http://2014.igem.org/wiki/index.php?title=Special:Contributions&feed=atom&limit=50&target=Eliblock&year=&month=2014.igem.org - User contributions [en]2024-03-29T01:53:48ZFrom 2014.igem.orgMediaWiki 1.16.5http://2014.igem.org/File:SBSiGEM_Drone_Map_1.pngFile:SBSiGEM Drone Map 1.png2015-01-12T04:55:28Z<p>Eliblock: uploaded a new version of &quot;File:SBSiGEM Drone Map 1.png&quot;: SBSiGEM 2014</p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Meet_Our_Team">Meet Our Team</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="intro">Team Members</a> ● <a href="#" id="methods">Advisors</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Attributions">Attributions</a></h7></div><br />
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<h6><center> The 2014 Stanford-Brown-Spelman iGEM Team, featuring NASA Ames Chief Scientist and Brown Alumna, Ellen Stofan.</center></h6><br />
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<div class="sub3"><img id="cellulosePic" src="https://static.igem.org/mediawiki/2014/6/6f/SBS_iGEM_2014_Cellulose_Icon.png"><h4><a href="http://www.eli-block.com" target="_blank">Eli Block</a></h4>Eli Block is a third year <a href="http://brown.edu" target="_blank">Brown</a> + <a href="http://www.risd.edu" target="_blank">RISD</a> Dual Degree student majoring in Industrial Design at RISD and concentrating in Biology at Brown; he's particularly interested in designed ecologies, evolutionary biology, and wearable technology. Eli worked on the wasp protein waterproofing project, built concept UAVs, cooked experimental biomaterials, and developed the team wiki. Eli loves dinosaurs, artificial intelligence, print design, and swimming like a merman in the Brown University pool.</div><br />
<div class="sub2"><img id="hellCellPic" src="https://static.igem.org/mediawiki/2014/c/c6/SBS_iGEM_2014_Hell_Cell.png" class="two"><h4>Jovita Byemerwa</h4>Jovita is from Tanzania, and is currently in her third year at Brown University concentrating in Computational Molecular Biology. This summer, she worked on the biodegradability project and explored human practices of UAVs. A huge interest of her's is language: she loves learning them, speaking them and even teaching them. She speaks Kiswahili (her mother tongue), English (of course), Italian and a bit of Spanish. </div><br />
<div class="sub3"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Ross Dispenza</h4>Ross is a junior at Brown University concentrating in Chemistry and French Studies. He worked on the cellulose acetate and cross-linker projects this summer. In high school, he once won a quiz bowl tournament for his team by answering a question about Lady Gaga.</div><br />
<div class="sub2"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Benjamin Doughty</h4>Ben is a sophomore at Brown University intending to concentrate in Biochemistry and Molecular Biology. Over the summer he worked on the <a class="links" href="https://2014.igem.org/Team:StanfordBrownSpelman/Cellulose_Acetate" target="_blank">Cellulose Acetate</a> and <a class="links" href="https://2014.igem.org/Team:StanfordBrownSpelman/Amberless_Hell_Cell" target="_blank">Amberless Hell Cell</a> projects. He can sing the alphabet backwards and once took home third place in a curling tournament. He also LOVES Gossip Girl and <a href="http://www.youtube.com/watch?v=wYa8EwX2Duw" target="_blank">Nicolas Cage</a>.</div><br />
<div class="sub3"><img id="cellulosePic" src="https://static.igem.org/mediawiki/2014/6/6f/SBS_iGEM_2014_Cellulose_Icon.png"><h4>Poorwa Godbole</h4>Poorwa is a junior at Stanford University majoring in Economics and planning to go to medical school after graduation. This summer she worked on the biodegradability and cellulose acetate projects, and made sure to hug each person on the team at least once a day. She enjoys dancing, laughing, and watching Blue Planet on Netflix. </div><br />
<div class="sub2"><img id="cellulosePic2" src="https://static.igem.org/mediawiki/2014/3/3a/SBS_iGEM_2014_Human_Practices.png"><h4>Jeannette Gonzales-Wright</h4>Jeannette is a junior in the Program in Liberal Medical Education (PLME) at Brown University. She is concentrating in Science & Society: Health/Medicine. This summer she worked on biodegradability, the human practices of UAVs, and the powerpoint presentation of our research. She identifies as a proud CODA and would prefer to talk to you in American Sign Language than in a spoken language. </div><br />
<div class="sub3"><img id="hellCellPic" src="https://static.igem.org/mediawiki/2014/c/c6/SBS_iGEM_2014_Hell_Cell.png" class="two"><h4>Ian Hull</h4> Ian somehow made it through his freshman year at Stanford without sustaining any major chemical burns. Apparently that makes him a sophomore now, and he's interested in bioengineering, chemistry, and science communication. This summer, he worked on the wasp protein and wax ester waterproofing projects. His hobbies include Bollywood dance and playing the trumpet. He loves the ocean and once swam with whale sharks.</div><br />
<div class="sub2"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Jotthe Kannappan</h4>Jotthe is a small and energetic junior in bioengineering at Stanford University. This summer, she worked primarily with wasp proteins in material waterproofing and biomaterials on the production of cellulose acetate. When she's not pipetting, she can be found humming obnoxiously, dancing, or curled up with a good book.</div><br />
<div class="sub3"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Alexander Lev<small></small>ine</h4>Alex is a junior at Brown University concentrating in Mathematical Physics. This summer, he worked mostly on getting <i>Gluconacetobacter hansenii</i> to produce cellulose acetate, and on creating software tools for synthetic biology. </div><br />
<div class="sub2"><img id="cellulosePic2" src="https://static.igem.org/mediawiki/2014/3/3a/SBS_iGEM_2014_Human_Practices.png"><h4>Raman Nelakanti</h4>Raman is the captain of the SBS iGEM team. He recently graduated from Stanford with a major in Bioengineering and worked on the <a class="links" href="https://2014.igem.org/Team:StanfordBrownSpelman/Amberless_Hell_Cell" target="_blank">Amberless Hell Cell</a> project this summer. Outside of science, his interests include singing, hiking, exploring, and living life. And one random fact about him - he once serenaded Justice Sandra Day O'Connor!</div><br />
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<div class="sub3"><img id="cellulosePic" src="https://static.igem.org/mediawiki/2014/6/6f/SBS_iGEM_2014_Cellulose_Icon.png"><h4>Lydia Ruffner</h4>Lydia is a recent graduate of Spelman College, where she majored in Biochemistry. Currently she is a first-year PhD student in the Chemistry and Chemical Biology Program at Northeastern University. This summer she worked on the Modeling portion of the Cellulose Acetate Project. Outside of the lab, she loves watching football (Go PATRIOTS!!), spending time with her family, and shopping.</div><br />
<div class="sub2"><img id="hellCellPic" src="https://static.igem.org/mediawiki/2014/c/c6/SBS_iGEM_2014_Hell_Cell.png" class="two"><h4>Alaina Shumate</h4>Alaina is a junior at Stanford University majoring in bioengineering. This summer she worked on the Amberless Hell Cell and Cellulose Cross-linker projects. While nothing makes her happier than Minipreps, she also enjoys drinking coffee, tap dancing, and convincing people that her home state of Wyoming is actually a fun place.</div><br />
<div class="sub3"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Aryo Sorayya</h4>Aryo is a sophomore at Stanford University, where he is studying Biochemistry and Computer Science. At Ames, Aryo worked on the Wasp Protein and Biodegradability projects, helped develop the team wiki, and strove to keep lab morale high! Aryo is a cheerful polyglot and enjoys learning about other cultures, playing pranks on his teammates, and traveling the world.<br />
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src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Kyla Ugwu</h4>Kyla Ugwu is a junior biochemistry major, mathematics minor at Spelman College. After graduation, she plans on getting a medical degree and a master’s in public health. During her time at NASA Ames Research Center, she worked on the Biodegradation project and Waterproofing projects. In her free time, she likes to sing with her quintet on campus and go to the movies as many times as her pocketbook will let her.</div><br />
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<h3><center>Meet Our Advisors</a></h3><br />
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<div class="sub3"><img id="cellulosePic" src="https://static.igem.org/mediawiki/2014/6/6f/SBS_iGEM_2014_Cellulose_Icon.png"><h4>Dr. Lynn Rothschild</h4>Lynn is the lead scientist in synthetic biology at NASA Ames Research Center where her lab has been working on cool projects ranging from the search for life in the universe and extremophiles, to pioneering how synthetic biology can be used to address NASA's missions. She is also an adjunct professor at Brown University, inter alia, <a href="http://vivo.brown.edu/display/lr3">Brown Home Page</a>. Her lab looks forward to hosting the team every year as they pioneer ways to take synthetic biology literally "out of this world". PS Yes, she really does play the <a href="http://www.nature.com/nature/journal/v422/n6932/full/422567a.html">bagpipes</a>! </div><br />
<div class="sub2"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Dr. Joseph Shih</h4>Joseph is the wet lab Lecturer in the Bioengineering Department at Stanford University. He got his Ph.D. in Molecular and Cellular Biology at Harvard University and did his post-doc in Pam Silver's lab at Harvard Medical School. He is always curious about biology and the potential for synthetic biology to change the world!</div><br />
<div class="sub3"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Dr. Gary Wessel</h4>Gary is a Professor of Biology at Brown University. He has been the faculty sponsor for the Brown University team since 2006 and teaches the synbio course at Brown "Bio 1210 Synthetic Biological Systems". <a href= "http://www.brown.edu/Research/Wessel_Lab/"> His research </a> focuses on anything germ line and reproduction and applies synthetic biological approaches to this research field. </div><br />
<div class="sub2"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Dr. Kimberly Jackson</h4>Kimberly is an Associate Professor of Biochemistry and co-director of the new Interdisciplinary Food Studies at Spelman College. She maintains an active research program in cancer therapeutics and drug discovery with funding from various agencies. Outside of being a professor, program director, mentor and researcher, Kimberly enjoys being a volleyball and soccer mom (of 3), wife of a research scientist, flutist and undercover foodie. One random fact—she completed part of her graduate studies as a NIH Fogarty fellow in Turku, FINLAND.</div><br />
<div class="sub3"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Dr. Jean Dimandja</h4>Jean-Marie Dimandja received his bachelor's in mathematics from Miami University, and his master's and doctorate in analytical chemistry from Southern Illinois University. Prior to joining the chemistry department at Spelman College in 2002, he worked at the NASA/Ames Research Center from 1991 to 1997, and the Centers for Disease Control and Prevention from 1997 to 2002 where he developed analytical methods for use in space research and environmental biomonitoring respectively. </div><br />
<div class="sub2"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Dr. Kosuke Fujishima</h4>Kosuke works as a researcher in Lynn Rothschild's lab tackling Origins of life questions using Synthetic Biology. He got his Ph.D in Systems Biology from Keio University (Japan) and is currently involved in multiple themes related to Astrobiology. He has been a technical advisor of the iGEM team since 2011. Aside from science, Kosuke has an absolutely adorable 14-month-old daughter named Sana, who the entire lab is in love with.</div><br />
<div class="sub3"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Ryan Kent</h4>Ryan was a member of the 2011 Brown/Stanford iGEM team and graduated from Stanford in 2012 with an M.S. in Biology. This is his second year as an iGEM mentor and as a member of Dr. Lynn Rothschild’s lab at NASA Ames Research Center. When he’s not whipping the team into shape, he enjoys writing about himself in the third person and surfing. -Ryan</div><br />
<div class="sub2"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Kendrick Wang</h4>Kendrick is a researcher at NASA Ames Research Center in Professor Lynn Rothschild’s lab. He recently earned his B.S. degree in Bioengineering at Stanford University. His research work focuses on the Origins of Life question, the prebiotic environment, and Astrobiology. He was a member of the 2012 Stanford Brown iGEM team and has been an advisor for iGEM teams since 2013. He is from California originally, but grew up abroad in Hong Kong and Singapore. For fun, he loves rock climbing and mountain biking. </div><br />
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</body></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Lynn_InterviewTeam:StanfordBrownSpelman/Lynn Interview2014-10-18T03:13:27Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Human_Practices">Interview with <br> Dr. Lynn Rothschild</a></h3><br />
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Dr. Lynn Rothschild is an evolutionary biologist from NASA Ames. She has worked at Ames since she was a post-doctoral fellow with the National Research Council in 1987. She has recently started pioneering the research related to biological UAVs and serves as an advisor to the Stanford-Brown-Spelman iGEM team.<br />
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<h5><center>Questions &amp; Answers</h5><br />
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<b>Q: For how long have you been working with UAVs?</b><br />
A: Even though I have been here a very long time, I only started getting interested in UAVs about six months to a year ago. For various reasons, I think UAVs are useful for the science that we conduct at Ames, and I think we can contribute to their building.<br />
<br><br><b>Q: Could you elaborate on the reasons UAVs are important in your career?</b><br />
A: My primary interest has been in looking for life elsewhere in the universe. One of the things UAVs could be particularly good for is surveying the surface of a planet. Now on the Earth, it seems that every square inch has been covered by Google Maps. But that isn’t true for Mars, or Titan, or Europa. When you land on another planetary body with a mothership, it might be very cool to be able to release UAVs at the surface and find out about interesting areas. I am also, of course, interested in planet Earth, given it is my home. I have been interested in the use of UAVs remotely in Earth Science. For example, to monitor coral reefs. I am mainly interested in ground-based research, and I can get much more detail about where I am looking by doing ground-base experiments. But once you get in the air, you can cover much more area.<br />
<br><br><b>Q: How are UAVs connected to synthetic biology?</b><br />
A: Well, in the past, UAVs have not been connected to synthetic biology at all. But I am in the Earth Science group at NASA Ames, and periodically UAVs get lost- for example, on coral reefs or in other sensitive habitats. As I started to hear about this, I thought, “Well, wouldn’t it be useful if the UAV was biodegradable, so if it crashed somewhere that was sensitive, it wouldn’t matter if it dissolved. Synthetic biology can do that. In addition, these UAVs could be lighter and certainly a lot cheaper to make. You can make many more and not harm the environment, so that’s why I got interested in combining the two.<br />
<br><br><b>Q: Do you think a biosynthetic UAV would be as efficient as it’s more conventional counterpart?</b><br />
A: I think that synthetic biology UAVs could be equally efficient. Where my dream is to make a UAV where every single part of it could be replaced with something you could make biologically, that may not be completely practical. For example, you might want to have a camera on a UAV, and it might be really difficult to have an organism perform the same function or produce images that are worth anything. So, realistically, this is going to be much more of a hybrid vehicle. But much of the body of the vehicle could certainly be made biologically. There are many biosensors, there are many bits and pieces that we could do. That’s one of the many things that my lab, particularly the Stanford-Brown-Spelman iGEM team is exploring this summer. <br />
<br><br><b>Q: What is the biggest drawback to using a biosynthetic UAV?</b><br />
A: I think the biggest drawback is having it crash. There’s a big difference between having a living organism on there, and just products an organism made. For example, our team is thinking about using microbial cellulose. Cellulose itself is in wood, most of cotton, and all around nature. Once the cells make it, it really doesn’t matter whether it came from a cotton plant, a tree, or the microbes in the lab. I’m not concerned about that. However, if you having living organisms acting as biosensors and then the plane crashes, there certainly could be problems as this plane interacts with the environment. Hopefully people could think of this in advance, and design such that this never became a problem. For example, on crashing, the cells might die. Or the cells could be attenuated. There are all sorts of other processes to keep them from contaminating the environment. But that, to me, is the largest concern with a biological UAV- having living things on the UAV.<br />
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<h5><center>Read the Other Interviews</h5><br />
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Read one or more of the other UAV-related interviews that we conducted. All of our interviews are catalogued here:<br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Lynn_Interview" target="_blank">● Interview with Dr. Lynn Rothschild, NASA Synthetic Biologist</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Vince_Interview" target="_blank">● Interview with Vince Ambrosia, NASA Earth Scientist</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Randy_Interview" target="_blank">● Interview with Randy Berthold, NASA Earth Scientist</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Matt_Interview" target="_blank">● Interview with Matt Fladeland, NASA Ames Scientist</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Jim_Interview" target="_blank">● Interview with Jim Head, Brown U. Planetary Scientist</a></div><br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Meet_Our_Team">Meet Our Team</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="intro">Team Members</a> ● <a href="#" id="methods">Advisors</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Attributions">Attributions</a></h7></div><br />
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<h6><center> The 2014 Stanford-Brown-Spelman iGEM Team, featuring NASA Ames Chief Scientist and Brown Alumna, Ellen Stofan.</center></h6><br />
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<div class="sub3"><img id="cellulosePic" src="https://static.igem.org/mediawiki/2014/6/6f/SBS_iGEM_2014_Cellulose_Icon.png"><h4><a href="http://www.behance.net/eliblock" target="_blank">Eli Block</a></h4>Eli Block is a third year <a href="http://brown.edu" target="_blank">Brown</a> + <a href="http://www.risd.edu" target="_blank">RISD</a> Dual Degree student majoring in Industrial Design at RISD and concentrating in Biology at Brown; he's particularly interested in designed ecologies, evolutionary biology, and wearable technology. Eli worked on the wasp protein waterproofing project, built concept UAVs, cooked experimental biomaterials, and developed the team wiki. Eli loves dinosaurs, artificial intelligence, print design, and swimming like a merman in the Brown University pool.</div><br />
<div class="sub2"><img id="hellCellPic" src="https://static.igem.org/mediawiki/2014/c/c6/SBS_iGEM_2014_Hell_Cell.png" class="two"><h4>Jovita Byemerwa</h4>Jovita is from Tanzania, and is currently in her third year at Brown University concentrating in Computational Molecular Biology. This summer, she worked on the biodegradability project and explored human practices of UAVs. A huge interest of her's is language: she loves learning them, speaking them and even teaching them. She speaks Kiswahili (her mother tongue), English (of course), Italian and a bit of Spanish. </div><br />
<div class="sub3"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Ross Dispenza</h4>Ross is a junior at Brown University concentrating in Chemistry and French Studies. He worked on the cellulose acetate and cross-linker projects this summer. In high school, he once won a quiz bowl tournament for his team by answering a question about Lady Gaga.</div><br />
<div class="sub2"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Benjamin Doughty</h4>Ben is a sophomore at Brown University intending to concentrate in Biochemistry and Molecular Biology. Over the summer he worked on the <a class="links" href="https://2014.igem.org/Team:StanfordBrownSpelman/Cellulose_Acetate" target="_blank">Cellulose Acetate</a> and <a class="links" href="https://2014.igem.org/Team:StanfordBrownSpelman/Amberless_Hell_Cell" target="_blank">Amberless Hell Cell</a> projects. He can sing the alphabet backwards and once took home third place in a curling tournament. He also LOVES Gossip Girl and <a href="http://www.youtube.com/watch?v=wYa8EwX2Duw" target="_blank">Nicolas Cage</a>.</div><br />
<div class="sub3"><img id="cellulosePic" src="https://static.igem.org/mediawiki/2014/6/6f/SBS_iGEM_2014_Cellulose_Icon.png"><h4>Poorwa Godbole</h4>Poorwa is a junior at Stanford University majoring in Economics and planning to go to medical school after graduation. This summer she worked on the biodegradability and cellulose acetate projects, and made sure to hug each person on the team at least once a day. She enjoys dancing, laughing, and watching Blue Planet on Netflix. </div><br />
<div class="sub2"><img id="cellulosePic2" src="https://static.igem.org/mediawiki/2014/3/3a/SBS_iGEM_2014_Human_Practices.png"><h4>Jeannette Gonzales-Wright</h4>Jeannette is a junior in the Program in Liberal Medical Education (PLME) at Brown University. She is concentrating in Science & Society: Health/Medicine. This summer she worked on biodegradability, the human practices of UAVs, and the powerpoint presentation of our research. She identifies as a proud CODA and would prefer to talk to you in American Sign Language than in a spoken language. </div><br />
<div class="sub3"><img id="hellCellPic" src="https://static.igem.org/mediawiki/2014/c/c6/SBS_iGEM_2014_Hell_Cell.png" class="two"><h4>Ian Hull</h4> Ian somehow made it through his freshman year at Stanford without sustaining any serious chemical burns. Apparently that makes him a sophomore now, and he's interested in bioengineering, chemistry, and science communication. This summer, he worked on the wasp protein and wax ester waterproofing projects. He loves the ocean and once swam with whale sharks.</div><br />
<div class="sub2"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Jotthe Kannappan</h4>Jotthe is a small and energetic junior in bioengineering at Stanford University. This summer, she worked primarily with wasp proteins in material waterproofing and biomaterials on the production of cellulose acetate. When she's not pipetting, she can be found humming obnoxiously, dancing, or curled up with a good book.</div><br />
<div class="sub3"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Alexander Levine</h4>Alex is a junior at Brown University concentrating in Mathematical Physics. This summer, he worked mostly on getting <i>Gluconacetobacter hansenii</i> to produce cellulose acetate, and on creating software tools for synthetic biology.</div><br />
<div class="sub2"><img id="cellulosePic2" src="https://static.igem.org/mediawiki/2014/3/3a/SBS_iGEM_2014_Human_Practices.png"><h4>Raman Nelakanti</h4>Raman is the captain of the SBS iGEM team. He recently graduated from Stanford with a major in Bioengineering and worked on the <a class="links" href="https://2014.igem.org/Team:StanfordBrownSpelman/Amberless_Hell_Cell" target="_blank">Amberless Hell Cell</a> project this summer. Outside of science, his interests include singing, hiking, exploring, and living life. And one random fact about him - he once serenaded Justice Sandra Day O'Connor!</div><br />
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<div class="sub3"><img id="cellulosePic" src="https://static.igem.org/mediawiki/2014/6/6f/SBS_iGEM_2014_Cellulose_Icon.png"><h4>Lydia Ruffner</h4>Lydia is a recent graduate of Spelman College, where she majored in Biochemistry. Currently she is a first-year PhD student in the Chemistry and Chemical Biology Program at Northeastern University. This summer she worked on the Modeling portion of the Cellulose Acetate Project. Outside of the lab, she loves watching football (Go PATRIOTS!!), spending time with her family, and shopping.</div><br />
<div class="sub2"><img id="hellCellPic" src="https://static.igem.org/mediawiki/2014/c/c6/SBS_iGEM_2014_Hell_Cell.png" class="two"><h4>Alaina Shumate</h4>Alaina is a junior at Stanford University majoring in bioengineering. This summer she worked on the Amberless Hell Cell and Cellulose Cross-linker projects. While nothing makes her happier than Minipreps, she also enjoys drinking coffee, tap dancing, and convincing people that her home state of Wyoming is actually a fun place.</div><br />
<div class="sub3"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Aryo Sorayya</h4>Aryo is a sophomore at Stanford University, where he is studying Biochemistry and Computer Science. At Ames, Aryo worked on the Wasp Protein and Biodegradability projects, helped develop the team wiki, and strove to keep lab morale high! Aryo is a cheerful polyglot and enjoys learning about other cultures, playing pranks on his teammates, and traveling the world.<br />
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src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Kyla Ugwu</h4>Kyla Ugwu is a junior biochemistry major, mathematics minor at Spelman College. After graduation, she plans on getting a medical degree and a master’s in public health. During her time at NASA Ames Research Center, she worked on the Biodegradation project and Waterproofing projects. In her free time, she likes to sing with her quintet on campus and go to the movies as many times as her pocketbook will let her.</div><br />
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<div class="sub3"><img id="cellulosePic" src="https://static.igem.org/mediawiki/2014/6/6f/SBS_iGEM_2014_Cellulose_Icon.png"><h4>Dr. Lynn Rothschild</h4>Lynn is the lead scientist in synthetic biology at NASA Ames Research Center where her lab has been working on cool projects ranging from the search for life in the universe and extremophiles, to pioneering how synthetic biology can be used to address NASA's missions. She is also an adjunct professor at Brown University, inter alia, <a href="http://vivo.brown.edu/display/lr3">Brown Home Page</a>. Her lab looks forward to hosting the team every year as they pioneer ways to take synthetic biology literally "out of this world". PS Yes, she really does play the <a href="http://www.nature.com/nature/journal/v422/n6932/full/422567a.html">bagpipes</a>! </div><br />
<div class="sub2"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Dr. Joseph Shih</h4>Joseph is the wet lab Lecturer in the Bioengineering Department at Stanford University. He got his Ph.D. in Molecular and Cellular Biology at Harvard University and did his post-doc in Pam Silver's lab at Harvard Medical School. He is always curious about biology and the potential for synthetic biology to change the world!</div><br />
<div class="sub3"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Dr. Gary Wessel</h4>Gary is a Professor of Biology at Brown University. He has been the faculty sponsor for the Brown University team since 2006 and teaches the synbio course at Brown "Bio 1210 Synthetic Biological Systems". <a href= "http://www.brown.edu/Research/Wessel_Lab/"> His research </a> focuses on anything germ line and reproduction and applies synthetic biological approaches to this research field. </div><br />
<div class="sub2"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Dr. Kimberly Jackson</h4>Kimberly is an Associate Professor of Biochemistry and co-director of the new Interdisciplinary Food Studies at Spelman College. She maintains an active research program in cancer therapeutics and drug discovery with funding from various agencies. Outside of being a professor, program director, mentor and researcher, Kimberly enjoys being a volleyball and soccer mom (of 3), wife of a research scientist, flutist and undercover foodie. One random fact—she completed part of her graduate studies as a NIH Fogarty fellow in Turku, FINLAND.</div><br />
<div class="sub3"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Dr. Jean Dimandja</h4>Jean-Marie Dimandja received his bachelor's in mathematics from Miami University, and his master's and doctorate in analytical chemistry from Southern Illinois University. Prior to joining the chemistry department at Spelman College in 2002, he worked at the NASA/Ames Research Center from 1991 to 1997, and the Centers for Disease Control and Prevention from 1997 to 2002 where he developed analytical methods for use in space research and environmental biomonitoring respectively. </div><br />
<div class="sub2"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Dr. Kosuke Fujishima</h4>Kosuke works as a researcher in Lynn Rothschild's lab tackling Origins of life questions using Synthetic Biology. He got his Ph.D in Systems Biology from Keio University (Japan) and is currently involved in multiple themes related to Astrobiology. He has been a technical advisor of the iGEM team since 2011. Aside from science, Kosuke has an absolutely adorable 14-month-old daughter named Sana, who the entire lab is in love with.</div><br />
<div class="sub3"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Ryan Kent</h4>Ryan was a member of the 2011 Brown/Stanford iGEM team and graduated from Stanford in 2012 with an M.S. in Biology. This is his second year as an iGEM mentor and as a member of Dr. Lynn Rothschild’s lab at NASA Ames Research Center. When he’s not whipping the team into shape, he enjoys writing about himself in the third person and surfing. -Ryan</div><br />
<div class="sub2"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Kendrick Wang</h4>Kendrick is a researcher at NASA Ames Research Center in Professor Lynn Rothschild’s lab. He recently earned his B.S. degree in Bioengineering at Stanford University. His research work focuses on the Origins of Life question, the prebiotic environment, and Astrobiology. He was a member of the 2012 Stanford Brown iGEM team and has been an advisor for iGEM teams since 2013. He is from California originally, but grew up abroad in Hong Kong and Singapore. For fun, he loves rock climbing and mountain biking. </div><br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="wax">Alternate Approach</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wishes to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/3/33/SBS_iGEM_wasp_pipe_cropped.JPG" height="110%" width="110%"><br><br />
<h6> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion [4]. Until now, scientific knowledge of this protein coating was limited mainly to total amino acid composition of all the proteins in the paper. To gain more insight into the specific proteins that may exist in wasp nest paper, we collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. We believe there may be a single protein in wasp saliva that is chiefly responsible for the hydrophobic nature of their nests. </h6><br />
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<h6>Our ultimate goal is to identify the gene that codes for this wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>A polyacrylamide gel containing proteins from the paper wasp nests we collected. We excised the dominant bands for protein analysis.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from an active nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a <a href = "http://www.sigmaaldrich.com/catalog/product/sigma/pe0230?lang=en&region=US">plant protein extraction kit</a>. We ran the proteins on two polyacrylamide gels, one with a ten minute 70ºC heat denaturation step and the other without. We then excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.<br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running of a wasp protein gel.</a></p></center><br><br />
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<h6>Our initial plan was to extract RNA from female nest-building wasps so we could purify their messenger RNA, generate a complementary DNA library, and get the library sequenced for use as a reference transcriptome. Fortunately for us, the <i>Polistes dominula</i> <a href = "http://goblinx.soic.indiana.edu/PdomGDB"> genome</a> was published shortly after we began our project [5], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. The genome was used as a reference for peptide mass fingerprinting, we saved our RNA extracts for eventual RT-PCR amplification, and the project moved onwards. We truly live in an exciting time for genetic engineering!<br />
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<h6> Once we obtained the mass fingerprinting data, we were faced with a long list of uncharacterized peptide fragments with hits in the Polistes dominula genome. We used the NCBI Position-Specific Iterated Basic Local Alignment Search Tool, or PSI-BLAST, to search online databases for proteins with similar sequences and domains. This gave us hints towards the possible functions of these uncharacterized proteins.</h6><br />
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<h6>We then narrowed our list down to six proteins with favorable PSI-BLAST hits and obtained the DNA sequences for these genes from the genome. Three of these genes were codon-optimized and synthesized for E. coli expression in the pF1A T7 Flexi® vector, while the other three were amplified from wasp RNA via RT-PCR for S. cerevisiae expression in the pYES2.1/V5-His-TOPO® vector.</h6><br />
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<div class="sub4"><a href="https://static.igem.org/mediawiki/2014/3/36/WaspProteins.pdf" target="_blank"><h6><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/3/36/WaspProteins.pdf"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></h6></div><br />
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<h5><center>Results</h5><br />
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We collected two wasp nests in the field: one new nest that we saw wasps actively working on, and an older, abandoned nest that Dr. Kavanaugh estimated to be about one year old. After extracting total protein from the nests, we ran the protein samples on two polyacrylamide gels – one with a ten minute 70ºC heat denaturation step and the other without – and obtained the images below.<br />
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<h6><center>Wasp nest protein extracts run on a polyacrylamide gel after a ten minute 70ºC denaturation step.</center></h6><br />
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<h6><center>Wasp nest protein extracts run on a polyacrylamide gel without any denaturation step.</center></h6><br />
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<h6>The presence of a few dominant bands in the protein samples indicated that there may be a single protein chiefly responsible for the hydrophobicity of wasp nest paper. The relative faintness of bands from the older nest also suggests that the protein may degrade over time, which supports our qualitative observations that the older nest was somewhat less waterproof.</h6><br />
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<h6>Once we obtained peptide mass fingerprinting data on the bands we excised from the protein gels, we were faced with a list of thirty uncharacterized proteins whose fragments had hits in the Polistes dominula genome. Interestingly, the proteins present in each gel slice did not always have the sizes we expected in comparison to the protein ladder. Why this was the case remains to be seen, but we postulate that proteins of different affinities traveled through the gel at different speeds, may have complexed and traveled more slowly, or have been so abundant that traces were present in multiple bands.</h6><br />
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<h6>We used the NCBI Position-Specific Iterated Basic Local Alignment Search Tool, or PSI-BLAST, to search online databases for proteins with similar sequences and domains. This gave us hints towards the possible functions of these uncharacterized proteins. </h6><br />
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<h6>Nearly all of the PSI-BLAST results came back with strong functional predictions for each of our proteins, the majority of which we deemed irrelevant to waterproofing ability. For example, many proteins appeared to be variants of ubiquitin, a small regulatory protein found in virtually all eukaryotic tissues. Others were similar to vitellogenin, an egg yolk precursor protein used to nurture larvae in related insect species such as Vespula vulgaris (also known as the common yellow-jacket) and Formica exsecta (narrow-headed ant). While it was interesting to see the diversity of proteins present in the wasp nests, we had to narrow our scope to test candidate genes in the lab.</h6><br />
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<h6>Click <a href = "https://static.igem.org/mediawiki/2014/c/c0/SBS_iGEM_2014_peptide_mass_fingerprinting_results_2.zip"> here</a> to download a .zip file containing the results of our peptide mass fingerprinting, as well as a document listing the thirty proteins present, their first PSI-BLAST hits, predicted sizes, and amino acid sequences.</h6><br />
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<h6>Out of this list of thirty proteins present in the protein gel slices, we narrowed our list down to six candidate proteins with favorable PSI-BLAST hits. The genes that coded for these candidate proteins are what we ultimately ended up working on in the lab. These six proteins are detailed in the table below.</h6><br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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<h6>We chose the first two proteins, PdomMRNAr1.2-03231.1 and PdomMRNAr1.2-08705.1 (nicknamed C1 and C2), because the PSI-BLAST predicted they would have chitin-binding domains based on similar proteins in related species. These proteins intrigued us because chitin is a close chemical relative of cellulose, implying that the proteins might also bind to cellulose, and there appeared to be a relatively high proportion of hydrophobic amino acids in the peptide sequences. Furthermore, fungal mycelium is rich in chitin, meaning that if these proteins exhibit chitin-binding activity and hydrophobicity, they might also be used to waterproof chitin.</h6><br />
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<h6>We chose the next two proteins, PdomMRNAr1.2-10508.1 and PdomMRNAr1.2-04156.1 (nicknamed G1 and G2), because the PSI-BLAST found similarity between these proteins and glucose dehydrogenases characterized in related species. This sparked our interest because it suggested that these proteins could have glucose binding domains, and cellulose is a glucose-based polysaccharide. The predicted sizes of these two proteins also best matched the sizes of the bands we excised from the protein gels.</h6><br />
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<h6>We chose the last two proteins, PdomMRNAr1.2-02758.1 and PdomMRNAr1.2-10259.1 (nicknamed U1 and U2), because the PSI-BLAST found no significant hits for similar proteins in its entire database. This indicates these two proteins are completely uncharacterized and may be unique to paper wasps, having evolved recently as an adaptation to impart hydrophobicity to their nests.</h6><br />
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<h6> We obtained the DNA sequences for these genes from the genome. Three of the genes – C1, C2, and U1 – were codon-optimized, synthesized, and ligated into the pF1A T7 Flexi® vector for E. coli expression. U1 was synthesized with a polyhistidine-tag for eventual purification, while C1 and C2 were planned to be purified with a chitin-coated magnetic bead purification kit.The other three genes – G1, G2, and U2 – were amplified from wasp RNA via reverse transcription PCR (RT-PCR) and ligated into the pYES2.1/V5-His-TOPO® vector (which contains a polyhistidine tag) for S. cerevisiae expression and purification.</h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/0/07/SBSiGEM_Ian_Hull_9-4-14_wasp_RT-PCR_labeled.png"><br><br />
<h6><center>Results of an RT-PCR performed on <i>Polistes dominula</i> total RNA extract. Primers were designed for G1, G2, U1, and U2. U1 was not successfully amplified, but fortunately was short enough to synthesize.</center></h6><br />
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<h6>As seen in the gel image above, G1, G2, and U2 were successfully isolated from wasp total RNA via RT-PCR. Curiously, after transforming E. coli with these three genes to prepare enough plasmid to transform into yeast, we noticed after a series of sequencing orders that the genes amplified from our Californian wasps varied slightly from the Polistes dominula whose genome was sequenced. These variants were often but not always silent point mutations. We also occasionally experienced mutations in all six of our genes when cloning in E. coli.</h6><br />
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<h6>Click <a href = "https://static.igem.org/mediawiki/2014/c/c9/SBS_iGEM_2014_Polistes_dominula_gene_sequences.zip"> here </a> to download a .zip file containing the DNA and amino acid sequences for all six of our candidate genes, including original genomic predictions, codon-optimized versions, California variant sequences, and bacterial cloning mutations.</h6><br />
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<h6><center><i>Saccharomyces cerevisiae</i> transformants plated on uracil-deficient selective medium. Clockwise from top left: G2, U2, G1, RFP control. The cells were given glucose as a carbon source to repress pYES2.1/V5-His-TOPO® expression and help stimulate growth.</center></h6><br />
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<h6>As seen above, G1, G2, and U2 have successfully been transformed into yeast and we are in the process of culturing the yeast in uracil-deficient selective medium with galactose as a carbon source to induce pYES2.1/V5-His-TOPO® expression. Stay tuned for updates on this subset of the project!</h6><br />
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<h6>At the time of writing, U1 was received relatively late from the synthesis company, and so has not yet been transformed successfully into T7 E. coli for expression and purification. However, C1 and C2 have been transformed and expressed in T7 E. coli. To attempt to purify the proteins from cell lysate, we used the New England Biolabs Chitin Magnetic Beads protein purification kit, which isolates proteins that bind to chitin-coated magnetic beads. After eluting from the magnetic beads, we ran the purified C1 and C2 extracts on a polyacrylamide gel.</h6><br />
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<h6>This gel image shows that although C1 is absent, C2 was successfully purified with the chitin magnetic beads, suggesting that it exhibits chitin-binding activity. Pending cellulose-binding and waterproofing assays, C2 is a candidate for waterproofing both fungal mycelium and bacterial cellulose. Stay tuned for updates on this subset of the project!</h6><br />
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<h6>We successfully BioBricked C1, C2, and U1. Check out our <a href = "https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks"> BioBricks </a> page for links to the parts in the Parts Registry, where we’ll have the most up-to-date characterization data!</h6><br />
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<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
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<h6>In addition to the wasp protein waterproofing project, we searched literature for other organisms that would be able to produce a highly hydrophobic substance to waterproof the UAV. We stumbled across Marinobacter hydrocarbonoclasticus, a marine bacterium that synthesizes isoprenoid wax esters for use as storage compounds [1].</h6><br />
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<h6><center> A phytol-based wax ester produced by <i>Marinobacter hydrocarbonoclasticus</i>.</center></h6><br />
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<h6>Wax esters, such as the example shown above, consist of two very long carbon chains joined by an ester group. These chains impart great hydrophobicity to the entire molecule, indicating that a wax ester coating on a surface could be used to repel water. We decided to repurpose the wax ester synthesis genes in Marinobacter to create a pathway through which Escherichia coli could synthesize the wax esters and use them to waterproof our UAV.</h6><br />
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<h6>The two enzymes in Marinobacter that are involved in the production of isoprenoid wax esters were wax synthase 1 (WS1) and wax synthase 2 (WS2). In the presence of a fatty alcohol and fatty acyl-CoA (carboxylic acid derivative), either of these two proteins can catalyze the synthesis of isoprenoid wax esters.</h6><br />
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<h6>The DNA sequences for both WS1 and WS2 were obtained from the NCBI database [3][4]. They were then codon optimized for Escherichia coli using a codon optimizer from the Integrated DNA Technologies (IDT) website and then synthesized. After the genes were received from IDT, a polymerase chain reaction (PCR) was done to amplify the proteins and to ensure that they were present at around 1.4kb. The proteins were then ligated to a promoter and RBS and also into a chloramphenicol vector, then transformed into E. coli. After completing a colony PCR on the transformants, colonies 5, 7, and 11 on the WS2 plate appeared to contain the WS2 gene. Liquid cultures were inoculated with colonies 5, 7, and 11 to create more cells with the gene. After the cultures were grown in the incubator, they were miniprepped in order to extract their plasmid DNA. The DNA was then sent for sequencing with a 15ul reaction volume.</h6><br />
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<h6>After sequencing the data came back with miniprepped genes that aligned with the original synthesized gene for roughly half the length of the sequence, but with a puzzling truncation that was present in all three plasmid samples:</h6><br />
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<h6><center>WS2 sequencing alignment in Geneious 7.</center></h6><br />
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<h6>Unfortunately, the wax esters project had to be discontinued due to an error in the synthesis of the WS2 gene. In order to insert the gene into the vector, there are restriction sites that are cleaved by their respective restriction enzymes. When we codon-optimized WS2 for E. coli expression, we accidentally created a PstI cut site at base 720 in the synthesized gene. Because there was a cut site in the middle of the gene, only a fragment of the WS2 gene could ever make its way into the chloramphenicol vector, thereby disabling the protein the gene would have coded for. We also discovered that the fatty acyl-CoA reagent we intended to use for our enzymatic synthesis reaction was exorbitantly expensive (over $60 per milligram). For this reason, and because it would have been too costly to resynthesize the WS2 gene, the project was discontinued.</h6><br />
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<h5><center>References</h5><br />
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1. Holtzapple, E <i>et al.</i> (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189: 3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>. <br></br><br />
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2. Holtzapple, E <i>et al.</i> (2007) WS1 sequence in <i>Marinobacter hydrocarbonoclasticus</i>. <a href = "http://www.ncbi.nlm.nih.gov/nuccore/EF219376">NCBI</a>. June 14.<br><br><br />
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3. Holtzapple, E <i>et al.</i> (2007) WS2 sequence in <i>Marinobacter hydrocarbonoclasticus</i>. <a href = "http://www.ncbi.nlm.nih.gov/nuccore/EF219377">NCBI</a>. June 14.<br><br><br />
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4. Kojima, Jun-ichi.; Carpenter, James M. (1997): A Taxonomic Note and Nest Description of an Australian Paper Wasp, <i>Polistes variabilis Fabricius Hymenoptera, Vespidae, Polistinae </i>. <i>Japanese Journal of Systematic Entomology</i>. November 15; 32: 117-122.<br><br><br />
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5. Toth, <i>et al.</i> (2014): <i>Polistes dominula </i>genome. <a href = "http://www.ncbi.nlm.nih.gov/bioproject/234105">NCBI</a>. January 10.<br><br><br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Material_WaterproofingTeam:StanfordBrownSpelman/Material Waterproofing2014-10-18T03:08:08Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="wax">Alternate Approach</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wishes to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/3/33/SBS_iGEM_wasp_pipe_cropped.JPG" height="110%" width="110%"><br><br />
<h6> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion [4]. Until now, scientific knowledge of this protein coating was limited mainly to total amino acid composition of all the proteins in the paper. To gain more insight into the specific proteins that may exist in wasp nest paper, we collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. We believe there may be a single protein in wasp saliva that is chiefly responsible for the hydrophobic nature of their nests. </h6><br />
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<h6>Our ultimate goal is to identify the gene that codes for this wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>A polyacrylamide gel containing proteins from the paper wasp nests we collected. We excised the dominant bands for protein analysis.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from an active nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a <a href = "http://www.sigmaaldrich.com/catalog/product/sigma/pe0230?lang=en&region=US">plant protein extraction kit</a>. We ran the proteins on two polyacrylamide gels, one with a ten minute 70ºC heat denaturation step and the other without. We then excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.<br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running of a wasp protein gel.</a></p></center><br><br />
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<h6>Our initial plan was to extract RNA from female nest-building wasps so we could purify their messenger RNA, generate a complementary DNA library, and get the library sequenced for use as a reference transcriptome. Fortunately for us, the <i>Polistes dominula</i> <a href = "http://goblinx.soic.indiana.edu/PdomGDB"> genome</a> was published shortly after we began our project [5], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. The genome was used as a reference for peptide mass fingerprinting, we saved our RNA extracts for eventual RT-PCR amplification, and the project moved onwards. We truly live in an exciting time for genetic engineering!<br />
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<h6> Once we obtained the mass fingerprinting data, we were faced with a long list of uncharacterized peptide fragments with hits in the Polistes dominula genome. We used the NCBI Position-Specific Iterated Basic Local Alignment Search Tool, or PSI-BLAST, to search online databases for proteins with similar sequences and domains. This gave us hints towards the possible functions of these uncharacterized proteins.</h6><br />
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<h6>We then narrowed our list down to six proteins with favorable PSI-BLAST hits and obtained the DNA sequences for these genes from the genome. Three of these genes were codon-optimized and synthesized for E. coli expression in the pF1A T7 Flexi® vector, while the other three were amplified from wasp RNA via RT-PCR for S. cerevisiae expression in the pYES2.1/V5-His-TOPO® vector.</h6><br />
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<div class="sub4"><a href="https://static.igem.org/mediawiki/2014/3/36/WaspProteins.pdf" target="_blank"><h6><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/3/36/WaspProteins.pdf"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></h6></div><br />
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<h5><center>Results</h5><br />
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We collected two wasp nests in the field: one new nest that we saw wasps actively working on, and an older, abandoned nest that Dr. Kavanaugh estimated to be about one year old. After extracting total protein from the nests, we ran the protein samples on two polyacrylamide gels – one with a ten minute 70ºC heat denaturation step and the other without – and obtained the images below.<br />
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<h6><center>Wasp nest protein extracts run on a polyacrylamide gel after a ten minute 70ºC denaturation step.</center></h6><br />
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<h6><center>Wasp nest protein extracts run on a polyacrylamide gel without any denaturation step.</center></h6><br />
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<h6>The presence of a few dominant bands in the protein samples indicated that there may be a single protein chiefly responsible for the hydrophobicity of wasp nest paper. The relative faintness of bands from the older nest also suggests that the protein may degrade over time, which supports our qualitative observations that the older nest was somewhat less waterproof.</h6><br />
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<h6>Once we obtained peptide mass fingerprinting data on the bands we excised from the protein gels, we were faced with a list of thirty uncharacterized proteins whose fragments had hits in the Polistes dominula genome. Interestingly, the proteins present in each gel slice did not always have the sizes we expected in comparison to the protein ladder. Why this was the case remains to be seen, but we postulate that proteins of different affinities traveled through the gel at different speeds, may have complexed and traveled more slowly, or have been so abundant that traces were present in multiple bands.</h6><br />
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<h6>We used the NCBI Position-Specific Iterated Basic Local Alignment Search Tool, or PSI-BLAST, to search online databases for proteins with similar sequences and domains. This gave us hints towards the possible functions of these uncharacterized proteins. </h6><br />
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<h6>Nearly all of the PSI-BLAST results came back with strong functional predictions for each of our proteins, the majority of which we deemed irrelevant to waterproofing ability. For example, many proteins appeared to be variants of ubiquitin, a small regulatory protein found in virtually all eukaryotic tissues. Others were similar to vitellogenin, an egg yolk precursor protein used to nurture larvae in related insect species such as Vespula vulgaris (also known as the common yellow-jacket) and Formica exsecta (narrow-headed ant). While it was interesting to see the diversity of proteins present in the wasp nests, we had to narrow our scope to test candidate genes in the lab.</h6><br />
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<h6>Click <a href = "https://static.igem.org/mediawiki/2014/c/c0/SBS_iGEM_2014_peptide_mass_fingerprinting_results_2.zip"> here</a> to download a .zip file containing the results of our peptide mass fingerprinting, as well as a document listing the thirty proteins present, their first PSI-BLAST hits, predicted sizes, and amino acid sequences.</h6><br />
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<h6>Out of this list of thirty proteins present in the protein gel slices, we narrowed our list down to six candidate proteins with favorable PSI-BLAST hits. The genes that coded for these candidate proteins are what we ultimately ended up working on in the lab. These six proteins are detailed in the table below.</h6><br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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<h6>We chose the first two proteins, PdomMRNAr1.2-03231.1 and PdomMRNAr1.2-08705.1 (nicknamed C1 and C2), because the PSI-BLAST predicted they would have chitin-binding domains based on similar proteins in related species. These proteins intrigued us because chitin is a close chemical relative of cellulose, implying that the proteins might also bind to cellulose, and there appeared to be a relatively high proportion of hydrophobic amino acids in the peptide sequences. Furthermore, fungal mycelium is rich in chitin, meaning that if these proteins exhibit chitin-binding activity and hydrophobicity, they might also be used to waterproof chitin.</h6><br />
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<h6>We chose the next two proteins, PdomMRNAr1.2-10508.1 and PdomMRNAr1.2-04156.1 (nicknamed G1 and G2), because the PSI-BLAST found similarity between these proteins and glucose dehydrogenases characterized in related species. This sparked our interest because it suggested that these proteins could have glucose binding domains, and cellulose is a glucose-based polysaccharide. The predicted sizes of these two proteins also best matched the sizes of the bands we excised from the protein gels.</h6><br />
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<h6>We chose the last two proteins, PdomMRNAr1.2-02758.1 and PdomMRNAr1.2-10259.1 (nicknamed U1 and U2), because the PSI-BLAST found no significant hits for similar proteins in its entire database. This indicates these two proteins are completely uncharacterized and may be unique to paper wasps, having evolved recently as an adaptation to impart hydrophobicity to their nests.</h6><br />
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<h6> We obtained the DNA sequences for these genes from the genome. Three of the genes – C1, C2, and U1 – were codon-optimized, synthesized, and ligated into the pF1A T7 Flexi® vector for E. coli expression. U1 was synthesized with a polyhistidine-tag for eventual purification, while C1 and C2 were planned to be purified with a chitin-coated magnetic bead purification kit.The other three genes – G1, G2, and U2 – were amplified from wasp RNA via reverse transcription PCR (RT-PCR) and ligated into the pYES2.1/V5-His-TOPO® vector (which contains a polyhistidine tag) for S. cerevisiae expression and purification.</h6><br />
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<div class="small-10 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/0/07/SBSiGEM_Ian_Hull_9-4-14_wasp_RT-PCR_labeled.png"><br><br />
<h6><center>Results of an RT-PCR performed on <i>Polistes dominula</i> total RNA extract. Primers were designed for G1, G2, U1, and U2. U1 was not successfully amplified, but fortunately was short enough to synthesize.</center></h6><br />
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<h6>As seen in the gel image above, G1, G2, and U2 were successfully isolated from wasp total RNA via RT-PCR. Curiously, after transforming E. coli with these three genes to prepare enough plasmid to transform into yeast, we noticed after a series of sequencing orders that the genes amplified from our Californian wasps varied slightly from the Polistes dominula whose genome was sequenced. These variants were often but not always silent point mutations. We also occasionally experienced mutations in all six of our genes when cloning in E. coli.</h6><br />
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<h6>Click <a href = "https://static.igem.org/mediawiki/2014/c/c9/SBS_iGEM_2014_Polistes_dominula_gene_sequences.zip"> here </a> to download a .zip file containing the DNA and amino acid sequences for all six of our candidate genes, including original genomic predictions, codon-optimized versions, California variant sequences, and bacterial cloning mutations.</h6><br />
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<h6><center><i>Saccharomyces cerevisiae</i> transformants plated on uracil-deficient selective medium. Clockwise from top left: G2, U2, G1, RFP control. The cells were given glucose as a carbon source to repress pYES2.1/V5-His-TOPO® expression and help stimulate growth.</center></h6><br />
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<h6>As seen above, G1, G2, and U2 have successfully been transformed into yeast and we are in the process of culturing the yeast in uracil-deficient selective medium with galactose as a carbon source to induce pYES2.1/V5-His-TOPO® expression. Stay tuned for updates on this subset of the project!</h6><br />
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<h6>At the time of writing, U1 was received relatively late from the synthesis company, and so has not yet been transformed successfully into T7 E. coli for expression and purification. However, C1 and C2 have been transformed and expressed in T7 E. coli. To attempt to purify the proteins from cell lysate, we used the New England Biolabs Chitin Magnetic Beads protein purification kit, which isolates proteins that bind to chitin-coated magnetic beads. After eluting from the magnetic beads, we ran the purified C1 and C2 extracts on a polyacrylamide gel.</h6><br />
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<h6>This gel image shows that although C1 is absent, C2 was successfully purified with the chitin magnetic beads, suggesting that it exhibits chitin-binding activity. Pending cellulose-binding and waterproofing assays, C2 is a candidate for waterproofing both fungal mycelium and bacterial cellulose. Stay tuned for updates on this subset of the project!</h6><br />
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<h6>We successfully BioBricked C1, C2, and U1. Check out our <a href = "https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks"> BioBricks </a> page for links to the parts in the Parts Registry, where we’ll have the most up-to-date characterization data!</h6><br />
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<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
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<h6>In addition to the wasp protein waterproofing project, we searched literature for other organisms that would be able to produce a highly hydrophobic substance to waterproof the UAV. We stumbled across Marinobacter hydrocarbonoclasticus, a marine bacterium that synthesizes isoprenoid wax esters for use as storage compounds [1].</h6><br />
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<h6><center> A phytol-based wax ester produced by <i>Marinobacter hydrocarbonoclasticus</i>.</center></h6><br />
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<h6>Wax esters, such as the example shown above, consist of two very long carbon chains joined by an ester group. These chains impart great hydrophobicity to the entire molecule, indicating that a wax ester coating on a surface could be used to repel water. We decided to repurpose the wax ester synthesis genes in Marinobacter to create a pathway through which Escherichia coli could synthesize the wax esters and use them to waterproof our UAV.</h6><br />
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<h6>The two enzymes in Marinobacter that are involved in the production of isoprenoid wax esters were wax synthase 1 (WS1) and wax synthase 2 (WS2). In the presence of a fatty alcohol and fatty acyl-CoA (carboxylic acid derivative), either of these two proteins can catalyze the synthesis of isoprenoid wax esters.</h6><br />
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<h6>The DNA sequences for both WS1 and WS2 were obtained from the NCBI database [3][4]. They were then codon optimized for Escherichia coli using a codon optimizer from the Integrated DNA Technologies (IDT) website and then synthesized. After the genes were received from IDT, a polymerase chain reaction (PCR) was done to amplify the proteins and to ensure that they were present at around 1.4kb. The proteins were then ligated to a promoter and RBS and also into a chloramphenicol vector, then transformed into E. coli. After completing a colony PCR on the transformants, colonies 5, 7, and 11 on the WS2 plate appeared to contain the WS2 gene. Liquid cultures were inoculated with colonies 5, 7, and 11 to create more cells with the gene. After the cultures were grown in the incubator, they were miniprepped in order to extract their plasmid DNA. The DNA was then sent for sequencing with a 15ul reaction volume.</h6><br />
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<h6>After sequencing the data came back with miniprepped genes that aligned with the original synthesized gene for roughly half the length of the sequence, but with a puzzling truncation that was present in all three plasmid samples:</h6><br />
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<h6><center>WS2 sequencing alignment in Geneious 7.</center></h6><br />
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<h6>Unfortunately, the wax esters project had to be discontinued due to an error in the synthesis of the WS2 gene. In order to insert the gene into the vector, there are restriction sites that are cleaved by their respective restriction enzymes. When we codon-optimized WS2 for E. coli expression, we accidentally created a PstI cut site at base 720 in the synthesized gene. Because there was a cut site in the middle of the gene, only a fragment of the WS2 gene could ever make its way into the chloramphenicol vector, thereby disabling the protein the gene would have coded for. We also discovered that the fatty acyl-CoA reagent we intended to use for our enzymatic synthesis reaction was exorbitantly expensive (over $60 per milligram). For this reason, and because it would have been too costly to resynthesize the WS2 gene, the project was discontinued.</h6><br />
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1. Holtzapple, E <i>et al.</i> (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189: 3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>. <br></br><br />
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2. Holtzapple, E <i>et al.</i> (2007) WS1 sequence in <i>Marinobacter hydrocarbonoclasticus</i>. <a href = "http://www.ncbi.nlm.nih.gov/nuccore/EF219376">NCBI</a>. June 14.<br><br><br />
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3. Holtzapple, E <i>et al.</i> (2007) WS2 sequence in <i>Marinobacter hydrocarbonoclasticus</i>. <a href = "http://www.ncbi.nlm.nih.gov/nuccore/EF219377">NCBI</a>. June 14.<br><br><br />
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4. Kojima, Jun-ichi.; Carpenter, James M. (1997): A Taxonomic Note and Nest Description of an Australian Paper Wasp, <i>Polistes variabilis Fabricius Hymenoptera, Vespidae, Polistinae </i>. <i>Japanese Journal of Systematic Entomology</i>. November 15; 32: 117-122.<br><br><br />
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5. Toth, <i>et al.</i> (2014): <i>Polistes dominula </i>genome. <a href = "http://www.ncbi.nlm.nih.gov/bioproject/234105">NCBI</a>. January 10.<br><br><br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Safety">Safety</a></h3><br />
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<h7><center><a href="#" id="methods">Live Wasps</a> ● <a href="#" id="pics">Wasp Trapping</a><br><a href="#" id="chassis">Amberless Chassis</a> ● <a href="#" id="links">Safety Form</a> ● <a href="#" id="pics">Our Lab</a></h7><br />
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<h5 id="int"><center>Summary</h5><br />
<h6> The Rothschild lab hosts student interns at NASA Ames Research Center. As such, we must complete online training courses in chemical hygiene, hearing conservation, HAZCOM 2012, hazardous waste/environmental safety, and personal protective equipment, as well as an instructor-led lab safety practical. <br></br><br />
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The most important factor in good laboratory practice is personal safety. To that end, we adhered to the use of nitrile gloves for all lab work, as well as safety goggles for any work with irritants, toxic compounds, or liquid nitrogen. In addition, we divided our workspace into separate areas for computer work, bench work (for daily lab procedures), and work in the fume hood (for work with corrosives and organic solvents).</h6><br />
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<h5><center>Working with Live Wasps</center></h5><br />
<h6> The public often responded with surprise and occasional gasps whenever we mentioned that we caught our own wasps for our waterproofing project. Wasps have frightening reputations, mostly because they defend themselves with venomous stings – but just like bacteria, some wasps are safer than others. We were fortunate enough to work with the European paper wasp (<i>Polistes dominula</i>), which is a relatively non-aggressive species of paper wasp. We also caught our wasps under the guidance of Dr. Dave Kavanaugh, chairman and curator of entomology at the California Academy of Sciences. <br></br><br />
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Dr. Kavanaugh's tutelage got us through our wasp wranglin' escapade injury-free and with minimal stress to the wasps. He provided us with butterfly nets, with which it was an easy feat to catch the wasps in mid-air, as they fly rather slowly when near their nests. The wasps are not aggressive unless their nest is disturbed or if they've been trapped in a net. While both of these situations did inevitably occur, we were safe to insert gloved hands into the nets to coax trapped wasps into empty 2mL tubes, as the wasps cannot sting through nitrile gloves. Once in the tubes, we placed the wasps in an insulated container with ice. This calmed them down to a lethargic state, at which point we could proceed with dissections and RNA extraction. </h6> <br />
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<li><img src="https://static.igem.org/mediawiki/2014/e/eb/SBSiGEM2014_Wasps2.jpg"></li><h6>Kyla Ugwu examines a wasp she trapped during our specimen collection expedition at the beginning of summer 2014.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/8/8e/SBSiGEM2014_Wasps4.jpg"></li><h6>Our team of wasp trappers celebrates our success with California Academy of Sciences entomologist Dave Kavanaugh.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/71/SBSiGEM2014_Wasps11.jpg"></li><h6>A macroscopic image of a paper wasp we collected and stored in a tube of RNA<i>later</i>® for tissue preservation.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/f/f6/SBSiGEM2014_Wasps5.jpg"></li><h6>Ben Doughty and Eli Block swing in attempts to catch flying paper wasps in Petaluma, California. Our team collected wasps inhabiting the eaves of an old barn. Paper wasps are surprisingly docile, and no one on our team was ever stung!</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/27/SBSiGEM2014_Wasps1.jpg"></li><h6>Ian Hull collecting a live paper wasp inside a sterile jar. When trapped, paper wasps only ever move upward and so fitting them into collection tubes from below is quite safe.</h6><br />
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<h5><center>The Amberless Chassis</center></h5><br />
<h6>In addition to safe laboratory practices, we also examined environmental safety concerns. An end goal of our project would be the flight of our biological drone in nature, and if such a system contained live cells, we would run the risk of the horizontal transfer of our engineered genes to wild organisms, which would be unpredictable and potentially harmful to the ecosystem. To combat this, we are looking at the use of an “amberless” system in which the tRNA of our model organism is modified such that the UAG stop codon – called “amber” – codes instead for leucine. This way, if these genes are transferred to wild-type organisms, they will interpret the UAGs as a stop, and the resulting polypeptide would be truncated and non-functional.<br></br><br />
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We have discussed this system with Mark Segal at the Environmental Protection Agency, and we are beginning to examine the possibility of regulating the environmental testing of genetically-engineered organisms using an amberless chassis to prevent gene transfer. Stay tuned! </h6> <br />
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<h5><center>Safety Form</h5><br />
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<center>Our lab safety form can be found <a href = "https://igem.org/Safety/Safety_Form?team_id=1499"> <u>here</u> </a>.</center></div><br />
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<h5><center>Our Lab</h5><br />
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</p>Our lab is unique in that it is located in building 239, the Astrobiology and Life Sciences Research Laboratory, at NASA Ames Research Center. The surface of the building is pockmarked to look like the surface of the moon.<br />
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</p>Besides being the home for the Stanford-Brown-Spelman iGEM team, The Astrobiology and Life Sciences Research Laboratories contain the Human Environmental Test Facility and the Advanced Studies Laboratories (ASL), used for research in biomedicine, astrobiology, ecosystem science, Closed Ecological Life-Support Systems (CELSS), Environmental Controls and Life Support Systems (ECLSS), nanotechnology, and Synthetic Biology. The Astrobiology facilities include basic and applied research laboratories in astrochemistry, the cosmic evolution of biogenic elements and molecules, planetary pre-biotic chemistry, geology, the early organization and evolution of life, the evolution of complex organisms, and ecological studies. Some laboratory facilities include instrument development capabilities and analytical equipment for the characterization of gas and aqueous chemistry, instruments for the detection of various biomarkers including sugars and organics, microbiology facilities, including the culture of microbial mat communities and planetary protection testing, electron and Raman microscopy facilities, molecular biology capabilities, and bioinformatics computational capabilities. Laboratories in this facility are operated by NASA personnel and the University of California. <br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Lab">Lab Techniques, Special<br> Protocols, &amp; Project Journals</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="intro">General Protocols</a> ● <a href="#" id="methods">Special Protocols</a> ● <a href="#" id="data">Project Journals</a> </h7></div><br />
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<h5><center>General Protocols</h5><br />
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Here are links to pages documenting all of our laboratory protocols and procedures, from general lab techniques to special protocols we developed while working on our biological UAV project. For general laboratory protocols with an emphasis on NASA Ames Research Center lab practice, check out the <a href="https://static.igem.org/mediawiki/2013/5/5c/The_iGEMer’s_Guide_to_the_Galaxy_(Stanford-Brown).pdf" target="_blank">iGEMer's Guide to the Galaxy.</a><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Lab_Techniques1">● Using the autoclave, media, and generating antibiotic sticks</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Lab_Techniques2">● Plates, Liquid Cultures, and Cryostocking</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Lab_Techniques3">● Miniprep, Nanodrop, and Digestion</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Lab_Techniques4">● Gel Casting, Loading, Extraction, and Cleanup</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Lab_Techniques5">● Ligation and Chemically Competent Transformation</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Lab_Techniques6">● Everything Protein Gel Related</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Lab_Techniques7">● Everything PCR (Including Gel Extraction)</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Lab_Techniques8">● Primers and Sequencing</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Lab_Techniques9">● Using the iGEM Parts Registry</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Lab_Techniques10">● Culturing <i>Bacillus subtilis</i></a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Lab_Techniques11">● Site Directed Mutagenesis</a></div><br />
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<h5><center>New Protocols for 2014</h5><br />
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Our projects this year required a few specific protocols that aren't covered in our iGEMer's Guide to the Galaxy. Check them out <a href="https://static.igem.org/mediawiki/2014/1/1e/Protocols.pdf"><u>here!</u></a></div><br />
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<h5><center>Detailed Project Journals</h5><br />
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Our team documented all of our iGEM work in shared online documents that anyone can view at the following links. Having begun lab work in late May, our team recorded our progress over the course of the summer and into the fall, filing over four months of detailed notes on our many project. Check out the details below:<br />
<div class="sub4"><a href="https://docs.google.com/document/d/1ZH8qGKbhKb7Xe48ewJp8Ggznt0BzsDU9TQngtSh1HrA/edit?usp=sharing"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/c/c5/CelluloseAcetateProduction.pdf"><b>Biomaterials Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
<div class="sub4"><a href="https://docs.google.com/document/d/1-GabZY2igffoCGSQ0G8Oom92DnfuWyL9RcDAqq_X7EE/edit?usp=sharing"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/3/30/AmberlessHellCell.pdf"><b>Amberless Hell Cell Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
<div class="sub4"><a href="https://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/3/36/WaspProteins.pdf"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
<div class="sub4"><a href="https://docs.google.com/document/d/1-xtwn-iB2w-IVkquCmboShdY0sgYeFM80Uoc9zjVmXw/edit?usp=sharing"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/a/ad/Biodegradability.pdf"><b>Biodegradability Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
<div class="sub4"><a href="https://docs.google.com/document/d/1-BS2AXdxk_gbYPC2qc5T6e1L7qtnXg4DxsOF-aoxIQg/edit?usp=sharing"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/2/2b/Cross-LinkingAdapter.pdf"><b>Cellulose Cross-Linker Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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<a class="links" href="https://2014.igem.org/Team:StanfordBrownSpelman/Meet_Our_Team" target="_blank">Meet our team!</a><br />
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Built atop Foundation. Content &amp; Development &copy; Stanford–Brown–Spelman iGEM 2014.<br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/AttributionsTeam:StanfordBrownSpelman/Attributions2014-10-18T03:04:12Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Attributions">Attributions</a></h3><br />
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We would like to thank everyone who contributed to the realization of our project! All that we've done would not be possible with the contributions made by those listed below. On this page, we've listed contributors by project. We've also added sections for our sponsors and for those who made contributions to our team outside of specific projects. Thank you again, everyone, for all you've done!<br />
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<h5><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Cellulose_Acetate" target="_blank">Biomaterial Production</a></h5><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://www.bchs.uh.edu/people/detail/?155622-961-5=tcooper" target="_blank">Tim Cooper at University of Houston for <i>Pseudomonas fluorescens</i>.</a></div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://www.spelman.edu/academics/faculty/jean-marie-dimandja" target="_blank">Jean-Marie Dimandja at Spelman College for discussions of 2D GC Analysis.</div><br />
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<h5><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing" target="_blank">Wasp Proteins &amp; Material Waterproofing</a></h5><br />
<h6><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://research.calacademy.org/ent/staff/dkavanaugh" target="_blank">Dave Kavanaugh at the California Academy of Sciences for helping us trap wasps.</a></div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://research.calacademy.org/ccg/staff/dkapan" target="_blank">Durrell Kapan at the California Academy of Sciences for advising us on wasp transcriptome and genome analysis.</a></div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://ib.berkeley.edu/people/directory/detail/6000/" target="_blank">Michael Sheehan at UC Berkeley for helping us identify wasp species.</a></div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://www.brown.edu/Research/Wessel_Lab/" target="_blank">Gary Wessel and his lab staff at Brown University for their help with peptide mass fingerprinting.</a></div><br />
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<h5><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Biodegradability" target="_blank">Biodegradability</a></h5><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://web.mit.edu/voigtlab/">Christopher Voigt at MIT for providing plasmids necessary for making our biodegradation constructs.</a></div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="https://www.linkedin.com/pub/timothy-brown/36/ab4/441" target="_blank">Timothy Brown from Thermo Fisher Scientific for teaching us how to use the flow cytometer to collect GFP data. </a></div><br />
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<h5><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Human_Practices" target="_blank">Policy and Human Practices</a></h5><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://geo.arc.nasa.gov/sg/cv/esddir3cv-Brass.html">Jim Brass</a>, Kevin Reynolds, and Bob Dahlgren for consulting with us about building, flying, and using UAVs.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://sites.nationalacademies.org/PGA/brdi/PGA_053682">Mark Segal at the EPA for his input on the regulations surrounding the release of recombinant biological materials into the environment.</a></div><br />
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<h5><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Outreach" target="_blank">Outreach</a></h5><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a style="text-decoration: none" href="http://www.dna20.com/" target="_blank">DNA 2.0 for their advice, sponsorship, and facility tour.</a></div><br />
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<h5><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Building_The_Drone" target="_blank">Building the UAV</a></h5><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://www.ecovativedesign.com" target="_blank">Ecovative for the production of our mycelium drone components.</a></div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://agic.cc" target="_blank">AgiC for providing us with silver ink pens for producing drawn circuits and for printing circuits on our cellulose-based biomaterials.</div><br />
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<h5><center>Art &amp; Design</h5><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Wiki built using the Zurb Foundation framework and brought to you by iGEM and MediaWiki.</div><br />
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<h4><center>Special Thanks to Our Sponsors</center></h4><br />
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<a style="text-decoration: none" href="http://www.dna20.com/" target="_blank">DNA 2.0</a> ●<br />
<a href="http://www.mathworks.com/" target="_blank">Mathworks</a> ● <br />
<a href="http://www.idt.com/" target="_blank">IDT </a> ●<br />
<a href="http://www.geneious.com/" target="_blank">Geneious </a><br><br />
<a href="http://www.planetary.brown.edu/RI_Space_Grant/" target="_blank">Rhode Island Space Grant </a> ● <br />
<a href="http://www.gasgc.org/" target="_blank">Georgia Space Grant </a><br><br />
<a href="http://www.nasa.gov/centers/ames/cct/office/cif/2014/index.html#.U9wAQPldVKI" target="_blank">NASA Ames Directors’ Investment Fund </a><br><br />
<a href="http://www.brown.edu/about/administration/president/" target="_blank">Brown University Office of the President </a><br><br />
<a href="http://www.brown.edu/academics/college/fellowships/utra/" target="_blank">Brown University UTRA </a> ● <br />
<a href="http://bioengineering.stanford.edu/education/REU.html" target="_blank">Stanford University REU</a><br> <br />
<a href="http://www.nasa.gov/centers/ames/cct/index.html#.VDzeHeeEjhM" target="_blank">NASA Ames Office of the Center Chief Technologist </a> </center> <br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks">Outreach</a></h3><br />
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<h7><center><a href="#" id="intro">Introduction</a> ● <a href="#" id="pics">Outreach Events</a> ● <a href="#" id="methods">People Hosted</a></h7><br />
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Synthetic biology is still a poorly understood tool by the public at large and even students. Working at NASA, our team had the additional responsibility of educating Agency officials and other members of the federal government. To this end, our outreach this summer included participation in the following events.<br />
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<h5><center>Outreach Events</h5><br />
<h6><center>Here are some of the outreach events we conducted this year.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/1/17/SBS_BayAreaMaker.JPG"></li><h6>Held a synthetic biology booth at the Bay Area Maker Faire. (May 17th, 2014)</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/8/86/SBSiGEM2014OutreachUCSFHosting.jpg"></li><h6>Hosted a visit by the UCSF-UC Berkeley iGEM team to NASA. (June 13th, 2014)</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/ba/SBSiGEM2014OutreachNYMakerFaire2.jpg"></li><h6>Taught children about biology through strawberry DNA extraction at World Maker Faire in New York. (Sept 20th, 2014)</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/51/SBSiGEM2014OutreachCalAcademy.jpg"></li><h6>We presented our project to California Academy of Science scientists and public engagement personnel, seen posing with us here under the Academy's T-Rex! (Aug 20th, 2014)</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/bd/SBSiGEM2014OutreachWorden.jpg"></li><h6>Our team met with and presented to NASA Ames Research Center Director and team sponsor Pete Worden. (June 20th, 2014)</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/02/SBSiGEM2014OutreachJimHead.jpg"></li><h6>We presented our project to and engaged with Jim Head, distinguished Prof. of Geological Sciences and Mars researcher from Brown University. Here, Prof. Head feels one our biomaterial samples. (July 21st, 2014)</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/c/cd/SBSiGEM2014OutreachEllenStofanJuly22.jpg"></li><h6>Our team poses with Ellen Stofan, NASA Chief Scientist after having presented our work to her and given her a tour of our lab. (July 22nd, 2014)</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/3/38/SBSiGEM2014OutreachBena4.jpg"></li><h6>Our team presented to California Congressman Ami Bera about our work in synthetic biology. (Aug 27th, 2014)</h6><br><br />
<!-- ==DUPLICATE IMAGE== <li><img src="https://static.igem.org/mediawiki/2014/0/02/SBSiGEM2014OutreachJimHead.jpg"></li><h6>We presented our project to and engaged with Jim Head, distinguished Prof. of Geological Sciences and Mars researcher from Brown University. Here, Prof. Head feels one our biomaterial samples. (July 21st, 2014)</h6> --><br />
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<li><img src="https://static.igem.org/mediawiki/2014/f/f8/SBSiGEM2014OutreachStanford.JPG"></li><h6>Gave a synthetic biology presentation at Stanford's high school Bioengineering Bootcamp. (July 24th, 2014)</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/a/af/SBS_DNA2.0.JPG"></li><h6>Presented to our sponsors at DNA2.0. (Aug 1st, 2014)</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014OutreachNYMakerFaire1.JPG"></li><h6>Taught synthetic biology at booth in the World Maker Faire, New York. (Sept 20th, 2014)</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7f/SBSiGEM2014OutreachCalAcademyMargaratTreeClimber.jpg"></li><h6>Met with Margaret D. Lowman or tree climbing "Canopy Meg," Chief of Science and Sustainability at the California Academy of Sciences. (Aug 20th, 2014)</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/6/6d/SBSiGEM2014OutreachWordenWasp.jpg"></li><h6>Pete Worden, NASA Ames Research Center Director and team sponsor is seen here approving our team's wasp wrangling expedition. (June 20th, 2014)</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/78/SBSiGEM2014OutreachHead2.jpg"></li><h6>Our team posing with Jim Head during his visit to our lab. (July 21st, 2014)</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/77/SBSiGEM2014OutreachUCSCMeetupAugust15.jpg"></li><h6>We participated in the Northern California iGEM Meetup hosted by the UCSC iGEM team. In this photo, we're sharing our project with UCSC team members. (Aug 15th, 2014)</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/d/d7/SBSiGEM2014OutreachBena2.jpg"></li><h6>A small portion of our team posing with Congressman Ami Bera after having given him a tour of our lab and answered his questions about synthetic biology. This was particularly exciting as he is on the House Science Committee and an MD. (Aug 27th, 2014)</h6><br><br />
<!-- ==DUPLICATE IMAGE== <li><img src="https://static.igem.org/mediawiki/2014/7/77/SBSiGEM2014OutreachUCSCMeetupAugust15.jpg"></li><h6>We participated in the Northern California iGEM Meetup hosted by the UCSC iGEM team. In this photo, we're sharing our project with UCSC team members. (Aug 15th, 2014)</h6> --><br />
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<h5><center>Additional Outreach</h5><br />
<h6>In addition to some of the outreach documented above, our team was also involved in sharing idea about synthetic biology through the following outlets:<br />
<div class="sub5">● Brown University's 250th open house (Sept 27th, 2014)</div><br />
<div class="sub5">● NASA Ames 75th anniversary open house (Oct 18th, 2014)</div><br />
<div class="sub5">● Contributions to the Paris-Bettencourt iGEM Newsletter</div><br />
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<h5><center>People Hosted</h5><br />
<h6>We were honored with the opportunity to host the following individuals in our lab at NASA Ames, some of whom are pictured above.<br />
<div class="sub5">● Congressman Ami Bera of California’s 7th Congressional District from the U.S. House of Representatives</div><br />
<div class="sub5">● NASA Ames Center Director S. Pete Worden</div><br />
<div class="sub5">● NASA Chief Scientist Ellen Stofan</div><br />
<div class="sub5">● NASA Chief Technologist David Miller and staff</div><br />
<div class="sub5">● Dr. Barry Groves, Superintendent of Mountain View/Los Altos School District and his staff.</div><br />
<div class="sub5">● Professional explorer, educator, and environmental advocate <a href="http://www.joshbernstein.com/site.php?/home/" target="_blank">Josh Bernstein</a>.</div><br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Human_PracticesTeam:StanfordBrownSpelman/Human Practices2014-10-18T03:00:22Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Human_Practices">Uses of UAVs &amp; Orthogonal <br> Systems in Nature</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="intro">Intro</a> ● <a href="#results" id="pics">EPA Partnership</a> ● <a href="#" id="methods">Uses of UAVs</a> ● <a href="#" id="data">Interviews</a> ● <a href="#" id="project">Survey</a></div> <br />
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Unmanned Aerial Vehicles (UAVs) (also known as Unmanned Aircraft Systems <a href="http://www.uavs.org/index.php?page=what_is">(UAS)</a> have a long history of usage. According to <br />
<a href="http://www.draganfly.com/">DraganFly Innovations Inc.</a>, early UAVs took the form of balloons and they were primarily used for military purposes for monitoring and eliminating enemies in the battlefield. However, in recent years, UAVs have been increasingly used by civilians to accomplish various scientific and humanitarian missions. Due to their promising ability to accomplish tasks that otherwise could have been tedious, unreachable or even dangerous to civilians, our team has considered the idea of improving the current models of UAVs in order to make them more biodegradable, modular and even cheaper and hence increasing their accessibility and practicability to the scientific and civilian societies. <br />
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In the midst of our scientific design process and laboratory work, our team has taken into serious consideration the risks, ethics and stigma of using UAVs for civilian uses. Our aim in conducting this iGEM human practices project was to dive deep into the social and economic impacts of using synthetic biology in general. Our second aim was to consider how to work around the stigma present in society regarding the uses of UAVs. Part of this project was also to discuss the regulations and policies involved in the flying of civilian UAVs and assess the accessibility and practicability of the current civilian UAVs. The main reason of doing this human practices project was to bring our laboratory work closer to humanity by assessing the impacts of our creation to the general society.<br />
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<h5><center>Our Work with the EPA—<br>Synthetic Biology in the Air: Biological<br>UAVs and Environmental Safety Concerns</h5><br />
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We are currently working on a series of projects towards the construction of a fully biological unmanned aerial vehicle (UAV) for use in scientific and humanitarian missions. The prospect of a biologically-produced UAV presents numerous advantages over the current manufacturing paradigm. First, a foundational architecture built by cells allows for construction or repair in locations where it would be difficult to bring traditional tools of production. Second, a major limitation of current research with UAVs is the size and high power consumption of analytical instruments, which require bulky electrical components and large fuselages to support their weight. By moving these functions into cells with biosensing capabilities – for example, a series of cells engineered to report GFP, green fluorescent protein, when conditions exceed a certain threshold concentration of a compound of interest, enabling their detection post-flight – these problems of scale can be avoided.<br />
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However, housing live cells on an aerial system presents a new set of problems, chief amongst which is the concern of horizontal gene transfer. Bacterial cells can perform conjugation, a process which allows for plasmids (small, circular strands of DNA) to be copied from one organism to another. In our case, we must take steps to protect the environment from our cells and avoid the possibility that our engineered genes proliferate throughout ecosystems. In addition, not dissimilar to the problem of mechanical UAVs getting lost or crashing and leaching toxins into the environment, we must address what might happen if our biological UAV were to crash and allow the cells on its biofilm to act as an invasive species.<br />
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We are harnessing the power of genetic engineering to mitigate both of these scenarios. To address horizontal gene transfer, we are recoding the cells on our UAV to use the UAG stop codon, which usually signals the truncation of a protein during translation, as leucine, an amino acid, instead. When we engineer the genes we wish to transform into our cells (an “amberless” strain, as UAG stop is called “amber”), all instances of UAG will be replaced with different stop codons, and all leucine residues will be coded for by UAG. Thus, any engineered genes which are passed from an amberless cell to a normal cell in the environment will not be read correctly, as each leucine residue will be instead be interpreted as “stop” and will result in the production of a non-functional protein fragment. We hope that our amberless strain can be adopted as a model for any engineered organisms one might wish to send into the environment. <br><br><br />
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Finally, to address the invasion of our cells into the environment, we are engineering a pressure-sensitive “kill switch.” Upon crash, the cells will activate quorum sensing (a method of intercellular communication), which will act as a signal to begin producing a set of proteins which can degrade the cellulose acetate base material. Much of the UAV (minus the metals) will degrade into glucose, which can be taken up by organisms in the crash environment, while the engineered cells will be killed by the free acetate, which, hopefully, will be in high enough concentration to harm the biofilm but not the crash environment.<br><br><br />
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In order to determine whether these measures would be enough to ensure the safe and ethical dispersal of recombinant DNA into the environment, we have begun conversations with Dr. Mark Segal at the US Environmental Protection Agency. These talks served two purposes: to understand the current status of regulations surrounding the release of engineered biological materials into the environment and to see whether our adapted amberless concept might serve to clarify certain gray areas and push regulations forward, making them more sensible for researchers and safer for ecosystems everywhere. Currently, the study of genetically-modified organisms in the environment is covered under the 1997 <a href="http://www.gpo.gov/fdsys/pkg/FR-1997-04-11/pdf/97-8669.pdf" target="_blank">Microbial Products of Biotechnology</a> section of the Toxic Substances Control Act (TSCA), which says that limited, controlled trials are exempt from seeking EPA approval. However, the ultimate application of these recombinant cells in remote sensing capabilities (i.e. on a biodegradable UAV) would require approval from the EPA in the form of a TSCA Environmental Release Application (TERA). We are still in the process of determining whether our amberless concept could act as a case study for the EPA to consider when drafting new regulations. <br />
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<h5><center>Uses of UAVs</h5><br />
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In order to fully understand the benefits, risks and ethics of using UAVs and to address the stigma surrounding their use, we first had to get first hand information from experts who work on, or use UAVs for their daily activities. To do this, we conducted short interviews (15 minutes long) with experts from various fields including Earth Sciences, Planetary Sciences, Synthetic Biology and Remote Sensing. The responses from these experts increased our knowledge on the current uses of UAVs, the capabilities of the current models, the regulations of handling UAVs and the future of this technology. We have used (and will continue to use) these responses as a tool to increase awareness to people of the good uses of UAVs.<br />
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In addition to interviewing experts, we conducted a social survey that was aimed at getting the general public’s opinion on the uses of UAVs for civilian uses. This survey correlated people’s knowledge of UAVs and their opinions on UAVs and apart from helping us get a general sense of the public’s opinion, it has made us believe that, the more people know about the beneficial uses of UAVs, the more they will be willing to try, use or even add to this new technology.<br />
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<h5><center>Part A: Interviews</h5><br />
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We interviewed 5 experts: 4 from NASA Ames Research Center and 1 from Brown University. From NASA Ames, we had the honor to interview Dr. Lynn Rothschild (expert in synthetic biology and the supervisor of S-B-S iGEM team), Vince Ambrosia (Earth Scientist), Matthew Fladeland and Randy Berthold. From Brown University, we had a pleasure of interview Prof. Jim Head who is an expert on planetary sciences and Satellite missions. <br />
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All of the interviewees were asked the same questions ranging from how they use UAVs in their specific jobs, their opinion on synthetic biology and the future of UAVs. The links of the videos have been posted on our wiki. Before giving the detailed responses of the interviews, we have compiled the answers of each question so as to get a list of uses of UAVS. <br />
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<div class="sub4"><a href="https://static.igem.org/mediawiki/2014/9/9f/SBSiGEM2014_Human_Practices_of_UAVs.pdf"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/9/9f/SBSiGEM2014_Human_Practices_of_UAVs.pdf">Download our interview series with geology, synthetic biology, and engineering experts about their opinions of the uses of Unmanned Aerial Vehicles.</a></div><br />
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<h5><center>Individual Interview Links</h5><br />
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Prefer not to download? Here are links to each individual UAV-related interview that we conducted, viewable on our wiki:<br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Lynn_Interview" target="_blank">● Interview with Dr. Lynn Rothschild, NASA Synthetic Biologist</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Vince_Interview" target="_blank">● Interview with Vince Ambrosia, NASA Earth Scientist</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Randy_Interview" target="_blank">● Interview with Randy Berthold, NASA Earth Scientist</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Matt_Interview" target="_blank">● Interview with Matt Fladeland, NASA Ames Scientist</a></div><br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Jim_Interview" target="_blank">● Interview with Jim Head, Brown U. Planetary Scientist</a></div><br />
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<h5><center>Summary of Answers to Interview Questions</h5><br />
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Below is a short summary of the major responses for 3 main questions: the uses of UAVs, the value of Synthetic Biology in general and in creating UAVs and how to address the stigma of using UAVs:<br />
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1. Uses of UAVs <br></h6><br />
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a. Looking for life elsewhere: <br />
UAVs are excellent tools for searching for life in areas where it might be too dangerous for humans to reach. Dr. Lynn Rothschild sees the potential in using UAVs for searching for new life forms on planet earth and other planets. She also sees the potential of linking synthetic biology in creating cheaper, safer-for-the environment and biodegradable UAVs<br />
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b. Disaster analysis and Wildfire Control:<br />
Would you send a human being in the middle of a natural disaster, or would you rather send a machine? Vince Ambrosia and Matthew Fladeland see the potential in using UAVs for wildfire control, natural disaster analysis and recording of real-time sensory data in order to have a better control of disaster management. <br />
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c. Working in harsh, unreachable areas:<br />
Dr. Randy Berthold sees potential in using UAVs in places that are hard to reach, and in places where noise/increase of sound might disturb the measurements. UAVs could reach areas with toxic gases and extreme temperatures.<br />
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d. Planetary Exploration:<br />
Prof. Jim Heads from Brown University finds a lot of potential in using UAVs for geological and planetary Exploration. UAVs will be an efficient way to record sensory data which cover a large area without worrying about transporting astronauts and the costs that go with that.<br />
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e. Coast Guard Exploration:<br />
Dr. Philip McGillivard, a science liaison from Coast Guard PACAREA who has been working on Autonomous Underwater Systems (AUS) finds a lot of potential in linking UAVs with AUS in exploring coasts. UAVs can be used to explore and identify features of interest such as an oceanographic front or an iceberg and later, UAVs can communicate this information to surface vehicles which will then send the information to Underwater Systems in order to study that specific ocean area. <br />
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2. Opinions on Synthetic biology<br />
All of our interviewees believed that synthetic biology presents great potentials and capabilities in technology, especially in creating cheaper and eco-friendly technology. Most admitted that the scientific society is yet to discover the full potential of using synthetic biology for their missions. The experts working on UAVs reported that one of the limitations of using UAVs in their jobs is the payload of the UAV. They believed that UAV technology could be largely improved if synthetic biology can create cheaper and lighter payloads with high efficient capabilities.<br />
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3. How can we address the stigma surrounding UAVs?<br />
All the interviewees agreed that people have concerns on the uses of UAVs mainly due to their connection with military uses, and also due to fear of their personal safety. The interviewed experts said this stigma could be addressed effectively if people are informed on the beneficial uses of UAVs, and if they are informed of the policies and regulations present on the uses of UAVs. <br />
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<h5><center>Part B: Social Survey</h5><br />
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The social survey was conducted primarily for the purpose of collecting general public’s opinion on the use of UAVs for civilian uses. The survey was posted in many different social networks such as tweeter and Facebook. Due to the nature of the survey and how it was advertised, we ended up having most of the respondents (over 90%) of age from 18-25 years. The total number of people who responded to the survey questions was 117.<br />
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<h6>Q1: This chart above shows that over 90% of the respondents of this survey where of age 18-29. This shows a limitation of our survey in that it did not reach a wide range of public. However, the survey got opinions of young people who are the active creators and makers in our current world and their opinion is very important to the future of the scientific world.</h6><br></div><br />
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<h6>Q2: The aim of asking people this question was to get a sense of how educated people are and find out how their level of education connects to their general opinion on UAVs. As it is obvious from the graph, around 80% of the respondents were college students, and some had just recently graduated from college. It is interesting to know what college students think since they represent the youthful part of the society that is very creative and innovative and very influential to the society. </h6><br></div><br />
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<h6>Q3: Around 70% of the total respondents reported to have first heard of UAVs through television and through local and national newspapers. 6.84% of the respondents did not know what drones were. Around 10% of respondents reported to have first heard of drones in other ways that the question did not specify and these responses included the Internet, family members, high school courses and through online articles. In general, the media has been the main source of information on UAVs and their uses. This response shows us that we can utilize the media, especially various types of social media to educate people on good uses of UAVs.</h6></div><br />
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<h6>Q4: This question was probably one of the most interesting in the survey. Around 70% of the respondents reported that the first thing that comes to mind when they think of UAVs os UAVs for military uses. Even though most of the respondents were college students and they presumably have heard of other uses of UAVs, they still relate UAVs tp their military uses. However, around 10% of the respondents did appreciate that UAVs are a great technology that can be used constructively in other areas of life.</h6></div><br />
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<h6>Q5: Even though 70% of respondents related UAVs to military uses, around 45% thought that using drones for civilian uses is a good idea. Although this number is not too low, around half of the respondents (around 55%) either were not interested in the UAV technology or did not think it was a good idea to introduce drones as a tool to use for societal functions. This indicated a window of opportunity for us to educate more and more people, starting with college students (who are potential creators and makers of our society) and reach other communities at large.</h6></div><br />
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<h6>Q6: Interestingly, even though half of the respondents responded against the use of UAVs for civilian uses, 70% of the total respondents believed that UAVs could be beneficial to society. This could mean that people do believe in the technology of UAVs and its potential, but are afraid of its use in society. This fear could be due to uncertainty on the control of using UAVs and the fear about people's personal safety and privacy. However, if policies and regulations controlling the uses of UAVs were made transparent to the general public, more people would feel safer and more accepting towards the introduction of UAVs for civilian uses.</h6></div></div><br />
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</body></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Building_The_DroneTeam:StanfordBrownSpelman/Building The Drone2014-10-18T03:00:07Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Building_The_Drone">Prototyping a Biological UAV</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="data">Materials &amp; Designs</a> ● <a href="#" id="methods">Downloads</a> ● <a href="#" id="links">References</a> ● <a href="#" id="intro">Futures</a></h7></div><br />
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Our team modeled, prototyped, and collaborated with <a href="http://www.ecovativedesign.com">Ecovative Design</a> to grow a mycelium-based chassis for our biological drone. Below you'll find process photos, part designs, and links to open source model files for downloading and additively manufacturing your own biological or bio-inspired unmanned aerial vehicle. Finally, you can see images of the biological, biodegradable UAV that we built and flew!<br />
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<li><img src="https://static.igem.org/mediawiki/2014/6/66/SBSiGEM2014BTD2.JPG"></li><h6>Experimenting with cellulose material shape.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/e/e6/SBSiGEM2014BTD4.JPG"></li><h6>Layering cellulose to create thicker leather, see here at the back of the hood.</h6><br />
<li><img src="https://static.igem.org/mediawiki/2014/d/d3/SBSiGEM2014BTD5.jpg"></li><h6>Mycelium drone chassis, modeled and 3D-designed by our team, produced by Ecovative.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/5/57/SBSiGEM2014_Cellulose_Circuit.jpg"></li><h6>Our team collaborated with a silicon valley start up <a href="http://agic.cc" target="_blank">AgiC Inc.</a> to print circuits onto our cellulose-based biomaterials in order to prototype how fully biodegradable circuitry might function on a biological UAV. See our <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Cellulose_Acetate">Biomaterials</a> page for details on the conductivity of this circuitry.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014BTD3.jpg"></li><h6>Variable thickness elements and experimental fragment attachment methods.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014BTD8.JPG"></li><h6>Spreading a cellulose sheet out to dry.</h6><br />
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<h5><center>Starting Small, Ending Big</h5><br />
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We began by experimenting with producing cellulose in sheets and cellulose acetate non-biologically. Seeing that primarily cellulose materials are extremely strong and tough, but tear easily and becomes soggy when wet, we sought to increase the durability of the cellulose by grinding it into pieces to create a cellulose paste (that became spreadable into sheets like paper made from wood pulp) and stretching and twisting it into ropes to add strength. A few of our material samples follow: </h6><br />
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<h6><center>A spiral rope made by weaving together several cellulose sheets and dehydrating them.</center></h6><br />
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<h6><center>A piece of cellulose leather generated by laying multiple sheets of cellulose together in perpendicular orientations.</center></h6><br />
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While experimenting with cellulose-based materials, we also explored traditional starch bioplastics to compare material functionality. Here is an example of a starch bioplastic that we produced synthetically in the lab:</h6><br />
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<h6><center>Common starch bioplastic, which is more voluminous but less strong than bacterial cellulose. Starch bioplastics, like bacterial cellulose materials, suffer disintegration when wet.</center></h6><br />
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Realizing that cellulose acetate is tough but thin, our team was in need of a building material that was durable and lightweight. So, we reached out to Ecovative Design, a pioneering fungal-mycelium-based biomaterial company, to prototype a mycelium form that could serve as the chassis of our vehicle. Ecovative shipped us mycelium samples (pictured below), that we skinned in bacterial cellulose.</h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/5/57/SBSiGEM2014_Mycelium_Sample2.jpg"><br><br />
<h6><center>6" by 6" by 1" sample of Ecovative's lightweight mycelium-based biomaterial.</center></h6><br />
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<h6><center>A piece of fungal mycelium skinned in bacterial cellulose.</center></h6><br />
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Thanks to Ecovative, we were able to construct a prototype biological unmanned aerial vehicle!<br><br>But we didn't stop there. Our team was enthusiastic about drone design and so we developed concept UAV designs meant to inspire future scientists and designers to think outside the box about how a future, partially living vehicle might look. Pseudo-natural and pseudo-industrial, our drone design references the traditional biological architecture of birds while embracing industrial additive manufacturability.<br><br>All 3D printable files for this concept drone are available in the downloads section. Images of our work follow:</h6><br />
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<h6><center>Concept UAV Design</center></h6><br />
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<h6><center>Biological UAV Concept, Exploded View</center></h6><br />
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<h6><br />
We succeeded in producing multiple viable chassis designs for mycelium UAV concept prototypes. You can download our basic chassis designs here. If you would like to receive a copy of the designs for our more involved, final UAV concept (pictured above), then please reach out to us! We would love to share our work! In the meantime, download and check out our other models below:<br />
<div class="sub4"><a href="https://static.igem.org/mediawiki/2014/6/66/Mycelium_Drone_1.SLDPRT.zip"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/6/66/Mycelium_Drone_1.SLDPRT.zip">Mycelium_Drone_1.SLDPRT: Download a 3D printable STL file for a secondary drone chassis mold concept. Modeled in house and ready for printing, molding, and biomaterial casting.</a></div><br />
<div class="sub4"><a href="https://static.igem.org/mediawiki/2014/c/cd/Mycelium_Drone_2.SLDPRT.zip"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/c/cd/Mycelium_Drone_2.SLDPRT.zip">Mycelium_Drone_2.SLDPRT: Download a 3D printable STL file for a third drone chassis. Shaped like a paper airplane, this mold can be used to make testable mycelium drone prototypes.</a></div><br />
<div class="sub4"><a href="https://static.igem.org/mediawiki/2014/0/02/Mycelium_Quad_1.SLDPRT.zip"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/0/02/Mycelium_Quad_1.SLDPRT.zip">Mycelium_Quad_1.SLDPRT: Download a 3D printable STL file for our mycelium quad chassic (pictured at top of page), which can accept four motors and serve as the foundation for a DIY biomaterial drone prototype.</a></div><br />
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<h5><center>Links & References</h5><br />
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Interested in what we've been working on and want to find more relevant information? Check out some of the following sites, companies, and people who either aided us in our production of biomaterials or collaborated with us in working to produce a viable biological unmanned aerial vehicle.<br />
<div class="sub5"><a href="http://www.ecovativedesign.com">● Ecovative — Fungal-mycelium-based biomaterial production company</a></div><br />
<div class="sub5"><a href="http://issuu.com/miriamribul/docs/miriam_ribul_recipes_for_material_a">● Miriam Ribul's <i>Recipes for Material Activism</i> documents bioplastic production with household ingredients</a></div><br />
<div class="sub5"><a href="http://cookingobjects.com/images">● Cooking Objects — Understanding objects, objectivity, and our relationship with sustainably produced, biodegradable household objects</a></div><br />
<div class="sub5"><a href="http://www.nasa.gov/centers/ames/spaceshop/about/index.html#.VD8Hq4f9pbw">● The NASA Space shop, providing resources and tools for rapid prototyping at the NASA Ames Research Center</a></div><br />
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<h5><center>Drone Futures</h5><br />
<h6><br />
Here is a collection of drone-related sites and speculative work that stimulated our team to think about synthetic biology, the future, and the role of personal unmanned aerial vehicles or biological devices in an evolving world of DIY craft, government surveillance, and channelled creativity.<br />
<div class="sub5"><a href="http://www.fabrica.it/projects/drone">● Frabrica — <i>Drone</i>, speculative fictions in the age of the drone</a></div><br />
<div class="sub5"><a href="http://www.dronesurvivalguide.org">● Drone Survival Guide — a poster series highlighting the uneasy relationship between the public and drones</a></div><br />
<div class="sub5"><a href="http://diydrones.com">● DIY Drones — a growing online community of makers committed to building unmanned aircraft</a></div><br />
<div class="sub5"><a href="http://www.dezeen.com/2014/04/29/drone-shadows-graphics-james-bridle-designs-of-the-year-2014/">● Drone shadows, a visual reminder of constant surveillance</a></div><br />
<div class="sub5"><a href="http://www.huffingtonpost.com/2013/04/03/anti-drone-hoodie-adam-harvey-surveillance_n_3007064.html#slide=2295806">● Anti-Drone hoodie</a></div><br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Modelling">Bioinformatics & Modeling</a><br />
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<h7><center><a href="#" id="data">DoubleOptimizer</a> ● <a href="#" id="methods">CompositionSearch</a><br><a href="#" id="pics">Flux Balance Analysis</a></h7><br />
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<h6 id="subheader">While working on our synthetic biology projects for this year's iGEM competition, we found ourselves in need of some computational tools for synthetic biology that did not yet exist. Therefore, we developed our own software packages to meet these needs. In particular, we have developed two pieces of software, specifically designed for the needs of synthetic biologists:<b></b><br />
<div class="sub5">● DoubleOptimizer: a tool that facilitates synthesis of repetitive genes by optimizing codon usage to both match a codon usage distribution for a desired organism, and to reduce and avoid repetitive nucleotide sequences, allowing for easier synthesis.</div><br />
<div class="sub5">● CompositionSearch: a tool that quickly ranks all proteins in a proteome by their similarity to a given amino acid distribution.</div><br />
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DoubleOptimizer </h5><br />
<h6><center>A utility for simultaneous codon and <br>gene synthesis optimization<br></h6><br />
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<center><iframe width="420" height="315" src="//www.youtube.com/embed/sO1qd3eTzRo" frameborder="0" allowfullscreen></iframe><br><br><br />
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<h5>Background</h5><br />
<h6>Gene synthesis as a tool for biological engineering presents both opportunities and challenges. One opportunity presented is the ability to optimize codon usage in a gene to match that of a host organism. Compared to traditional cloning methods, this can increase protein yields in the host organism by several fold.[1] However, while there exist a large number of freely-usable programs that perform codon optimization, there is no guarantee that the sequences these programs provide will be able to be synthesized. Specifically, in the case of genes with repetitive amino acid sequences, these programs will often generate outputs that contain too many repeated short DNA sequences to be synthesized commercially. <br></br><br />
<br />
<br />
As an example, the hypothetical protein X777_06170 from the ant species <i>Cerapachys biroi</i> has an amino acid sequence that appears to be somewhat repetitive: <br><br><br />
<br />
<div class="sub6">001 mklfkclvpv vvlllikdss arpglirdfv ggtvgsilep fqilkpkdsy adanshasah <br></br><br />
061 nlggtfslgp vslggglssa sasssasang gglasasska daqaggygyg gsnanaqasa <br></br><br />
121 sanaqgggyg nggihgiypg qqgvhggnpf lggagsnana naiananaqa naggnngglg<br></br><br />
181 syggyqqggn ypidsstgpi gnnpflsggh gdgnanaaan anagasaign gggpidvnnp<br></br><br />
241 flhggaansg agginyqpgn aggiilsekp lglptiypgq hppayldsig spgansnaga<br></br><br />
301 napcsecgss gatilgyegq glggikesgs sgatilgyeg qglggikesg ssgatilgye<br></br><br />
361 gqglggikes gssgatilgs ydgqgpsgat ilgdyngqgl ggikessgvt vlgdyegqgl<br></br><br />
421 ggisgphggh gqaganagan ananagatvg ssggvlggvg dhggyhgyng hdgssglnlg<br></br><br />
481 gygggsnana qassnalass ggsssatsda lsnahssggs alanssskas angsgsanan<br></br><br />
541 ahassnassg shglgsktsa ssqasasadt rdmlifs[2]</div><br><br />
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<h6> Note that this sequence is not simply the same sequence repeated multiple times, but instead contains several motifs on the order of 10 - 20 amino acids in length that occur several times. When this sequence was run through the codon optimization program for expression in ''<i>E. coli</i>'' provided by a major DNA synthesis firm, the resulting output could not be synthesized by the very same firm: the "optimized" DNA sequence contained too many recurring short (> 8 nucleotide) DNA sequences to allow for synthesis.<br><br><br />
<br />
Manually correcting for repeats in the codon-optimized DNA sequence is a sub-optimal solution: not only is this process time-consuming, but it has the tendency to undo the codon-optimization: if a sequence of amino acids occurs several times, one may be forced to use all possible codon-combinations to represent this sequence to avoid nucleotide-sequence repetition. Unless corrected for by skewing codon usage elsewhere in the sequence, this will tend to make the codon usage more uniform than is optimal for the expression vector. Additionally, any changes made in either correcting for repeats or re-correcting for codon usage may in turn introduce additional repeats.<br />
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<h5><center>Solution: DoubleOptimizer</h5><br />
<h6>DoubleOptimizer is a software tool we have created to optimize codon usage in a gene both to match a given codon usage distribution and to avoid repetition of nucleotide sequences. Given a DNA or amino acid sequence and a desired codon distribution, DoubleOptimizer will produce, within a matter of seconds, an equivalent sequence that has substantially reduced DNA sequence repetition, while also closely matching the desired codon usage.<br />
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<h5><center>Availability and Usage</h5><br />
<h6>DoubleOptimizer may be downloaded <a href = "http://drive.google.com/a/brown.edu/file/d/0B6Q5Eo65G4cPZC1SZWEzbUtrYUU/view?usp=sharing"><u>here</u></a>. DoubleOptimizer is a command line utility, provided as a Java jar file. It can be invoked from command line on any system with Java 7 or later installed, with the following syntax:<br></br><br />
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<div class="sub6">java -jar DoubleOptimizer.jar seq.txt codons.txt <br></br> </div><br />
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where "seq.txt" is a DNA sequence, stored as a plain text file, and "codons.txt" is a file containing the desired codon distribution to match. It should be formatted as plain text, according to the following example template: <br><br><br />
<br />
<div class="sub6"><br />
A<br></br><br />
GCG .36<br></br><br />
GCC .27<br></br><br />
GCA .21<br></br><br />
GCT .16<br></br><br />
R<br></br><br />
CGC .40<br></br><br />
CGT .38<br></br><br />
CGG .10<br></br><br />
CGA .06<br></br><br />
AGA .04<br></br><br />
AGG .02<br></br><br />
N<br></br><br />
AAC .55<br></br><br />
AAT .45<br></br><br />
D<br></br><br />
GAT .63<br></br><br />
GAC .37<br></br><br />
C<br></br><br />
TGC .55<br></br><br />
TGT .45<br></br><br />
E<br></br><br />
GAA .69<br></br><br />
GAG .31<br></br><br />
Q<br></br><br />
CAG .65<br></br><br />
CAA .35<br></br><br />
G<br></br><br />
GGC .40<br></br><br />
GGT .34<br></br><br />
GGG .15<br></br><br />
GGA .11<br></br><br />
H<br></br><br />
CAT .57<br></br><br />
CAC .43<br></br><br />
I<br></br><br />
ATT .51<br></br><br />
ATC .42<br></br><br />
ATA .07<br></br><br />
L<br></br><br />
CTG .50<br></br><br />
TTG .13<br></br><br />
TTA .13<br></br><br />
CTT .10<br></br><br />
CTC .10<br></br><br />
CTA .04<br></br><br />
K<br></br><br />
AAA .77<br></br><br />
AAG .23<br></br><br />
M<br></br><br />
ATG 1<br></br><br />
F<br></br><br />
TTT .57<br></br><br />
TTC .43<br></br><br />
P<br></br><br />
CCG .52<br></br><br />
CCA .19<br></br><br />
CCT .16<br></br><br />
CCC .12<br></br><br />
S<br></br><br />
AGC .28<br></br><br />
AGT .15<br></br><br />
TCG .15<br></br><br />
TCT .15<br></br><br />
TCC .15<br></br><br />
TCA .12<br></br><br />
*<br></br><br />
TAA .64<br></br><br />
TGA .29<br></br><br />
TAG .07<br></br><br />
T<br></br><br />
ACC .44<br></br><br />
ACG .27<br></br><br />
ACT .17<br></br><br />
ACA .13<br></br><br />
W<br></br><br />
TGG 1<br></br><br />
Y<br></br><br />
TAT .57<br></br><br />
TAC .43<br></br><br />
V<br></br><br />
GTG .37<br></br><br />
GTT .26<br></br><br />
GTC .22<br></br><br />
GTA .15<br></br><br />
</div><br />
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(Note that the above example is actually the codon usage distribution of <i>E. coli</i>.[3]) <br></br><br />
<br />
DoubleOptimizer supports non-canonical codon assignments: the amino acid-codon groupings can by specified in whatever way the user wants in the codon distribution file.<br></br><br />
<br />
When executed, DoubleOptimizer will first display the input sequence with repetitive regions highlighted. It will also give the fraction of the sequence that initially consists of repetitive regions (defined by default as regions of 8 nucleotides or more that occur more than once in the sequence, including as their reverse complement), and a chi-squared value for the goodness-of-fit to the desired codon distribution (lower is better).<br></br><br />
<br />
DoubleOptimizer will then compute and display the optimized sequence (By default, it will produce the best sequence it can find after 10 seconds of computation time). Again, repetitive regions will be highlighted, and the same measurements of repetitiveness and codon fit will be given.<br></br><br />
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The following optional flags may be used to change the program's behavior:<br></br><br />
<br />
<div><h5><ul><li>-A</ul></h5></div><br />
<br />
This allows for an amino-acid sequence, specified in single-letter code, to be used as input instead of a DNA sequence. The initial sequence statistics displayed will be for a uniform random reverse translation of the given amino acid sequence. <br></br><br />
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Example: <br><br />
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<div class="sub6">java -jar DoubleOptimizer.jar aaseq.txt codons.txt -A <br></br><br />
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<div><h5><ul><li>-T##</ul></h5></div><br />
This allows the user to specify, in seconds, a different run-time for the program other than the default 10 seconds. While 10 seconds should be sufficient to produce a well-optimized result for most genes of moderate length on modern desktop computers, longer times may produce better-optimized results on slower machines or on longer sequences. <br></br><br />
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Example:<br><br />
<br />
<div class="sub6">java -jar DoubleOptimizer.jar aaseq.txt codons.txt -A -T30 <br></br><br />
<br />
</div><br />
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<div><h5><ul><li>-L##</ul></h5></div><br />
This allows the user to specify a different minimum length for what is considered a repeat, other than the default 8 nucleotides.<br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.txt codons.txt -L7 -T15 <br></br><br />
<br />
</div><br />
<div><h5><ul><li>-S##, -E##</ul></h5></div><br />
These allow the user to specify the starting and ending nucleotide, respectively, of the coding region in a construct sequence to be sythesized. Nucleotides outside this frame will be ignored for codon usage optimization, and will never be modified. This option is useful for preventing repetitions, within the coding region, of fixed sequences that occur at the ends of a construct to be sythesized, outside of the coding region. The default values are the beginning of the sequence, and the end of the last complete codon. These options may be used together or independently. Values are one-indexed. If used with ''-A'', these will be interpreted as amino acid indices.<br></br><br />
<br />
Example:<br><br />
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<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -R1000 -D100<br />
<br />
</div><br />
<div><h5><ul><li>-D##</ul></h5></div><br />
This option will make the program periodically display the current best sequence, and associated statistics, as it runs. The number given is the number of optoimization cycles the program will perform between each round of displaying the sequence. This provides a continuous measure of progress on long optimization runs. Note that, when given 10 seconds, the program may execute several thousand cycles of optimization, so an argument on the order of 100 may be reasonable.<br></br> <br />
<br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -R1000 -D100<br />
<br />
</div><br />
<div><h5><ul><li>-R##</ul></h5></div><br />
This option will make the program halt optimization after a certain number of optimization cycles. This may be used with a large value of -T to standardize optimization quality between computers of different speeds. Because this option is mostly only useful for testing the efficiency of this program itself, it may be removed from future releases.<br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -T1000 -R1000<br />
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<h5><center>Examples of Use</h5><br />
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<h6>As an example of the normal operation of the program on an amino acid sequence, consider the above-listed example of a repetitive amino acid sequence, hypothetical protein X777_06170 from the ant species <i>Cerapachys biroi</i>. At one point in our waterproofing project, this protein was considered for sythesis as a possible homologue of the waterproofing <i>Polistes</i> protein. DoubleOptimizer was originally written to overcome difficulties in synthesising this protein for expression in <i> E. coli</i>. (Due to further advances in the <i>Polistes</i> project, this gene was never synthesized). <b></b><br />
A screenshot of a run of DoubleOptimizer on the amino acid sequence for this protein (listed above), with the <i> E. coli</i> codon usage table also listed above, with default settings for 10 seconds on a laptop computer (2011 model Macbook Pro) is shown below: <br />
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<div class="small-10 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/8/85/AADoubleOptExample.png"><br />
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<br />
Note that the number of repeated sequences is significantly reduced, and the closeness-of-fit to the desired codon sequence is much improved, compared to the initial random reverse-translation. As an example of DoubleOptimizer run on a DNA sequence, a somewhat-repetitive <i>Homo sapiens</i> gene was arbitrarily selected from the NCBI database. Specifically, the exon sequence for <a href="http://www.ncbi.nlm.nih.gov/nuccore/18028443"><u>Ca+2-binding protein CBP86 form VII</u></a> was optimized for synthesis, again in <i> E. coli</i>. The same computer setup was used, and DoubleOptimizer was run for the default 10 seconds: <br />
</h6></div></div><br />
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<div class="small-10 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/0/09/DNADoubleOptExample.png"><br />
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<br />
In this case, the sequence went from being over 47% composed of repetitive sequences 8 nucleotides or more in length to being completely non-repetitive. Also, the codon usage distribution is now a near-perfect match to the <i> E. coli</i> codon usage distribution.<br />
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</h6><br />
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<!-- ======Algorithm ====== --><br />
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<h5><center>Algorithm</h5><br />
<h6>DoubleOptimizer uses a genetic optimization algorithm.[4] This algorithm mimics natural selection by taking the original gene sequence and generating several variants by copying the sequence and creating random silent point mutations in each copy. Each variant is then assigned a fitness score based on its repetitiveness and closeness-of-fit to the desired codon usage distribution, and the most fit variants are kept. These are then re-copied, re-mutated and re-scored, and again only the most fit variants are kept, through several thousand rounds of selection. When optimization time runs out, the most fit member of the population after the final round of selection is displayed. <br></br> <br />
One challenge in implementing this algorithm efficiently is that scoring for repetitiveness is a time-consuming process (the time belongs to O(n<sup>2</sup>) in the length of the sequence). Because many thousands of sequences are scored in a single optimization, finding a way to reduce the computational burden of this step was a task of paramount importance. Our solution was to store for each sequence a matrix representing the locations in the sequence where repetitions are already known to occur, and to only attempt to add or remove repetitions that could by affected by the point mutations that were made. This significantly improved run-time, compared to the unsophisticated technique of independently re-scoring each modified sequence.<br />
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<h5><center>References</h5><br />
<h6>1. Several references exist that establish the link between codon usage and expression. To give some highly-cited examples:<br> </br><br />
<br />
Ikemura, T. (1985) Codon usage and tRNA content in unicellular and multicellular organisms. <i>Mol Biol Evol. </i> 2 (1): 13-34. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/3916708" target="_blank">3916708 </a> <br></br> <br />
<br />
Gouy, M. and Gautier, C. (1982) Codon usage in bacteria: correlation with gene expressivity. <i>Nucl. Acids Res.</i> 10 (22): 7055-7074. PMCID: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC326988/" target="_blank"> PMC326988 </a> <br></br> <br />
<br />
Sharp, PM. and Li, W.-H. (1987) The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications <i>Nucl. Acids Res.</i> 15 (3): 1281-1295. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/3547335/" target="_blank"> 3547335 </a> <br></br> <br />
<br />
2. Protein sequence from NCBI, <a href="http://www.ncbi.nlm.nih.gov/protein/607359946" target="_blank"><u> here.</u></a><br> <br />
Original paper: Oxley PR, <i>et al.</i> (2014) The genome of the clonal raider ant Cerapachys biroi. <i> Curr Biol. </i> 24(4):451-8. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/24508170" target="_blank"> 24508170 </a> <br></br> <br />
<br />
3. Nakamura, Y. (2007) Codon Usage Database: Escherichia coli W3110. <a href="http://www.kazusa.or.jp/codon/cgi-bin/showcodon.cgi?species=316407"" target="_blank"> http://www.kazusa.or.jp/codon/cgi-bin/showcodon.cgi?species=316407</a>. <br></br> <br />
4. For a good introduction to genetic algorithms, see:<br></br> <br />
Goldberg, DE. (1989) <i>Genetic Algorithms in Search, Optimization, and Machine Learning.</i> Reading, Mass: Addison-Wesley Pub. Co.<br />
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<h5 ><center> CompositionSearch</h5><br />
<h6><center>A fast, local search of protein sequences<br>against a specified amino acid distribution</h6><br> </br><br> <br />
<h5><center>Background</h5> <br />
<h6>When the relative proportions of amino acids in an unknown protein product have been chemically determined, it is often useful to search a proteome for proteins that have similar amino acid distributions, in order to identify this protein product. While at least one online utility for performing this task already exists (provided by the <a href = "http://web.expasy.org/aacompident/aacompfree.html"> <u>Swiss Institute of Bioinformatics</u> </a>), the web-based nature of this program creates some limitations. Firstly, the SIB provides computational resources for the calculation, resulting in slower turnaround for the user (searches take about 15 minutes). Secondly, this program will only search for proteins already in the Swiss-Prot / TrEMBL databases. (At last check, only the smaller Swiss-Prot search was functional.) If an organism is being newly studied and has just been sequenced, its predicted proteome will not be in these databases. Thirdly, due to limited resources, only the top matches to a given search are provided. This does not allow for statistical comparison to the "typical" protein within a given proteome. Fourthly, for very-high-security tasks, uploading data to a third party may be undesirable.<br />
</h6><br />
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<!-- ====== CompositionSearch: Solution ====== --><br />
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<h5><center>Solution: CompositionSearch</h5><br />
<h6>CompositionSearch is a software tool we have created to address these issues by allowing an individual to rank all proteins in a proteome by similarity (minimum Euclidian distance) to a reference amino acid distribution locally on one's own computer. This ranking can be generated in a matter of seconds, rather than taking several minutes. Because it ranks all proteins in a proteome, CompositionSearch can also generate a figure for the significance of the similarity of a given protein to a given amino acid distribution, using the similarity of the rest of the proteome as a statistical distribution function. <br />
</h6><br />
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<!-- ====== CompositionSearch: Availability and Usage ====== --><br />
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<h5><center>Availability and Usage</h5><br />
<h6>CompositionSearch may be downloaded <a href= "http://drive.google.com/a/brown.edu/file/d/0B6Q5Eo65G4cPbnpQYlNlcU1QUE0/view?usp=sharing"><u>here</u></a>. <br></br><br />
<br />
CompositionSearch is a command line utility, provided as a Java jar file. It can be invoked from command line on any system with Java 7 or later installed, with the following syntax:<br></br><br />
<br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv <br></br> </div><br />
<br />
where "prot.fasta" is a FASTA-formatted proteome, and "freqs.txt" is a file containing the desired amino distribution to match. It should be formatted as plain text, according to the following example template:<br><br />
<br />
<div class="sub6"><br />
A 0.134 <br></br> <br />
D 0.044 <br></br> <br />
E 0.04 <br></br><br />
G 0.228 <br></br> <br />
I 0.033 <br></br> <br />
K 0.021<br></br><br />
L 0.04 <br></br><br />
P 0.08 <br></br><br />
R 0.009<br></br><br />
S 0.151<br></br><br />
T 0.038<br></br><br />
V 0.083<br></br><br />
</div> <br />
<br />
Note that this may contain as many or as few amino acids as desired. The frequencies, however, are interpreted as absolute, so if all amino acids are represented, they should add up to 1. (See below for discussion of how the optional -N and -X flags affect this interpretation). <br></br> <br />
<br />
"out.csv" in the above example line is the destination path to store the results of the calculation. The output will be a spreadsheet in csv format, which may be imported into your favorite desktop spreadsheet application (e.g. Microsoft Excel, LibreOffice Calc, etc.).<br></br><br />
<br />
After execution, out.csv will contain a spreadsheet showing the reference distribution, and, in order by similarity to the reference distribution, the amino acid distributions of all proteins in the proteome. Protein name, similarity ranking, similarity score (Euclidian distance between amino acid distributions; lower is better), and similarity p-value (on the curve of other proteins in the proteome) will be listed for each protein. <br></br><br />
<br />
The following optional flags may be used to change the program's behavior: <br></br><br />
<br />
<div><h5><ul><li>-N</ul></h5></div><br />
<br />
This will cause the program to ignore amino acids in the proteins that are not in the distribution list. In other words, this means that the frequencies given refer to frequencies relative to only the the other amino acids listed, instead of all amino acids. To clarify:<br></br><br />
<br />
Without the -N flag, the line in the above example distribution list: <br><br />
<div class="sub6"> P 0.08 <br></br> </div><br />
means that 8% of all amino acid residues in the matching protein are expected to be proline residues. <br></br><br />
<br />
With the -N flag, the line in the above example distribution list:<br><br />
<div class="sub6"> P 0.08 <br></br> </div> <br />
means that 8% of amino acid residues that belong in the set {ADEGIKLPRSTV} (the amino acids with defined frequences) in the matching protein are expected to be proline residues. <br></br><br />
<br />
Example:<br><br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv -N <br></br><br />
<br />
</div><br />
<div><h5><ul><li>-X$$</ul></h5></div><br />
This will cause the program to completely disregard certain amino acid residue symbols in the proteome, regardless of use of the N flag.<br></br><br />
<br />
The default value of this set of characters is 'X,' often used to represent unknown amino acid residues. Therefore the flag -XX is equivalent to normal behavior. Note that the set of characters being ignored is replaced by the -X flag, so it is always advisable to list X when using this flag.<br></br><br />
<br />
Example:<br><br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv -XX*-<br></br> </div><br />
<br />
This will cause the symbols X,*, and - to be ignored in the proteome. <br />
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Cellulose Pathway Modeling </h5><br />
<br />
<!-- ====== Flux Balance Analysis ====== --><br />
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<h6><center>Flux Balance Analysis</h6><br><br />
<br />
<h6><br />
Modeling allows for efficient experimental design by optimizing specific conditions before testing with in vitro methods. Flux Balance Analysis (FBA) is used to optimize the growth medium for the bioengineered <i>Gluconacetobacter hansenii</i> to yield bacterial cellulose to be used as biomaterial for a biodegradable drone. FBA is a mathematical method used to examine how metabolites relate to each network and makes predictions for the growth of an organism and product output. It is a direct application of linear programming to biological systems that used the stoichiometric coefficient for each reaction in the system as the set of constraints for the optimization. <br />
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<div class="small-8 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/9/97/SBS_ModelingFigure.png"><br><br />
<h6><center>Sample pathway, stoichiometric matrix, and constraints vectors.</center></h6><br />
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Flux Balance Analysis is perfomed under steady state conditions and requires information about the stoichiometry of metabolic pathways, metabolic demands, and strain specific parameters. At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption [3].<br />
<br />
</br></br>FBA will be used to optimize the growth conditions of <i>G. hansenii</i> <br />
in order to maximize the product output of cellulose. The exchange reactions determine the metabolites that are beneficial to the medium. Changing the composition will allow us to be able to determine the effect the composition has on the efficiency of the production media.<br />
</h6><br><br><br />
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<!-- ====== Model SEED- ModelView ====== --><br />
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<h5><center>Model SEED- ModelView</h5><br />
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<h6>Model SEED is an online analysis software with its own genome and media formulation database. An analysis of <i>G. hansenii</i> is done with the curated model including a biomass reaction set for optimal organismal growth and a control of complete media. The biomass reaction describes the rate, which all the biomass precursors are made in correct proportions [2]. A biomass reaction requires a knowledge of the composition of a cell and the energy requirements that allow for the cell to grow. The biomass equation consists of three levels: macromolecular level (RNA, protein), intermediate level (biosynthetic energy), advanced level (vitamins, elements, and cofactors). The biomass equation also list to stochiometric coefficients for each part of the biomass equation. Using this biomass equation the growth rate was determined to be 66.48 gm per biomass CDW/hr was obtained. Since the flux is positive this explains that the flux of the analysis is indeed towards growth a negative or zero flux would have explained that the pathway is not used or it is impossible. <br />
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<h5><center>Future Directions/Modifications</h5><br />
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<h6>Although a growth rate is obtained using Model SEED, this rate is for an environment where a complete medium is used. The growth medium being used contains dextrose, peptone, yeast extract, Na2HPO4, citric acid, agar and water. Model SEED does not have the capabilities to create a media file, so all further analysis will be done using KBase because it has the ability to customize the media used for analysis. Kbase is a software that allows for online analysis of models, where models and the corresponding media for organismal growth can be optimized. The genome database contains all genomes including the <i>G. hansenii</i> genome (as a JSON file). A full database for media that exist and capabilities to create the media being used in experimentation through the IRIS interface, which is the command line environment used by Kbase. Using IRIS and the subsequent FBA tutorial, a flux balance analysis was attempted but several issue were observed because of command line issues. Kbase will allow for the majority of modifications to be done in order to obtain the correct model and medium, further help will be sought out in order to conquer the error messages seen. Once the error messages are fixed, further literature searches and consultations will be done in order to change the biomass reaction to one that will target the product growth of cellulose instead of organismal growth of <i>G. hansenii</i>. <br />
Once an accurate growth rate with the proper medium is found, it will then be possible to plot the organism growth rate vs. product growth rate. Plotting this data will determine if there is a direct or inverse relationship between the two quantities.<br />
</h6><br><br><br />
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<!-- ====== References ====== --><br />
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<h5><center>References</h5><br />
<br />
<h6>1. Covert, M. W. and B. O. Palsson. Constraints-based models: regulation of gene expression reduces the steady-state solution space. J Theor Biol [Online] 2003 221(3), 309-325.</br></br><br />
<br />
2. Feist, Adam M, and Bernhard O Palsson. The biomass objective function. Current Opinion in Microbiology. [Online] 2010,13.3, 344-349. </br></br><br />
<br />
3. Orth, Jeffrey D, Ines Thiele, and Bernhard Ø Palsson. What is flux balance analysis? Nature Biotechnology [Online] 2010, 28.3, 245-248.</br></br><br />
<br />
4. Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nature Protocols [Online], 2011, 6:1290-1307.<br />
</h6><br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks">BioBricks</a></h3><br />
<br><br />
<h6><br />
A major goal of the iGEM foundation is to allow for the black-boxing of DNA parts that can later be utilized and recombined innovatively to create new, useful things. These black-boxed DNA parts are called BioBricks. Each of our projects has contributed to the <a href="http://parts.igem.org/Catalog">Registry of Standard Biological Parts</a>.<br />
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<h5 id="CA"><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Cellulose_Acetate">Cellulose Acetate</a></h5><br />
<h6><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499000"><u>Part BBa_K1499000</u></a>: This part is for the wssF region of the wss operon isolated from <i>Pseudomonas fluorescens</i>. This operon is responsible for the acetylation of cellulose.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499001"><u>Part BBa_K1499001</u></a>: This part is for the wssG region of the wss operon isolated from <i>Pseudomonas fluorescens</i>. This operon is responsible for the acetylation of cellulose.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499002"><u>Part BBa_K1499002</u></a>: This part is for the wssH region of the wss operon isolated from <i>Pseudomonas fluorescens</i>. This operon is responsible for the acetylation of cellulose.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499003"><u>Part BBa_K1499003</u></a>: This part is for the wssI region of the wss operon isolated from <i>Pseudomonas fluorescens</i>. This operon is responsible for the acetylation of cellulose.</div><br />
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<h5 id="CCL"><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Cellulose_Cross_Linker">Cellulose Cross Linker</a></h5><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499004"><u>Part BBa_K1499004</u></a>: This part encodes the expression of a cellulose cross linking protein. It contains two different cellulose binding domains taken from species <i>Clostridium cellulovorans</i>, and a streptavidin domain in between that allows living cells expressing a biotinylated AviTag to attach to the cellulose.</div><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499200"><u>Part BBa_K1499200</u></a>: This part encodes uvsE, a putative UV damage endonuclease found in <i>D. radiodurans</i> that protects cells from radiation-induced DNA damage.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499201"><u>Part BBa_K1499201</u></a>: This parts encodes uracil glycosylase 1, a protein involved in the base-excision repair pathway that protects cells from DNA damage by removing uracil residues from the DNA double helix.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499202"><u>Part BBa_K1499202</u></a>: This parts encodes uracil glycosylase 2, a protein involved in the base-excision repair pathway that protects cells from DNA damage by removing uracil residues from the DNA double helix.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499203"><u>Part BBa_K1499203</u></a>: This part encodes the SdaB protein generator with 5 UAG stop codons incorporated at leucine residues. Serine deamine B is a protein involved in resistance to alkaline conditions.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499205"><u>Part BBa_K1499205</u></a>: This part encodes the manganese transporter MntH with a UAG stop codon in place of a leucine and a promoter for expression. MntH is involved with manganese homeostasis, a critical process for dealing with free radicals generated upon exposure to radiation that could potentially cause DNA damage.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499250"><u>Part BBa_K1499250</u></a>: GFP (E0040) with two amber stop codons. This GFP contains two stop codons in place of leucine residues. In an amberless cell that also contains supP tRNA, GFP will be translated normally, otherwise, it will be truncated.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499251"><u>Part BBa_K1499251</u></a>: This part encodes the amber-suppressing tRNA supP, a mutant form of the tRNA leuX which normally inserts leucine at UUG. This is a UAG-leucine tRNA, so a leucine is added every time a UAG codon is encountered. 100bp upstream and 50bp downstream are also included to ensure proper splicing and folding.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499252"><u>Part BBa_K1499252</u></a>: This part encodes a GFP generator followed by the supP tRNA. The GFP produced is E0040 with 2 UAG stop codons in place of leucine residues, which can only be properly translated in amberless cells also containing supP.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499253"><u>Part BBa_K1499253</u></a>: This part encodes an aeBlue generator followed by the supP tRNA. The aeBlue produced is K864401 with 3 UAG stop codons in place of leucine residues, which can only be properly translated in amberless cells also containing supP.</div><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499400"><u>Part BBa_K1499400</u></a>: This is one version of a chitin binding proteins found in the <i> Polistes dominula </i> saliva that may be responsible for the waterproofing capability of cellulose seen in paper wasp nests. </div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499401"><u>Part BBa_K1499401</u></a>: This is another version of a chitin binding proteins found in the <i> Polistes dominula </i> saliva that may be responsible for the waterproofing capability of cellulose seen in paper wasp nests.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499402"><u>Part BBa_K1499402</u></a>: This is a completely uncharacterized protein from the<i> Polistes dominula </i> transcriptome that may be a major player in the paper wasp's ability to waterproof cellulose. </div><br />
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<h5 id="B"><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Biodegradability">Biodegradability</a></h5><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499500"><u>Part BBa_K1499500</u></a>: This is a part that encodes quorum sensing machinery to activate GFP expression. It is fundamentally a combination of two parts, <a href= "http://parts.igem.org/Part:BBa_I13202" >BBa_I13202</a> and <a href = "http://parts.igem.org/Part:BBa_T9002">BBa_T9002</a>. </div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/d/d2/SBS_iGEM_2014_bioBrick.png"><a href="http://parts.igem.org/Part:BBa_K1499501"><u>Part BBa_K1499501</u></a>: This is a part that encodes the endo-1,4-beta-glucanase, or cellulase gene. This protein is a means of breaking down cellulose and is is specific for 1,4-beta linkages in cellulose. It was isolated from<i> Neisseria sicca</i>. </div><br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Cellulose_Cross_LinkerTeam:StanfordBrownSpelman/Cellulose Cross Linker2014-10-17T23:36:54Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Cellulose_Cross_Linker">Cellulose Cross-Linker</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="intro">Introduction</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#CCL">BioBricks</a></h7></div><br />
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<h6 id="int"><br />
The goal of this subproject is to create a cellulose cross-linking protein to increase material strength and allow for the modular attachment of biological sensors. This fusion protein contains two distinct cellulose-binding domains[1] on either side of a streptavidin domain. The cellulose-binding domains cross link the cellulose fibers while the streptavidin serves as a binding domain for biological sensors. The interaction between SA (streptavidin) and biotin is one of the strongest non-covalent interactions in nature [2]. Therefore a cell expressing an outer membrane protein that has been biotinylated will bind tightly to this domain. This will allow our UAV to make use of a number of biological sensors.<br />
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<h5><center>Approach & Methods</h5><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/5/57/Cross_linker_SBSIGEM.png"><br><br />
<h6><b>Figure 1.</b> An illustration of cellulose binding domains cross-linking cellulose fibers with a streptavidin domain in the middle. The biosensing cell is expressing a biotinylated AviTag which will bind to the streptavidin .</center></h6><br />
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<h6><b></b>Our initial approach was to use the cellulose binding domains from <i>C. cellulovorans </i> <a href="http://parts.igem.org/Part:BBa_K863111">(part BBa_K863111)</a></a> on either side of the streptavidin domain <a href="http://parts.igem.org/Part:BBa_K283010">(part BBa_K283010) under a T7 promoter in the PSB1A3 backbone.</a></a>We also included a His-Tag for protein purification. The protein is then expressed in <i> E. coli </i>. Once purified, the cross-linking protein is tested on bacterial cellulose we grew in our lab from the organism <i>G. hansenii</i>. By dotting the protein on the cellulose, the cellulose binding domains will bind to the cellulose fibers and leave the streptavidin domain unbound and ready to bind biotin.</center></h6><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1-BS2AXdxk_gbYPC2qc5T6e1L7qtnXg4DxsOF-aoxIQg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/2/2b/Cross-LinkingAdapter.pdf">Click here to go to our project journal, which details our design and engineering process and included descriptions of the protocols we developed and used.</a></div><br />
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<h5><center>Results</h5><br />
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Using the same cellulose binding domain on either side of the streptavidin caused problems that lead us to revaluate our approach. Due to the repetitive nature of the sequence and potential homologous recombination, we had many issues with molecular cloning. We changed our approach to using two different cellulose-binding domains with different sequences. The first cellulose binding domain remained the same, but rather than repeating that same sequence on the other side of the streptavidin, we instead used the cellulose anchoring protein cohesin from the organism <i>C. cellulovorans </i>This allowed us to successfully conduct the molecular cloning.<br />
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<h6><b>Figure 2.</b> Sequencing data for the cross-linking protein. The solid green bar indicates a perfect match between our sequence and the expected sequence.The first 1000 base pairs are sequenced in this forward sequence.</center></h6><br />
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<h6><b>Figure 3.</b> Sequencing data for the cross-linking protein. The solid green bar indicates a perfect match between our sequence and the expected sequence.The last 1000 base pairs are sequenced in this reverse sequence. This in combination with the perfect sequencing of the first 1000 base pairs shows our construct matches the CBD-Streptavidin-CBD protein exactly.</center></h6><br />
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After obtaining a good sequence and inducing the protein with IPTG, we used a procedure similar to a western blot to test for functionality. After dotting the protein on bacterial cellulose and incubating with skim milk, unbound proteins were washed off with TBS buffer. The cellulose was then incubated with Biotin (5-fluorescein) conjugate which would bind to the streptavidin domain. While our results are still inconclusive, if we can detect fluorescence on the cellulose sheet, we will know that the cellulose binding domains are functional. The biotin conjugate will only bind to the streptavidin domain which will only be present on the cellulose sheet if the binding domains are functional. <br />
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<h5><center>References</h5><br />
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1. Linderm N and T T Teeri (1996) The cellulose-binding domain of the major cellobiohydrolase of Trichoderma reesei exhibits true reversibility and a high exchange rate on crystalline cellulose. <i>PNAS</i> 122251 PMID: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC37976/?page=1">24136966</a>.<br><br><br />
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2. Claire E. CHIVERS, Apurba L. KONER, Edward D. LOWE and Mark HOWARTH (2011) How the biotin–streptavidin interaction was made even stronger:<br />
investigation via crystallography and a chimaeric tetramer <i>Biochem.J.</i> 55 PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/21241253">2981802</a>.</a></div><br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Biodegradability">Biodegradability</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="intro">Introduction</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#B">BioBricks</a></h7></div><br />
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Creating a biodegradable unmanned aerial vehicle (UAV) will reduce collateral waste, lightening the footprint of unmanned science missions on planetary environments and micro-ecosystems. Since we are using Bacterial Cellulose Acetate (BCOAc) for the construction of our UAV, we investigated in nature to find organisms that can naturally break down Cellulose Acetate. We found out that <i>Niesseria sicca</i> uses cellulose acetate as its carbon source by producing two genes that break down cellulose acetate. The first gene is Cellulose Acetate Esterase which de-acetylates BCOAc, and the second is Endo-1,4-beta-glucanase, a cellulase that breaks down the glycosidic linkages in cellulose to produce glucose monomers.<br />
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<h6><b>Figure 1. </b> This image describes the degradation process, with the esterase enzyme de-acetylating cellulose acetate, and the cellulase enzyme breaking down the cellulose into glucose monomers.</h6><br />
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<h6>In order to trigger the onset of degradation our UAV at specific conditions, we investigated pressure-sensitive promoters for their abilities to stimulate degradation upon impact and time-sensitive promoters that will control the amount of time the UAV should fly before it degrades. After a series of experimentations, we chose to use quorum sensing as a time delay mechanism to control the length of time our UAV can fly before degradation enzymes are produced. <h6><br />
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<h5><center>Approach & Methods</center></h5><br />
<h6>We extracted the esterase and cellulase gene sequences from <i>Niesseria sicca</i> and transformed them in <i>E. coli</i>. Since the genes were synthesized with a T7 promoter, we induced the expression of the proteins by adding IPTG to the cell cultures. The Esterase was successfully purified and its presence was confirmed by observing a band at 43kDa when a protein gel was run. The activity of the Esterase enzyme was tested by soaking in blue stain reagent and testing for intensity of the stain. As for cellulase, we are currently working to express the cellulase in <i>E. coli</i> so as we can purify it and test its activity. </h6><br />
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<h6>In addition to working with the degradation enzymes, we created a mechanism where quorum sensing can cause a needed time delay for degradation so that our UAV does not degrade as soon as it starts to fly. Quorum sensing is a cell to cell communication mechanism, and here we use it to control when and how fast degradation should occur. </h6><br />
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<h6>Figure 2. </b>Our ideal quorum sensing system works in a loop: quorum sensing will activate a promoter that will trigger the expression of the degradation enzymes, which will activate positive feedback on the loop.</center></h6><br />
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<div class="sub4"><h6><a href="http://docs.google.com/document/d/1-xtwn-iB2w-IVkquCmboShdY0sgYeFM80Uoc9zjVmXw/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1-xtwn-iB2w-IVkquCmboShdY0sgYeFM80Uoc9zjVmXw/edit?usp=sharing">Click here to go to our project journal, which details our design and engineering process and included descriptions of the protocols we developed and used.</a></div><br />
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<h5><center>Results</h5><br />
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Our goal for this project was not only to isolate biodegradation enzymes but also to control the release of these enzymes, so that our UAV would not degrade uncontrollably. In order to control the initiation of biodegradation, we first considered using a pressure sensor. This would allow the UAV to begin degrading after the impact of a crash. The 2008 Tokyo Tech iGem team found that the ptet promoter was pressure sensitive and increased its activity 3-fold after undergoing 30 MPa of pressure. We got their construct from the distribution kit and tested its functionality by monitoring GFP expression. We found that this promoter is always turned on even at very low pressures (The cells were bright green even with no pressure at all). When we introduced a repressor for the Ptet promoter, we found out that the repressor is too strong, and even at high pressures, the Ptet promoter will still be repressed. The photo and graphs below show our testing of the Ptet promoter in the presence and absence of the Tet repressor. </h6><br />
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<h6><b>Figure 3. </b>A graph showing the relationship between pressure applied and expression of GFP under control of the ptet promoter.</center></h6><br />
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The graph above shows that there was not a clear correlation between the amount of pressure applied on the ptet promoter to the amount of GFP expression: For instance, application of 14000rpm pressure on cells with ptet promoter for 30 seconds led to production of less fluorescence than application of 5000rpm pressure to the same batch of cells. When Tet Repressor was used, its effect was too strong and we could not find a repressor that could inhibit ptet just enough to control the biodegradation process.<br />
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On top of testing the ptet promoter, we analyzed the impact a crash would have on our UAV by using a force plate ,and we found it unlikely that the impact of the crash would ever reach as high a pressure as 30 MPa, based on projections from the data we collected. (see graph below). <br />
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<h6><b>Figure 4. </b>A graph demonstrating the relationship between impact pressure and height of crash.</center></h6><br />
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In the graph above, we measured the pressure upon impact of an object similar in shape and weight to our UAV. We took measurements by dropping or throwing the object from various heights and angles. Based on our data, it is projected that even from a drop height of 10m, the pressure upon impact would not even be 1 MPa, making it unlikely that any crash would ever cause 30 MPa of pressure.<br />
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We next tried to initiate degradation using a light sensor, which would activate degradation in the darkness, allowing our UAV to have a flight time of one day. We planned to do this using the construct from the UT Austin and UCSF 2004 Coliroid project. However, the strain of <i>E. coli</i> (EnvZ) that we needed to work with to use this construct was resistant to all 4 of the main antibiotics we had in our lab, making the bacteria difficult and expensive to work with.<h6><br />
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<h6>Finally, we decided to use quorum sensing as a means of creating a time delay for initiation of degradation. Two previous constructs, BBa_I13202 and BBa_T9002, when combined, create a quorum sensing cascade used to initiate expression of GFP. We ligated these two parts together to create our novel part, BBa_K1499500, which we used for our assays. We found that, in lac deficient cells, the quorum sensing construct can be initiated by induction with IPTG. After this induction, GFP expression increases with time, implying that the construct is doing its job. Our data for this assay is shown below. </h6><br />
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<h5><center>Quorum Sensing Data</h5> <br><br />
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<h6><b>Figure 5.</b> Flow cytometer data & histogram plots from fluorescence testing for quorum sensing construct. </center></h6><br />
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<h6>Flow cytometry is a laser-based technology that has a wide-range of uses, including multiparametric cell counting and cell sorting. In this study, we utilized the cell counting ability of the Life Technologies Attune® NxT Acoustic Focusing flow cytometer in order to determine how many of our cells were expressing GFP after being induced with IPTG (a lac analog). The three graphs and tables above represent the flow cytometer data obtained from three samples of lac-deficient <i>E. coli</i> cells. One of the samples was a negative control of LB-cultured <i>E. coli</i> cells (Negative Control), and the other two samples (IPTG-Positive and IPTG-Negative) had the GFP-quorum sensing construct. The IPTG-Positive sample had a ~6X increase in GFP expression over the background IPTG-Negative cells, showing our construct worked correctly.<br />
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The “X-Median” seen on the three tables above shows the fluorescence intensity of all of the cells that were counted that were not considered dead (Median). <br />
Each sample measured 50,000 cells (Seen under All Events - Count), and then counted all of the cells that were alive (Median - Count), where each sample had between 47,000 and 49,000 live cells. From the data, and as illustrated in the graphs above, we can gather that the fluorescence intensity (X-Median) of the LB only cells was approximately 63 arbitrary fluorescence units (AFU), the IPTG-negative cells was ~17,000 AFU, and the intensity of the IPTG-positive cells was ~107,000 AFU, meaning that the IPTG-positive quorum sensing cells increased fluorescence intensity over 6X the background intensity (IPTG-negative cells).<br />
These results indicate that our quorum sensing construct works and greatly increases GFP expression when activated through IPTG-induction. The IPTG-negative cells also expressed some non-negligible degree of GFP, which suggests that the construct could still be improved further. However, the successful proof of concept of our quorum sensing construct is incredibly promising and allows us to proceed toward the next step.<br />
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Since we know that the quorum sensing construct is functional and inducible with IPTG. We can now work towards replacing the GFP gene with the genes for our degradation enzymes, allowing us to control degradation by applying IPTG at different time points.<br />
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In conjunction with working on controlling the initiation of degradation, we simultaneously worked with our two degradation enzymes, esterase and cellulase. After successfully transforming the esterase gene into E. coli, and confirming via colony PCR, we grew up a large culture of transformed cells and used this to extract and purify the esterase protein.<br />
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<h6><b>Figure 6. </b>This is an image of a gel electrophoresis done after running colony PCR with E. coli colonies transformed with our esterase gene (1.5 kB).</h6><br />
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<h6><b>Figure 7. </b>This is the SDS-page gel that was run after purifying the esterase protein from bacteria. The band is at 43 kDa, the expected size of the esterase protein.</h6><br />
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<h6>Once we had isolated our protein we were able to do functional assays with the esterase enzyme. By using a cellulose-binding dye that selectively binds to cellulose and not cellulose acetate, we were able to test whether or not the esterase enzyme was effective in de-acetylating commercial grade cellulose acetate. We soaked the cellulose acetate in the esterase protein at its optimal temperature of 30ºC and tested with the stain at multiple points. The results of our assay (shown below) demonstrate that over time the protein was working to degrade the cellulose acetate, as the blue stain intensity increased over time. In the future, we can better characterize our esterase protein by doing assays with more replicates, longer time periods, and varying amounts of protein.<h6><br />
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<h6><b>Figure 8. </b>Pieces of cellulose acetate were soaked in esterase enzyme for varying amounts of time before their level of degradation was tested using the blue cellulose-binding dye.</h6><br />
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<h6>We are currently working on functional assays of the cellulase gene, and have submitted it as a <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks">BioBrick </a><a href="http://parts.igem.org/Part:BBa_K1499501">(BBa_K1499501)</a>.<h6><br />
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<div class="sub5"><a href="http://cat.inist.fr/?aModele=afficheN&cpsidt=2524737">(1) Sakai K; Yamauchi T; Nakasu F; Ohe T. (1996) Biodegradation of cellulose acetate by Neisseria sicca. Bioscience Biotechnology Biochemistry.(10):1617-22.<br />
http://www.ncbi.nlm.nih.gov/pubmed/<br />
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<div class="sub5">(2) Barry Canton (2004) 3OC6HSL Sender Controlled by Lac Repressible Promoter. Registry of Biological Parts. <a href = http://parts.igem.org/Part:BBa_I13202"> Part:BBa_I13202 </a></div><br />
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<div class="sub5"><a href="http://onlinelibrary.wiley.com/doi/10.1002/047122197X.ch16/pdf">(5) Fuqua, Clay; R. Parsek, Mattew (2002) Molecular Mechanisms of Quorum Sensing Modern Microbial Genetics 2:362-382. ISBNs:0-471-38665-0 </a></div><br />
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<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="wax">Alternate Approach</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wishes to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<h6> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. Until now, scientific knowledge of this protein coating was limited mainly to total amino acid composition of all the proteins in the paper. To gain more insight into the specific proteins that may exist in wasp nest paper, we collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. We believe there may be a single protein in wasp saliva that is chiefly responsible for the hydrophobic nature of their nests. </h6><br />
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<h6>Our ultimate goal is to identify the gene that codes for this wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nests we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. We ran the proteins on two polyacrylamide gels, one with a ten minute 70ºC heat denaturation step and the other without. We then excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.<br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running of a wasp protein gel.</a></p></center><br><br />
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<h6>Our initial plan was to extract RNA from female nest-building wasps so we could purify their messenger RNA, generate a complementary DNA library, and get the library sequenced for use as a reference transcriptome. Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. The genome was used as a reference for peptide mass fingerprinting, we saved our RNA extracts for eventual RT-PCR amplification, and the project moved onwards. We truly live in an exciting time for genetic engineering!<br />
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<h6> Once we obtained the mass fingerprinting data, we were faced with a long list of uncharacterized peptide fragments with hits in the Polistes dominula genome. We used the NCBI Position-Specific Iterated Basic Local Alignment Search Tool, or PSI-BLAST, to search online databases for proteins with similar sequences and domains. This gave us hints towards the possible functions of these uncharacterized proteins.</h6><br />
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<h6>We then narrowed our list down to six proteins with favorable PSI-BLAST hits and obtained the DNA sequences for these genes from the genome. Three of these genes were codon-optimized and synthesized for E. coli expression in the pF1A T7 Flexi® vector, while the other three were amplified from wasp RNA via RT-PCR for S. cerevisiae expression in the pYES2.1/V5-His-TOPO® vector.</h6><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><h6><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></h6></div><br />
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<h5><center>Results</h5><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
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<h6>In addition to the wasp protein waterproofing project, we searched literature for other organisms that would be able to produce a highly hydrophobic substance to waterproof the UAV. We stumbled across Marinobacter hydrocarbonoclasticus, a marine bacterium that synthesizes isoprenoid wax esters for use as storage compounds CITATION NEEDED.</h6><br />
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<h6><center> A phytol-based wax ester produced by <i>Marinobacter hydrocarbonoclasticus</i>.</center></h6><br />
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<h6>Wax esters, such as the example shown above, consist of two very long carbon chains joined by an ester group. These chains impart great hydrophobicity to the entire molecule, indicating that a wax ester coating on a surface could be used to repel water. We decided to repurpose the wax ester synthesis genes in Marinobacter to create a pathway through which Escherichia coli could synthesize the wax esters and use them to waterproof our UAV.</h6><br />
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<h6>The two enzymes in Marinobacter that are involved in the production of isoprenoid wax esters were wax synthase 1 (WS1) and wax synthase 2 (WS2). In the presence of a fatty alcohol and fatty acyl-CoA (carboxylic acid derivative), either of these two proteins can catalyze the synthesis of isoprenoid wax esters.</h6><br />
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<h6>The DNA sequences for both WS1 and WS2 were obtained from the NCBI database CITATION NEEDED. They were then codon optimized for Escherichia coli using a codon optimizer from the Integrated DNA Technologies (IDT) website and then synthesized. After the genes were received from IDT, a polymerase chain reaction (PCR) was done to amplify the proteins and to ensure that they were present at around 1.4kb. The proteins were then ligated to a promoter and RBS and also into a chloramphenicol vector, then transformed into E. coli. After completing a colony PCR on the transformants, colonies 5, 7, and 11 on the WS2 plate appeared to contain the WS2 gene. Liquid cultures were inoculated with colonies 5, 7, and 11 to create more cells with the gene. After the cultures were grown in the incubator, they were miniprepped in order to extract their plasmid DNA. The DNA was then sent for sequencing with a 15ul reaction volume.</h6><br />
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<h6>After sequencing the data came back with miniprepped genes that aligned with the original synthesized gene for roughly half the length of the sequence, but with a puzzling truncation that was present in all three plasmid samples:</h6><br />
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<h6><center>WS2 sequencing alignment in Geneious 7.</center></h6><br />
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<h6>Unfortunately, the wax esters project had to be discontinued due to an error in the synthesis of the WS2 gene. In order to insert the gene into the vector, there are restriction sites that are cleaved by their respective restriction enzymes. When we codon-optimized WS2 for E. coli expression, we accidentally created a PstI cut site at base 720 in the synthesized gene. Because there was a cut site in the middle of the gene, only a fragment of the WS2 gene could ever make its way into the chloramphenicol vector, thereby disabling the protein the gene would have coded for. We also discovered that the fatty acyl-CoA reagent we intended to use for our enzymatic synthesis reaction was exorbitantly expensive (over $60 per milligram). For this reason, and because it would have been too costly to resynthesize the WS2 gene, the project was discontinued.</h6><br />
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<h5><center>References</h5><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
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2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
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3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Amberless_Hell_CellTeam:StanfordBrownSpelman/Amberless Hell Cell2014-10-17T23:35:53Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Amberless_Hell_Cell">Amberless Hell Cell</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="intro">Introduction</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#AHC">BioBricks</a></h7></div><br />
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For an application of synthetic biology where live, genetically-modified cells will come in direct contact with the environment, such as biological sensors on a UAV, two concerns must be addressed. First, the cells need to be resistant to widely-varying conditions that may be present in the environment. Second, in order to address ethical concerns about releasing genetically-modified organisms, it is desirable to reduce horizontal gene transfer from the engineered cells into cells naturally present in the environment. In order to solve both of these issues, and therefore to create an ideal chassis for synthetic biology in environmental applications, we will combine two research goals:<br />
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<b>1.</b> The <a href="https://2012.igem.org/Team:Stanford-Brown/HellCell/Introduction" target="_blank">"Hell Cell" project</a> by the 2012 Stanford-Brown iGEM team isolated genes from extremophile bacterial species and inserted them into <i>Escherichia coli</i>, in order to create bacteria that are resistant to extremes in pH, temperature, and moisture. We sought to further characterize, improve, and search for new resistance genes that would help our chassis survive in earth and space applications. <br />
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<b>2.</b> The <a href="http://arep.med.harvard.edu/" target="_blank">Church Lab</a> at Harvard Medical School in 2013 created a strain of <i>E. coli</i> <a href="http://www.addgene.org/49018/" target="_blank">(C321.ΔA)</a> in which all 321 instances of the UAG ("Amber") stop codon in the <i>E. coli</i> genome had been replaced with the UAA stop codon [1]. Release factor 1, which terminates translation at UAG, was also removed. With this system, the Church group incorporated artificial amino acids with a tRNA that recognizes UAG as its codon. </div></div> <br />
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<h6><b>Figure 1.</b> Our approach to the Amberless Hell Cell. By recoding the UAG stop codon to translate into an amino acid, only cells that have a tRNA with the anticodon AUC will produce the complete protein. In our experiment, we used a tRNA that charges with leucine to translate the UAG codon.</h6><br />
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We developed a novel approach for preventing horizontal transfer of engineered genes into the environment by inserting a UAG-leucine tRNA, and using UAG for leucine in all of the inserted, engineered genes. The engineered genes will not have any effect in naturally-occurring bacteria in the environment, which lack the ability to translate UAG into leucine. We call this strategy <b>Codon Security</b>. Our project involves synthesizing UAG-leucine coded versions of the Hell Cell genes and inserting them into the amberless <i>E. coli</i> strain, along with a UAG-leucine (supP) tRNA [2]. This will create a strain of bacteria that is both resilient and safe for environmental applications.<br />
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<h5><center>Approach & Methods</h5><br />
<h6>We were interested in two avenues of research. The first was test our hypothesis that the amberless chassis would enable us to create an orthogonal protein expression system that would not function properly in other bacteria. By replacing 2-4 leucines with TAG stops in a gene, we make it such that an organism that does not express the supP tRNA, which translates UAG into leucine, produces a truncated product. We have named this novel system <b>Codon Security</b>. The second goal of the project was to apply Codon Security to the Hell Cell genes from our 2012 team in order to limit the horizontal transfer of resistance genes when using synthetic biology in the environment. Applying the Leucine->Stop modifications to the Hell Cell genes and transforming them into amberless cells, we could make the world's first <b>Amberless Hell Cell</b>.<br />
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<h6><b>Figure 2.</b> We transformed DH5-alpha and amberless cells with test plasmids containing GFP or aeBlue reporter genes with stop codons and the supP tRNA in order to establish a proof-of-concept for Codon Security.</h6><br />
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<h6>In order to test our hypothesis for Codon Security, we designed two test plasmids with GFP and aeBlue reporter genes. The BioBrick parts pages for these constructs are <a href="http://parts.igem.org/Part:BBa_K1499252" target="_blank"><u>BBa_K1499252</u></a> and <a href="http://parts.igem.org/Part:BBa_K1499253" target="_blank"><u>BBa_K1499253</u></a> respectively. The GFP generator construct was synthesized by taking the <a href="http://parts.igem.org/Part:BBa_E0040" target="_blank">BBa_E0040</a> sequence, then modifying two leucine codons into TAG stops. Similarly, the aeBlue construct was modified from iGEM Team Uppsala 2012's chromoprotein <a href="http://parts.igem.org/Part:BBa_K864401" target="_blank">BBa_K864401</a> with three TAG stops coding for leucine. We chose to have the supP tRNA, which translates UAG to leucine, downstream of the terminator for the reporter genes. The supP tRNA sequence was found in Thorbjarnardóttir <i>et al.</i> [2]. However, we decided to include 100bp downstream and upstream of the tRNA coding region in the original organism to include any native promoters and assembly sequences to ensure normal tRNA expression in <i>E. coli</i>. Thus we created a BioBrick of the supP tRNA that included 100bp upstream and downstream of the tRNA gene and validated that it indeed worked in amberless cells: <a href="http://parts.igem.org/Part:BBa_K1499251" target="_blank"><u>BBa_K1499251</u></a>.<br><br><br />
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We thought of several design strategies for implementing Codon Security. Among them were placing the supP tRNA on a separate plasmid, incorporating supP into the amberless genomic DNA, and placing it under an inducible promoter. We moved forward with a testing construct with the reporter gene + stop codons and tRNA in the same plasmid. The reason was that we wanted to perform the hardest test on system first and then employ more creative solutions only if it failed this robustness test. With the gene and tRNA on the same construct, it is easier for non-amberless cells to express the protein. However, we hypothesized that the supP tRNA would be toxic enough to non-amberless cells, because it would read through UAG stops, that mutations to the tRNA would be selected for. We tested this hypothesis by transforming DH5-alpha and amberless cells with the test plasmids and measuring protein output.<br />
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<h6><b>Figure 3.</b> Workflow for Amberless Hell Cell side of the project. We isolated 5 radiation resistance genes from <i>D. radiodurans</i> genomic DNA using PCR. We moved forward with 2 candidates, uvsE and MntH. We first tested the radiation resistance they conferred in DH5-alpha. Then, we performed mutagenesis PCR at 2-3 leucine codons to create nonsense mutations, tested non-functionality in DH5-alpha, and recovered radiation resistance in amberless by adding the supP tRNA construct.</h6><br />
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<h6>For the Amberless Hell Cell, we are taking resistance genes found in extremophiles in nature and mutating leucine codons into stop codons before putting the genes into the Amberless chassis. In this way, we produce cells that can withstand stresses, like dessication, pH, and radiation, but cannot transfer those capabilities to other organisms. We focused on radiation resistance genes this summer, drawing both from bricks produced by the 2012 Stanford-Brown iGEM Team and new bricks produced by this year's team.<br><br><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1-GabZY2igffoCGSQ0G8Oom92DnfuWyL9RcDAqq_X7EE/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1-GabZY2igffoCGSQ0G8Oom92DnfuWyL9RcDAqq_X7EE/edit?usp=sharing" target="_blank">Click here to go to our project journal, which details our design and engineering process and included descriptions of the protocols we developed and used.</a></div><br />
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<h5><center>Results</h5><br />
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We first tested the <b>Codon Security</b> hypothesis with the GFP test plasmid. We cloned the synthesized GFP+tRNA construct into pSB1C3. We initially tried to transform it into DH5-alpha cells, but effeciency was very low. Sequencing of the growing colonies showed interesting deletions or mutations in the tRNA portion of the construct, giving some evidence supporting our hypothesis that the tRNA was able to be expressed but toxic to the cells. We then BioBricked the GFP and tRNA portions separately. The GFP with 2 stop codons but no tRNA (GFP-2S) was stably transformed into DH5-alpha and amberless cells, and as expected, neither cell-type had fluorescent colonies. The tRNA biobrick could not be cloned in DH5-alpha cells; all colonies came back with mutations in the tRNA, further supporting the idea that non-amberless cells cannot tolerate the supP tRNA. We could only biobrick the tRNA in the Amberless chassis.<br><br><br />
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We were then able to generate amberless clones that had the full sequence-verified GFP-2S+tRNA construct in pSB1C3. These were significantly fluorescent, compared to the amberless with just the GFP-2S that had no measurable fluorescence. This demonstrated that the supP tRNA was expressed and translated UAG codons into leucine to produce the complete protein product. We then took a fluorescent amberless GFP-2S+tRNA clone, miniprepped the construct, and attempted a heat-shock transformation under the exact same conditions for competent DH5-alpha and amberless cells. Although there were more successful transformants in the amberless plate, both plates had visibly fluorescing cells. In order to accurately compare GFP expression, we grew one fluorescent clone from DH5-alpha and amberless for flow cytometry analysis.<br />
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<h6><b>Figure 4.</b>FACS histograms showing amberless cells strongly express GFP while there is a mixed population of expressing and non expressing wild type cells.<br />
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Figures 4 and 5 demonstrate that GFP expression is 40X greater in amberless <i>E. coli</i> than in wild type <i>E. coli</i> given they both have the supP tRNA . The wild type cells with the GFP-2S serve as a negative control. In wild type cells that contain the GFP-2S and supP tRNA, fluorescence is observed in a small population of cells, and it is much less intense than in the amberless cells. Normally, the supP tRNA is toxic to wild type <i>E. coli</i>, but when these cells expressing some GFP were sequenced, we found mutations in the tRNA. This indicates that with some mutations, the supP tRNA can be tolerated in wild type cells. There mixed population of low-GFP expressing and non-expressing DH5-alpha cells suggest that the mutations render less efficient tRNAs that impair the translation of the complete protein product. This indicates that our Codon Security strategy is effective in significantly reducing protein expression in wild type cells. <br />
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<h6><b>Figure 5.</b> A bar graph from FACS data showing a high mean fluorescence in amberless cells and a much lower mean fluorescence in wild type cells due to a mixed population.<br />
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<h6>Our tests with the aeBlue+tRNA construct further demonstrates the effectiveness of Codon Security in preventing gene expression in non-amberless <i>E. coli</i>.<br />
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<h6><b>Figure 7.</b> Figure caption here.<br />
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<h6><b>Figure 8.</b> Figure caption here.<br />
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1. Lajoie, MJ <i>et al.</i> (2013) Genomically Recoded Organisms Impart New Biological Functions. <i>Science</i> 342: 357-60. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/24136966" target="_blank">24136966</a>.<br><br><br />
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2. Thorbjarnardóttir, S <i>et al.</i> (1985) Leucine tRNA family of Escherichia coli: nucleotide sequence of the supP(Am) suppressor gene. <i>J. Bacteriol.</i> 161: 219–22. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/2981802" target="_blank">2981802</a>.</a></div><br />
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● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Human_Practices#EPA"><u>Read about</u></a> how our submitted Amberless Hell Cell idea was used as a government regulatory case study on synthetic biology. We then began a conversation with Dr. Mark Segal at the EPA about the regulation and safety of the use of engineered bacteria in the environment.<br><br><br />
● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#AHC"><u>Submitted biobricks</u></a>: We submitted 9 biobricks for this sub-project. Six of these bricks include parts that can enable other teams to use the Amberless chassis as a system for more responsible synthetic biology.<br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Cellulose_AcetateTeam:StanfordBrownSpelman/Cellulose Acetate2014-10-17T23:35:32Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Cellulose_Acetate">Biomaterials</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="intro">Intro</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="moreinfo">Links</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#CA">BioBricks</a></h7></div><br />
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The body of the UAV is designed to consist of a styrofoam-like filler made of fungal mycelia, coated with a cellulose acetate covering. The skin will be biologically <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing" target="_blank">waterproofed</a>. <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Amberless_Hell_Cell" target="_blank">Biosensors</a> can be added to the cellulose acetate skin through a biological <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Cellulose_Cross_Linker" target="_blank">cross-linker</a>.<br />
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Cellulose acetate is a biodegradable thermoplastic polymer used for a variety of industrial applications [1]. The monomer of cellulose acetate is glucose with one or more of its available hydroxyl groups replaced with acetyl groups. Cellulose acetate is produced industrially by treating cellulose, typically from wood or cotton, with acetic anhydride and sulfuric acid at high temperatures [1]. <br />
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</p>Our aim is to engineer bacterial cells to produce industrial-grade cellulose acetate biologically, allowing this plastic to be produced anywhere that bacterial colonies can be grown (e.g., in space). This material could then be used as a basis or coating for a biodegradable UAV. Many species of bacteria produce cellulose fibers; however, <i>Gluconacetobacter hansenii</i> has been identified as the species producing the highest yield of cellulose [2]. Another strain of bacteria, the SBW25 isolate of the species <i>Pseudomonas fluorescens</i>, produces a biofilm containing cellulose fibers with a small degree of acetylation (0.14 acetyl groups per glucose monomer) [3]. Industrial-grade cellulose acetate must have at least 1.71 acetyl groups per glucose monomer [4]. In order to engineer a bacterium to efficiently produce cellulose acetate, our strategy is to transform <i>G. hansenii</i> with the four genes, wssF, wssG, wssH, and wssI, that have been identified [3] as being involved in cellulose acetylation in <i>P. fluorescens</i>, and to use directed evolution to further increase percent acetylation of the polymer.<br />
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</p>In addition, we seek to create a streptavidin/cellulose-binding-domain fusion protein which will have the capacity to both cross-link bacterial cellulose acetate polymers (improving material properties) and allow the modular addition of cells (e.g., biosensors). This will be accomplished through the expression on the cells of a biotinylated membrane protein. This will allow biological sensors to be added directly to our cellulose acetate fibers, allowing bacterial sensors to be attached directly to the body of our UAV. <br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our goal was to turn bacterial cellulose into cellulose acetate.</h6><br />
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<h6><center>Fig. 1: Cellulose on the left transformed into cellulose acetate on the right.</center></h6><br />
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<h6>To accomplish this we looked to transform the genes responsible for the acetylation of cellulose in <i>P. fluorescens</i>, wssF-I [3], into our model cellulose-producing organism <i>G. hansenii.</i></h6><br />
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<h6><center>Fig. 2: Acetylation genes. Image via [7].</center></h6><br />
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<h6>However, <i>G. hansenii</i> is not a well-characterized organism for standard synthetic biology lab procedures. In particular, the standard pSB1C3 backbone does not contain a suitable origin of replication for <i>G. hansenii</i>. Instead, we utilized the multi-host shuttle vector pUCD4 [6], which allowed us to grow the plasmid to large quantities in <i>E. coli</i> before transforming it into <i>G. hansenii.</i> For the transformation we adapted the electroporation protocol found in [5]. Note that [5] gives a transformation protocol for a cellulose-negative strain of <i>G. hansenii.</i> However, we were able to successfully transform cellulose-producing bacteria using the same procedure. </h6><br />
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<h6><center>Fig. 3: Design of pUCD4 plasmid<br /><b>Note:</b> pUCD4 differs from pUCD2 in only one restriction site.</center></h6><br />
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We were able to extract total DNA from <i>P. fluorescens</i> and amplify the four acetylation genes.<br />
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<h6><center>Fig. 4: Gel verification confirming amplification of four acetylation genes from total <i>P. fluorescens</i> DNA.</center></h6><br />
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<h6>The prefix and suffix were added onto these PCR products, and then they were inserted (separately) into pSB1C3 for biobricking (see <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#CA" target="_blank"><u>Submitted Bricks</u></a>).</h6><br />
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<h6><center>Fig. 5: wssF sequencing alignment.</center></h6><br />
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<h6>We were also able to show that the pUCD4 shuttle vector was effective in making <i>G. hansenii</i> a suitable chassis for carrying synthetic information, an important step in the process of studying cellulose derivate polymers. By plating both transformed and untransformed cells on antibiotic selection plates and using colony PCR to screen for the presence of the plasmid, we found that pUCD4 was effective at providing resistances to multiple antibiotics.</h6><br />
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<h6><center>Fig. 6: pUCD4 verification gel.</center></h6><br />
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<h6>The first step in working towards producing our building material was to grow cultures of cellulose producing bacteria in sterile trays. After these cultures grew for 1-2 weeks, we removed the produced cellulose sheet from the culture to test various methods of drying. We experimented with drying the sheet in an oven, to produce an extremely thin layer of cellulose. We also wrapped fungal mycelium blocks provided by Ecovative, which we intend to be the body of our UAV, with wet cellulose, and allowed the cellulose to dry on its own. This will provide the platform for us to alter the biomaterial for flight, by, for example, making it waterproof.</h6> <br />
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<h6>Fig. 7: Production of dried cellulose. a) A wet cellulose sheet, soaking in 50% alcohol solution. b) The cellulose was placed between two acrylic gel casters and left in a 75ºC oven for 2 days. c) A thin, dry cellulose sheet. d) Fungal mycelium wrapped in dry cellulose.</h6><br />
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<h6>One alteration we intend to make in order to produce a functional UAV is to draw circuits on the biomaterial to conduct electricity. In order to produce a biodegradable circuit, we worked with a company called <a href="http://agic.cc" target="_blank">AgiC</a>, which prints circuits out of silver nano particles (see our <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Building_The_Drone"><u>Building a UAV</u></a> page for a full circuit). By taking silver ink and painting it on to our bacterial cellulose, we were able to test the conductive capabilities of our building material.</h6> <br />
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<h6>Fig. 8: Making cellulose electrically conductive. a) The silver ink used to paint cellulose. b) Silver nano particles painted onto cellulose covered mycelium. c) Positive Control: Aluminum foil has a resistance of 0.5 ohms. d) Negative Control: Unaltered cellulose has no resistance, and thus no conductivity. e) Experimental: Cellulose painted with silver nano particles has a resistance of 1.6 ohms. </h6><br />
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<h5><center>Additional Information</h5><br />
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Interested in what we've been working on and want to find more relevant information? Check out some of the following sites, companies, and people who either aided us in our production of biomaterials or collaborated with us in working to produce a viable biological unmanned aerial vehicle.<br />
<div class="sub5"><a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#CA">● Click here for a list of submitted biobricks, the four genes found in <i>P. fluorescens</i> responsible for the acetylation of the cellulose polymer. These genes are all found individually in the biobrick backbone and are fully biobrick compatible.</a></div><br />
<div class="sub5"><a href="http://docs.google.com/document/d/1ZH8qGKbhKb7Xe48ewJp8Ggznt0BzsDU9TQngtSh1HrA/edit?usp=sharing">● Click here for more information on the specific protocols we used for working with <i>G. hansenii</i> and bacterial cellulose, including our recipe for <i>Acetobacter</i> Medium, an electroporation transformation protocol, and instructions on generating, cleaning, and drying bacterial cellulose.</a></div><br />
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Our team modeled, prototyped, and collaborated with Ecovative Design to grow a mycelium-based chassis for our biological drone. For more information on this process, including part designs, links to open source model files, and photographs of the biological and biodegradable UAV we built and flew, <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Building_The_Drone">click here</a>!<br />
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1. Fischer S <i>et al.</i> (2008) Properties and Applications of Cellulose Acetate. <i>Macromol. Symp.</i> 262: 89-96. DOI: <a href="http://blogs.unpad.ac.id/evyerna/files/2010/11/ca.pdf" target="_blank">10.1002/masy.200850210</a><br><br><br />
2. Ross P <i>et al.</i> (1991) Cellulose Biosynthesis and Function in Bacteria. <i>Microbiological Reviews</i> 55: 35-58. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/2030672" target="_blank">2030672 </a><br><br><br />
3. Spiers AJ <i>et al.</i> (2003) Biofilm formation at the air–liquid interface by the <i>Pseudomonas fluorescens</i> SBW25 wrinkly spreader requires an acetylated form of cellulose. <i>Molecular Microbiology</i> 50: 15-27. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/14507360" target="_blank">14507360 </a><br><br><br />
4. The United States Pharmacopeial Convention. Cellulose Acetate. USP-NF. 2013. <br><br><br />
5. Hall PE <i>et al.</i> (1992) Transformation of Acetobacter xylinum with Plasmid DNA by Electroporation. <i>Plasmid</i> 28: 194-200. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/1461938" target="_blank">1461938 </a><br><br><br />
6. Close TJ <i>et al.</i> (1984) Design and Development of Amplifiable Broad-Host-Range Cloning Vectors: Analysis of the wir Region of Agrobacterium tumefaciens Plasmid pTiC58. <i>Plasmid</i> 12: 111-118. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/6095350" target="_blank">6095350 </a><br><br><br />
7. Spiers AJ <i>et al.</i> (2013) Cellulose Expression in <i>Pseudomonas fluorescens</i> SBW25 and Other Environmental Pseudomonads in <i>Cellulose - Medical, Pharmaceutical, and Electronic Applications</i>. DOI: <a href="http://cdn.intechopen.com/pdfs-wm/45637.pdf" target="_blank">10.5772/53736</a><br><br />
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</body></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Material_WaterproofingTeam:StanfordBrownSpelman/Material Waterproofing2014-10-17T20:45:47Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="wax">Alternate Approach</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wishes to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<h6><center> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. Until now, scientific knowledge of this protein coating was limited mainly to total amino acid composition of all the proteins in the paper. To gain more insight into the specific proteins that may exist in wasp nest paper, we collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. We believe there may be a single protein in wasp saliva that is chiefly responsible for the hydrophobic nature of their nests. </h6><br />
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<h6>Our ultimate goal is to identify the gene that codes for this wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running of a wasp protein gel.</a></p></center><br><br />
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<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!</h6><br />
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<h6> Once we obtained the mass fingerprinting data, we were faced with a long list of uncharacterized peptide fragments with hits in the Polistes dominula genome. We used the NCBI Position-Specific Iterated Basic Local Alignment Search Tool, or PSI-BLAST, to search online databases for proteins with similar sequences and domains. This gave us hints towards the possible functions of these uncharacterized proteins.</h6><br />
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<h6>We then narrowed our list down to six proteins with favorable PSI-BLAST hits and obtained the DNA sequences for these genes from the genome. Three of these genes were codon-optimized and synthesized for E. coli expression in the pF1A T7 Flexi® vector, while the other three were amplified from wasp RNA via RT-PCR for S. cerevisiae expression in the pYES2.1/V5-His-TOPO® vector.</h6><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><h6><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></h6></div><br />
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<h5><center>Results</h5><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
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<h6>In addition to the wasp protein waterproofing project, we searched literature for other organisms that would be able to produce a highly hydrophobic substance to waterproof the UAV. We stumbled across Marinobacter hydrocarbonoclasticus, a marine bacterium that synthesizes isoprenoid wax esters for use as storage compounds CITATION NEEDED.</h6><br />
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<h6><center> A phytol-based wax ester produced by <i>Marinobacter hydrocarbonoclasticus</i>.</center></h6><br />
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<h6>Wax esters, such as the example shown above, consist of two very long carbon chains joined by an ester group. These chains impart great hydrophobicity to the entire molecule, indicating that a wax ester coating on a surface could be used to repel water. We decided to repurpose the wax ester synthesis genes in Marinobacter to create a pathway through which Escherichia coli could synthesize the wax esters and use them to waterproof our UAV.</h6><br />
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<h6>The two enzymes in Marinobacter that are involved in the production of isoprenoid wax esters were wax synthase 1 (WS1) and wax synthase 2 (WS2). In the presence of a fatty alcohol and fatty acyl-CoA (carboxylic acid derivative), either of these two proteins can catalyze the synthesis of isoprenoid wax esters.</h6><br />
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<h6>The DNA sequences for both WS1 and WS2 were obtained from the NCBI database CITATION NEEDED. They were then codon optimized for Escherichia coli using a codon optimizer from the Integrated DNA Technologies (IDT) website and then synthesized. After the genes were received from IDT, a polymerase chain reaction (PCR) was done to amplify the proteins and to ensure that they were present at around 1.4kb. The proteins were then ligated to a promoter and RBS and also into a chloramphenicol vector, then transformed into E. coli. After completing a colony PCR on the transformants, colonies 5, 7, and 11 on the WS2 plate appeared to contain the WS2 gene. Liquid cultures were inoculated with colonies 5, 7, and 11 to create more cells with the gene. After the cultures were grown in the incubator, they were miniprepped in order to extract their plasmid DNA. The DNA was then sent for sequencing with a 15ul reaction volume.</h6><br />
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<h6>After sequencing the data came back with miniprepped genes that aligned with the original synthesized gene for roughly half the length of the sequence, but with a puzzling truncation that was present in all three plasmid samples:</h6><br />
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<h6><center>WS2 sequencing alignment in Geneious 7.</center></h6><br />
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<h6>Unfortunately, the wax esters project had to be discontinued due to an error in the synthesis of the WS2 gene. In order to insert the gene into the vector, there are restriction sites that are cleaved by their respective restriction enzymes. When we codon-optimized WS2 for E. coli expression, we accidentally created a PstI cut site at base 720 in the synthesized gene. Because there was a cut site in the middle of the gene, only a fragment of the WS2 gene could ever make its way into the chloramphenicol vector, thereby disabling the protein the gene would have coded for. We also discovered that the fatty acyl-CoA reagent we intended to use for our enzymatic synthesis reaction was exorbitantly expensive (over $60 per milligram). For this reason, and because it would have been too costly to resynthesize the WS2 gene, the project was discontinued.</h6><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
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2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
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3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Material_WaterproofingTeam:StanfordBrownSpelman/Material Waterproofing2014-10-17T20:45:29Z<p>Eliblock: </p>
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<div class="small-5 small-centered columns projecticon-cellulose-acetate"><img class="projecticon-cellulose-acetate" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"/><br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="wax">Alternate Approach</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wishes to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<h6><center> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. Until now, scientific knowledge of this protein coating was limited mainly to total amino acid composition of all the proteins in the paper. To gain more insight into the specific proteins that may exist in wasp nest paper, we collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. We believe there may be a single protein in wasp saliva that is chiefly responsible for the hydrophobic nature of their nests. </h6><br />
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<h6>Our ultimate goal is to identify the gene that codes for this wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running of a wasp protein gel.</a></p></center><br><br />
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<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!</h6><br />
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<h6> Once we obtained the mass fingerprinting data, we were faced with a long list of uncharacterized peptide fragments with hits in the Polistes dominula genome. We used the NCBI Position-Specific Iterated Basic Local Alignment Search Tool, or PSI-BLAST, to search online databases for proteins with similar sequences and domains. This gave us hints towards the possible functions of these uncharacterized proteins.</h6><br />
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<h6>We then narrowed our list down to six proteins with favorable PSI-BLAST hits and obtained the DNA sequences for these genes from the genome. Three of these genes were codon-optimized and synthesized for E. coli expression in the pF1A T7 Flexi® vector, while the other three were amplified from wasp RNA via RT-PCR for S. cerevisiae expression in the pYES2.1/V5-His-TOPO® vector.</h6><br><br><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><h6><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></h6></div><br />
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<h5><center>Results</h5><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
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<h6>In addition to the wasp protein waterproofing project, we searched literature for other organisms that would be able to produce a highly hydrophobic substance to waterproof the UAV. We stumbled across Marinobacter hydrocarbonoclasticus, a marine bacterium that synthesizes isoprenoid wax esters for use as storage compounds CITATION NEEDED.</h6><br />
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<h6><center> A phytol-based wax ester produced by <i>Marinobacter hydrocarbonoclasticus</i>.</center></h6><br />
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<h6>Wax esters, such as the example shown above, consist of two very long carbon chains joined by an ester group. These chains impart great hydrophobicity to the entire molecule, indicating that a wax ester coating on a surface could be used to repel water. We decided to repurpose the wax ester synthesis genes in Marinobacter to create a pathway through which Escherichia coli could synthesize the wax esters and use them to waterproof our UAV.</h6><br />
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<h6>The two enzymes in Marinobacter that are involved in the production of isoprenoid wax esters were wax synthase 1 (WS1) and wax synthase 2 (WS2). In the presence of a fatty alcohol and fatty acyl-CoA (carboxylic acid derivative), either of these two proteins can catalyze the synthesis of isoprenoid wax esters.</h6><br />
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<h6>The DNA sequences for both WS1 and WS2 were obtained from the NCBI database CITATION NEEDED. They were then codon optimized for Escherichia coli using a codon optimizer from the Integrated DNA Technologies (IDT) website and then synthesized. After the genes were received from IDT, a polymerase chain reaction (PCR) was done to amplify the proteins and to ensure that they were present at around 1.4kb. The proteins were then ligated to a promoter and RBS and also into a chloramphenicol vector, then transformed into E. coli. After completing a colony PCR on the transformants, colonies 5, 7, and 11 on the WS2 plate appeared to contain the WS2 gene. Liquid cultures were inoculated with colonies 5, 7, and 11 to create more cells with the gene. After the cultures were grown in the incubator, they were miniprepped in order to extract their plasmid DNA. The DNA was then sent for sequencing with a 15ul reaction volume.</h6><br />
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<h6>After sequencing the data came back with miniprepped genes that aligned with the original synthesized gene for roughly half the length of the sequence, but with a puzzling truncation that was present in all three plasmid samples:</h6><br />
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<h6><center>WS2 sequencing alignment in Geneious 7.</center></h6><br />
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<h6>Unfortunately, the wax esters project had to be discontinued due to an error in the synthesis of the WS2 gene. In order to insert the gene into the vector, there are restriction sites that are cleaved by their respective restriction enzymes. When we codon-optimized WS2 for E. coli expression, we accidentally created a PstI cut site at base 720 in the synthesized gene. Because there was a cut site in the middle of the gene, only a fragment of the WS2 gene could ever make its way into the chloramphenicol vector, thereby disabling the protein the gene would have coded for. We also discovered that the fatty acyl-CoA reagent we intended to use for our enzymatic synthesis reaction was exorbitantly expensive (over $60 per milligram). For this reason, and because it would have been too costly to resynthesize the WS2 gene, the project was discontinued.</h6><br />
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<h5><center>References</h5><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
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2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
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3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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<div class="sub3"><img id="cellulosePic" src="https://static.igem.org/mediawiki/2014/6/6f/SBS_iGEM_2014_Cellulose_Icon.png"><h4><a href="http://www.behance.net/eliblock" target="_blank">Eli Block</a></h4>Eli Block is a third year <a href="http://brown.edu" target="_blank">Brown</a> + <a href="http://www.risd.edu" target="_blank">RISD</a> Dual Degree student majoring in Industrial Design at RISD and concentrating in Biology at Brown; he's particularly interested in designed ecologies, evolutionary biology, and wearable technology. Eli worked on the wasp protein waterproofing project, built concept UAVs, cooked experimental biomaterials, and developed the team wiki. Eli loves dinosaurs, artificial intelligence, print design, and swimming like a merman in the Brown University pool.</div><br />
<div class="sub2"><img id="hellCellPic" src="https://static.igem.org/mediawiki/2014/c/c6/SBS_iGEM_2014_Hell_Cell.png" class="two"><h4>Jovita Byemerwa</h4>Jovita is from Tanzania, and is currently in her third year at Brown University concentrating in Computational Molecular Biology. This summer, she worked on the biodegradability project and explored human practices of UAVs. A huge interest of her's is language: she loves learning them, speaking them and even teaching them. She speaks Kiswahili (her mother tongue), English (of course), Italian and a bit of Spanish. </div><br />
<div class="sub3"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Ross Dispenza</h4>Ross is a junior at Brown University concentrating in Chemistry and French Studies. He worked on the cellulose acetate and cross-linker projects this summer. In high school, he once won a quiz bowl tournament for his team by answering a question about Lady Gaga.</div><br />
<div class="sub2"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Benjamin Doughty</h4>Ben is a sophomore at Brown University intending to concentrate in Biochemistry and Molecular Biology. Over the summer he worked on the <a class="links" href="https://2014.igem.org/Team:StanfordBrownSpelman/Cellulose_Acetate" target="_blank">Cellulose Acetate</a> and <a class="links" href="https://2014.igem.org/Team:StanfordBrownSpelman/Amberless_Hell_Cell" target="_blank">Amberless Hell Cell</a> projects. In high school he took home third place at a curling tournament, and he can sing the alphabet backwards.</div><br />
<div class="sub3"><img id="cellulosePic" src="https://static.igem.org/mediawiki/2014/6/6f/SBS_iGEM_2014_Cellulose_Icon.png"><h4>Poorwa Godbole</h4>Poorwa is a junior at Stanford University majoring in Economics and planning to go to medical school after graduation. This summer she worked on the biodegradability and cellulose acetate projects, and made sure to hug each person on the team at least once a day. She enjoys dancing, laughing, and watching Blue Planet on Netflix. </div><br />
<div class="sub2"><img id="cellulosePic2" src="https://static.igem.org/mediawiki/2014/3/3a/SBS_iGEM_2014_Human_Practices.png"><h4>Jeannette Gonzales-Wright</h4>Jeannette is a junior in the Program in Liberal Medical Education (PLME) at Brown University. She is concentrating in Science & Society: Health/Medicine. This summer she worked on biodegradability, the human practices of UAVs, and the powerpoint presentation of our research. She identifies as a proud CODA and would prefer to talk to you in American Sign Language than in a spoken language. </div><br />
<div class="sub3"><img id="hellCellPic" src="https://static.igem.org/mediawiki/2014/c/c6/SBS_iGEM_2014_Hell_Cell.png" class="two"><h4>Ian Hull</h4> Ian somehow made it through his freshman year at Stanford without sustaining any serious chemical burns. Apparently that makes him a sophomore now, and he's interested in bioengineering, chemistry, and science communication. This summer, he worked on the wasp protein and wax ester waterproofing projects. He loves the ocean and once swam with whale sharks.</div><br />
<div class="sub2"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Jotthe Kannappan</h4>Jotthe is a small and energetic junior in bioengineering at Stanford University. This summer, she worked primarily with wasp proteins in material waterproofing and biomaterials on the production of cellulose acetate. When she's not pipetting, she can be found humming obnoxiously, dancing, or curled up with a good book.</div><br />
<div class="sub3"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Alexander Levine</h4>Alex is a junior at Brown University concentrating in Mathematical Physics. This summer, he worked mostly on getting <i>Gluconacetobacter hansenii</i> to produce cellulose acetate, and on creating software tools for synthetic biology.</div><br />
<div class="sub2"><img id="cellulosePic2" src="https://static.igem.org/mediawiki/2014/3/3a/SBS_iGEM_2014_Human_Practices.png"><h4>Raman Nelakanti</h4>Raman is the captain of the SBS iGEM team. He recently graduated from Stanford with a major in Bioengineering and worked on the <a class="links" href="https://2014.igem.org/Team:StanfordBrownSpelman/Amberless_Hell_Cell" target="_blank">Amberless Hell Cell</a> project this summer. Outside of science, his interests include singing, hiking, exploring, and living life. And one random fact about him - he once serenaded Justice Sandra Day O'Connor!</div><br />
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<div class="sub3"><img id="cellulosePic" src="https://static.igem.org/mediawiki/2014/6/6f/SBS_iGEM_2014_Cellulose_Icon.png"><h4>Lydia Ruffner</h4>Lydia is a recent graduate of Spelman College, where she majored in Biochemistry. Currently she is a first-year PhD student in the Chemistry and Chemical Biology Program at Northeastern University. This summer she worked on the Modeling portion of the Cellulose Acetate Project. Outside of the lab, she loves watching football (Go PATRIOTS!!), spending time with her family, and shopping.</div><br />
<div class="sub2"><img id="hellCellPic" src="https://static.igem.org/mediawiki/2014/c/c6/SBS_iGEM_2014_Hell_Cell.png" class="two"><h4>Alaina Shumate</h4>Alaina is a junior at Stanford University majoring in bioengineering. This summer she worked on the Amberless Hell Cell and Cellulose Cross-linker projects. While nothing makes her happier than Minipreps, she also enjoys drinking coffee, tap dancing, and convincing people that her home state of Wyoming is actually a fun place.</div><br />
<div class="sub3"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Aryo Sorayya</h4>Aryo is a sophomore at Stanford University, where he is studying Biochemistry and Computer Science. At Ames, Aryo worked on the Wasp Protein and Biodegradability projects, helped develop the team wiki, and strove to keep lab morale high! Aryo is a cheerful polyglot and enjoys learning about other cultures, playing pranks on his teammates, and traveling the world.<br />
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src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Kyla Ugwu</h4>Kyla Ugwu is a junior biochemistry major, mathematics minor at Spelman College. After graduation, she plans on getting a medical degree and a master’s in public health. During her time at NASA Ames Research Center, she worked on the Biodegradation project and Waterproofing projects. In her free time, she likes to sing with her quintet on campus and go to the movies as many times as her pocketbook will let her.</div><br />
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<div class="sub3"><img id="cellulosePic" src="https://static.igem.org/mediawiki/2014/6/6f/SBS_iGEM_2014_Cellulose_Icon.png"><h4>Dr. Lynn Rothschild</h4>Lynn is the lead scientist in synthetic biology at NASA Ames Research Center where her lab has been working on cool projects ranging from the search for life in the universe and extremophiles, to pioneering how synthetic biology can be used to address NASA's missions. She is also an adjunct professor at Brown University, inter alia, <a href="https://vivo.brown.edu/display/lr3">Brown Home Page</a>. Her lab looks forward to hosting the team every year as they pioneer ways to take synthetic biology literally "out of this world". PS Yes, she really does play the <a href="http://www.nature.com/nature/journal/v422/n6932/full/422567a.html">bagpipes</a>! </div><br />
<div class="sub2"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Dr. Joseph Shih</h4>Joseph is the wet lab Lecturer in the Bioengineering Department at Stanford University. He got his Ph.D. in Molecular and Cellular Biology at Harvard University and did his post-doc in Pam Silver's lab at Harvard Medical School. He is always curious about biology and the potential for synthetic biology to change the world!</div><br />
<div class="sub3"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Dr. Gary Wessel</h4>Gary is a Professor of Biology at Brown University. He has been the faculty sponsor for the Brown University team since 2006 and teaches the synbio course at Brown "Bio 1210 Synthetic Biological Systems". <a href= "http://www.brown.edu/Research/Wessel_Lab/"> His research </a> focuses on anything germ line and reproduction and applies synthetic biological approaches to this research field. </div><br />
<div class="sub2"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Dr. Kimberly Jackson</h4>Kimberly is an Associate Professor of Biochemistry and co-director of the new Interdisciplinary Food Studies at Spelman College. She maintains an active research program in cancer therapeutics and drug discovery with funding from various agencies. Outside of being a professor, program director, mentor and researcher, Kimberly enjoys being a volleyball and soccer mom (of 3), wife of a research scientist, flutist and undercover foodie. One random fact—she completed part of her graduate studies as a NIH Fogarty fellow in Turku, FINLAND.</div><br />
<div class="sub3"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Dr. Jean Dimandja</h4>Jean-Marie Dimandja received his bachelor's in mathematics from Miami University, and his master's and doctorate in analytical chemistry from Southern Illinois University. Prior to joining the chemistry department at Spelman College in 2002, he worked at the NASA/Ames Research Center from 1991 to 1997, and the Centers for Disease Control and Prevention from 1997 to 2002 where he developed analytical methods for use in space research and environmental biomonitoring respectively. </div><br />
<div class="sub2"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Dr. Kosuke Fujishima</h4>Kosuke works as a researcher in Lynn Rothschild's lab tackling Origins of life questions using Synthetic Biology. He got his Ph.D in Systems Biology from Keio University (Japan) and is currently involved in multiple themes related to Astrobiology. He has been a technical advisor of the iGEM team since 2011. Aside from science, Kosuke has an absolutely adorable 14-month-old daughter named Sana, who the entire lab is in love with.</div><br />
<div class="sub3"><img id="waterPic" src="https://static.igem.org/mediawiki/2014/1/18/SBS_iGEM_2014_Waterproofing.png"><h4>Ryan Kent</h4>Ryan was a member of the 2011 Brown/Stanford iGEM team and graduated from Stanford in 2012 with an M.S. in Biology. This is his second year as an iGEM mentor and as a member of Dr. Lynn Rothschild’s lab at NASA Ames Research Center. When he’s not whipping the team into shape, he enjoys writing about himself in the third person and surfing. -Ryan</div><br />
<div class="sub2"><img id="biodegradePic" src="https://static.igem.org/mediawiki/2014/7/71/SBS_iGEM_2014_Biodegradation.png" class="two"><h4>Kendrick Wang</h4>Kendrick is a researcher at NASA Ames Research Center in Professor Lynn Rothschild’s lab. He recently earned his B.S. degree in Bioengineering at Stanford University. His research work focuses on the Origins of Life question, the prebiotic environment, and Astrobiology. He was a member of the 2012 Stanford Brown iGEM team and has been an advisor for iGEM teams since 2013. He is from California originally, but grew up abroad in Hong Kong and Singapore. For fun, he loves rock climbing and mountain biking. </div><br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Achievements">Achievements</a></h3><br />
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Following is documentation of the <a href="https://2014.igem.org/Judging/Awards" target="_blank">2014 iGEM Competition judging criteria</a> and how our team fulfilled the various medal requirements. We feel our team performed strongly in a number of areas, and we would like to share our accomplishments! Check them out:<br />
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<h5><center><a href="https://2014.igem.org/Judging/Awards" target="_blank">Bronze Medal Requirements</a></h5><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">We completed our team <a href="https://igem.org/Team.cgi?year=2014&team_name=StanfordBrownSpelman" target="_blank"><u>registration</u></a> in April, and plan to attend the iGEM Jamboree in October.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">We have completed our Judging Form and submitted it by the October deadline.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">We have produced a <a href="https://2014.igem.org/Team:StanfordBrownSpelman"><u>wiki</u></a> that documents our team's various project results and the processes we went through to obtain them.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">We have prepared a poster and a talk for the 2014 iGEM Jamboree. Registration paid, hotels reserved, UAVs assembled, and rearing to go!</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">We have acknowledged our advisors, sponsors, and collaborators in the <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Attributions"><u>Attributions</u></a> section of our wiki.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">We have documented multiple new standard biobrick Parts or Devices. Check out our <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks" target="_blank"><u>BioBricks page</u></a> for more information about our bricks and find our parts in the <a href="http://parts.igem.org/Main_Page" target="_blank">registry</a> to see the complete documentation.</div><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Experimentally validate at least one biobrick part—we validated the <a href="http://parts.igem.org/Part:BBa_K1499200" target="_blank">radiation resistance of uvsE.</a></div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Document the characterization of the biobrick on the parts registry page and submit part to registry. Both completed for <a href="http://parts.igem.org/Part:BBa_K1499200" target="_blank">radiation resistance gene uvsE.</a></div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Articulate questions about the project and answer them. Visit our <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Human_Practices" target="_blank">Human Practices</a> page to see some of the questions and answers about biological drone use that we generated.</div><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Improve the function or characterization of an existing biobrick part or device.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Help any registered iGEM team by: characterizing a part, debugging a construct, modeling their system.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"> We have been working with the EPA to write regulations for synthetic biology use in the environment. Our project addresses policy and outlines best use practices for biological UAVs and environmental safety concerns with respect to synthetic biology that affect the application of synthetic biology across the country. Read more about our collaboration with the EPA to address the release of synthetic organisms into the environment and our evaluation of our own approach on our <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Human_Practices" target="_blank"><u>Human Practices</u></a> page.</div><br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Material_WaterproofingTeam:StanfordBrownSpelman/Material Waterproofing2014-10-17T20:06:53Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="wax">Alternate Approach</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wishes to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<h6><center> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. Until now, scientific knowledge of this protein coating was limited mainly to total amino acid composition of all the proteins in the paper. To gain more insight into the specific proteins that may exist in wasp nest paper, we collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. We believe there may be a single protein in wasp saliva that is chiefly responsible for the hydrophobic nature of their nests. </h6><br />
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<h6>Our ultimate goal is to identify the gene that codes for this wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running of a wasp protein gel.</a></p></center><br><br />
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<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!<br><br><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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<h5><center>Results</h5><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
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<h6>In addition to the wasp protein waterproofing project, we searched literature for other organisms that would be able to produce a highly hydrophobic substance to waterproof the UAV. We stumbled across Marinobacter hydrocarbonoclasticus, a marine bacterium that synthesizes isoprenoid wax esters for use as storage compounds CITATION NEEDED.</h6><br />
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<h6><center> A phytol-based wax ester produced by <i>Marinobacter hydrocarbonoclasticus</i>.</center></h6><br />
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<h6>Wax esters, such as the example shown above, consist of two very long carbon chains joined by an ester group. These chains impart great hydrophobicity to the entire molecule, indicating that a wax ester coating on a surface could be used to repel water. We decided to repurpose the wax ester synthesis genes in Marinobacter to create a pathway through which Escherichia coli could synthesize the wax esters and use them to waterproof our UAV.</h6><br />
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<h6>The two enzymes in Marinobacter that are involved in the production of isoprenoid wax esters were wax synthase 1 (WS1) and wax synthase 2 (WS2). In the presence of a fatty alcohol and fatty acyl-CoA (carboxylic acid derivative), either of these two proteins can catalyze the synthesis of isoprenoid wax esters.</h6><br />
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<h6>The DNA sequences for both WS1 and WS2 were obtained from the NCBI database CITATION NEEDED. They were then codon optimized for Escherichia coli using a codon optimizer from the Integrated DNA Technologies (IDT) website and then synthesized. After the genes were received from IDT, a polymerase chain reaction (PCR) was done to amplify the proteins and to ensure that they were present at around 1.4kb. The proteins were then ligated to a promoter and RBS and also into a chloramphenicol vector, then transformed into E. coli. After completing a colony PCR on the transformants, colonies 5, 7, and 11 on the WS2 plate appeared to contain the WS2 gene. Liquid cultures were inoculated with colonies 5, 7, and 11 to create more cells with the gene. After the cultures were grown in the incubator, they were miniprepped in order to extract their plasmid DNA. The DNA was then sent for sequencing with a 15ul reaction volume.</h6><br />
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<h6>After sequencing the data came back with miniprepped genes that aligned with the original synthesized gene for roughly half the length of the sequence, but with a puzzling truncation that was present in all three plasmid samples:</h6><br />
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<h6><center>WS2 sequencing alignment in Geneious 7.</center></h6><br />
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<h6>Unfortunately, the wax esters project had to be discontinued due to an error in the synthesis of the WS2 gene. In order to insert the gene into the vector, there are restriction sites that are cleaved by their respective restriction enzymes. When we codon-optimized WS2 for E. coli expression, we accidentally created a PstI cut site at base 720 in the synthesized gene. Because there was a cut site in the middle of the gene, only a fragment of the WS2 gene could ever make its way into the chloramphenicol vector, thereby disabling the protein the gene would have coded for. We also discovered that the fatty acyl-CoA reagent we intended to use for our enzymatic synthesis reaction was exorbitantly expensive (over $60 per milligram). For this reason, and because it would have been too costly to resynthesize the WS2 gene, the project was discontinued.</h6><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
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2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
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3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Material_WaterproofingTeam:StanfordBrownSpelman/Material Waterproofing2014-10-17T20:06:29Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="wax">Alternate Approach</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center><iframe width="420" height="315" src="//www.youtube.com/embed/57SGD95KY6s" frameborder="0" allowfullscreen></iframe><br><br> <br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wishes to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<h6><center> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. Until now, scientific knowledge of this protein coating was limited mainly to total amino acid composition of all the proteins in the paper. To gain more insight into the specific proteins that may exist in wasp nest paper, we collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. We believe there may be a single protein in wasp saliva that is chiefly responsible for the hydrophobic nature of their nests. </h6><br />
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<h6>Our ultimate goal is to identify the gene that codes for this wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running of a wasp protein gel.</a></p></center><br><br />
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<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!<br><br><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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<h5><center>Results</h5><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
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<h6>In addition to the wasp protein waterproofing project, we searched literature for other organisms that would be able to produce a highly hydrophobic substance to waterproof the UAV. We stumbled across Marinobacter hydrocarbonoclasticus, a marine bacterium that synthesizes isoprenoid wax esters for use as storage compounds CITATION NEEDED.</h6><br />
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<h6><center> A phytol-based wax ester produced by <i>Marinobacter hydrocarbonoclasticus</i>.</center></h6><br />
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<h6>Wax esters, such as the example shown above, consist of two very long carbon chains joined by an ester group. These chains impart great hydrophobicity to the entire molecule, indicating that a wax ester coating on a surface could be used to repel water. We decided to repurpose the wax ester synthesis genes in Marinobacter to create a pathway through which Escherichia coli could synthesize the wax esters and use them to waterproof our UAV.</h6><br />
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<h6>The two enzymes in Marinobacter that are involved in the production of isoprenoid wax esters were wax synthase 1 (WS1) and wax synthase 2 (WS2). In the presence of a fatty alcohol and fatty acyl-CoA (carboxylic acid derivative), either of these two proteins can catalyze the synthesis of isoprenoid wax esters.</h6><br />
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<h6>The DNA sequences for both WS1 and WS2 were obtained from the NCBI database CITATION NEEDED. They were then codon optimized for Escherichia coli using a codon optimizer from the Integrated DNA Technologies (IDT) website and then synthesized. After the genes were received from IDT, a polymerase chain reaction (PCR) was done to amplify the proteins and to ensure that they were present at around 1.4kb. The proteins were then ligated to a promoter and RBS and also into a chloramphenicol vector, then transformed into E. coli. After completing a colony PCR on the transformants, colonies 5, 7, and 11 on the WS2 plate appeared to contain the WS2 gene. Liquid cultures were inoculated with colonies 5, 7, and 11 to create more cells with the gene. After the cultures were grown in the incubator, they were miniprepped in order to extract their plasmid DNA. The DNA was then sent for sequencing with a 15ul reaction volume.</h6><br />
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<h6>After sequencing the data came back with miniprepped genes that aligned with the original synthesized gene for roughly half the length of the sequence, but with a puzzling truncation that was present in all three plasmid samples:</h6><br />
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<h6><center>WS2 sequencing alignment in Geneious 7.</center></h6><br />
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<h6>Unfortunately, the wax esters project had to be discontinued due to an error in the synthesis of the WS2 gene. In order to insert the gene into the vector, there are restriction sites that are cleaved by their respective restriction enzymes. When we codon-optimized WS2 for E. coli expression, we accidentally created a PstI cut site at base 720 in the synthesized gene. Because there was a cut site in the middle of the gene, only a fragment of the WS2 gene could ever make its way into the chloramphenicol vector, thereby disabling the protein the gene would have coded for. We also discovered that the fatty acyl-CoA reagent we intended to use for our enzymatic synthesis reaction was exorbitantly expensive (over $60 per milligram). For this reason, and because it would have been too costly to resynthesize the WS2 gene, the project was discontinued.</h6><br />
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<h5><center>References</h5><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
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2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
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3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="wax">Alternate Approach</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wishes to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<h6><center> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. Until now, scientific knowledge of this protein coating was limited mainly to total amino acid composition of all the proteins in the paper. To gain more insight into the specific proteins that may exist in wasp nest paper, we collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. We believe there may be a single protein in wasp saliva that is chiefly responsible for the hydrophobic nature of their nests. </h6><br />
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<h6>Our ultimate goal is to identify the gene that codes for this wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running of a wasp protein gel.</a></p></center><br><br />
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<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!<br><br><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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<h5><center>Results</h5><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
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<h6>In addition to the wasp protein waterproofing project, we searched literature for other organisms that would be able to produce a highly hydrophobic substance to waterproof the UAV. We stumbled across Marinobacter hydrocarbonoclasticus, a marine bacterium that synthesizes isoprenoid wax esters for use as storage compounds CITATION NEEDED.</h6><br />
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<h6><center> A phytol-based wax ester produced by <i>Marinobacter hydrocarbonoclasticus</i>.</center></h6><br />
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<h6>Wax esters, such as the example shown above, consist of two very long carbon chains joined by an ester group. These chains impart great hydrophobicity to the entire molecule, indicating that a wax ester coating on a surface could be used to repel water. We decided to repurpose the wax ester synthesis genes in Marinobacter to create a pathway through which Escherichia coli could synthesize the wax esters and use them to waterproof our UAV.</h6><br />
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<h6>The two enzymes in Marinobacter that are involved in the production of isoprenoid wax esters were wax synthase 1 (WS1) and wax synthase 2 (WS2). In the presence of a fatty alcohol and fatty acyl-CoA (carboxylic acid derivative), either of these two proteins can catalyze the synthesis of isoprenoid wax esters.</h6><br />
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<h6>The DNA sequences for both WS1 and WS2 were obtained from the NCBI database CITATION NEEDED. They were then codon optimized for Escherichia coli using a codon optimizer from the Integrated DNA Technologies (IDT) website and then synthesized. After the genes were received from IDT, a polymerase chain reaction (PCR) was done to amplify the proteins and to ensure that they were present at around 1.4kb. The proteins were then ligated to a promoter and RBS and also into a chloramphenicol vector, then transformed into E. coli. After completing a colony PCR on the transformants, colonies 5, 7, and 11 on the WS2 plate appeared to contain the WS2 gene. Liquid cultures were inoculated with colonies 5, 7, and 11 to create more cells with the gene. After the cultures were grown in the incubator, they were miniprepped in order to extract their plasmid DNA. The DNA was then sent for sequencing with a 15ul reaction volume.</h6><br />
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<h6>After sequencing the data came back with miniprepped genes that aligned with the original synthesized gene for roughly half the length of the sequence, but with a puzzling truncation that was present in all three plasmid samples:</h6><br />
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<h6><center>WS2 sequencing alignment in Geneious 7.</center></h6><br />
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<h6>Unfortunately, the wax esters project had to be discontinued due to an error in the synthesis of the WS2 gene. In order to insert the gene into the vector, there are restriction sites that are cleaved by their respective restriction enzymes. When we codon-optimized WS2 for E. coli expression, we accidentally created a PstI cut site at base 720 in the synthesized gene. Because there was a cut site in the middle of the gene, only a fragment of the WS2 gene could ever make its way into the chloramphenicol vector, thereby disabling the protein the gene would have coded for. We also discovered that the fatty acyl-CoA reagent we intended to use for our enzymatic synthesis reaction was exorbitantly expensive (over $60 per milligram). For this reason, and because it would have been too costly to resynthesize the WS2 gene, the project was discontinued.</h6><br />
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<h5><center>References</h5><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
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2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
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3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="wax">Alternate Approach</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wishes to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/3/33/SBS_iGEM_wasp_pipe_cropped.JPG" height="110%" width="110%"><br><br />
<h6><center> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. Until now, scientific knowledge of this protein coating was limited mainly to total amino acid composition of all the proteins in the paper. To gain more insight into the specific proteins that may exist in wasp nest paper, we collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. We believe there may be a single protein in wasp saliva that is chiefly responsible for the hydrophobic nature of their nests. </h6><br />
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<h6>Our ultimate goal is to identify the gene that codes for this wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running of a wasp protein gel.</a></p></center><br><br />
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<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!<br><br><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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<h5><center>Results</h5><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
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<h6>In addition to the wasp protein waterproofing project, we searched literature for other organisms that would be able to produce a highly hydrophobic substance to waterproof the UAV. We stumbled across Marinobacter hydrocarbonoclasticus, a marine bacterium that synthesizes isoprenoid wax esters for use as storage compounds CITATION NEEDED.</h6><br />
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<h6><center> A phytol-based wax ester produced by <i>Marinobacter hydrocarbonoclasticus</i>.</center></h6><br />
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<h6>Wax esters, such as the example shown above, consist of two very long carbon chains joined by an ester group. These chains impart great hydrophobicity to the entire molecule, indicating that a wax ester coating on a surface could be used to repel water. We decided to repurpose the wax ester synthesis genes in Marinobacter to create a pathway through which Escherichia coli could synthesize the wax esters and use them to waterproof our UAV.</h6><br />
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<h6>The two enzymes in Marinobacter that are involved in the production of isoprenoid wax esters were wax synthase 1 (WS1) and wax synthase 2 (WS2). In the presence of a fatty alcohol and fatty acyl-CoA (carboxylic acid derivative), either of these two proteins can catalyze the synthesis of isoprenoid wax esters.</h6><br />
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<h6>The DNA sequences for both WS1 and WS2 were obtained from the NCBI database CITATION NEEDED. They were then codon optimized for Escherichia coli using a codon optimizer from the Integrated DNA Technologies (IDT) website and then synthesized. After the genes were received from IDT, a polymerase chain reaction (PCR) was done to amplify the proteins and to ensure that they were present at around 1.4kb. The proteins were then ligated to a promoter and RBS and also into a chloramphenicol vector, then transformed into E. coli. After completing a colony PCR on the transformants, colonies 5, 7, and 11 on the WS2 plate appeared to contain the WS2 gene. Liquid cultures were inoculated with colonies 5, 7, and 11 to create more cells with the gene. After the cultures were grown in the incubator, they were miniprepped in order to extract their plasmid DNA. The DNA was then sent for sequencing with a 15ul reaction volume.</h6><br />
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<h6>After sequencing the data came back with miniprepped genes that aligned with the original synthesized gene for roughly half the length of the sequence, but with a puzzling truncation that was present in all three plasmid samples:</h6><br />
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<h6><center>WS2 sequencing alignment in Geneious 7.</center></h6><br />
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<h6>Unfortunately, the wax esters project had to be discontinued due to an error in the synthesis of the WS2 gene. In order to insert the gene into the vector, there are restriction sites that are cleaved by their respective restriction enzymes. When we codon-optimized WS2 for E. coli expression, we accidentally created a PstI cut site at base 720 in the synthesized gene. Because there was a cut site in the middle of the gene, only a fragment of the WS2 gene could ever make its way into the chloramphenicol vector, thereby disabling the protein the gene would have coded for. We also discovered that the fatty acyl-CoA reagent we intended to use for our enzymatic synthesis reaction was exorbitantly expensive (over $60 per milligram). For this reason, and because it would have been too costly to resynthesize the WS2 gene, the project was discontinued.</h6><br />
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<h5><center>References</h5><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
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2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
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3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="wax">Alternate Approach</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wishes to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<h6><center> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. Until now, scientific knowledge of this protein coating was limited mainly to total amino acid composition of all the proteins in the paper. To gain more insight into the specific proteins that may exist in wasp nest paper, we collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. We believe there may be a single protein in wasp saliva that is chiefly responsible for the hydrophobic nature of their nests. </h6><br />
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<h6>Our ultimate goal is to identify the gene that codes for this wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running of a wasp protein gel.</a></p></center><br><br />
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<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!<br><br><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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<h5><center>Results</h5><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
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<h6>In addition to the wasp protein waterproofing project, we searched literature for other organisms that would be able to produce a highly hydrophobic substance to waterproof the UAV. We stumbled across Marinobacter hydrocarbonoclasticus, a marine bacterium that synthesizes isoprenoid wax esters for use as storage compounds CITATION NEEDED.</h6><br />
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<h6><center> A phytol-based wax ester produced by <i>Marinobacter hydrocarbonoclasticus</i>.</center></h6><br />
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<h6>Wax esters, such as the example shown above, consist of two very long carbon chains joined by an ester group. These chains impart great hydrophobicity to the entire molecule, indicating that a wax ester coating on a surface could be used to repel water. We decided to repurpose the wax ester synthesis genes in Marinobacter to create a pathway through which Escherichia coli could synthesize the wax esters and use them to waterproof our UAV.</h6><br />
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<h6>The two enzymes in Marinobacter that are involved in the production of isoprenoid wax esters were wax synthase 1 (WS1) and wax synthase 2 (WS2). In the presence of a fatty alcohol and fatty acyl-CoA (carboxylic acid derivative), either of these two proteins can catalyze the synthesis of isoprenoid wax esters.</h6><br />
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<h6>The DNA sequences for both WS1 and WS2 were obtained from the NCBI database CITATION NEEDED. They were then codon optimized for Escherichia coli using a codon optimizer from the Integrated DNA Technologies (IDT) website and then synthesized. After the genes were received from IDT, a polymerase chain reaction (PCR) was done to amplify the proteins and to ensure that they were present at around 1.4kb. The proteins were then ligated to a promoter and RBS and also into a chloramphenicol vector, then transformed into E. coli. After completing a colony PCR on the transformants, colonies 5, 7, and 11 on the WS2 plate appeared to contain the WS2 gene. Liquid cultures were inoculated with colonies 5, 7, and 11 to create more cells with the gene. After the cultures were grown in the incubator, they were miniprepped in order to extract their plasmid DNA. The DNA was then sent for sequencing with a 15ul reaction volume.</h6><br />
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<h6>After sequencing the data came back with miniprepped genes that aligned with the original synthesized gene for roughly half the length of the sequence, but with a puzzling truncation that was present in all three plasmid samples:</h6><br />
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<h6><center>WS2 sequencing alignment in Geneious 7.</center></h6><br />
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<h6>Unfortunately, the wax esters project had to be discontinued due to an error in the synthesis of the WS2 gene. In order to insert the gene into the vector, there are restriction sites that are cleaved by their respective restriction enzymes. When we codon-optimized WS2 for E. coli expression, we accidentally created a PstI cut site at base 720 in the synthesized gene. Because there was a cut site in the middle of the gene, only a fragment of the WS2 gene could ever make its way into the chloramphenicol vector, thereby disabling the protein the gene would have coded for. We also discovered that the fatty acyl-CoA reagent we intended to use for our enzymatic synthesis reaction was exorbitantly expensive (over $60 per milligram). For this reason, and because it would have been too costly to resynthesize the WS2 gene, the project was discontinued.</h6><br />
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<h5><center>References</h5><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
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2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
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3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Attributions">Attributions</a></h3><br />
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We would like to thank everyone who contributed to the realization of our project! All that we've done would not be possible with the contributions made by those listed below. On this page, we've listed contributors by project. We've also added sections for our sponsors and for those who made contributions to our team outside of specific projects. Thank you again, everyone, for all you've done!<br />
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<h5><center><a href="https://2014.igem.org/Judging/Awards" target="_blank">Biomaterial Production</a></h5><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://www.bchs.uh.edu/people/detail/?155622-961-5=tcooper" target="_blank">Tim Cooper at University of Houston for <i>Pseudomonas fluorescens</i>.</a></div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://www.spelman.edu/academics/faculty/jean-marie-dimandja" target="_blank">Jean-Marie Dimandja at Spelman College for discussions of 2D GC Analysis.</div><br />
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<h5><center><a href="https://2014.igem.org/Judging/Awards" target="_blank">Amberless Hell Cell</a></h5><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Experimentally validate at least one biobrick part—we validated the <a href="http://parts.igem.org/Part:BBa_K1499200" target="_blank">radiation resistance of uvsE.</a></div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Document the characterization of the biobrick on the parts registry page and submit part to registry. Both completed for <a href="http://parts.igem.org/Part:BBa_K1499200" target="_blank">radiation resistance gene uvsE.</a></div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Articulate questions about the project and answer them. Visit our <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Human_Practices" target="_blank">Human Practices page to see some of the questions and answers about biological drone use that we generated.</a></div><br />
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<h5><center><a href="https://2014.igem.org/Judging/Awards" target="_blank">Wasp Proteins &amp; Material Waterproofing</a></h5><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://research.calacademy.org/ent/staff/dkavanaugh" target="_blank">Dave Kavanaugh at the California Academy of Sciences for helping us trap wasps.</a></div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://research.calacademy.org/ccg/staff/dkapan" target="_blank">Durrell Kapan at the California Academy of Sciences for advising us on wasp transcriptome and genome analysis.</a></div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://ib.berkeley.edu/people/directory/detail/6000/" target="_blank">Michael Sheehan at UC Berkeley for helping us identify wasp species.</a></div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://www.brown.edu/Research/Wessel_Lab/" target="_blank">Gary Wessel and his lab staff at Brown University for their help with peptide mass fingerprinting.</a></div><br />
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<h5><center><a href="https://2014.igem.org/Judging/Awards" target="_blank">Biodegradability</a></h5><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://web.mit.edu/voigtlab/">Christopher Voigt at MIT for providing plasmids necessary for making our biodegradation constructs.</a></div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="https://www.linkedin.com/pub/timothy-brown/36/ab4/441" target="_blank">Timothy Brown from Thermo Fisher Scientific for teaching us how to use the flow-cytometer to collect GFP data. </a></div><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Improve the function or characterization of an existing biobrick part or device.</div><br />
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<h5><center><a href="https://2014.igem.org/Judging/Awards" target="_blank">Modeling</a></h5><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Improve the function or characterization of an existing biobrick part or device.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Help any registered iGEM team by: characterizing a part, debugging a construct, modeling their system.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Describe an approach to address policy and practices, evaluate your approach, mention how it affects us. We've been working with the EPA to write regulations for synthetic biology in the air. Our project addresses policy and outlines best use practices for biological UAVs and environmental safety concerns with respect to synthetic biology that affect the application of synthetic biology across the country. Read more about our collaboration with the EPA to address the release of synthetic organisms into the environment and our evaluation of our own approach on our <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Human_Practices" target="_blank">Human Practices page.</a></div><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://geo.arc.nasa.gov/sg/cv/esddir3cv-Brass.html">Jim Brass</a>, Kevin Reynolds, and Bob Dahlgren for consulting with us about building, flying, and using UAVs.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Help any registered iGEM team by: characterizing a part, debugging a construct, modeling their system.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Describe an approach to address policy and practices, evaluate your approach, mention how it affects us. We've been working with the EPA to write regulations for synthetic biology in the air. Our project addresses policy and outlines best use practices for biological UAVs and environmental safety concerns with respect to synthetic biology that affect the application of synthetic biology across the country. Read more about our collaboration with the EPA to address the release of synthetic organisms into the environment and our evaluation of our own approach on our <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Human_Practices" target="_blank">Human Practices page.</a></div><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://www.ecovativedesign.com" target="_blank">Ecovative for the production of our mycelium drone components.</a></div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png"><a href="http://agic.cc" target="_blank">AgiC for providing us with silver ink pens for producing drawn circuits and for printing circuits on our cellulose-based biomaterials.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Starch bioplastic samples produced by Eli Block and Jotthe Kappannan with contributions from other team members. Biomaterial molds design by Eli Block. Extensive cellulose material experimentation by Poorwa Godbole.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Preliminary UAV concept and mycelium UAV drone part designs by Eli Block.</div><br />
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<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Wiki design by Eli Block, Aryo Sorayya, and Jotthe Kappannan. Built using the Zurb Foundation framework and brought to you by iGEM and MediaWiki.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Team poster, banner, and wiki assets designed and produced by Eli Block. Material product photography by Eli Block.</div><br />
<div class="sub"><img src="https://static.igem.org/mediawiki/2014/3/3d/SBSiGEM2014_Check_Icon.png">Team presentation and peace sign drone logo designed by Jeannette Gonzales-Wright.</a></div><br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Material_WaterproofingTeam:StanfordBrownSpelman/Material Waterproofing2014-10-17T20:02:21Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="wax">Alternate Approach</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center><iframe width="420" height="315" src="//www.youtube.com/embed/57SGD95KY6s" frameborder="0" allowfullscreen></iframe><br><br> <br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wishes to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<h6><center> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. Until now, scientific knowledge of this protein coating was limited mainly to total amino acid composition of all the proteins in the paper. To gain more insight into the specific proteins that may exist in wasp nest paper, we collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. We believe there may be a single protein in wasp saliva that is chiefly responsible for the hydrophobic nature of their nests. </h6><br />
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<h6>Our ultimate goal is to identify the gene that codes for this wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running of a wasp protein gel.</a></p></center><br><br />
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<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!<br><br><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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<h5><center>Results</h5><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
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<h6>In addition to the wasp protein waterproofing project, we searched literature for other organisms that would be able to produce a highly hydrophobic substance to waterproof the UAV. We stumbled across Marinobacter hydrocarbonoclasticus, a marine bacterium that synthesizes isoprenoid wax esters for use as storage compounds CITATION NEEDED.</h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/c/cf/SBSiGEM_Ian_Hull_wax_ester_chemdraw.png" height="110%" width="110%"><br><br />
<h6><center> A phytol-based wax ester produced by <i>Marinobacter hydrocarbonoclasticus</i>.</center></h6><br />
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<h6>Wax esters, such as the example shown above, consist of two very long carbon chains joined by an ester group. These chains impart great hydrophobicity to the entire molecule, indicating that a wax ester coating on a surface could be used to repel water. We decided to repurpose the wax ester synthesis genes in Marinobacter to create a pathway through which Escherichia coli could synthesize the wax esters and use them to waterproof our UAV.</h6><br />
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<h6>The two enzymes in Marinobacter that are involved in the production of isoprenoid wax esters were wax synthase 1 (WS1) and wax synthase 2 (WS2). In the presence of a fatty alcohol and fatty acyl-CoA (carboxylic acid derivative), either of these two proteins can catalyze the synthesis of isoprenoid wax esters.</h6><br />
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<h6>The DNA sequences for both WS1 and WS2 were obtained from the NCBI database CITATION NEEDED. They were then codon optimized for Escherichia coli using a codon optimizer from the Integrated DNA Technologies (IDT) website and then synthesized. After the genes were received from IDT, a polymerase chain reaction (PCR) was done to amplify the proteins and to ensure that they were present at around 1.4kb. The proteins were then ligated to a promoter and RBS and also into a chloramphenicol vector, then transformed into E. coli. After completing a colony PCR on the transformants, colonies 5, 7, and 11 on the WS2 plate appeared to contain the WS2 gene. Liquid cultures were inoculated with colonies 5, 7, and 11 to create more cells with the gene. After the cultures were grown in the incubator, they were miniprepped in order to extract their plasmid DNA. The DNA was then sent for sequencing with a 15ul reaction volume.</h6><br />
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<h6>After sequencing the data came back with miniprepped genes that aligned with the original synthesized gene for roughly half the length of the sequence, but with a puzzling truncation that was present in all three plasmid samples:</h6><br />
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<h6><center>WS2 sequencing alignment in Geneious 7.</center></h6><br />
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<h6>Unfortunately, the wax esters project had to be discontinued due to an error in the synthesis of the WS2 gene. In order to insert the gene into the vector, there are restriction sites that are cleaved by their respective restriction enzymes. When we codon-optimized WS2 for E. coli expression, we accidentally created a PstI cut site at base 720 in the synthesized gene. Because there was a cut site in the middle of the gene, only a fragment of the WS2 gene could ever make its way into the chloramphenicol vector, thereby disabling the protein the gene would have coded for. We also discovered that the fatty acyl-CoA reagent we intended to use for our enzymatic synthesis reaction was exorbitantly expensive (over $60 per milligram). For this reason, and because it would have been too costly to resynthesize the WS2 gene, the project was discontinued.</h6><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
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2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
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3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center><iframe width="420" height="315" src="//www.youtube.com/embed/57SGD95KY6s" frameborder="0" allowfullscreen></iframe><br><br> <br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wishes to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/3/33/SBS_iGEM_wasp_pipe_cropped.JPG" height="110%" width="110%"><br><br />
<h6><center> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. Until now, scientific knowledge of this protein coating was limited mainly to total amino acid composition of all the proteins in the paper. To gain more insight into the specific proteins that may exist in wasp nest paper, we collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. We believe there may be a single protein in wasp saliva that is chiefly responsible for the hydrophobic nature of their nests. </h6><br />
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<h6>Our ultimate goal is to identify the gene that codes for this wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running of a wasp protein gel.</a></p></center><br><br />
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<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!<br><br><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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<h5><center>Results</h5><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
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<h6>In addition to the wasp protein waterproofing project, we searched literature for other organisms that would be able to produce a highly hydrophobic substance to waterproof the UAV. We stumbled across Marinobacter hydrocarbonoclasticus, a marine bacterium that synthesizes isoprenoid wax esters for use as storage compounds CITATION NEEDED.</h6><br />
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<h6><center> A phytol-based wax ester produced by <i>Marinobacter hydrocarbonoclasticus</i>.</center></h6><br />
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<h6>Wax esters, such as the example shown above, consist of two very long carbon chains joined by an ester group. These chains impart great hydrophobicity to the entire molecule, indicating that a wax ester coating on a surface could be used to repel water. We decided to repurpose the wax ester synthesis genes in Marinobacter to create a pathway through which Escherichia coli could synthesize the wax esters and use them to waterproof our UAV.</h6><br />
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<h6>The two enzymes in Marinobacter that are involved in the production of isoprenoid wax esters were wax synthase 1 (WS1) and wax synthase 2 (WS2). In the presence of a fatty alcohol and fatty acyl-CoA (carboxylic acid derivative), either of these two proteins can catalyze the synthesis of isoprenoid wax esters.</h6><br />
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<h6>The DNA sequences for both WS1 and WS2 were obtained from the NCBI database CITATION NEEDED. They were then codon optimized for Escherichia coli using a codon optimizer from the Integrated DNA Technologies (IDT) website and then synthesized. After the genes were received from IDT, a polymerase chain reaction (PCR) was done to amplify the proteins and to ensure that they were present at around 1.4kb. The proteins were then ligated to a promoter and RBS and also into a chloramphenicol vector, then transformed into E. coli. After completing a colony PCR on the transformants, colonies 5, 7, and 11 on the WS2 plate appeared to contain the WS2 gene. Liquid cultures were inoculated with colonies 5, 7, and 11 to create more cells with the gene. After the cultures were grown in the incubator, they were miniprepped in order to extract their plasmid DNA. The DNA was then sent for sequencing with a 15ul reaction volume.</h6><br />
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<h6>After sequencing the data came back with miniprepped genes that aligned with the original synthesized gene for roughly half the length of the sequence, but with a puzzling truncation that was present in all three plasmid samples:</h6><br />
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<h6><center>WS2 sequencing alignment in Geneious 7.</center></h6><br />
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<h6>Unfortunately, the wax esters project had to be discontinued due to an error in the synthesis of the WS2 gene. In order to insert the gene into the vector, there are restriction sites that are cleaved by their respective restriction enzymes. When we codon-optimized WS2 for E. coli expression, we accidentally created a PstI cut site at base 720 in the synthesized gene. Because there was a cut site in the middle of the gene, only a fragment of the WS2 gene could ever make its way into the chloramphenicol vector, thereby disabling the protein the gene would have coded for. We also discovered that the fatty acyl-CoA reagent we intended to use for our enzymatic synthesis reaction was exorbitantly expensive (over $60 per milligram). For this reason, and because it would have been too costly to resynthesize the WS2 gene, the project was discontinued.</h6><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
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2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
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3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Material_WaterproofingTeam:StanfordBrownSpelman/Material Waterproofing2014-10-17T19:59:16Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wishes to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/3/33/SBS_iGEM_wasp_pipe_cropped.JPG" height="110%" width="110%"><br><br />
<h6><center> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. Until now, scientific knowledge of this protein coating was limited mainly to total amino acid composition of all the proteins in the paper. To gain more insight into the specific proteins that may exist in wasp nest paper, we collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. We believe there may be a single protein in wasp saliva that is chiefly responsible for the hydrophobic nature of their nests. </h6><br />
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<h6>Our ultimate goal is to identify the gene that codes for this wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running of a wasp protein gel.</a></p></center><br><br />
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<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!<br><br><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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<h5><center>Results</h5><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
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<h6>In addition to the wasp protein waterproofing project, we searched literature for other organisms that would be able to produce a highly hydrophobic substance to waterproof the UAV. We stumbled across Marinobacter hydrocarbonoclasticus, a marine bacterium that synthesizes isoprenoid wax esters for use as storage compounds CITATION NEEDED.</h6><br />
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<h6><center> A phytol-based wax ester produced by <i>Marinobacter hydrocarbonoclasticus</i>.</center></h6><br />
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<h6>Wax esters, such as the example shown above, consist of two very long carbon chains joined by an ester group. These chains impart great hydrophobicity to the entire molecule, indicating that a wax ester coating on a surface could be used to repel water. We decided to repurpose the wax ester synthesis genes in Marinobacter to create a pathway through which Escherichia coli could synthesize the wax esters and use them to waterproof our UAV.</h6><br />
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<h6>The two enzymes in Marinobacter that are involved in the production of isoprenoid wax esters were wax synthase 1 (WS1) and wax synthase 2 (WS2). In the presence of a fatty alcohol and fatty acyl-CoA (carboxylic acid derivative), either of these two proteins can catalyze the synthesis of isoprenoid wax esters.</h6><br />
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<h6>The DNA sequences for both WS1 and WS2 were obtained from the NCBI database CITATION NEEDED. They were then codon optimized for Escherichia coli using a codon optimizer from the Integrated DNA Technologies (IDT) website and then synthesized. After the genes were received from IDT, a polymerase chain reaction (PCR) was done to amplify the proteins and to ensure that they were present at around 1.4kb. The proteins were then ligated to a promoter and RBS and also into a chloramphenicol vector, then transformed into E. coli. After completing a colony PCR on the transformants, colonies 5, 7, and 11 on the WS2 plate appeared to contain the WS2 gene. Liquid cultures were inoculated with colonies 5, 7, and 11 to create more cells with the gene. After the cultures were grown in the incubator, they were miniprepped in order to extract their plasmid DNA. The DNA was then sent for sequencing with a 15ul reaction volume.</h6><br />
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<h6>After sequencing the data came back with miniprepped genes that aligned with the original synthesized gene for roughly half the length of the sequence, but with a puzzling truncation that was present in all three plasmid samples:</h6><br />
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<h6><center>WS2 sequencing alignment in Geneious 7.</center></h6><br />
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<h6>Unfortunately, the wax esters project had to be discontinued due to an error in the synthesis of the WS2 gene. In order to insert the gene into the vector, there are restriction sites that are cleaved by their respective restriction enzymes. When we codon-optimized WS2 for E. coli expression, we accidentally created a PstI cut site at base 720 in the synthesized gene. Because there was a cut site in the middle of the gene, only a fragment of the WS2 gene could ever make its way into the chloramphenicol vector, thereby disabling the protein the gene would have coded for. We also discovered that the fatty acyl-CoA reagent we intended to use for our enzymatic synthesis reaction was exorbitantly expensive (over $60 per milligram). For this reason, and because it would have been too costly to resynthesize the WS2 gene, the project was discontinued.</h6><br />
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<h5><center>References</h5><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
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2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
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3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wants to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<h6><center> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. Until now, scientific knowledge of this protein coating was limited mainly to total amino acid composition of all the proteins in the paper. To gain more insight into the specific proteins that may exist in wasp nest paper, we collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. We believe there may be a single protein in wasp saliva that is chiefly responsible for the hydrophobic nature of their nests. Our ultimate goal is to identify the gene that codes for this wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
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<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
<h6>The biodegradable unmanned aerial vehicle (UAV) would be best improved if it had waterproofing capabilities. As such, various waterproofing mechanisms are under investigation for application [1]. One of the mechanisms includes the biological manipulation of the protein involved in the secretion of lipophilic wax esters from the avian uropygial gland of a pelican. Previous research has revealed that the chemical composition of the uropygial gland secretion is primarily composed of unique variations of methylhexanoic acid and fatty alcohols that react to produce wax esters. The enzymes responsible for catalyzing the esterification reaction are wax synthases. Various wax synthases have been identified across many eukaryotic and prokaryotic organisms including plants, mammals, protozoa, and bacteria. However, the current focus is bacterial and protozoan production of wax esters. Bacterial production of wax esters is most commonly associated with the <i>Acinetobacter calcoaceticus</i> bacterium and isoprenoid wax ester production in <i>Marinobacter hydrocarbonoclasticus</i> [2-3]. <i>M. hydrocarbonoclasticus</i> and <i>Euglena gracilis</i>, a bacterium and protist respectively, were the primary focus for the synthesis of wax esters. There were two proteins that efficiently catalyzed the production of isoprenoid wax esters: wax synthase 1 and wax synthase 2. Both of these proteins can be found in <i>M. hydrocarbonoclasticus</i>. Therefore, we used molecular biology to investigate the biosynthetic production of wax esters in <i>E. coli</i> for waterproofing capabilities. </h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running of a wasp protein gel.</a></p></center><br><br />
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<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!<br><br><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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<h5><center>Results</h5><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
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2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
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3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wants to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/3/33/SBS_iGEM_wasp_pipe_cropped.JPG" height="110%" width="110%"><br><br />
<h6><center> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. We believe there may be a single protein in wasp saliva that is responsible for the hydrophobic nature of their nests. We collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. Our ultimate goal is to identify the gene that codes for the wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
</h6><br />
<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
<h6>The biodegradable unmanned aerial vehicle (UAV) would be best improved if it had waterproofing capabilities. As such, various waterproofing mechanisms are under investigation for application [1]. One of the mechanisms includes the biological manipulation of the protein involved in the secretion of lipophilic wax esters from the avian uropygial gland of a pelican. Previous research has revealed that the chemical composition of the uropygial gland secretion is primarily composed of unique variations of methylhexanoic acid and fatty alcohols that react to produce wax esters. The enzymes responsible for catalyzing the esterification reaction are wax synthases. Various wax synthases have been identified across many eukaryotic and prokaryotic organisms including plants, mammals, protozoa, and bacteria. However, the current focus is bacterial and protozoan production of wax esters. Bacterial production of wax esters is most commonly associated with the <i>Acinetobacter calcoaceticus</i> bacterium and isoprenoid wax ester production in <i>Marinobacter hydrocarbonoclasticus</i> [2-3]. <i>M. hydrocarbonoclasticus</i> and <i>Euglena gracilis</i>, a bacterium and protist respectively, were the primary focus for the synthesis of wax esters. There were two proteins that efficiently catalyzed the production of isoprenoid wax esters: wax synthase 1 and wax synthase 2. Both of these proteins can be found in <i>M. hydrocarbonoclasticus</i>. Therefore, we used molecular biology to investigate the biosynthetic production of wax esters in <i>E. coli</i> for waterproofing capabilities. </h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
<br />
<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <h6><a href="http://vimeo.com/102973720">A short time lapse video documenting the running<br>of one of our our wasp nest protein gel.</a></h6></center><br><br />
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<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!<br><br><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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<h5><center>Results</h5><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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<h5><center>References</h5><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
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2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
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3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Material_WaterproofingTeam:StanfordBrownSpelman/Material Waterproofing2014-10-17T15:02:56Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center><iframe width="420" height="315" src="//www.youtube.com/embed/57SGD95KY6s" frameborder="0" allowfullscreen></iframe><br><br> <br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wants to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/3/33/SBS_iGEM_wasp_pipe_cropped.JPG" height="110%" width="110%"><br><br />
<h6><center> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. We believe there may be a single protein in wasp saliva that is responsible for the hydrophobic nature of their nests. We collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. Our ultimate goal is to identify the gene that codes for the wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
</h6><br />
<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
<h6>The biodegradable unmanned aerial vehicle (UAV) would be best improved if it had waterproofing capabilities. As such, various waterproofing mechanisms are under investigation for application [1]. One of the mechanisms includes the biological manipulation of the protein involved in the secretion of lipophilic wax esters from the avian uropygial gland of a pelican. Previous research has revealed that the chemical composition of the uropygial gland secretion is primarily composed of unique variations of methylhexanoic acid and fatty alcohols that react to produce wax esters. The enzymes responsible for catalyzing the esterification reaction are wax synthases. Various wax synthases have been identified across many eukaryotic and prokaryotic organisms including plants, mammals, protozoa, and bacteria. However, the current focus is bacterial and protozoan production of wax esters. Bacterial production of wax esters is most commonly associated with the <i>Acinetobacter calcoaceticus</i> bacterium and isoprenoid wax ester production in <i>Marinobacter hydrocarbonoclasticus</i> [2-3]. <i>M. hydrocarbonoclasticus</i> and <i>Euglena gracilis</i>, a bacterium and protist respectively, were the primary focus for the synthesis of wax esters. There were two proteins that efficiently catalyzed the production of isoprenoid wax esters: wax synthase 1 and wax synthase 2. Both of these proteins can be found in <i>M. hydrocarbonoclasticus</i>. Therefore, we used molecular biology to investigate the biosynthetic production of wax esters in <i>E. coli</i> for waterproofing capabilities. </h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
<br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <h6><a href="http://vimeo.com/102973720">A short time lapse video documenting the running<br>of one of our our wasp nest protein gel.</a></h6></center><br />
<br />
<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!<br><br><br />
<br />
<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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<h5><center>Results</h5><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
<br />
2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
<br />
3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Material_WaterproofingTeam:StanfordBrownSpelman/Material Waterproofing2014-10-17T14:59:58Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center><iframe width="420" height="315" src="//www.youtube.com/embed/57SGD95KY6s" frameborder="0" allowfullscreen></iframe><br><br> <br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water. This poses a problem for anyone who wants to fly our UAV on a rainy day. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties.</h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/3/33/SBS_iGEM_wasp_pipe_cropped.JPG" height="110%" width="110%"><br><br />
<h6><center> <i>Polistes dominula</i>, also known as the European paper wasp. The nest paper is a durable mixture of saliva and cellulose pulp.</center></h6><br />
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<h6>The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. We believe there may be a single protein in wasp saliva that is responsible for the hydrophobic nature of their nests. We collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. Our ultimate goal is to identify the gene that codes for the wasp waterproofing protein and transform into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shield lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
</h6><br />
<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
<h6>The biodegradable unmanned aerial vehicle (UAV) would be best improved if it had waterproofing capabilities. As such, various waterproofing mechanisms are under investigation for application [1]. One of the mechanisms includes the biological manipulation of the protein involved in the secretion of lipophilic wax esters from the avian uropygial gland of a pelican. Previous research has revealed that the chemical composition of the uropygial gland secretion is primarily composed of unique variations of methylhexanoic acid and fatty alcohols that react to produce wax esters. The enzymes responsible for catalyzing the esterification reaction are wax synthases. Various wax synthases have been identified across many eukaryotic and prokaryotic organisms including plants, mammals, protozoa, and bacteria. However, the current focus is bacterial and protozoan production of wax esters. Bacterial production of wax esters is most commonly associated with the <i>Acinetobacter calcoaceticus</i> bacterium and isoprenoid wax ester production in <i>Marinobacter hydrocarbonoclasticus</i> [2-3]. <i>M. hydrocarbonoclasticus</i> and <i>Euglena gracilis</i>, a bacterium and protist respectively, were the primary focus for the synthesis of wax esters. There were two proteins that efficiently catalyzed the production of isoprenoid wax esters: wax synthase 1 and wax synthase 2. Both of these proteins can be found in <i>M. hydrocarbonoclasticus</i>. Therefore, we used molecular biology to investigate the biosynthetic production of wax esters in <i>E. coli</i> for waterproofing capabilities. </h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running<br>of one of our our wasp nest protein gel.</a></p></center><br />
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<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!<br><br><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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<h5><center>Results</h5><br />
<h6><br />
From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
<br />
2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
<br />
3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Material_WaterproofingTeam:StanfordBrownSpelman/Material Waterproofing2014-10-17T14:25:46Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water from wet environments. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties. The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. We believe there may be a single protein in wasp saliva that is responsible for the hydrophobic nature of their nests. We collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. Our ultimate goal is to transform the gene coding for the wasp waterproofing protein into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shielding lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
</h6><br />
<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
<h6>The biodegradable unmanned aerial vehicle (UAV) would be best improved if it had waterproofing capabilities. As such, various waterproofing mechanisms are under investigation for application [1]. One of the mechanisms includes the biological manipulation of the protein involved in the secretion of lipophilic wax esters from the avian uropygial gland of a pelican. Previous research has revealed that the chemical composition of the uropygial gland secretion is primarily composed of unique variations of methylhexanoic acid and fatty alcohols that react to produce wax esters. The enzymes responsible for catalyzing the esterification reaction are wax synthases. Various wax synthases have been identified across many eukaryotic and prokaryotic organisms including plants, mammals, protozoa, and bacteria. However, the current focus is bacterial and protozoan production of wax esters. Bacterial production of wax esters is most commonly associated with the <i>Acinetobacter calcoaceticus</i> bacterium and isoprenoid wax ester production in <i>Marinobacter hydrocarbonoclasticus</i> [2-3]. <i>M. hydrocarbonoclasticus</i> and <i>Euglena gracilis</i>, a bacterium and protist respectively, were the primary focus for the synthesis of wax esters. There were two proteins that efficiently catalyzed the production of isoprenoid wax esters: wax synthase 1 and wax synthase 2. Both of these proteins can be found in <i>M. hydrocarbonoclasticus</i>. Therefore, we used molecular biology to investigate the biosynthetic production of wax esters in <i>E. coli</i> for waterproofing capabilities. </h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
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<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running<br>of one of our our wasp nest protein gel.</a></p></center><br />
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<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!<br><br><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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<h5><center>Results</h5><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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<h5><center>References</h5><br />
<h6><br />
1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
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2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
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3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Material_WaterproofingTeam:StanfordBrownSpelman/Material Waterproofing2014-10-17T14:23:33Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water from wet environments. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties. The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. We believe there may be a single protein in wasp saliva that is responsible for the hydrophobic nature of their nests. We collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. Our ultimate goal is to transform the gene coding for the wasp waterproofing protein into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shielding lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
</h6><br />
<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
<h6>The biodegradable unmanned aerial vehicle (UAV) would be best improved if it had waterproofing capabilities. As such, various waterproofing mechanisms are under investigation for application [1]. One of the mechanisms includes the biological manipulation of the protein involved in the secretion of lipophilic wax esters from the avian uropygial gland of a pelican. Previous research has revealed that the chemical composition of the uropygial gland secretion is primarily composed of unique variations of methylhexanoic acid and fatty alcohols that react to produce wax esters. The enzymes responsible for catalyzing the esterification reaction are wax synthases. Various wax synthases have been identified across many eukaryotic and prokaryotic organisms including plants, mammals, protozoa, and bacteria. However, the current focus is bacterial and protozoan production of wax esters. Bacterial production of wax esters is most commonly associated with the <i>Acinetobacter calcoaceticus</i> bacterium and isoprenoid wax ester production in <i>Marinobacter hydrocarbonoclasticus</i> [2-3]. <i>M. hydrocarbonoclasticus</i> and <i>Euglena gracilis</i>, a bacterium and protist respectively, were the primary focus for the synthesis of wax esters. There were two proteins that efficiently catalyzed the production of isoprenoid wax esters: wax synthase 1 and wax synthase 2. Both of these proteins can be found in <i>M. hydrocarbonoclasticus</i>. Therefore, we used molecular biology to investigate the biosynthetic production of wax esters in <i>E. coli</i> for waterproofing capabilities. </h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
<br />
<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" badge="0" loop="1" portrait="0" title="0" byline="0" color="34C129" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running of one of our our wasp nest protein gel.</a>.</p></center><br />
<br />
<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!<br><br><br />
<br />
<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
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2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
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3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Material_WaterproofingTeam:StanfordBrownSpelman/Material Waterproofing2014-10-17T14:17:37Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center><iframe width="420" height="315" src="//www.youtube.com/embed/57SGD95KY6s" frameborder="0" allowfullscreen></iframe><br><br> <br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water from wet environments. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties. The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. We believe there may be a single protein in wasp saliva that is responsible for the hydrophobic nature of their nests. We collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. Our ultimate goal is to transform the gene coding for the wasp waterproofing protein into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shielding lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
</h6><br />
<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
<h6>The biodegradable unmanned aerial vehicle (UAV) would be best improved if it had waterproofing capabilities. As such, various waterproofing mechanisms are under investigation for application [1]. One of the mechanisms includes the biological manipulation of the protein involved in the secretion of lipophilic wax esters from the avian uropygial gland of a pelican. Previous research has revealed that the chemical composition of the uropygial gland secretion is primarily composed of unique variations of methylhexanoic acid and fatty alcohols that react to produce wax esters. The enzymes responsible for catalyzing the esterification reaction are wax synthases. Various wax synthases have been identified across many eukaryotic and prokaryotic organisms including plants, mammals, protozoa, and bacteria. However, the current focus is bacterial and protozoan production of wax esters. Bacterial production of wax esters is most commonly associated with the <i>Acinetobacter calcoaceticus</i> bacterium and isoprenoid wax ester production in <i>Marinobacter hydrocarbonoclasticus</i> [2-3]. <i>M. hydrocarbonoclasticus</i> and <i>Euglena gracilis</i>, a bacterium and protist respectively, were the primary focus for the synthesis of wax esters. There were two proteins that efficiently catalyzed the production of isoprenoid wax esters: wax synthase 1 and wax synthase 2. Both of these proteins can be found in <i>M. hydrocarbonoclasticus</i>. Therefore, we used molecular biology to investigate the biosynthetic production of wax esters in <i>E. coli</i> for waterproofing capabilities. </h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
<br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
<br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br><br />
<br />
<center><iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">A short time lapse video documenting the running of one of our our wasp nest protein gel.</a>.</p></center><br />
<br />
<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!<br><br><br />
<br />
<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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<h5><center>Results</h5><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
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2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
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3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Material_Waterproofing">Material Waterproofing</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="pics">Images</a> ● <a href="#" id="methods">Methods</a> ● <a href="#" id="data">Results</a> ● <a href="#" id="links">References</a> ● <a href="https://2014.igem.org/Team:StanfordBrownSpelman/BioBricks#MW">BioBricks</a></h7></div><br />
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<h5 id="int"><center><iframe width="420" height="315" src="//www.youtube.com/embed/57SGD95KY6s" frameborder="0" allowfullscreen></iframe><br><br> <br />
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<h5 id="int"><center>Primary approach: Paper wasp protein</h5><br />
<h6>While cellulose-based biomaterials have promising applications in aeronautics and various other fields due to their lightweight, biodegradable nature, they risk structural failure if they absorb too much water from wet environments. Fortunately, nature has provided us with a potential solution. Paper wasps of the genus <i>Polistes</i> are well known for their ability to collect cellulose from the plants around them, mix it with their saliva, and use the resulting cement to construct nests with paper-like properties. The most significant property of the wasp-produced paper is that it is hydrophobic and therefore waterproof. Research has shown that the nest paper is composed primarily of cellulose, but coated with a protein-rich oral secretion CITATION NEEDED. We believe there may be a single protein in wasp saliva that is responsible for the hydrophobic nature of their nests. We collected <i>Polistes dominula</i>, an invasive European species of paper wasp, and sequenced the proteins found in their nests using peptide mass fingerprinting. Our ultimate goal is to transform the gene coding for the wasp waterproofing protein into <i>Saccharomyces cerevisiae</i> so that we can produce an inherently biomimetic solution to shielding lightweight bacterial cellulose (BC) or bacterial cellulose acetate (BCOAc) films from water in the environment. This project is particularly exciting because of its potential for discovery; never before have the proteins in wasp saliva been identified or applied as functional biomaterials.<br />
</h6><br />
<h5><center>Alternate approach: Wax ester biosynthesis</h5><br />
<h6>The biodegradable unmanned aerial vehicle (UAV) would be best improved if it had waterproofing capabilities. As such, various waterproofing mechanisms are under investigation for application [1]. One of the mechanisms includes the biological manipulation of the protein involved in the secretion of lipophilic wax esters from the avian uropygial gland of a pelican. Previous research has revealed that the chemical composition of the uropygial gland secretion is primarily composed of unique variations of methylhexanoic acid and fatty alcohols that react to produce wax esters. The enzymes responsible for catalyzing the esterification reaction are wax synthases. Various wax synthases have been identified across many eukaryotic and prokaryotic organisms including plants, mammals, protozoa, and bacteria. However, the current focus is bacterial and protozoan production of wax esters. Bacterial production of wax esters is most commonly associated with the <i>Acinetobacter calcoaceticus</i> bacterium and isoprenoid wax ester production in <i>Marinobacter hydrocarbonoclasticus</i> [2-3]. <i>M. hydrocarbonoclasticus</i> and <i>Euglena gracilis</i>, a bacterium and protist respectively, were the primary focus for the synthesis of wax esters. There were two proteins that efficiently catalyzed the production of isoprenoid wax esters: wax synthase 1 and wax synthase 2. Both of these proteins can be found in <i>M. hydrocarbonoclasticus</i>. Therefore, we used molecular biology to investigate the biosynthetic production of wax esters in <i>E. coli</i> for waterproofing capabilities. </h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/9/97/SBSiGEM2014_Wasps6.jpg"></li><h6>Our first, successful gel containing proteins from the three paper wasp nest we collected. The presence of several strong bands indicated that the waterproofing effect was likely the result of the interactions of only a few key proteins.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/2/2f/SBSiGEM2014_Wasps3.jpg"></li><h6>Frozen wasp paper sample collected during the summer from a live but vacated nest.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/53/SBSiGEM2014_Wasps8.jpg"></li><h6>A paper wasp on the nest we cultivated on the roof of our lab at the NASA Ames Research Center. Our personal wasp nest provided us with the freshest possible samples.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014_Wasps7.jpg"></li><h6>Protein samples extracted from three paper wasp nests collected with Dave Kavanaugh, entomologist from the California Academy of Sciences.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/0/04/SBSiGEM2014_Wasps9.jpg"></li><h6>Jotthe Kannappan grinds a frozen wasp in the process of extracting RNA.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/7/7c/SBSiGEM2014_Wasps10.jpg"></li><h6>A macroscopic photo of one of our paper wasps used for species identification.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/b/b3/SBSiGEM2014_Wasps12.jpg"></li><h6>Paper wasp actively working on building its nest.</h6><br />
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<h5><center>Approach & Methods</h5><br />
<h6>Our approach to identifying the <i>Polistes dominula</i> waterproofing protein relied on the acquisition of nest samples and of individual wasps. Our plan, detailed in the graphic below, was to extract protein from the nest samples and use analytical techniques such as peptide mass fingerprinting to gather information on the proteins present, and then to use this information to identify candidate <i>Polistes dominula</i> genes for cloning and testing in model organisms.</h6><br />
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<h6><center><b>Figure 1.</b> Schematic for wasp nest protein identification via peptide mass fingerprinting.</center></h6><br />
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<h6>We extracted total protein from the nest samples using a plant protein extraction kit [CITATION NEEDED]. After denaturing the proteins and running them on a polyacrylamide gel, we excised all dominant individual bands and sent them to Dr. Gary Wessel’s lab at Brown University for peptide mass fingerprinting.</h6><br />
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<h6><center>A small sample of a <i>Polistes dominula</i> nest waiting to be ground with a mortar and pestle for protein extraction.</center></h6><br />
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<h6>Peptide mass fingerprinting is an analytical protein identification technique in which the protein of interest is cleaved into small fragments via site-specific proteolytic enzymes such as trypsin. The molecular masses of these fragments can be measured accurately through mass spectrometry. Once these masses are known, they can be compared with computer predictions based on a reference genome or transcriptome to see which of the reference’s proteins are most abundant.</h6><br />
<br />
<iframe src="//player.vimeo.com/video/102973720" width="500" height="281" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe> <p><a href="http://vimeo.com/102973720">S-B-S iGEM Wasp Protein Gel Time Lapse</a> from <a href="http://vimeo.com/eliblock">Eli Block</a> on <a href="https://vimeo.com">Vimeo</a>.</p><br />
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<h6>Fortunately for us, the <i>Polistes dominula</i> genome was published shortly after we began our project [CITATION NEEDED], saving us the trouble and extreme expense of sequencing wasp RNA to create a reference transcriptome ourselves. We truly live in an exciting time for genetic engineering!<br><br><br />
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<div class="sub4"><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="http://docs.google.com/document/d/1jpWJ48e9fPUjGYJ3KOXdE5UEZzHpWGWydRSrWBVN_Kg/edit?usp=sharing"><b>Material Waterproofing Project Notebook:</b> Complete documentation of our methods, protocols, and scientific processes can be found in the linked document.</a></div><br />
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From the peptide mass fingerprinting data, we obtained a list of thirty fragments with hits in the wasp genome. After running a PSI-BLAST on the amino acid sequences of each fragment to look for similar, characterized sequences in related species, we chose six genes as candidates for the waterproofing protein.<br />
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<h6><center>Wasp candidate genes.</center></h6><br />
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1. Biester, EM <i>et al.</i> (2012) Identification of avian wax synthases. <i>BMC Biochemistry</i> 13:4. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/22305293" target="_blank">22305293</a>.<br><br><br />
<br />
2. Kalscheuer, R & Steinbüchel, A (2003) A novel bifunctional wax ester synthase/acyl-CoA:diacylglycerol acyltransferase mediates wax ester and triacylglycerol biosynthesis in Acinetobacter calcoaceticus ADP1. <i>J. Biol. Chem.</i> 278(10):8075-82. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/12502715" target="_blank">12502715</a>.<br><br><br />
<br />
3. Holtzapple, E & Schmidt-Dannert, C (2007) Biosynthesis of Isoprenoid Wax Ester in <i>Marinobacter hydrocarbonoclasticus</i> DSM 8798: Identification and Characterization of Isoprenoid Coenzyme A Synthetase and Wax Ester Synthases. <i>J. Bacteriology</i> 189:3804-3812. PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/17351040" target="_blank">17351040</a>.<br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Building_The_Drone">Prototyping a Biological UAV</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="data">Materials &amp; Designs</a> ● <a href="#" id="methods">Downloads</a> ● <a href="#" id="links">References</a> ● <a href="#" id="intro">Futures</a></h7></div><br />
<h6><br />
Our team modeled, prototyped, and collaborated with <a href="http://www.ecovativedesign.com">Ecovative Design</a> to grow a mycelium-based chassis for our biological drone. Below you'll find process photos, part designs, and links to open source model files for downloading and additively manufacturing your own biological or bio-inspired unmanned aerial vehicle. Finally, you can see images of the biological, biodegradable UAV that we built and flew!<br />
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<li><img src="https://static.igem.org/mediawiki/2014/f/ff/SBSiGEM2014BTD1.jpg"></li><h6>Harvesting a pure bacterial cellulose sheet.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/6/66/SBSiGEM2014BTD2.JPG"></li><h6>Experimenting with cellulose material shape.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/e/e6/SBSiGEM2014BTD4.JPG"></li><h6>Layering cellulose to create thicker leather, see here at the back of the hood.</h6><br />
<li><img src="https://static.igem.org/mediawiki/2014/d/d3/SBSiGEM2014BTD5.jpg"></li><h6>Mycelium drone chassis, modeled and 3D-designed by our team, produced by Ecovative.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/5/57/SBSiGEM2014_Cellulose_Circuit.jpg"></li><h6>Our team collaborated with <a href="http://agic.cc" target="_blank">AgiC</a> to print circuits onto our cellulose-based biomaterials in order to prototype how fully biodegradable circuitry might function on a biological UAV. See our <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Cellulose_Acetate">Biomaterials</a> page for details on the conductivity of this circuitry.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014BTD3.jpg"></li><h6>Variable thickness elements and experimental fragment attachment methods.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014BTD8.JPG"></li><h6>Spreading a cellulose sheet out to dry.</h6><br />
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<h5><center>Starting Small, Ending Big</h5><br />
<h6><br />
We began by experimenting with producing cellulose in sheets and cellulose acetate non-biologically. Seeing that primarily cellulose materials are extremely strong and tough, but tear easily and becomes soggy when wet, we sought to increase the durability of the cellulose by grinding it into pieces to create a cellulose paste (that became spreadable into sheets like paper made from wood pulp) and stretching and twisting it into ropes to add strength. A few of our material samples follow: </h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/3/38/SBSiGEM2014_Cellulose_Screw.jpg"><br><br />
<h6><center>A spiral rope made by weaving together several cellulose sheets and dehydrating them.</center></h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/d/d2/SBSiGEM2014_Cellulose_Leather.jpg"><br><br />
<h6><center>A piece of cellulose leather generated by laying multiple sheets of cellulose together in perpendicular orientations.</center></h6><br />
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While experimenting with cellulose-based materials, we also explored traditional starch bioplastics to compare material functionality. Here is an example of a starch bioplastic that we produced synthetically in the lab:</h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/e/e4/SBSiGEM2014_Mycelium_Sample.jpg"><br><br />
<h6><center>Common starch bioplastic, which is more voluminous but less strong than bacterial cellulose. Starch bioplastics, like bacterial cellulose materials, suffer disintegration when wet.</center></h6><br />
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Realizing that cellulose acetate is tough but thin, our team was in need of a building material that was durable and lightweight. So, we reached out to Ecovative Design, a pioneering fungal-mycelium-based biomaterial company, to prototype a mycelium form that could serve as the chassis of our vehicle. Ecovative shipped us mycelium samples (pictured below), that we skinned in bacterial cellulose.</h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/5/57/SBSiGEM2014_Mycelium_Sample2.jpg"><br><br />
<h6><center>6" by 6" by 1" sample of Ecovative's lightweight mycelium-based biomaterial.</center></h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/1/1b/SBSiGEM2014_Skinned_Mycelium.jpg"><br><br />
<h6><center>A piece of fungal mycelium skinned in bacterial cellulose.</center></h6><br />
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Thanks to Ecovative, we were able to construct a prototype biological unmanned aerial vehicle!<br><br>But we didn't stop there. Our team was enthusiastic about drone design and so we developed concept UAV designs meant to inspire future scientists and designers to think outside the box about how a future, partially living vehicle might look. Pseudo-natural and pseudo-industrial, our drone design references the traditional biological architecture of birds while embracing industrial additive manufacturability.<br><br>All 3D printable files for this concept drone are available in the downloads section. Images of our work follow:</h6><br />
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<h6><center>Concept UAV Design</center></h6><br />
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<h6><center>Biological UAV Concept, Exploded View</center></h6><br />
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<h5><center>Designed Parts &amp; Downloads</h5><br />
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We succeeded in producing multiple viable chassis designs for mycelium UAV concept prototypes. You can download our basic chassis designs here. If you would like to receive a copy of the designs for our more involved, final UAV concept (pictured above), then please reach out to us! We would love to share our work! In the meantime, download and check out our other models below:<br />
<div class="sub4"><a href="https://static.igem.org/mediawiki/2014/6/66/Mycelium_Drone_1.SLDPRT.zip"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/6/66/Mycelium_Drone_1.SLDPRT.zip">Mycelium_Drone_1.SLDPRT: Download a 3D printable STL file for a secondary drone chassis mold concept. Modeled in house and ready for printing, molding, and biomaterial casting.</a></div><br />
<div class="sub4"><a href="https://static.igem.org/mediawiki/2014/c/cd/Mycelium_Drone_2.SLDPRT.zip"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/c/cd/Mycelium_Drone_2.SLDPRT.zip">Mycelium_Drone_2.SLDPRT: Download a 3D printable STL file for a third drone chassis. Shaped like a paper airplane, this mold can be used to make testable mycelium drone prototypes.</a></div><br />
<div class="sub4"><a href="https://static.igem.org/mediawiki/2014/0/02/Mycelium_Quad_1.SLDPRT.zip"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/0/02/Mycelium_Quad_1.SLDPRT.zip">Mycelium_Quad_1.SLDPRT: Download a 3D printable STL file for our mycelium quad chassic (pictured at top of page), which can accept four motors and serve as the foundation for a DIY biomaterial drone prototype.</a></div><br />
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<h5><center>Links & References</h5><br />
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Interested in what we've been working on and want to find more relevant information? Check out some of the following sites, companies, and people who either aided us in our production of biomaterials or collaborated with us in working to produce a viable biological unmanned aerial vehicle.<br />
<div class="sub5"><a href="http://www.ecovativedesign.com">● Ecovative — Fungal-mycelium-based biomaterial production company</a></div><br />
<div class="sub5"><a href="http://issuu.com/miriamribul/docs/miriam_ribul_recipes_for_material_a">● Miriam Ribul's <i>Recipes for Material Activism</i> documents bioplastic production with household ingredients</a></div><br />
<div class="sub5"><a href="http://cookingobjects.com/images">● Cooking Objects — Understanding objects, objectivity, and our relationship with sustainably produced, biodegradable household objects</a></div><br />
<div class="sub5"><a href="http://www.nasa.gov/centers/ames/spaceshop/about/index.html#.VD8Hq4f9pbw">● The NASA Space shop, providing resources and tools for rapid prototyping at the NASA Ames Research Center</a></div><br />
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<h5><center>Drone Futures</h5><br />
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Here is a collection of drone-related sites and speculative work that stimulated our team to think about synthetic biology, the future, and the role of personal unmanned aerial vehicles or biological devices in an evolving world of DIY craft, government surveillance, and channelled creativity.<br />
<div class="sub5"><a href="http://www.fabrica.it/projects/drone">● Frabrica — <i>Drone</i>, speculative fictions in the age of the drone</a></div><br />
<div class="sub5"><a href="http://www.dronesurvivalguide.org">● Drone Survival Guide — a poster series highlighting the uneasy relationship between the public and drones</a></div><br />
<div class="sub5"><a href="http://diydrones.com">● DIY Drones — a growing online community of makers committed to building unmanned aircraft</a></div><br />
<div class="sub5"><a href="http://www.dezeen.com/2014/04/29/drone-shadows-graphics-james-bridle-designs-of-the-year-2014/">● Drone shadows, a visual reminder of constant surveillance</a></div><br />
<div class="sub5"><a href="http://www.huffingtonpost.com/2013/04/03/anti-drone-hoodie-adam-harvey-surveillance_n_3007064.html#slide=2295806">● Anti-Drone hoodie</a></div><br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Building_The_DroneTeam:StanfordBrownSpelman/Building The Drone2014-10-17T04:29:31Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Building_The_Drone">Prototyping a Biological UAV</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="data">Materials &amp; Designs</a> ● <a href="#" id="methods">Downloads</a> ● <a href="#" id="links">References</a> ● <a href="#" id="future">Futures</a></h7></div><br />
<h6 id="int"><br />
Our team modeled, prototyped, and collaborated with <a href="http://www.ecovativedesign.com">Ecovative Design</a> to grow a mycelium-based chassis for our biological drone. Below you'll find process photos, part designs, and links to open source model files for downloading and additively manufacturing your own biological or bio-inspired unmanned aerial vehicle. Finally, you can see images of the biological, biodegradable UAV that we built and flew!<br />
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<li><img src="https://static.igem.org/mediawiki/2014/6/66/SBSiGEM2014BTD2.JPG"></li><h6>Experimenting with cellulose material shape.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/e/e6/SBSiGEM2014BTD4.JPG"></li><h6>Layering cellulose to create thicker leather, see here at the back of the hood.</h6><br />
<li><img src="https://static.igem.org/mediawiki/2014/d/d3/SBSiGEM2014BTD5.jpg"></li><h6>Mycelium drone chassis, modeled and 3D-designed by our team, produced by Ecovative.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014BTD3.jpg"></li><h6>Variable thickness elements and experimental fragment attachment methods.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014BTD8.JPG"></li><h6>Spreading a cellulose sheet out to dry.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/57/SBSiGEM2014_Cellulose_Circuit.jpg"></li><h6>Our team collaborated with <a href="http://agic.cc" target="_blank">AgiC</a> to print circuits onto our cellulose-based biomaterials in order to prototype how fully biodegradable circuitry might function on a biological UAV. See our <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Cellulose_Acetate">Biomaterials</a> page for details on the conductivity of this circuitry.</h6><br />
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<h5><center>Starting Small, Ending Big</h5><br />
<h6><br />
We began by experimenting with producing cellulose in sheets and cellulose acetate non-biologically. Seeing that primarily cellulose materials are extremely strong and tough, but tear easily and becomes soggy when wet, we sought to increase the durability of the cellulose by grinding it into pieces to create a cellulose paste (that became spreadable into sheets like paper made from wood pulp) and stretching and twisting it into ropes to add strength. A few of our material samples follow: </h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/3/38/SBSiGEM2014_Cellulose_Screw.jpg"><br><br />
<h6><center>A spiral rope made by weaving together several cellulose sheets and dehydrating them.</center></h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/d/d2/SBSiGEM2014_Cellulose_Leather.jpg"><br><br />
<h6><center>A piece of cellulose leather generated by laying multiple sheets of cellulose together in perpendicular orientations.</center></h6><br />
</div><br />
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<div id="subheader" class="small-8 small-centered columns"><h6><br />
While experimenting with cellulose-based materials, we also explored traditional starch bioplastics to compare material functionality. Here is an example of a starch bioplastic that we produced synthetically in the lab:</h6><br />
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<h6><center>Common starch bioplastic, which is more voluminous but less strong than bacterial cellulose. Starch bioplastics, like bacterial cellulose materials, suffer disintegration when wet.</center></h6><br />
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Realizing that cellulose acetate is tough but thin, our team was in need of a building material that was durable and lightweight. So, we reached out to Ecovative Design, a pioneering fungal-mycelium-based biomaterial company, to prototype a mycelium form that could serve as the chassis of our vehicle. Ecovative shipped us mycelium samples (pictured below), that we skinned in bacterial cellulose.</h6><br />
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<h6><center>6" by 6" by 1" sample of Ecovative's lightweight mycelium-based biomaterial.</center></h6><br />
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<h6><center>A piece of fungal mycelium skinned in bacterial cellulose.</center></h6><br />
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Thanks to Ecovative, we were able to construct a prototype biological unmanned aerial vehicle!<br><br>But we didn't stop there. Our team was enthusiastic about drone design and so we developed concept UAV designs meant to inspire future scientists and designers to think outside the box about how a future, partially living vehicle might look. Pseudo-natural and pseudo-industrial, our drone design references the traditional biological architecture of birds while embracing industrial additive manufacturability.<br><br>All 3D printable files for this concept drone are available in the downloads section. Images of our work follow:</h6><br />
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<h6><center>Concept UAV Design</center></h6><br />
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<h6><center>Biological UAV Concept, Exploded View</center></h6><br />
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We succeeded in producing multiple viable chassis designs for mycelium UAV concept prototypes. You can download our basic chassis designs here. If you would like to receive a copy of the designs for our more involved, final UAV concept (pictured above), then please reach out to us! We would love to share our work! In the meantime, download and check out our other models below:<br />
<div class="sub4"><a href="https://static.igem.org/mediawiki/2014/6/66/Mycelium_Drone_1.SLDPRT.zip"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/6/66/Mycelium_Drone_1.SLDPRT.zip">Mycelium_Drone_1.SLDPRT: Download a 3D printable STL file for a secondary drone chassis mold concept. Modeled in house and ready for printing, molding, and biomaterial casting.</a></div><br />
<div class="sub4"><a href="https://static.igem.org/mediawiki/2014/c/cd/Mycelium_Drone_2.SLDPRT.zip"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/c/cd/Mycelium_Drone_2.SLDPRT.zip">Mycelium_Drone_2.SLDPRT: Download a 3D printable STL file for a third drone chassis. Shaped like a paper airplane, this mold can be used to make testable mycelium drone prototypes.</a></div><br />
<div class="sub4"><a href="https://static.igem.org/mediawiki/2014/0/02/Mycelium_Quad_1.SLDPRT.zip"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/0/02/Mycelium_Quad_1.SLDPRT.zip">Mycelium_Quad_1.SLDPRT: Download a 3D printable STL file for our mycelium quad chassic (pictured at top of page), which can accept four motors and serve as the foundation for a DIY biomaterial drone prototype.</a></div><br />
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Interested in what we've been working on and want to find more relevant information? Check out some of the following sites, companies, and people who either aided us in our production of biomaterials or collaborated with us in working to produce a viable biological unmanned aerial vehicle.<br />
<div class="sub5"><a href="http://www.ecovativedesign.com">● Ecovative — Fungal-mycelium-based biomaterial production company</a></div><br />
<div class="sub5"><a href="http://issuu.com/miriamribul/docs/miriam_ribul_recipes_for_material_a">● Miriam Ribul's <i>Recipes for Material Activism</i> documents bioplastic production with household ingredients</a></div><br />
<div class="sub5"><a href="http://cookingobjects.com/images">● Cooking Objects — Understanding objects, objectivity, and our relationship with sustainably produced, biodegradable household objects</a></div><br />
<div class="sub5"><a href="http://www.nasa.gov/centers/ames/spaceshop/about/index.html#.VD8Hq4f9pbw">● The NASA Space shop, providing resources and tools for rapid prototyping at the NASA Ames Research Center</a></div><br />
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Here is a collection of speculative work that stimulated our team to think about synthetic biology, the future, and the role of personal unmanned aerial vehicles or biological devices in an evolving world of DIY craft, government surveillance, and channelled creativity.<br />
<div class="sub5"><a href="http://www.fabrica.it/projects/drone">● Frabrica — <i>Drone</i>, speculative fictions in the age of the drone</a></div><br />
<div class="sub5"><a href="http://www.dronesurvivalguide.org">● Drone Survival Guide — a poster series highlighting the uneasy relationship between the public and drones</a></div><br />
<div class="sub5"><a href="http://www.huffingtonpost.com/2013/04/03/anti-drone-hoodie-adam-harvey-surveillance_n_3007064.html#slide=2295806">● Anti-Drone hoodie</a></div><br />
<div class="sub5"><a href="http://diydrones.com">● DIY Drones — a growing online community of makers committed to building unmanned aircraft</a></div><br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/Building_The_DroneTeam:StanfordBrownSpelman/Building The Drone2014-10-17T04:29:08Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Building_The_Drone">Prototyping a Biological UAV</a></h3><br />
<div class="boxedmenu"><h7><center><a href="#" id="intro">Intro</a> ● <a href="#" id="data">Materials &amp; Designs</a> ● <a href="#" id="methods">Downloads</a> ● <a href="#" id="links">References</a> ● <a href="#" id="future">Futures</a></h7></div><br />
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Our team modeled, prototyped, and collaborated with <a href="http://www.ecovativedesign.com">Ecovative Design</a> to grow a mycelium-based chassis for our biological drone. Below you'll find process photos, part designs, and links to open source model files for downloading and additively manufacturing your own biological or bio-inspired unmanned aerial vehicle. Finally, you can see images of the biological, biodegradable UAV that we built and flew!<br />
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<li><img src="https://static.igem.org/mediawiki/2014/6/66/SBSiGEM2014BTD2.JPG"></li><h6>Experimenting with cellulose material shape.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/e/e6/SBSiGEM2014BTD4.JPG"></li><h6>Layering cellulose to create thicker leather, see here at the back of the hood.</h6><br />
<li><img src="https://static.igem.org/mediawiki/2014/d/d3/SBSiGEM2014BTD5.jpg"></li><h6>Mycelium drone chassis, modeled and 3D-designed by our team, produced by Ecovative.</h6><br />
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<li><img src="https://static.igem.org/mediawiki/2014/c/c5/SBSiGEM2014BTD8.JPG"></li><h6>Spreading a cellulose sheet out to dry.</h6><br><br />
<li><img src="https://static.igem.org/mediawiki/2014/5/57/SBSiGEM2014_Cellulose_Circuit.jpg"></li><h6>Our team collaborated with <a href="http://agic.cc" target="_blank">AgiC</a> to print circuits onto our cellulose-based biomaterials in order to prototype how fully biodegradable circuitry might function on a biological UAV. See our <a href="https://2014.igem.org/Team:StanfordBrownSpelman/Cellulose_Acetate">Biomaterials</a> page for details on the conductivity of this circuitry.</h6><br />
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<h5><center>Starting Small, Ending Big</h5><br />
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We began by experimenting with producing cellulose in sheets and cellulose acetate non-biologically. Seeing that primarily cellulose materials are extremely strong and tough, but tear easily and becomes soggy when wet, we sought to increase the durability of the cellulose by grinding it into pieces to create a cellulose paste (that became spreadable into sheets like paper made from wood pulp) and stretching and twisting it into ropes to add strength. A few of our material samples follow: </h6><br />
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<h6><center>A spiral rope made by weaving together several cellulose sheets and dehydrating them.</center></h6><br />
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<h6><center>A piece of cellulose leather generated by laying multiple sheets of cellulose together in perpendicular orientations.</center></h6><br />
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While experimenting with cellulose-based materials, we also explored traditional starch bioplastics to compare material functionality. Here is an example of a starch bioplastic that we produced synthetically in the lab:</h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/e/e4/SBSiGEM2014_Mycelium_Sample.jpg"><br><br />
<h6><center>Common starch bioplastic, which is more voluminous but less strong than bacterial cellulose. Starch bioplastics, like bacterial cellulose materials, suffer disintegration when wet.</center></h6><br />
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Realizing that cellulose acetate is tough but thin, our team was in need of a building material that was durable and lightweight. So, we reached out to Ecovative Design, a pioneering fungal-mycelium-based biomaterial company, to prototype a mycelium form that could serve as the chassis of our vehicle. Ecovative shipped us mycelium samples (pictured below), that we skinned in bacterial cellulose.</h6><br />
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<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/5/57/SBSiGEM2014_Mycelium_Sample2.jpg"><br><br />
<h6><center>6" by 6" by 1" sample of Ecovative's lightweight mycelium-based biomaterial.</center></h6><br />
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<h6><center>A piece of fungal mycelium skinned in bacterial cellulose.</center></h6><br />
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Thanks to Ecovative, we were able to construct a prototype biological unmanned aerial vehicle!<br><br>But we didn't stop there. Our team was enthusiastic about drone design and so we developed concept UAV designs meant to inspire future scientists and designers to think outside the box about how a future, partially living vehicle might look. Pseudo-natural and pseudo-industrial, our drone design references the traditional biological architecture of birds while embracing industrial additive manufacturability.<br><br>All 3D printable files for this concept drone are available in the downloads section. Images of our work follow:</h6><br />
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<h6><center>Concept UAV Design</center></h6><br />
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<h6><center>Biological UAV Concept, Exploded View</center></h6><br />
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We succeeded in producing multiple viable chassis designs for mycelium UAV concept prototypes. You can download our basic chassis designs here. If you would like to receive a copy of the designs for our more involved, final UAV concept (pictured above), then please reach out to us! We would love to share our work! In the meantime, download and check out our other models below:<br />
<div class="sub4"><a href="https://static.igem.org/mediawiki/2014/6/66/Mycelium_Drone_1.SLDPRT.zip"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/6/66/Mycelium_Drone_1.SLDPRT.zip">Mycelium_Drone_1.SLDPRT: Download a 3D printable STL file for a secondary drone chassis mold concept. Modeled in house and ready for printing, molding, and biomaterial casting.</a></div><br />
<div class="sub4"><a href="https://static.igem.org/mediawiki/2014/c/cd/Mycelium_Drone_2.SLDPRT.zip"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/c/cd/Mycelium_Drone_2.SLDPRT.zip">Mycelium_Drone_2.SLDPRT: Download a 3D printable STL file for a third drone chassis. Shaped like a paper airplane, this mold can be used to make testable mycelium drone prototypes.</a></div><br />
<div class="sub4"><a href="https://static.igem.org/mediawiki/2014/0/02/Mycelium_Quad_1.SLDPRT.zip"><img src="https://static.igem.org/mediawiki/2014/2/25/SBS_iGEM_2014_download.png"></a><a href="https://static.igem.org/mediawiki/2014/0/02/Mycelium_Quad_1.SLDPRT.zip">Mycelium_Quad_1.SLDPRT: Download a 3D printable STL file for our mycelium quad chassic (pictured at top of page), which can accept four motors and serve as the foundation for a DIY biomaterial drone prototype.</a></div><br />
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Interested in what we've been working on and want to find more relevant information? Check out some of the following sites, companies, and people who either aided us in our production of biomaterials or collaborated with us in working to produce a viable biological unmanned aerial vehicle.<br />
<div class="sub5"><a href="http://www.ecovativedesign.com">● Ecovative — Fungal-mycelium-based biomaterial production company</a></div><br />
<div class="sub5"><a href="http://issuu.com/miriamribul/docs/miriam_ribul_recipes_for_material_a">● Miriam Ribul's <i>Recipes for Material Activism</i> documents bioplastic production with household ingredients</a></div><br />
<div class="sub5"><a href="http://cookingobjects.com/images">● Cooking Objects — Understanding objects, objectivity, and our relationship with sustainably produced, biodegradable household objects</a></div><br />
<div class="sub5"><a href="http://www.nasa.gov/centers/ames/spaceshop/about/index.html#.VD8Hq4f9pbw">● The NASA Space shop, providing resources and tools for rapid prototyping at the NASA Ames Research Center</a></div><br />
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<h5><center>Drone Futures</h5><br />
<h6><br />
Here is a collection of speculative work that stimulated our team to think about synthetic biology, the future, and the role of personal unmanned aerial vehicles or biological devices in an evolving world of DIY craft, government surveillance, and channelled creativity.<br />
<div class="sub5"><a href="http://www.fabrica.it/projects/drone">● Frabrica — <i>Drone</i>, speculative fictions in the age of the drone</a></div><br />
<div class="sub5"><a href="http://www.dronesurvivalguide.org">● Drone Survival Guide — a poster series highlighting the uneasy relationship between the public and drones</a></div><br />
<div class="sub5"><a href="http://www.huffingtonpost.com/2013/04/03/anti-drone-hoodie-adam-harvey-surveillance_n_3007064.html#slide=2295806">● Anti-Drone hoodie</a></div><br />
<div class="sub5"><a href="http://diydrones.com">● DIY Drones — a growing online community of makers committed to building unmanned aircraft</a></div><br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Modelling">Bioinformatics & Modeling</a><br />
</h3><br />
<center><div class="boxedmenu"><br />
<h7><center><a href="#" id="data">DoubleOptimizer</a> ● <a href="#" id="methods">CompositionSearch</a><br><a href="#" id="pics">Flux Balance Analysis</a></h7></div></center><br />
<br />
<h6 id="subheader">While working on our synthetic biology projects for this year's iGEM competition, we found ourselves in need of some computational tools for synthetic biology that did not yet exist. Therefore, we developed our own software packages to meet these needs. In particular, we have developed two pieces of software, specifically designed for the needs of synthetic biologists:<b></b><br />
<div class="sub5">● DoubleOptimizer: a tool that facilitates synthesis of repetitive genes by optimizing codon usage to both match a codon usage distribution for a desired organism, and to reduce and avoid repetitive nucleotide sequences, allowing for easier synthesis.</div><br />
<div class="sub5">● CompositionSearch: a tool that quickly ranks all proteins in a proteome by their similarity to a given amino acid distribution.</div><br />
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<h5><center><br />
DoubleOptimizer </h5><br />
<h6><center>A utility for simultaneous codon and <br>gene synthesis optimization<br></h6><br />
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<center><iframe width="420" height="315" src="//www.youtube.com/embed/sO1qd3eTzRo" frameborder="0" allowfullscreen></iframe><br><br><br />
<h6><br />
Gene synthesis as a tool for biological engineering presents both opportunities and challenges. One opportunity presented is the ability to optimize codon usage in a gene to match that of a host organism. Compared to traditional cloning methods, this can increase protein yields in the host organism by several fold.[1] However, while there exist a large number of freely-usable programs that perform codon optimization, there is no guarantee that the sequences these programs provide will be able to be synthesized. Specifically, in the case of genes with repetitive amino acid sequences, these programs will often generate outputs that contain too many repeated short DNA sequences to be synthesized commercially. <br></br><br />
<br />
<br />
As an example, the hypothetical protein X777_06170 from the ant species <i>Cerapachys biroi</i> has an amino acid sequence that appears to be somewhat repetitive: <br><br><br />
<br />
<div class="sub6">001 mklfkclvpv vvlllikdss arpglirdfv ggtvgsilep fqilkpkdsy adanshasah <br></br><br />
061 nlggtfslgp vslggglssa sasssasang gglasasska daqaggygyg gsnanaqasa <br></br><br />
121 sanaqgggyg nggihgiypg qqgvhggnpf lggagsnana naiananaqa naggnngglg<br></br><br />
181 syggyqqggn ypidsstgpi gnnpflsggh gdgnanaaan anagasaign gggpidvnnp<br></br><br />
241 flhggaansg agginyqpgn aggiilsekp lglptiypgq hppayldsig spgansnaga<br></br><br />
301 napcsecgss gatilgyegq glggikesgs sgatilgyeg qglggikesg ssgatilgye<br></br><br />
361 gqglggikes gssgatilgs ydgqgpsgat ilgdyngqgl ggikessgvt vlgdyegqgl<br></br><br />
421 ggisgphggh gqaganagan ananagatvg ssggvlggvg dhggyhgyng hdgssglnlg<br></br><br />
481 gygggsnana qassnalass ggsssatsda lsnahssggs alanssskas angsgsanan<br></br><br />
541 ahassnassg shglgsktsa ssqasasadt rdmlifs[2]</div><br><br />
</h6><br />
<br />
<h6> Note that this sequence is not simply the same sequence repeated multiple times, but instead contains several motifs on the order of 10 - 20 amino acids in length that occur several times. When this sequence was run through the codon optimization program for expression in ''E. coli'' provided by a major DNA synthesis firm, the resulting output could not be synthesized by the very same firm: the "optimized" DNA sequence contained too many recurring short (> 8 nucleotide) DNA sequences to allow for synthesis.<br><br><br />
<br />
Manually correcting for repeats in the codon-optimized DNA sequence is a sub-optimal solution: not only is this process time-consuming, but it has the tendency to undo the codon-optimization: if a sequence of amino acids occurs several times, one may be forced to use all possible codon-combinations to represent this sequence to avoid nucleotide-sequence repetition. Unless corrected for by skewing codon usage elsewhere in the sequence, this will tend to make the codon usage more uniform than is optimal for the expression vector. Additionally, any changes made in either correcting for repeats or re-correcting for codon usage may in turn introduce additional repeats.<br />
</h6><br />
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<h5><center>Solution</h5><br />
<h6>DoubleOptimizer is a software tool we have created to optimize codon usage in a gene both to match a given codon usage distribution and to avoid repetition of nucleotide sequences. Given a DNA or amino acid sequence and a desired codon distribution, DoubleOptimizer will produce, within a matter of minutes, an equivalent sequence that has substantially reduced DNA sequence repetition, while also closely matching the desired codon usage.<br />
</h6><br />
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<h5><center>Availability and Usage</h5><br />
<h6>DoubleOptimizer may be downloaded <a href = "http://drive.google.com/a/brown.edu/file/d/0B6Q5Eo65G4cPZC1SZWEzbUtrYUU/view?usp=sharing"><u>here</u></a>. DoubleOptimizer is a command line utility, provided as a Java jar file. It can be invoked from command line on any system with Java 7 or later installed, with the following syntax:<br></br><br />
<br />
<div class="sub6">java -jar DoubleOptimizer.jar seq.txt codons.txt <br></br> </div><br />
<br />
where "seq.txt" is a DNA sequence, stored as a plain text file, and "codons.txt" is a file containing the desired codon distribution to match. It should be formatted as plain text, according to the following example template: <br><br><br />
<br />
<div class="sub6"><br />
A<br></br><br />
GCG .36<br></br><br />
GCC .27<br></br><br />
GCA .21<br></br><br />
GCT .16<br></br><br />
R<br></br><br />
CGC .40<br></br><br />
CGT .38<br></br><br />
CGG .10<br></br><br />
CGA .06<br></br><br />
AGA .04<br></br><br />
AGG .02<br></br><br />
N<br></br><br />
AAC .55<br></br><br />
AAT .45<br></br><br />
D<br></br><br />
GAT .63<br></br><br />
GAC .37<br></br><br />
C<br></br><br />
TGC .55<br></br><br />
TGT .45<br></br><br />
E<br></br><br />
GAA .69<br></br><br />
GAG .31<br></br><br />
Q<br></br><br />
CAG .65<br></br><br />
CAA .35<br></br><br />
G<br></br><br />
GGC .40<br></br><br />
GGT .34<br></br><br />
GGG .15<br></br><br />
GGA .11<br></br><br />
H<br></br><br />
CAT .57<br></br><br />
CAC .43<br></br><br />
I<br></br><br />
ATT .51<br></br><br />
ATC .42<br></br><br />
ATA .07<br></br><br />
L<br></br><br />
CTG .50<br></br><br />
TTG .13<br></br><br />
TTA .13<br></br><br />
CTT .10<br></br><br />
CTC .10<br></br><br />
CTA .04<br></br><br />
K<br></br><br />
AAA .77<br></br><br />
AAG .23<br></br><br />
M<br></br><br />
ATG 1<br></br><br />
F<br></br><br />
TTT .57<br></br><br />
TTC .43<br></br><br />
P<br></br><br />
CCG .52<br></br><br />
CCA .19<br></br><br />
CCT .16<br></br><br />
CCC .12<br></br><br />
S<br></br><br />
AGC .28<br></br><br />
AGT .15<br></br><br />
TCG .15<br></br><br />
TCT .15<br></br><br />
TCC .15<br></br><br />
TCA .12<br></br><br />
*<br></br><br />
TAA .64<br></br><br />
TGA .29<br></br><br />
TAG .07<br></br><br />
T<br></br><br />
ACC .44<br></br><br />
ACG .27<br></br><br />
ACT .17<br></br><br />
ACA .13<br></br><br />
W<br></br><br />
TGG 1<br></br><br />
Y<br></br><br />
TAT .57<br></br><br />
TAC .43<br></br><br />
V<br></br><br />
GTG .37<br></br><br />
GTT .26<br></br><br />
GTC .22<br></br><br />
GTA .15<br></br><br />
</div><br />
<br />
(Note that the above example is actually the codon usage distribution of <i>E. coli</i>.[3]) <br></br><br />
<br />
DoubleOptimizer supports non-canonical codon assignments: the amino acid-codon groupings can by specified in whatever way the user wants in the codon distribution file.<br></br><br />
<br />
When executed, DoubleOptimizer will first display the input sequence with repetitive regions highlighted. It will also give the fraction of the sequence that initially consists of repetitive regions (defined by default as regions of 8 nucleotides or more that occur more than once in the sequence, including as their reverse complement), and a chi-squared value for the goodness-of-fit to the desired codon distribution (lower is better).<br></br><br />
<br />
DoubleOptimizer will then compute and display the optimized sequence (By default, it will produce the best sequence it can find after 10 seconds of computation time). Again, repetitive regions will be highlighted, and the same measurements of repetitiveness and codon fit will be given.<br></br><br />
<br />
The following optional flags may be used to change the program's behavior:<br></br><br />
<br />
<div><h5><ul><li>-A</ul></h5></div><br />
<br />
This allows for an amino-acid sequence, specified in single-letter code, to be used as input instead of a DNA sequence. The initial sequence statistics displayed will be for a uniform random reverse translation of the given amino acid sequence. <br></br><br />
<br />
Example: <br><br />
<br />
<div class="sub6">java -jar DoubleOptimizer.jar aaseq.txt codons.txt -A <br></br><br />
<br />
</div><br />
<br />
<div><h5><ul><li>-T##</ul></h5></div><br />
This allows the user to specify, in seconds, a different run-time for the program other than the default 10 seconds. While 10 seconds should be sufficient to produce a well-optimized result for most genes of moderate length on modern desktop computers, longer times may produce better-optimized results on slower machines or on longer sequences. <br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6">java -jar DoubleOptimizer.jar aaseq.txt codons.txt -A -T30 <br></br><br />
<br />
</div><br />
<br />
<div><h5><ul><li>-L##</ul></h5></div><br />
This allows the user to specify a different minimum length for what is considered a repeat, other than the default 8 nucleotides.<br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.txt codons.txt -L7 -T15 <br></br><br />
*-S##, -E##<br></br> <br />
</div><br />
<br />
These allow the user to specify the starting and ending nucleotide, respectively, of the coding region in a construct sequence to be sythesized. Nucleotides outside this frame will be ignored for codon usage optimization, and will never be modified. This option is useful for preventing repetitions, within the coding region, of fixed sequences that occur at the ends of a construct to be sythesized, outside of the coding region. The default values are the beginning of the sequence, and the end of the last complete codon. These options may be used together or independently. Values are one-indexed. If used with ''-A'', these will be interpreted as amino acid indices.<br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -R1000 -D100<br />
<br />
</div><br />
<div><h5><ul><li>-D##</ul></h5></div><br />
This option will make the program periodically display the current best sequence, and associated statistics, as it runs. The number given is the number of optoimization cycles the program will perform between each round of displaying the sequence. This provides a continuous measure of progress on long optimization runs. Note that, when given 10 seconds, the program may execute several thousand cycles of optimization, so an argument on the order of 100 may be reasonable.<br></br> <br />
<br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -R1000 -D100<br />
<br />
</div><br />
<div><h5><ul><li>-R##</ul></h5></div><br />
This option will make the program halt optimization after a certain number of optimization cycles. This may be used with a large value of -T to standardize optimization quality between computers of different speeds. Because this option is mostly only useful for testing the efficiency of this program itself, it may be removed from future releases.<br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -T1000 -R1000<br />
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<h5><center>Examples of Use</h5><br />
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<h6>As an example of the normal operation of the program on an amino acid sequence, consider the above-listed example of a repetitive amino acid sequence, hypothetical protein X777_06170 from the ant species <i>Cerapachys biroi</i>. At one point in our waterproofing project, this protein was considered for sythesis as a possible homologue of the waterproofing <i>Polistes</i> protein. DoubleOptimizer was originally written to overcome difficulties in synthesising this protein for expression in <i> E. coli</i>. (Due to further advances in the <i>Polistes</i> project, this gene was never synthesized). <b></b><br />
A screenshot of a run of DoubleOptimizer on the amino acid sequence for this protein (listed above), with the <i> E. coli</i> codon usage table also listed above, with default settings for 10 seconds on a laptop computer (2011 model Macbook Pro) is shown below: <b></b></h6></div><br />
<div><br><center><img src="https://static.igem.org/mediawiki/2014/8/85/AADoubleOptExample.png" width="1300" height="800"><br><br />
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<b></b><br />
Note that the number of repeated sequences is significantly reduced, and the closeness-of-fit to the desired codon sequence is much improved, compared to the initial random reverse-translation.<br />
<b></b><b></b><br />
As an example of DoubleOptimizer run on a DNA sequence, a somewhat-repetitive <i>Homo sapiens</i> gene was arbitrarily selected from the NCBI database. Specifically, the exon sequence for <a href="http://www.ncbi.nlm.nih.gov/nuccore/18028443"><u>Ca+2-binding protein CBP86 form VII</u></a> was optimized for synthesis, again in <i> E. coli</i>. The same computer setup was used, and DoubleOptimizer was run for the default 10 seconds: <b></b><br />
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<div><br><center><img src="https://static.igem.org/mediawiki/2014/0/09/DNADoubleOptExample.png" width="1300" height="800"><br><br />
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<b></b><br />
In this case, the sequence went from being over 47% composed of repetitive sequences 8 nucleotides or more in length to being completely non-repetitive. Also, the codon usage distribution is now a near-perfect match to the <i> E. coli</i> codon usage distribution.<b><br />
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<h5><center>Algorithm</h5><br />
<h6>DoubleOptimizer uses a genetic optimization algorithm.[4] This algorithm mimics natural selection by taking the original gene sequence and generating several variants by copying the sequence and creating random silent point mutations in each copy. Each variant is then assigned a fitness score based on its repetitiveness and closeness-of-fit to the desired codon usage distribution, and the most fit variants are kept. These are then re-copied, re-mutated and re-scored, and again only the most fit variants are kept, through several thousand rounds of selection. When optimization time runs out, the most fit member of the population after the final round of selection is displayed. <br></br> <br />
One challenge in implementing this algorithm efficiently is that scoring for repetitiveness is a time-consuming process (the time belongs to O(n<sup>2</sup>) in the length of the sequence). Because many thousands of sequences are scored in a single optimization, finding a way to reduce the computational burden of this step was a task of paramount importance. Our solution was to store for each sequence a matrix representing the locations in the sequence where repetitions are already known to occur, and to only attempt to add or remove repetitions that could by affected by the point mutations that were made. This significantly improved run-time, compared to the unsophisticated technique of independently re-scoring each modified sequence.<br />
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<h5><center>References</h5><br />
<h6>1. Several references exist that establish the link between codon usage and expression. To give some highly-cited examples:<br> <br />
<br />
Ikemura, T. Codon usage and tRNA content in unicellular and multicellular organisms. Mol Biol Evol (1985) 2 (1): 13-34 <br></br> <br />
Gouy, M. and Gautier, C. Codon usage in bacteria: correlation with gene expressivity Nucl. Acids Res. (1982) 10 (22): 7055-7074 doi:10.1093/nar/10.22.7055 <br></br> <br />
Sharp, Paul M. and Li, Wen-Hsiung. The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications Nucl. Acids Res. (1987) 15 (3): 1281-1295 doi:10.1093/nar/15.3.1281 <br></br> <br />
2. http://www.ncbi.nlm.nih.gov/protein/607359946. Original paper: Oxley PR, Ji L, Fetter-Pruneda I, McKenzie SK, Li C, Hu H, Zhang G, Kronauer DJ. The genome of the clonal raider ant Cerapachys biroi. Curr Biol. 2014 Feb 17;24(4):451-8. doi: 10.1016/j.cub.2014.01.018. Epub 2014 Feb 6. PubMed PMID: 24508170; PubMed Central PMCID: PMC3961065. <br></br> <br />
3. Kazuna DNA Research Institute. Codon Usage Database. http://www.kazusa.or.jp/codon/cgi-bin/showcodon.cgi?species=316407. <br></br> <br />
4. For a good introduction to genetic algorithms, see:<br></br> <br />
Goldberg, David E. Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, Mass: Addison-Wesley Pub. Co, 1989. <br />
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<h5 ><center> CompositionSearch</h5><br />
<h6><center>A fast, local search of protein sequences<br>against a specified amino acid distribution</h6><br> <br />
<h6>When the relative proportions of amino acids in an unknown protein product have been chemically determined, it is often useful to search a proteome for proteins that have similar amino acid distributions, in order to identify this protein product. While at least one online utility for performing this task already exists (provided by the <a href = "http://web.expasy.org/aacompident/aacompfree.html"> <u>Swiss Institute of Bioinformatics</u> </a>), the web-based nature of this program creates some limitations. Firstly, the SIB provides computational resources for the calculation, resulting in slower turnaround for the user (searches take about 15 minutes). Secondly, this program will only search for proteins already in the Swiss-Prot / TrEMBL databases. (At last check, only the smaller Swiss-Prot search was functional.) If an organism is being newly studied and has just been sequenced, its predicted proteome will not be in these databases. Thirdly, due to limited resources, only the top matches to a given search are provided. This does not allow for statistical comparison to the "typical" protein within a given proteome. Fourthly, for very-high-security tasks, uploading data to a third party may be undesirable.<br />
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<h5><center>Solution</h5><br />
<h6>CompositionSearch is a software tool we have created to address these issues by allowing an individual to rank all proteins in a proteome by similarity (minimum Euclidian distance) to a reference amino acid distribution locally on one's own computer. This ranking can be generated in a matter of seconds, rather than taking several minutes. Because it ranks all proteins in a proteome, CompositionSearch can also generate a figure for the significance of the similarity of a given protein to a given amino acid distribution, using the similarity of the rest of the proteome as a statistical distribution function. <br />
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<h5><center>Availability and Usage</h5><br />
<h6>CompositionSearch may be downloaded <a href= "http://drive.google.com/a/brown.edu/file/d/0B6Q5Eo65G4cPbnpQYlNlcU1QUE0/view?usp=sharing"><u>here</u></a>. <br></br><br />
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CompositionSearch is a command line utility, provided as a Java jar file. It can be invoked from command line on any system with Java 7 or later installed, with the following syntax:<br></br><br />
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<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv <br></br> </div><br />
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where "prot.fasta" is a FASTA-formatted proteome, and "freqs.txt" is a file containing the desired amino distribution to match. It should be formatted as plain text, according to the following example template:<br><br />
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<div class="sub6"><br />
A 0.134 <br></br> <br />
D 0.044 <br></br> <br />
E 0.04 <br></br><br />
G 0.228 <br></br> <br />
I 0.033 <br></br> <br />
K 0.021<br></br><br />
L 0.04 <br></br><br />
P 0.08 <br></br><br />
R 0.009<br></br><br />
S 0.151<br></br><br />
T 0.038<br></br><br />
V 0.083<br></br><br />
</div> <br />
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Note that this may contain as many or as few amino acids as desired. The frequencies, however, are interpreted as absolute, so if all amino acids are represented, they should add 1. (See below for discussion of how the optional -N and -X flags affect this interpretation). <br></br> <br />
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"out.csv" in the above example line is the destination path to store the results of the calculation. The output will be a spreadsheet in csv format, which may be imported into your favorite desktop spreadsheet application (i.e. Microsoft Excel, LibreOffice Calc, etc.).<br></br><br />
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After execution, out.csv will contain a spreadsheet showing the reference distribution, and, in order by similarity to the reference distribution, the amino acid distributions of all proteins in the proteome. Protein name, similarity ranking, similarity score (Euclidian distance between amino acid distributions; lower is better), and similarity p-value (on the curve of other proteins in the proteome) will be listed for each protein. <br></br><br />
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The following optional flags may be used to change the program's behavior: <br></br><br />
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<div><h5><ul><li>-N</ul></h5></div><br />
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This will cause the program to ignore amino acids in the proteins that are not in the distribution list. In other words, this means that the frequencies given refer to frequencies relative to only the the other amino acids listed, instead of all amino acids. To clarify:<br></br><br />
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Without the -N flag, the line in the above example distribution list: <br><br />
<div class="sub6"> P 0.08 <br></br> </div><br />
means that 8% of all amino acid residues in the matching protein are expected to be proline residues. <br></br><br />
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With the -N flag, the line in the above example distribution list:<br><br />
<div class="sub6"> P 0.08 <br></br> </div> <br />
means that 8% of amino acid residues that belong in the set {ADEGIKLPRSTV} (the amino acids with defined frequences) in the matching protein are expected to be proline residues. <br></br><br />
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Example:<br><br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv -N <br></br><br />
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</div><br />
<div><h5><ul><li>-X$$</ul></h5></div><br />
This will cause the program to completely disregard certain amino acid residue symbols in the proteome, regardless of use of the N flag.<br></br><br />
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The default value of this set of characters is 'X,' often used to represent unknown amino acid residues. Therefore the flag -XX is equivalent to normal behavior. Note that the set of characters being ignored is replaced by the -X flag, so it is always advisable to list X when using this flag.<br></br><br />
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Example:<br><br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv -XX*-<br></br> </div><br />
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This will cause the symbols X,*, and - to be ignored in the proteome. <br />
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Cellulose Pathway Modeling </h5><br />
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<h6><center>Flux Balance Analysis</h6><br><br />
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Modeling allows for efficient experimental design by optimizing specific conditions before testing with in vitro methods. Flux Balance Analysis (FBA) is used to optimize the growth medium for the bioengineered gluconacetobacter hansenii to yield bacterial cellulose to be used as biomaterial for a biodegradable drone. FBA is a mathematical method used to examine how metabolites relate to each network and makes predictions for the growth of an organism and product output. It is a direct application of linear programming to biological systems that used the stoichiometric coefficient for each reaction in the system as the set of constraints for the optimization. (add glycolysis stoichiometric picture here)<br />
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</br></br>Flux Balance Analysis is perfomed under steady state conditions and requires information about the stoichiometry of metabolic pathways, metabolic demands, and strain specific parameters. At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption (3).<br />
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</br></br>FBA will be used to optimize the growth conditions of g. hansenii in order to maximize the product output of cellulose. The exchange reactions determine the metabolites that are beneficial to the medium. Changing the composition will allow us to be able to determine the effect the composition has on the efficiency of the production media.<br />
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<h5><center>Model SEED- ModelView</h5><br />
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<h6>Model SEED is an online analysis software with its own genome and media formulation database. An analysis of the G. hansenii is done with the curated model including a biomass reaction set for optimal organismal growth and a control of complete media. The biomass reaction describes the rate, which all the biomass precursors are made in correct proportions.(2) Biomass reactions include the information listed in Figure 1.(Biomass reaction picture) A biomass reactions requires a knowledge of the composition of a cell and the energy requirements that allow for the cell to grow. The biomass equation consists of three levels : macromolecular level (RNA, protein), intermediate level (biosynthetic energy), advanced level (vitamins, elements, and cofactors). The biomass equation also list to stochiometric coefficients for each part of the biomass equation. Using this biomass equation the growth rate was determined to be 66.48 gm per biomass CDW/hr was obtained. Since the flux is positive this explains that the flux of the analysis is indeed towards growth a negative or zero flux would have explained that the pathway is not used or it is impossible. <br />
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<h5><center>Future Directions/Modifications</h5><br />
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<h6>Although a growth rate is obtained using Model SEED, this rate is for an environment where a complete medium is used. The growth medium being used contains dextrose, peptone, yeast extract, Na2HPO4, citric acid, agar and water. Model SEED does not have the capabilities to create a media file, so all further analysis will be done using KBase because it has the ability to customize the media used for analysis. Kbase is a software that allows for online analysis of models, where models and the corresponding media for organismal growth can be optimized. The genome database contains all genomes including the G. hansenii genome (as a JSON file). A full database for medias that exist and capabilities to create the media being used in experimentation through the IRIS interface, which is the command line environment used by Kbase. Using IRIS and the subsequent FBA tutorial, a flux balance analysis was attempted but several issue were observed because of command line issues. Kbase will allow for the majority of modifications to be done in order to obtain the correct model and medium, further help will be sought out in order to conquer the error messages seen. Once the error messages are fixed, further literature searches and consultations will be done in order to change the biomass reaction to one that will target the product growth of cellulose instead of organismal growth of G. hansenii. Again once an accurate growth rate with the proper medium is found, it will then be possible to plot the organism growth rate vs. product growth rate. Plotting this data will determine if there is a direct or inverse relationship between the two quantities.<br />
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<h5><center>References</h5><br />
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<h6>1. Covert, M. W. and B. O. Palsson. Constraints-based models: regulation of gene expression reduces the steady-state solution space. J Theor Biol [Online] 2003 221(3), 309-325.</br></br><br />
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2. Feist, Adam M, and Bernhard O Palsson. The biomass objective function. Current Opinion in Microbiology. [Online] 2010,13.3, 344-349. </br></br><br />
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3. Orth, Jeffrey D, Ines Thiele, and Bernhard Ø Palsson. What is flux balance analysis? Nature Biotechnology [Online] 2010, 28.3, 245-248.</br></br><br />
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4. Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nature Protocols [Online], 2011, 6:1290-1307.<br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Modelling">Bioinformatics & Modeling</a><br />
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<h7><center><a href="#" id="data">DoubleOptimizer</a> ● <a href="#" id="methods">CompositionSearch</a><br><a href="#" id="pics">Flux Balance Analysis</a></h7></div></center><br />
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<h6 id="subheader">While working on our synthetic biology projects for this year's iGEM competition, we found ourselves in need of some computational tools for synthetic biology that did not yet exist. Therefore, we developed our own software packages to meet these needs. In particular, we have developed two pieces of software, specifically designed for the needs of synthetic biologists:<b></b><br />
<div class="sub5">● DoubleOptimizer: a tool that facilitates synthesis of repetitive genes by optimizing codon usage to both match a codon usage distribution for a desired organism, and to reduce and avoid repetitive nucleotide sequences, allowing for easier synthesis.</div><br />
<div class="sub5">● CompositionSearch: a tool that quickly ranks all proteins in a proteome by their similarity to a given amino acid distribution.</div><br />
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<!-- ====== DOUBLE OPTIMIZER ====== --><br />
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<h5><center><br />
DoubleOptimizer </h5><br />
<h6><center>A utility for simultaneous codon and <br>gene synthesis optimization<br></h6><br />
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<center><iframe width="420" height="315" src="//www.youtube.com/embed/sO1qd3eTzRo" frameborder="0" allowfullscreen></iframe><br><br><br />
<h6><br />
Gene synthesis as a tool for biological engineering presents both opportunities and challenges. One opportunity presented is the ability to optimize codon usage in a gene to match that of a host organism. Compared to traditional cloning methods, this can increase protein yields in the host organism by several fold.[1] However, while there exist a large number of freely-usable programs that perform codon optimization, there is no guarantee that the sequences these programs provide will be able to be synthesized. Specifically, in the case of genes with repetitive amino acid sequences, these programs will often generate outputs that contain too many repeated short DNA sequences to be synthesized commercially. <br></br><br />
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As an example, the hypothetical protein X777_06170 from the ant species <i>Cerapachys biroi</i> has an amino acid sequence that appears to be somewhat repetitive: <br><br><br />
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<div class="sub6">001 mklfkclvpv vvlllikdss arpglirdfv ggtvgsilep fqilkpkdsy adanshasah <br></br><br />
061 nlggtfslgp vslggglssa sasssasang gglasasska daqaggygyg gsnanaqasa <br></br><br />
121 sanaqgggyg nggihgiypg qqgvhggnpf lggagsnana naiananaqa naggnngglg<br></br><br />
181 syggyqqggn ypidsstgpi gnnpflsggh gdgnanaaan anagasaign gggpidvnnp<br></br><br />
241 flhggaansg agginyqpgn aggiilsekp lglptiypgq hppayldsig spgansnaga<br></br><br />
301 napcsecgss gatilgyegq glggikesgs sgatilgyeg qglggikesg ssgatilgye<br></br><br />
361 gqglggikes gssgatilgs ydgqgpsgat ilgdyngqgl ggikessgvt vlgdyegqgl<br></br><br />
421 ggisgphggh gqaganagan ananagatvg ssggvlggvg dhggyhgyng hdgssglnlg<br></br><br />
481 gygggsnana qassnalass ggsssatsda lsnahssggs alanssskas angsgsanan<br></br><br />
541 ahassnassg shglgsktsa ssqasasadt rdmlifs[2]</div><br><br />
</h6><br />
<br />
<h6> Note that this sequence is not simply the same sequence repeated multiple times, but instead contains several motifs on the order of 10 - 20 amino acids in length that occur several times. When this sequence was run through the codon optimization program for expression in ''E. coli'' provided by a major DNA synthesis firm, the resulting output could not be synthesized by the very same firm: the "optimized" DNA sequence contained too many recurring short (> 8 nucleotide) DNA sequences to allow for synthesis.<br><br><br />
<br />
Manually correcting for repeats in the codon-optimized DNA sequence is a sub-optimal solution: not only is this process time-consuming, but it has the tendency to undo the codon-optimization: if a sequence of amino acids occurs several times, one may be forced to use all possible codon-combinations to represent this sequence to avoid nucleotide-sequence repetition. Unless corrected for by skewing codon usage elsewhere in the sequence, this will tend to make the codon usage more uniform than is optimal for the expression vector. Additionally, any changes made in either correcting for repeats or re-correcting for codon usage may in turn introduce additional repeats.<br />
</h6><br />
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</div><br />
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<!-- ====== Solution: DoubleOptimizer ====== --><br />
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<h5><center>Solution</h5><br />
<h6>DoubleOptimizer is a software tool we have created to optimize codon usage in a gene both to match a given codon usage distribution and to avoid repetition of nucleotide sequences. Given a DNA or amino acid sequence and a desired codon distribution, DoubleOptimizer will produce, within a matter of minutes, an equivalent sequence that has substantially reduced DNA sequence repetition, while also closely matching the desired codon usage.<br />
</h6><br />
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<!-- ====== Availability and Usage ====== --><br />
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<h5><center>Availability and Usage</h5><br />
<h6>DoubleOptimizer may be downloaded <a href = "http://drive.google.com/a/brown.edu/file/d/0B6Q5Eo65G4cPZC1SZWEzbUtrYUU/view?usp=sharing"><u>here</u></a>. DoubleOptimizer is a command line utility, provided as a Java jar file. It can be invoked from command line on any system with Java 7 or later installed, with the following syntax:<br></br><br />
<br />
<div class="sub6">java -jar DoubleOptimizer.jar seq.txt codons.txt <br></br> </div><br />
<br />
where "seq.txt" is a DNA sequence, stored as a plain text file, and "codons.txt" is a file containing the desired codon distribution to match. It should be formatted as plain text, according to the following example template: <br><br><br />
<br />
<div class="sub6"><br />
A<br></br><br />
GCG .36<br></br><br />
GCC .27<br></br><br />
GCA .21<br></br><br />
GCT .16<br></br><br />
R<br></br><br />
CGC .40<br></br><br />
CGT .38<br></br><br />
CGG .10<br></br><br />
CGA .06<br></br><br />
AGA .04<br></br><br />
AGG .02<br></br><br />
N<br></br><br />
AAC .55<br></br><br />
AAT .45<br></br><br />
D<br></br><br />
GAT .63<br></br><br />
GAC .37<br></br><br />
C<br></br><br />
TGC .55<br></br><br />
TGT .45<br></br><br />
E<br></br><br />
GAA .69<br></br><br />
GAG .31<br></br><br />
Q<br></br><br />
CAG .65<br></br><br />
CAA .35<br></br><br />
G<br></br><br />
GGC .40<br></br><br />
GGT .34<br></br><br />
GGG .15<br></br><br />
GGA .11<br></br><br />
H<br></br><br />
CAT .57<br></br><br />
CAC .43<br></br><br />
I<br></br><br />
ATT .51<br></br><br />
ATC .42<br></br><br />
ATA .07<br></br><br />
L<br></br><br />
CTG .50<br></br><br />
TTG .13<br></br><br />
TTA .13<br></br><br />
CTT .10<br></br><br />
CTC .10<br></br><br />
CTA .04<br></br><br />
K<br></br><br />
AAA .77<br></br><br />
AAG .23<br></br><br />
M<br></br><br />
ATG 1<br></br><br />
F<br></br><br />
TTT .57<br></br><br />
TTC .43<br></br><br />
P<br></br><br />
CCG .52<br></br><br />
CCA .19<br></br><br />
CCT .16<br></br><br />
CCC .12<br></br><br />
S<br></br><br />
AGC .28<br></br><br />
AGT .15<br></br><br />
TCG .15<br></br><br />
TCT .15<br></br><br />
TCC .15<br></br><br />
TCA .12<br></br><br />
*<br></br><br />
TAA .64<br></br><br />
TGA .29<br></br><br />
TAG .07<br></br><br />
T<br></br><br />
ACC .44<br></br><br />
ACG .27<br></br><br />
ACT .17<br></br><br />
ACA .13<br></br><br />
W<br></br><br />
TGG 1<br></br><br />
Y<br></br><br />
TAT .57<br></br><br />
TAC .43<br></br><br />
V<br></br><br />
GTG .37<br></br><br />
GTT .26<br></br><br />
GTC .22<br></br><br />
GTA .15<br></br><br />
</div><br />
<br />
(Note that the above example is actually the codon usage distribution of <i>E. coli</i>.[3]) <br></br><br />
<br />
DoubleOptimizer supports non-canonical codon assignments: the amino acid-codon groupings can by specified in whatever way the user wants in the codon distribution file.<br></br><br />
<br />
When executed, DoubleOptimizer will first display the input sequence with repetitive regions highlighted. It will also give the fraction of the sequence that initially consists of repetitive regions (defined by default as regions of 8 nucleotides or more that occur more than once in the sequence, including as their reverse complement), and a chi-squared value for the goodness-of-fit to the desired codon distribution (lower is better).<br></br><br />
<br />
DoubleOptimizer will then compute and display the optimized sequence (By default, it will produce the best sequence it can find after 10 seconds of computation time). Again, repetitive regions will be highlighted, and the same measurements of repetitiveness and codon fit will be given.<br></br><br />
<br />
The following optional flags may be used to change the program's behavior:<br></br><br />
<br />
<div><h5><ul><li>-A</ul></h5></div><br />
<br />
This allows for an amino-acid sequence, specified in single-letter code, to be used as input instead of a DNA sequence. The initial sequence statistics displayed will be for a uniform random reverse translation of the given amino acid sequence. <br></br><br />
<br />
Example: <br><br />
<br />
<div class="sub6">java -jar DoubleOptimizer.jar aaseq.txt codons.txt -A <br></br><br />
<br />
</div><br />
<br />
<div><h5><ul><li>-T##</ul></h5></div><br />
This allows the user to specify, in seconds, a different run-time for the program other than the default 10 seconds. While 10 seconds should be sufficient to produce a well-optimized result for most genes of moderate length on modern desktop computers, longer times may produce better-optimized results on slower machines or on longer sequences. <br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6">java -jar DoubleOptimizer.jar aaseq.txt codons.txt -A -T30 <br></br><br />
<br />
</div><br />
<br />
<div><h5><ul><li>-L##</ul></h5></div><br />
This allows the user to specify a different minimum length for what is considered a repeat, other than the default 8 nucleotides.<br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.txt codons.txt -L7 -T15 <br></br><br />
*-S##, -E##<br></br> <br />
</div><br />
<br />
These allow the user to specify the starting and ending nucleotide, respectively, of the coding region in a construct sequence to be sythesized. Nucleotides outside this frame will be ignored for codon usage optimization, and will never be modified. This option is useful for preventing repetitions, within the coding region, of fixed sequences that occur at the ends of a construct to be sythesized, outside of the coding region. The default values are the beginning of the sequence, and the end of the last complete codon. These options may be used together or independently. Values are one-indexed. If used with ''-A'', these will be interpreted as amino acid indices.<br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -R1000 -D100<br />
<br />
</div><br />
<div><h5><ul><li>-D##</ul></h5></div><br />
This option will make the program periodically display the current best sequence, and associated statistics, as it runs. The number given is the number of optoimization cycles the program will perform between each round of displaying the sequence. This provides a continuous measure of progress on long optimization runs. Note that, when given 10 seconds, the program may execute several thousand cycles of optimization, so an argument on the order of 100 may be reasonable.<br></br> <br />
<br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -R1000 -D100<br />
<br />
</div><br />
<div><h5><ul><li>-R##</ul></h5></div><br />
This option will make the program halt optimization after a certain number of optimization cycles. This may be used with a large value of -T to standardize optimization quality between computers of different speeds. Because this option is mostly only useful for testing the efficiency of this program itself, it may be removed from future releases.<br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -T1000 -R1000<br />
</div><br />
<br />
</h6><br />
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<!-- ====== Examples of Use ====== --><br />
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<h5><center>Examples of Use</h5><br />
<br />
<h6>As an example of the normal operation of the program on an amino acid sequence, consider the above-listed example of a repetitive amino acid sequence, hypothetical protein X777_06170 from the ant species <i>Cerapachys biroi</i>. At one point in our waterproofing project, this protein was considered for sythesis as a possible homologue of the waterproofing <i>Polistes</i> protein. DoubleOptimizer was originally written to overcome difficulties in synthesising this protein for expression in <i> E. coli</i>. (Due to further advances in the <i>Polistes</i> project, this gene was never synthesized). <b></b><br />
A screenshot of a run of DoubleOptimizer on the amino acid sequence for this protein (listed above), with the <i> E. coli</i> codon usage table also listed above, with default settings for 10 seconds on a laptop computer (2011 model Macbook Pro) is shown below: <b></b></h6></div><br />
<div><br><center><img src="https://static.igem.org/mediawiki/2014/8/85/AADoubleOptExample.png" width="1300" height="800"><br><br />
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<h6><br />
<b></b><br />
Note that the number of repeated sequences is significantly reduced, and the closeness-of-fit to the desired codon sequence is much improved, compared to the initial random reverse-translation.<br />
<b></b><b></b><br />
As an example of DoubleOptimizer run on a DNA sequence, a somewhat-repetitive <i>Homo sapiens</i> gene was arbitrarily selected from the NCBI database. Specifically, the exon sequence for <a href="http://www.ncbi.nlm.nih.gov/nuccore/18028443"><u>Ca+2-binding protein CBP86 form VII</u></a> was optimized for synthesis, again in <i> E. coli</i>. The same computer setup was used, and DoubleOptimizer was run for the default 10 seconds: <b></b><br />
</h6></div><br />
<div><br><center><img src="https://static.igem.org/mediawiki/2014/0/09/DNADoubleOptExample.png" width="1300" height="800"><br><br />
<br />
</div><br />
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<div id="subheader" class="small-8 small-centered columns"><br />
<h6><br />
<br />
<br />
<b></b><br />
In this case, the sequence went from being over 47% composed of repetitive sequences 8 nucleotides or more in length to being completely non-repetitive. Also, the codon usage distribution is now a near-perfect match to the <i> E. coli</i> codon usage distribution.<b><br />
<br />
</h6><br />
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<!-- ======Algorithm ====== --><br />
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<h5><center>Algorithm</h5><br />
<h6>DoubleOptimizer uses a genetic optimization algorithm.[4] This algorithm mimics natural selection by taking the original gene sequence and generating several variants by copying the sequence and creating random silent point mutations in each copy. Each variant is then assigned a fitness score based on its repetitiveness and closeness-of-fit to the desired codon usage distribution, and the most fit variants are kept. These are then re-copied, re-mutated and re-scored, and again only the most fit variants are kept, through several thousand rounds of selection. When optimization time runs out, the most fit member of the population after the final round of selection is displayed. <br></br> <br />
One challenge in implementing this algorithm efficiently is that scoring for repetitiveness is a time-consuming process (the time belongs to O(n<sup>2</sup>) in the length of the sequence). Because many thousands of sequences are scored in a single optimization, finding a way to reduce the computational burden of this step was a task of paramount importance. Our solution was to store for each sequence a matrix representing the locations in the sequence where repetitions are already known to occur, and to only attempt to add or remove repetitions that could by affected by the point mutations that were made. This significantly improved run-time, compared to the unsophisticated technique of independently re-scoring each modified sequence.<br />
<br><br />
</h6><br />
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<!-- ====== References ====== --><br />
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<h5><center>References</h5><br />
<h6>1. Several references exist that establish the link between codon usage and expression. To give some highly-cited examples:<br> <br />
<br />
Ikemura, T. Codon usage and tRNA content in unicellular and multicellular organisms. Mol Biol Evol (1985) 2 (1): 13-34 <br></br> <br />
Gouy, M. and Gautier, C. Codon usage in bacteria: correlation with gene expressivity Nucl. Acids Res. (1982) 10 (22): 7055-7074 doi:10.1093/nar/10.22.7055 <br></br> <br />
Sharp, Paul M. and Li, Wen-Hsiung. The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications Nucl. Acids Res. (1987) 15 (3): 1281-1295 doi:10.1093/nar/15.3.1281 <br></br> <br />
2. http://www.ncbi.nlm.nih.gov/protein/607359946. Original paper: Oxley PR, Ji L, Fetter-Pruneda I, McKenzie SK, Li C, Hu H, Zhang G, Kronauer DJ. The genome of the clonal raider ant Cerapachys biroi. Curr Biol. 2014 Feb 17;24(4):451-8. doi: 10.1016/j.cub.2014.01.018. Epub 2014 Feb 6. PubMed PMID: 24508170; PubMed Central PMCID: PMC3961065. <br></br> <br />
3. Kazuna DNA Research Institute. Codon Usage Database. http://www.kazusa.or.jp/codon/cgi-bin/showcodon.cgi?species=316407. <br></br> <br />
4. For a good introduction to genetic algorithms, see:<br></br> <br />
Goldberg, David E. Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, Mass: Addison-Wesley Pub. Co, 1989. <br />
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<!-- ====== COMPOSITION SEARCH ====== --><br />
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<h5 id="process"><center> CompositionSearch</h5><br />
<h6><center>A fast, local search of protein sequences<br>against a specified amino acid distribution</h6><br> <br />
<h6>When the relative proportions of amino acids in an unknown protein product have been chemically determined, it is often useful to search a proteome for proteins that have similar amino acid distributions, in order to identify this protein product. While at least one online utility for performing this task already exists (provided by the <a href = "http://web.expasy.org/aacompident/aacompfree.html"> <u>Swiss Institute of Bioinformatics</u> </a>), the web-based nature of this program creates some limitations. Firstly, the SIB provides computational resources for the calculation, resulting in slower turnaround for the user (searches take about 15 minutes). Secondly, this program will only search for proteins already in the Swiss-Prot / TrEMBL databases. (At last check, only the smaller Swiss-Prot search was functional.) If an organism is being newly studied and has just been sequenced, its predicted proteome will not be in these databases. Thirdly, due to limited resources, only the top matches to a given search are provided. This does not allow for statistical comparison to the "typical" protein within a given proteome. Fourthly, for very-high-security tasks, uploading data to a third party may be undesirable.<br />
</h6><br />
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<!-- ====== CompositionSearch: Solution ====== --><br />
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<h5><center>Solution</h5><br />
<h6>CompositionSearch is a software tool we have created to address these issues by allowing an individual to rank all proteins in a proteome by similarity (minimum Euclidian distance) to a reference amino acid distribution locally on one's own computer. This ranking can be generated in a matter of seconds, rather than taking several minutes. Because it ranks all proteins in a proteome, CompositionSearch can also generate a figure for the significance of the similarity of a given protein to a given amino acid distribution, using the similarity of the rest of the proteome as a statistical distribution function. <br />
</h6><br />
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<!-- ====== CompositionSearch: Availability and Usage ====== --><br />
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<h5><center>Availability and Usage</h5><br />
<h6>CompositionSearch may be downloaded <a href= "http://drive.google.com/a/brown.edu/file/d/0B6Q5Eo65G4cPbnpQYlNlcU1QUE0/view?usp=sharing"><u>here</u></a>. <br></br><br />
<br />
CompositionSearch is a command line utility, provided as a Java jar file. It can be invoked from command line on any system with Java 7 or later installed, with the following syntax:<br></br><br />
<br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv <br></br> </div><br />
<br />
where "prot.fasta" is a FASTA-formatted proteome, and "freqs.txt" is a file containing the desired amino distribution to match. It should be formatted as plain text, according to the following example template:<br><br />
<br />
<div class="sub6"><br />
A 0.134 <br></br> <br />
D 0.044 <br></br> <br />
E 0.04 <br></br><br />
G 0.228 <br></br> <br />
I 0.033 <br></br> <br />
K 0.021<br></br><br />
L 0.04 <br></br><br />
P 0.08 <br></br><br />
R 0.009<br></br><br />
S 0.151<br></br><br />
T 0.038<br></br><br />
V 0.083<br></br><br />
</div> <br />
<br />
Note that this may contain as many or as few amino acids as desired. The frequencies, however, are interpreted as absolute, so if all amino acids are represented, they should add 1. (See below for discussion of how the optional -N and -X flags affect this interpretation). <br></br> <br />
<br />
"out.csv" in the above example line is the destination path to store the results of the calculation. The output will be a spreadsheet in csv format, which may be imported into your favorite desktop spreadsheet application (i.e. Microsoft Excel, LibreOffice Calc, etc.).<br></br><br />
<br />
After execution, out.csv will contain a spreadsheet showing the reference distribution, and, in order by similarity to the reference distribution, the amino acid distributions of all proteins in the proteome. Protein name, similarity ranking, similarity score (Euclidian distance between amino acid distributions; lower is better), and similarity p-value (on the curve of other proteins in the proteome) will be listed for each protein. <br></br><br />
<br />
The following optional flags may be used to change the program's behavior: <br></br><br />
<br />
<div><h5><ul><li>-N</ul></h5></div><br />
<br />
This will cause the program to ignore amino acids in the proteins that are not in the distribution list. In other words, this means that the frequencies given refer to frequencies relative to only the the other amino acids listed, instead of all amino acids. To clarify:<br></br><br />
<br />
Without the -N flag, the line in the above example distribution list: <br><br />
<div class="sub6"> P 0.08 <br></br> </div><br />
means that 8% of all amino acid residues in the matching protein are expected to be proline residues. <br></br><br />
<br />
With the -N flag, the line in the above example distribution list:<br><br />
<div class="sub6"> P 0.08 <br></br> </div> <br />
means that 8% of amino acid residues that belong in the set {ADEGIKLPRSTV} (the amino acids with defined frequences) in the matching protein are expected to be proline residues. <br></br><br />
<br />
Example:<br><br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv -N <br></br><br />
<br />
</div><br />
<div><h5><ul><li>-X$$</ul></h5></div><br />
This will cause the program to completely disregard certain amino acid residue symbols in the proteome, regardless of use of the N flag.<br></br><br />
<br />
The default value of this set of characters is 'X,' often used to represent unknown amino acid residues. Therefore the flag -XX is equivalent to normal behavior. Note that the set of characters being ignored is replaced by the -X flag, so it is always advisable to list X when using this flag.<br></br><br />
<br />
Example:<br><br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv -XX*-<br></br> </div><br />
<br />
This will cause the symbols X,*, and - to be ignored in the proteome. <br />
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<!-- ====== Cellulose Modeling ====== --><br />
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<h5 id="results"><center><br />
Cellulose Pathway Modeling </h5><br />
<br />
<!-- ====== Flux Balance Analysis ====== --><br />
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<h6><center>Flux Balance Analysis</h6><br><br />
<br />
<h6><br />
Modeling allows for efficient experimental design by optimizing specific conditions before testing with in vitro methods. Flux Balance Analysis (FBA) is used to optimize the growth medium for the bioengineered gluconacetobacter hansenii to yield bacterial cellulose to be used as biomaterial for a biodegradable drone. FBA is a mathematical method used to examine how metabolites relate to each network and makes predictions for the growth of an organism and product output. It is a direct application of linear programming to biological systems that used the stoichiometric coefficient for each reaction in the system as the set of constraints for the optimization. (add glycolysis stoichiometric picture here)<br />
<br />
</br></br>Flux Balance Analysis is perfomed under steady state conditions and requires information about the stoichiometry of metabolic pathways, metabolic demands, and strain specific parameters. At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption (3).<br />
<br />
</br></br>FBA will be used to optimize the growth conditions of g. hansenii in order to maximize the product output of cellulose. The exchange reactions determine the metabolites that are beneficial to the medium. Changing the composition will allow us to be able to determine the effect the composition has on the efficiency of the production media.<br />
</h6><br><br><br />
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<!-- ====== Model SEED- ModelView ====== --><br />
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<h5><center>Model SEED- ModelView</h5><br />
<br />
<h6>Model SEED is an online analysis software with its own genome and media formulation database. An analysis of the G. hansenii is done with the curated model including a biomass reaction set for optimal organismal growth and a control of complete media. The biomass reaction describes the rate, which all the biomass precursors are made in correct proportions.(2) Biomass reactions include the information listed in Figure 1.(Biomass reaction picture) A biomass reactions requires a knowledge of the composition of a cell and the energy requirements that allow for the cell to grow. The biomass equation consists of three levels : macromolecular level (RNA, protein), intermediate level (biosynthetic energy), advanced level (vitamins, elements, and cofactors). The biomass equation also list to stochiometric coefficients for each part of the biomass equation. Using this biomass equation the growth rate was determined to be 66.48 gm per biomass CDW/hr was obtained. Since the flux is positive this explains that the flux of the analysis is indeed towards growth a negative or zero flux would have explained that the pathway is not used or it is impossible. <br />
</h6><br><br><br />
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<h5><center>Future Directions/Modifications</h5><br />
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<h6>Although a growth rate is obtained using Model SEED, this rate is for an environment where a complete medium is used. The growth medium being used contains dextrose, peptone, yeast extract, Na2HPO4, citric acid, agar and water. Model SEED does not have the capabilities to create a media file, so all further analysis will be done using KBase because it has the ability to customize the media used for analysis. Kbase is a software that allows for online analysis of models, where models and the corresponding media for organismal growth can be optimized. The genome database contains all genomes including the G. hansenii genome (as a JSON file). A full database for medias that exist and capabilities to create the media being used in experimentation through the IRIS interface, which is the command line environment used by Kbase. Using IRIS and the subsequent FBA tutorial, a flux balance analysis was attempted but several issue were observed because of command line issues. Kbase will allow for the majority of modifications to be done in order to obtain the correct model and medium, further help will be sought out in order to conquer the error messages seen. Once the error messages are fixed, further literature searches and consultations will be done in order to change the biomass reaction to one that will target the product growth of cellulose instead of organismal growth of G. hansenii. Again once an accurate growth rate with the proper medium is found, it will then be possible to plot the organism growth rate vs. product growth rate. Plotting this data will determine if there is a direct or inverse relationship between the two quantities.<br />
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<h5><center>References</h5><br />
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<h6>1. Covert, M. W. and B. O. Palsson. Constraints-based models: regulation of gene expression reduces the steady-state solution space. J Theor Biol [Online] 2003 221(3), 309-325.</br></br><br />
<br />
2. Feist, Adam M, and Bernhard O Palsson. The biomass objective function. Current Opinion in Microbiology. [Online] 2010,13.3, 344-349. </br></br><br />
<br />
3. Orth, Jeffrey D, Ines Thiele, and Bernhard Ø Palsson. What is flux balance analysis? Nature Biotechnology [Online] 2010, 28.3, 245-248.</br></br><br />
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4. Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nature Protocols [Online], 2011, 6:1290-1307.<br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Modelling">Bioinformatics & Modeling</a><br />
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<h7><center><a href="#" id="data">DoubleOptimizer</a> ● <a href="#" id="methods">CompositionSearch</a><br><a href="#" id="pics">Flux Balance Analysis</a></h7></div></center><br />
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<h6 id="subheader">While working on our synthetic biology projects for this year's iGEM competition, we found ourselves in need of some computational tools for synthetic biology that did not yet exist. Therefore, we developed our own software packages to meet these needs. In particular, we have developed two pieces of software, specifically designed for the needs of synthetic biologists:<b></b><br />
<div class="sub5">● DoubleOptimizer: a tool that facilitates synthesis of repetitive genes by optimizing codon usage to both match a codon usage distribution for a desired organism, and to reduce and avoid repetitive nucleotide sequences, allowing for easier synthesis.</div><br />
<div class="sub5">● CompositionSearch: a tool that quickly ranks all proteins in a proteome by their similarity to a given amino acid distribution.</div><br />
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DoubleOptimizer </h5><br />
<h6><center>A utility for simultaneous codon and <br>gene synthesis optimization<br></h6><br />
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<center><iframe width="420" height="315" src="//www.youtube.com/embed/sO1qd3eTzRo" frameborder="0" allowfullscreen></iframe><br><br><br />
<h6><br />
Gene synthesis as a tool for biological engineering presents both opportunities and challenges. One opportunity presented is the ability to optimize codon usage in a gene to match that of a host organism. Compared to traditional cloning methods, this can increase protein yields in the host organism by several fold.[1] However, while there exist a large number of freely-usable programs that perform codon optimization, there is no guarantee that the sequences these programs provide will be able to be synthesized. Specifically, in the case of genes with repetitive amino acid sequences, these programs will often generate outputs that contain too many repeated short DNA sequences to be synthesized commercially. <br></br><br />
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<br />
As an example, the hypothetical protein X777_06170 from the ant species <i>Cerapachys biroi</i> has an amino acid sequence that appears to be somewhat repetitive: <br><br><br />
<br />
<div class="sub6">001 mklfkclvpv vvlllikdss arpglirdfv ggtvgsilep fqilkpkdsy adanshasah <br></br><br />
061 nlggtfslgp vslggglssa sasssasang gglasasska daqaggygyg gsnanaqasa <br></br><br />
121 sanaqgggyg nggihgiypg qqgvhggnpf lggagsnana naiananaqa naggnngglg<br></br><br />
181 syggyqqggn ypidsstgpi gnnpflsggh gdgnanaaan anagasaign gggpidvnnp<br></br><br />
241 flhggaansg agginyqpgn aggiilsekp lglptiypgq hppayldsig spgansnaga<br></br><br />
301 napcsecgss gatilgyegq glggikesgs sgatilgyeg qglggikesg ssgatilgye<br></br><br />
361 gqglggikes gssgatilgs ydgqgpsgat ilgdyngqgl ggikessgvt vlgdyegqgl<br></br><br />
421 ggisgphggh gqaganagan ananagatvg ssggvlggvg dhggyhgyng hdgssglnlg<br></br><br />
481 gygggsnana qassnalass ggsssatsda lsnahssggs alanssskas angsgsanan<br></br><br />
541 ahassnassg shglgsktsa ssqasasadt rdmlifs[2]</div><br><br />
</h6><br />
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<h6> Note that this sequence is not simply the same sequence repeated multiple times, but instead contains several motifs on the order of 10 - 20 amino acids in length that occur several times. When this sequence was run through the codon optimization program for expression in ''E. coli'' provided by a major DNA synthesis firm, the resulting output could not be synthesized by the very same firm: the "optimized" DNA sequence contained too many recurring short (> 8 nucleotide) DNA sequences to allow for synthesis.<br><br><br />
<br />
Manually correcting for repeats in the codon-optimized DNA sequence is a sub-optimal solution: not only is this process time-consuming, but it has the tendency to undo the codon-optimization: if a sequence of amino acids occurs several times, one may be forced to use all possible codon-combinations to represent this sequence to avoid nucleotide-sequence repetition. Unless corrected for by skewing codon usage elsewhere in the sequence, this will tend to make the codon usage more uniform than is optimal for the expression vector. Additionally, any changes made in either correcting for repeats or re-correcting for codon usage may in turn introduce additional repeats.<br />
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<!-- ====== Solution: DoubleOptimizer ====== --><br />
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<h5><center>Solution</h5><br />
<h6>DoubleOptimizer is a software tool we have created to optimize codon usage in a gene both to match a given codon usage distribution and to avoid repetition of nucleotide sequences. Given a DNA or amino acid sequence and a desired codon distribution, DoubleOptimizer will produce, within a matter of minutes, an equivalent sequence that has substantially reduced DNA sequence repetition, while also closely matching the desired codon usage.<br />
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<h5><center>Availability and Usage</h5><br />
<h6>DoubleOptimizer may be downloaded <a href = "http://drive.google.com/a/brown.edu/file/d/0B6Q5Eo65G4cPZC1SZWEzbUtrYUU/view?usp=sharing"><u>here</u></a>. DoubleOptimizer is a command line utility, provided as a Java jar file. It can be invoked from command line on any system with Java 7 or later installed, with the following syntax:<br></br><br />
<br />
<div class="sub6">java -jar DoubleOptimizer.jar seq.txt codons.txt <br></br> </div><br />
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where "seq.txt" is a DNA sequence, stored as a plain text file, and "codons.txt" is a file containing the desired codon distribution to match. It should be formatted as plain text, according to the following example template: <br><br><br />
<br />
<div class="sub6"><br />
A<br></br><br />
GCG .36<br></br><br />
GCC .27<br></br><br />
GCA .21<br></br><br />
GCT .16<br></br><br />
R<br></br><br />
CGC .40<br></br><br />
CGT .38<br></br><br />
CGG .10<br></br><br />
CGA .06<br></br><br />
AGA .04<br></br><br />
AGG .02<br></br><br />
N<br></br><br />
AAC .55<br></br><br />
AAT .45<br></br><br />
D<br></br><br />
GAT .63<br></br><br />
GAC .37<br></br><br />
C<br></br><br />
TGC .55<br></br><br />
TGT .45<br></br><br />
E<br></br><br />
GAA .69<br></br><br />
GAG .31<br></br><br />
Q<br></br><br />
CAG .65<br></br><br />
CAA .35<br></br><br />
G<br></br><br />
GGC .40<br></br><br />
GGT .34<br></br><br />
GGG .15<br></br><br />
GGA .11<br></br><br />
H<br></br><br />
CAT .57<br></br><br />
CAC .43<br></br><br />
I<br></br><br />
ATT .51<br></br><br />
ATC .42<br></br><br />
ATA .07<br></br><br />
L<br></br><br />
CTG .50<br></br><br />
TTG .13<br></br><br />
TTA .13<br></br><br />
CTT .10<br></br><br />
CTC .10<br></br><br />
CTA .04<br></br><br />
K<br></br><br />
AAA .77<br></br><br />
AAG .23<br></br><br />
M<br></br><br />
ATG 1<br></br><br />
F<br></br><br />
TTT .57<br></br><br />
TTC .43<br></br><br />
P<br></br><br />
CCG .52<br></br><br />
CCA .19<br></br><br />
CCT .16<br></br><br />
CCC .12<br></br><br />
S<br></br><br />
AGC .28<br></br><br />
AGT .15<br></br><br />
TCG .15<br></br><br />
TCT .15<br></br><br />
TCC .15<br></br><br />
TCA .12<br></br><br />
*<br></br><br />
TAA .64<br></br><br />
TGA .29<br></br><br />
TAG .07<br></br><br />
T<br></br><br />
ACC .44<br></br><br />
ACG .27<br></br><br />
ACT .17<br></br><br />
ACA .13<br></br><br />
W<br></br><br />
TGG 1<br></br><br />
Y<br></br><br />
TAT .57<br></br><br />
TAC .43<br></br><br />
V<br></br><br />
GTG .37<br></br><br />
GTT .26<br></br><br />
GTC .22<br></br><br />
GTA .15<br></br><br />
</div><br />
<br />
(Note that the above example is actually the codon usage distribution of <i>E. coli</i>.[3]) <br></br><br />
<br />
DoubleOptimizer supports non-canonical codon assignments: the amino acid-codon groupings can by specified in whatever way the user wants in the codon distribution file.<br></br><br />
<br />
When executed, DoubleOptimizer will first display the input sequence with repetitive regions highlighted. It will also give the fraction of the sequence that initially consists of repetitive regions (defined by default as regions of 8 nucleotides or more that occur more than once in the sequence, including as their reverse complement), and a chi-squared value for the goodness-of-fit to the desired codon distribution (lower is better).<br></br><br />
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DoubleOptimizer will then compute and display the optimized sequence (By default, it will produce the best sequence it can find after 10 seconds of computation time). Again, repetitive regions will be highlighted, and the same measurements of repetitiveness and codon fit will be given.<br></br><br />
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The following optional flags may be used to change the program's behavior:<br></br><br />
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<div><h5><ul><li>-A</ul></h5></div><br />
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This allows for an amino-acid sequence, specified in single-letter code, to be used as input instead of a DNA sequence. The initial sequence statistics displayed will be for a uniform random reverse translation of the given amino acid sequence. <br></br><br />
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Example: <br><br />
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<div class="sub6">java -jar DoubleOptimizer.jar aaseq.txt codons.txt -A <br></br><br />
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</div><br />
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<div><h5><ul><li>-T##</ul></h5></div><br />
This allows the user to specify, in seconds, a different run-time for the program other than the default 10 seconds. While 10 seconds should be sufficient to produce a well-optimized result for most genes of moderate length on modern desktop computers, longer times may produce better-optimized results on slower machines or on longer sequences. <br></br><br />
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Example:<br><br />
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<div class="sub6">java -jar DoubleOptimizer.jar aaseq.txt codons.txt -A -T30 <br></br><br />
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</div><br />
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<div><h5><ul><li>-L##</ul></h5></div><br />
This allows the user to specify a different minimum length for what is considered a repeat, other than the default 8 nucleotides.<br></br><br />
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Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.txt codons.txt -L7 -T15 <br></br><br />
*-S##, -E##<br></br> <br />
</div><br />
<br />
These allow the user to specify the starting and ending nucleotide, respectively, of the coding region in a construct sequence to be sythesized. Nucleotides outside this frame will be ignored for codon usage optimization, and will never be modified. This option is useful for preventing repetitions, within the coding region, of fixed sequences that occur at the ends of a construct to be sythesized, outside of the coding region. The default values are the beginning of the sequence, and the end of the last complete codon. These options may be used together or independently. Values are one-indexed. If used with ''-A'', these will be interpreted as amino acid indices.<br></br><br />
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Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -R1000 -D100<br />
<br />
</div><br />
<div><h5><ul><li>-D##</ul></h5></div><br />
This option will make the program periodically display the current best sequence, and associated statistics, as it runs. The number given is the number of optoimization cycles the program will perform between each round of displaying the sequence. This provides a continuous measure of progress on long optimization runs. Note that, when given 10 seconds, the program may execute several thousand cycles of optimization, so an argument on the order of 100 may be reasonable.<br></br> <br />
<br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -R1000 -D100<br />
<br />
</div><br />
<div><h5><ul><li>-R##</ul></h5></div><br />
This option will make the program halt optimization after a certain number of optimization cycles. This may be used with a large value of -T to standardize optimization quality between computers of different speeds. Because this option is mostly only useful for testing the efficiency of this program itself, it may be removed from future releases.<br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -T1000 -R1000<br />
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<h5><center>Examples of Use</h5><br />
<br />
<h6>As an example of the normal operation of the program on an amino acid sequence, consider the above-listed example of a repetitive amino acid sequence, hypothetical protein X777_06170 from the ant species <i>Cerapachys biroi</i>. At one point in our waterproofing project, this protein was considered for sythesis as a possible homologue of the waterproofing <i>Polistes</i> protein. DoubleOptimizer was originally written to overcome difficulties in synthesising this protein for expression in <i> E. coli</i>. (Due to further advances in the <i>Polistes</i> project, this gene was never synthesized). <b></b><br />
A screenshot of a run of DoubleOptimizer on the amino acid sequence for this protein (listed above), with the <i> E. coli</i> codon usage table also listed above, with default settings for 10 seconds on a laptop computer (2011 model Macbook Pro) is shown below: <b></b></h6></div><br />
<div><br><center><img src="https://static.igem.org/mediawiki/2014/8/85/AADoubleOptExample.png" width="1300" height="800"><br><br />
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<b></b><br />
Note that the number of repeated sequences is significantly reduced, and the closeness-of-fit to the desired codon sequence is much improved, compared to the initial random reverse-translation.<br />
<b></b><b></b><br />
As an example of DoubleOptimizer run on a DNA sequence, a somewhat-repetitive <i>Homo sapiens</i> gene was arbitrarily selected from the NCBI database. Specifically, the exon sequence for <a href="http://www.ncbi.nlm.nih.gov/nuccore/18028443"><u>Ca+2-binding protein CBP86 form VII</u></a> was optimized for synthesis, again in <i> E. coli</i>. The same computer setup was used, and DoubleOptimizer was run for the default 10 seconds: <b></b><br />
</h6></div><br />
<div><br><center><img src="https://static.igem.org/mediawiki/2014/0/09/DNADoubleOptExample.png" width="1300" height="800"><br><br />
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<br />
<br />
<b></b><br />
In this case, the sequence went from being over 47% composed of repetitive sequences 8 nucleotides or more in length to being completely non-repetitive. Also, the codon usage distribution is now a near-perfect match to the <i> E. coli</i> codon usage distribution.<b><br />
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<h5><center>Algorithm</h5><br />
<h6>DoubleOptimizer uses a genetic optimization algorithm.[4] This algorithm mimics natural selection by taking the original gene sequence and generating several variants by copying the sequence and creating random silent point mutations in each copy. Each variant is then assigned a fitness score based on its repetitiveness and closeness-of-fit to the desired codon usage distribution, and the most fit variants are kept. These are then re-copied, re-mutated and re-scored, and again only the most fit variants are kept, through several thousand rounds of selection. When optimization time runs out, the most fit member of the population after the final round of selection is displayed. <br></br> <br />
One challenge in implementing this algorithm efficiently is that scoring for repetitiveness is a time-consuming process (the time belongs to O(n<sup>2</sup>) in the length of the sequence). Because many thousands of sequences are scored in a single optimization, finding a way to reduce the computational burden of this step was a task of paramount importance. Our solution was to store for each sequence a matrix representing the locations in the sequence where repetitions are already known to occur, and to only attempt to add or remove repetitions that could by affected by the point mutations that were made. This significantly improved run-time, compared to the naive technique of independently re-scoring each modified sequence.<br />
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<h5><center>References</h5><br />
<h6>1. Several references exist that establish the link between codon usage and expression. To give some highly-cited examples:<br> <br />
<br />
Ikemura, T. Codon usage and tRNA content in unicellular and multicellular organisms. Mol Biol Evol (1985) 2 (1): 13-34 <br></br> <br />
Gouy, M. and Gautier, C. Codon usage in bacteria: correlation with gene expressivity Nucl. Acids Res. (1982) 10 (22): 7055-7074 doi:10.1093/nar/10.22.7055 <br></br> <br />
Sharp, Paul M. and Li, Wen-Hsiung. The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications Nucl. Acids Res. (1987) 15 (3): 1281-1295 doi:10.1093/nar/15.3.1281 <br></br> <br />
2. http://www.ncbi.nlm.nih.gov/protein/607359946. Original paper: Oxley PR, Ji L, Fetter-Pruneda I, McKenzie SK, Li C, Hu H, Zhang G, Kronauer DJ. The genome of the clonal raider ant Cerapachys biroi. Curr Biol. 2014 Feb 17;24(4):451-8. doi: 10.1016/j.cub.2014.01.018. Epub 2014 Feb 6. PubMed PMID: 24508170; PubMed Central PMCID: PMC3961065. <br></br> <br />
3. Kazuna DNA Research Institute. Codon Usage Database. http://www.kazusa.or.jp/codon/cgi-bin/showcodon.cgi?species=316407. <br></br> <br />
4. For a good introduction to genetic algorithms, see:<br></br> <br />
Goldberg, David E. Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, Mass: Addison-Wesley Pub. Co, 1989. <br />
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<h5 id="process"><center> CompositionSearch</h5><br />
<h6><center>A fast, local search of protein sequences<br>against a specified amino acid distribution</h6><br> <br />
<h6>When the relative proportions of amino acids in an unknown protein product have been chemically determined, it is often useful to search a proteome for proteins that have similar amino acid distributions, in order to identify this protein product. While at least one online utility for performing this task already exists (provided by the <a href = "http://web.expasy.org/aacompident/aacompfree.html"> <u>Swiss Institute of Bioinformatics</u> </a>), the web-based nature of this program creates some limitations. Firstly, the SIB provides computational resources for the calculation, resulting in slower turnaround for the user (searches take about 15 minutes). Secondly, this program will only search for proteins already in the Swiss-Prot / TrEMBL databases. (At last check, only the smaller Swiss-Prot search was functional.) If an organism is being newly studied and has just been sequenced, its predicted proteome will not be in these databases. Thirdly, due to limited resources, only the top matches to a given search are provided. This does not allow for statistical comparison to the "typical" protein within a given proteome. Fourthly, for very-high-security tasks, uploading data to a third party may be undesirable.<br />
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<h5><center>Solution</h5><br />
<h6>CompositionSearch is a software tool we have created to address these issues by allowing an individual to rank all proteins in a proteome by similarity (minimum Euclidian distance) to a reference amino acid distribution locally on one's own computer. This ranking can be generated in a matter of seconds, rather than taking several minutes. Because it ranks all proteins in a proteome, CompositionSearch can also generate a figure for the significance of the similarity of a given protein to a given amino acid distribution, using the similarity of the rest of the proteome as a statistical distribution function. <br />
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<h5><center>Availability and Usage</h5><br />
<h6>CompositionSearch may be downloaded <a href= "http://drive.google.com/a/brown.edu/file/d/0B6Q5Eo65G4cPbnpQYlNlcU1QUE0/view?usp=sharing"><u>here</u></a>. <br></br><br />
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CompositionSearch is a command line utility, provided as a Java jar file. It can be invoked from command line on any system with Java 7 or later installed, with the following syntax:<br></br><br />
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<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv <br></br> </div><br />
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where "prot.fasta" is a FASTA-formatted proteome, and "freqs.txt" is a file containing the desired amino distribution to match. It should be formatted as plain text, according to the following example template:<br><br />
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<div class="sub6"><br />
A 0.134 <br></br> <br />
D 0.044 <br></br> <br />
E 0.04 <br></br><br />
G 0.228 <br></br> <br />
I 0.033 <br></br> <br />
K 0.021<br></br><br />
L 0.04 <br></br><br />
P 0.08 <br></br><br />
R 0.009<br></br><br />
S 0.151<br></br><br />
T 0.038<br></br><br />
V 0.083<br></br><br />
</div> <br />
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Note that this may contain as many or as few amino acids as desired. The frequencies, however, are interpreted as absolute, so if all amino acids are represented, they should add 1. (See below for discussion of how the optional -N and -X flags affect this interpretation). <br></br> <br />
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"out.csv" in the above example line is the destination path to store the results of the calculation. The output will be a spreadsheet in csv format, which may be imported into your favorite desktop spreadsheet application (i.e. Microsoft Excel, LibreOffice Calc, etc.).<br></br><br />
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After execution, out.csv will contain a spreadsheet showing the reference distribution, and, in order by similarity to the reference distribution, the amino acid distributions of all proteins in the proteome. Protein name, similarity ranking, similarity score (Euclidian distance between amino acid distributions; lower is better), and similarity p-value (on the curve of other proteins in the proteome) will be listed for each protein. <br></br><br />
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The following optional flags may be used to change the program's behavior: <br></br><br />
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<div><h5><ul><li>-N</ul></h5></div><br />
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This will cause the program to ignore amino acids in the proteins that are not in the distribution list. In other words, this means that the frequencies given refer to frequencies relative to only the the other amino acids listed, instead of all amino acids. To clarify:<br></br><br />
<br />
Without the -N flag, the line in the above example distribution list: <br><br />
<div class="sub6"> P 0.08 <br></br> </div><br />
means that 8% of all amino acid residues in the matching protein are expected to be proline residues. <br></br><br />
<br />
With the -N flag, the line in the above example distribution list:<br><br />
<div class="sub6"> P 0.08 <br></br> </div> <br />
means that 8% of amino acid residues that belong in the set {ADEGIKLPRSTV} (the amino acids with defined frequences) in the matching protein are expected to be proline residues. <br></br><br />
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Example:<br><br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv -N <br></br><br />
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</div><br />
<div><h5><ul><li>-X$$</ul></h5></div><br />
This will cause the program to completely disregard certain amino acid residue symbols in the proteome, regardless of use of the N flag.<br></br><br />
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The default value of this set of characters is 'X,' often used to represent unknown amino acid residues. Therefore the flag -XX is equivalent to normal behavior. Note that the set of characters being ignored is replaced by the -X flag, so it is always advisable to list X when using this flag.<br></br><br />
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Example:<br><br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv -XX*-<br></br> </div><br />
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This will cause the symbols X,*, and - to be ignored in the proteome. <br />
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Cellulose Pathway Modeling </h5><br />
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<h6><center>Flux Balance Analysis</h6><br><br />
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Modeling allows for efficient experimental design by optimizing specific conditions before testing with in vitro methods. Flux Balance Analysis (FBA) is used to optimize the growth medium for the bioengineered gluconacetobacter hansenii to yield bacterial cellulose to be used as biomaterial for a biodegradable drone. FBA is a mathematical method used to examine how metabolites relate to each network and makes predictions for the growth of an organism and product output. It is a direct application of linear programming to biological systems that used the stoichiometric coefficient for each reaction in the system as the set of constraints for the optimization. (add glycolysis stoichiometric picture here)<br />
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</br></br>Flux Balance Analysis is perfomed under steady state conditions and requires information about the stoichiometry of metabolic pathways, metabolic demands, and strain specific parameters. At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption (3).<br />
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</br></br>FBA will be used to optimize the growth conditions of g. hansenii in order to maximize the product output of cellulose. The exchange reactions determine the metabolites that are beneficial to the medium. Changing the composition will allow us to be able to determine the effect the composition has on the efficiency of the production media.<br />
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<h5><center>Model SEED- ModelView</h5><br />
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<h6>Model SEED is an online analysis software with its own genome and media formulation database. An analysis of the G. hansenii is done with the curated model including a biomass reaction set for optimal organismal growth and a control of complete media. The biomass reaction describes the rate, which all the biomass precursors are made in correct proportions.(2) Biomass reactions include the information listed in Figure 1.(Biomass reaction picture) A biomass reactions requires a knowledge of the composition of a cell and the energy requirements that allow for the cell to grow. The biomass equation consists of three levels : macromolecular level (RNA, protein), intermediate level (biosynthetic energy), advanced level (vitamins, elements, and cofactors). The biomass equation also list to stochiometric coefficients for each part of the biomass equation. Using this biomass equation the growth rate was determined to be 66.48 gm per biomass CDW/hr was obtained. Since the flux is positive this explains that the flux of the analysis is indeed towards growth a negative or zero flux would have explained that the pathway is not used or it is impossible. <br />
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<h5><center>Future Directions/Modifications</h5><br />
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<h6>Although a growth rate is obtained using Model SEED, this rate is for an environment where a complete medium is used. The growth medium being used contains dextrose, peptone, yeast extract, Na2HPO4, citric acid, agar and water. Model SEED does not have the capabilities to create a media file, so all further analysis will be done using KBase because it has the ability to customize the media used for analysis. Kbase is a software that allows for online analysis of models, where models and the corresponding media for organismal growth can be optimized. The genome database contains all genomes including the G. hansenii genome (as a JSON file). A full database for medias that exist and capabilities to create the media being used in experimentation through the IRIS interface, which is the command line environment used by Kbase. Using IRIS and the subsequent FBA tutorial, a flux balance analysis was attempted but several issue were observed because of command line issues. Kbase will allow for the majority of modifications to be done in order to obtain the correct model and medium, further help will be sought out in order to conquer the error messages seen. Once the error messages are fixed, further literature searches and consultations will be done in order to change the biomass reaction to one that will target the product growth of cellulose instead of organismal growth of G. hansenii. Again once an accurate growth rate with the proper medium is found, it will then be possible to plot the organism growth rate vs. product growth rate. Plotting this data will determine if there is a direct or inverse relationship between the two quantities.<br />
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<h5><center>References</h5><br />
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<h6>1. Covert, M. W. and B. O. Palsson. Constraints-based models: regulation of gene expression reduces the steady-state solution space. J Theor Biol [Online] 2003 221(3), 309-325.</br></br><br />
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2. Feist, Adam M, and Bernhard O Palsson. The biomass objective function. Current Opinion in Microbiology. [Online] 2010,13.3, 344-349. </br></br><br />
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3. Orth, Jeffrey D, Ines Thiele, and Bernhard Ø Palsson. What is flux balance analysis? Nature Biotechnology [Online] 2010, 28.3, 245-248.</br></br><br />
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4. Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nature Protocols [Online], 2011, 6:1290-1307.<br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Modelling">Bioinformatics & Modeling</a><br />
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<h7><center><a href="#" id="data">DoubleOptimizer</a> ● <a href="#" id="methods">CompositionSearch</a><br><a href="#" id="pics">Flux Balance Analysis</a></h7></div></center><br />
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<h6 id="int"> While working on our synthetic biology projects for this year's iGEM competition, we found ourselves in need of some computational tools for synthetic biology that did not yet exist. Therefore, we developed our own software packages to meet these needs. In particular, we have developed two pieces of software, specifically designed for the needs of synthetic biologists:<b></b><br />
<div class="sub5">● DoubleOptimizer: a tool that facilitates synthesis of repetitive genes by optimizing codon usage to both match a codon usage distribution for a desired organism, and to reduce and avoid repetitive nucleotide sequences, allowing for easier synthesis.</div><br />
<div class="sub5">● CompositionSearch: a tool that quickly ranks all proteins in a proteome by their similarity to a given amino acid distribution.</div><br />
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<!-- ====== DOUBLE OPTIMIZER ====== --><br />
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DoubleOptimizer </h5><br />
<h6><center>A utility for simultaneous codon and <br>gene synthesis optimization<br></h6><br />
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<center><iframe width="420" height="315" src="//www.youtube.com/embed/sO1qd3eTzRo" frameborder="0" allowfullscreen></iframe><br><br><br />
<h6><br />
Gene synthesis as a tool for biological engineering presents both opportunities and challenges. One opportunity presented is the ability to optimize codon usage in a gene to match that of a host organism. Compared to traditional cloning methods, this can increase protein yields in the host organism by several fold.[1] However, while there exist a large number of freely-usable programs that perform codon optimization, there is no guarantee that the sequences these programs provide will be able to be synthesized. Specifically, in the case of genes with repetitive amino acid sequences, these programs will often generate outputs that contain too many repeated short DNA sequences to be synthesized commercially. <br></br><br />
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As an example, the hypothetical protein X777_06170 from the ant species <i>Cerapachys biroi</i> has an amino acid sequence that appears to be somewhat repetitive: <br><br><br />
<br />
<div class="sub6">001 mklfkclvpv vvlllikdss arpglirdfv ggtvgsilep fqilkpkdsy adanshasah <br></br><br />
061 nlggtfslgp vslggglssa sasssasang gglasasska daqaggygyg gsnanaqasa <br></br><br />
121 sanaqgggyg nggihgiypg qqgvhggnpf lggagsnana naiananaqa naggnngglg<br></br><br />
181 syggyqqggn ypidsstgpi gnnpflsggh gdgnanaaan anagasaign gggpidvnnp<br></br><br />
241 flhggaansg agginyqpgn aggiilsekp lglptiypgq hppayldsig spgansnaga<br></br><br />
301 napcsecgss gatilgyegq glggikesgs sgatilgyeg qglggikesg ssgatilgye<br></br><br />
361 gqglggikes gssgatilgs ydgqgpsgat ilgdyngqgl ggikessgvt vlgdyegqgl<br></br><br />
421 ggisgphggh gqaganagan ananagatvg ssggvlggvg dhggyhgyng hdgssglnlg<br></br><br />
481 gygggsnana qassnalass ggsssatsda lsnahssggs alanssskas angsgsanan<br></br><br />
541 ahassnassg shglgsktsa ssqasasadt rdmlifs[2]</div><br><br />
</h6><br />
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<h6> Note that this sequence is not simply the same sequence repeated multiple times, but instead contains several motifs on the order of 10 - 20 amino acids in length that occur several times. When this sequence was run through the codon optimization program for expression in ''E. coli'' provided by a major DNA synthesis firm, the resulting output could not be synthesized by the very same firm: the "optimized" DNA sequence contained too many recurring short (> 8 nucleotide) DNA sequences to allow for synthesis.<br><br><br />
<br />
Manually correcting for repeats in the codon-optimized DNA sequence is a sub-optimal solution: not only is this process time-consuming, but it has the tendency to undo the codon-optimization: if a sequence of amino acids occurs several times, one may be forced to use all possible codon-combinations to represent this sequence to avoid nucleotide-sequence repetition. Unless corrected for by skewing codon usage elsewhere in the sequence, this will tend to make the codon usage more uniform than is optimal for the expression vector. Additionally, any changes made in either correcting for repeats or re-correcting for codon usage may in turn introduce additional repeats.<br />
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<h5><center>Solution</h5><br />
<h6>DoubleOptimizer is a software tool we have created to optimize codon usage in a gene both to match a given codon usage distribution and to avoid repetition of nucleotide sequences. Given a DNA or amino acid sequence and a desired codon distribution, DoubleOptimizer will produce, within a matter of minutes, an equivalent sequence that has substantially reduced DNA sequence repetition, while also closely matching the desired codon usage.<br />
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<!-- ====== Availability and Usage ====== --><br />
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<h5><center>Availability and Usage</h5><br />
<h6>DoubleOptimizer may be downloaded <a href = "http://drive.google.com/a/brown.edu/file/d/0B6Q5Eo65G4cPZC1SZWEzbUtrYUU/view?usp=sharing"><u>here</u></a>. DoubleOptimizer is a command line utility, provided as a Java jar file. It can be invoked from command line on any system with Java 7 or later installed, with the following syntax:<br></br><br />
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<div class="sub6">java -jar DoubleOptimizer.jar seq.txt codons.txt <br></br> </div><br />
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where "seq.txt" is a DNA sequence, stored as a plain text file, and "codons.txt" is a file containing the desired codon distribution to match. It should be formatted as plain text, according to the following example template: <br><br><br />
<br />
<div class="sub6"><br />
A<br></br><br />
GCG .36<br></br><br />
GCC .27<br></br><br />
GCA .21<br></br><br />
GCT .16<br></br><br />
R<br></br><br />
CGC .40<br></br><br />
CGT .38<br></br><br />
CGG .10<br></br><br />
CGA .06<br></br><br />
AGA .04<br></br><br />
AGG .02<br></br><br />
N<br></br><br />
AAC .55<br></br><br />
AAT .45<br></br><br />
D<br></br><br />
GAT .63<br></br><br />
GAC .37<br></br><br />
C<br></br><br />
TGC .55<br></br><br />
TGT .45<br></br><br />
E<br></br><br />
GAA .69<br></br><br />
GAG .31<br></br><br />
Q<br></br><br />
CAG .65<br></br><br />
CAA .35<br></br><br />
G<br></br><br />
GGC .40<br></br><br />
GGT .34<br></br><br />
GGG .15<br></br><br />
GGA .11<br></br><br />
H<br></br><br />
CAT .57<br></br><br />
CAC .43<br></br><br />
I<br></br><br />
ATT .51<br></br><br />
ATC .42<br></br><br />
ATA .07<br></br><br />
L<br></br><br />
CTG .50<br></br><br />
TTG .13<br></br><br />
TTA .13<br></br><br />
CTT .10<br></br><br />
CTC .10<br></br><br />
CTA .04<br></br><br />
K<br></br><br />
AAA .77<br></br><br />
AAG .23<br></br><br />
M<br></br><br />
ATG 1<br></br><br />
F<br></br><br />
TTT .57<br></br><br />
TTC .43<br></br><br />
P<br></br><br />
CCG .52<br></br><br />
CCA .19<br></br><br />
CCT .16<br></br><br />
CCC .12<br></br><br />
S<br></br><br />
AGC .28<br></br><br />
AGT .15<br></br><br />
TCG .15<br></br><br />
TCT .15<br></br><br />
TCC .15<br></br><br />
TCA .12<br></br><br />
*<br></br><br />
TAA .64<br></br><br />
TGA .29<br></br><br />
TAG .07<br></br><br />
T<br></br><br />
ACC .44<br></br><br />
ACG .27<br></br><br />
ACT .17<br></br><br />
ACA .13<br></br><br />
W<br></br><br />
TGG 1<br></br><br />
Y<br></br><br />
TAT .57<br></br><br />
TAC .43<br></br><br />
V<br></br><br />
GTG .37<br></br><br />
GTT .26<br></br><br />
GTC .22<br></br><br />
GTA .15<br></br><br />
</div><br />
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(Note that the above example is actually the codon usage distribution of <i>E. coli</i>.[3]) <br></br><br />
<br />
DoubleOptimizer supports non-canonical codon assignments: the amino acid-codon groupings can by specified in whatever way the user wants in the codon distribution file.<br></br><br />
<br />
When executed, DoubleOptimizer will first display the input sequence with repetitive regions highlighted. It will also give the fraction of the sequence that initially consists of repetitive regions (defined by default as regions of 8 nucleotides or more that occur more than once in the sequence, including as their reverse complement), and a chi-squared value for the goodness-of-fit to the desired codon distribution (lower is better).<br></br><br />
<br />
DoubleOptimizer will then compute and display the optimized sequence (By default, it will produce the best sequence it can find after 10 seconds of computation time). Again, repetitive regions will be highlighted, and the same measurements of repetitiveness and codon fit will be given.<br></br><br />
<br />
The following optional flags may be used to change the program's behavior:<br></br><br />
<br />
<div><h5><ul><li>-A</ul></h5></div><br />
<br />
This allows for an amino-acid sequence, specified in single-letter code, to be used as input instead of a DNA sequence. The initial sequence statistics displayed will be for a uniform random reverse translation of the given amino acid sequence. <br></br><br />
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Example: <br><br />
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<div class="sub6">java -jar DoubleOptimizer.jar aaseq.txt codons.txt -A <br></br><br />
<br />
</div><br />
<br />
<div><h5><ul><li>-T##</ul></h5></div><br />
This allows the user to specify, in seconds, a different run-time for the program other than the default 10 seconds. While 10 seconds should be sufficient to produce a well-optimized result for most genes of moderate length on modern desktop computers, longer times may produce better-optimized results on slower machines or on longer sequences. <br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6">java -jar DoubleOptimizer.jar aaseq.txt codons.txt -A -T30 <br></br><br />
<br />
</div><br />
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<div><h5><ul><li>-L##</ul></h5></div><br />
This allows the user to specify a different minimum length for what is considered a repeat, other than the default 8 nucleotides.<br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.txt codons.txt -L7 -T15 <br></br><br />
*-S##, -E##<br></br> <br />
</div><br />
<br />
These allow the user to specify the starting and ending nucleotide, respectively, of the coding region in a construct sequence to be sythesized. Nucleotides outside this frame will be ignored for codon usage optimization, and will never be modified. This option is useful for preventing repetitions, within the coding region, of fixed sequences that occur at the ends of a construct to be sythesized, outside of the coding region. The default values are the beginning of the sequence, and the end of the last complete codon. These options may be used together or independently. Values are one-indexed. If used with ''-A'', these will be interpreted as amino acid indices.<br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -R1000 -D100<br />
<br />
</div><br />
<div><h5><ul><li>-D##</ul></h5></div><br />
This option will make the program periodically display the current best sequence, and associated statistics, as it runs. The number given is the number of optoimization cycles the program will perform between each round of displaying the sequence. This provides a continuous measure of progress on long optimization runs. Note that, when given 10 seconds, the program may execute several thousand cycles of optimization, so an argument on the order of 100 may be reasonable.<br></br> <br />
<br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -R1000 -D100<br />
<br />
</div><br />
<div><h5><ul><li>-R##</ul></h5></div><br />
This option will make the program halt optimization after a certain number of optimization cycles. This may be used with a large value of -T to standardize optimization quality between computers of different speeds. Because this option is mostly only useful for testing the efficiency of this program itself, it may be removed from future releases.<br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -T1000 -R1000<br />
</div><br />
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</h6><br />
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<!-- ====== Examples of Use ====== --><br />
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<h5><center>Examples of Use</h5><br />
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<h6>As an example of the normal operation of the program on an amino acid sequence, consider the above-listed example of a repetitive amino acid sequence, hypothetical protein X777_06170 from the ant species <i>Cerapachys biroi</i>. At one point in our waterproofing project, this protein was considered for sythesis as a possible homologue of the waterproofing <i>Polistes</i> protein. DoubleOptimizer was originally written to overcome difficulties in synthesising this protein for expression in <i> E. coli</i>. (Due to further advances in the <i>Polistes</i> project, this gene was never synthesized). <b></b><br />
A screenshot of a run of DoubleOptimizer on the amino acid sequence for this protein (listed above), with the <i> E. coli</i> codon usage table also listed above, with default settings for 10 seconds on a laptop computer (2011 model Macbook Pro) is shown below: <b></b></h6></div><br />
<div><br><center><img src="https://static.igem.org/mediawiki/2014/8/85/AADoubleOptExample.png" width="1300" height="800"><br><br />
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<b></b><br />
Note that the number of repeated sequences is significantly reduced, and the closeness-of-fit to the desired codon sequence is much improved, compared to the initial random reverse-translation.<br />
<b></b><b></b><br />
As an example of DoubleOptimizer run on a DNA sequence, a somewhat-repetitive <i>Homo sapiens</i> gene was arbitrarily selected from the NCBI database. Specifically, the exon sequence for <a href="http://www.ncbi.nlm.nih.gov/nuccore/18028443"><u>Ca+2-binding protein CBP86 form VII</u></a> was optimized for synthesis, again in <i> E. coli</i>. The same computer setup was used, and DoubleOptimizer was run for the default 10 seconds: <b></b><br />
</h6></div><br />
<div><br><center><img src="https://static.igem.org/mediawiki/2014/0/09/DNADoubleOptExample.png" width="1300" height="800"><br><br />
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<h6><br />
<br />
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<b></b><br />
In this case, the sequence went from being over 47% composed of repetitive sequences 8 nucleotides or more in length to being completely non-repetitive. Also, the codon usage distribution is now a near-perfect match to the <i> E. coli</i> codon usage distribution.<b><br />
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</h6><br />
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<!-- ======Algorithm ====== --><br />
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<h5><center>Algorithm</h5><br />
<h6>DoubleOptimizer uses a genetic optimization algorithm.[4] This algorithm mimics natural selection by taking the original gene sequence and generating several variants by copying the sequence and creating random silent point mutations in each copy. Each variant is then assigned a fitness score based on its repetitiveness and closeness-of-fit to the desired codon usage distribution, and the most fit variants are kept. These are then re-copied, re-mutated and re-scored, and again only the most fit variants are kept, through several thousand rounds of selection. When optimization time runs out, the most fit member of the population after the final round of selection is displayed. <br></br> <br />
One challenge in implementing this algorithm efficiently is that scoring for repetitiveness is a time-consuming process (the time belongs to O(n<sup>2</sup>) in the length of the sequence). Because many thousands of sequences are scored in a single optimization, finding a way to reduce the computational burden of this step was a task of paramount importance. Our solution was to store for each sequence a matrix representing the locations in the sequence where repetitions are already known to occur, and to only attempt to add or remove repetitions that could by affected by the point mutations that were made. This significantly improved run-time, compared to the naive technique of independently re-scoring each modified sequence.<br />
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</h6><br />
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<!-- ====== References ====== --><br />
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<h5><center>References</h5><br />
<h6>1. Several references exist that establish the link between codon usage and expression. To give some highly-cited examples:<br> <br />
<br />
Ikemura, T. Codon usage and tRNA content in unicellular and multicellular organisms. Mol Biol Evol (1985) 2 (1): 13-34 <br></br> <br />
Gouy, M. and Gautier, C. Codon usage in bacteria: correlation with gene expressivity Nucl. Acids Res. (1982) 10 (22): 7055-7074 doi:10.1093/nar/10.22.7055 <br></br> <br />
Sharp, Paul M. and Li, Wen-Hsiung. The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications Nucl. Acids Res. (1987) 15 (3): 1281-1295 doi:10.1093/nar/15.3.1281 <br></br> <br />
2. http://www.ncbi.nlm.nih.gov/protein/607359946. Original paper: Oxley PR, Ji L, Fetter-Pruneda I, McKenzie SK, Li C, Hu H, Zhang G, Kronauer DJ. The genome of the clonal raider ant Cerapachys biroi. Curr Biol. 2014 Feb 17;24(4):451-8. doi: 10.1016/j.cub.2014.01.018. Epub 2014 Feb 6. PubMed PMID: 24508170; PubMed Central PMCID: PMC3961065. <br></br> <br />
3. Kazuna DNA Research Institute. Codon Usage Database. http://www.kazusa.or.jp/codon/cgi-bin/showcodon.cgi?species=316407. <br></br> <br />
4. For a good introduction to genetic algorithms, see:<br></br> <br />
Goldberg, David E. Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, Mass: Addison-Wesley Pub. Co, 1989. <br />
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<!-- ====== COMPOSITION SEARCH ====== --><br />
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<h5 id="process"><center> CompositionSearch</h5><br />
<h6><center>A fast, local search of protein sequences<br>against a specified amino acid distribution</h6><br> <br />
<h6>When the relative proportions of amino acids in an unknown protein product have been chemically determined, it is often useful to search a proteome for proteins that have similar amino acid distributions, in order to identify this protein product. While at least one online utility for performing this task already exists (provided by the <a href = "http://web.expasy.org/aacompident/aacompfree.html"> <u>Swiss Institute of Bioinformatics</u> </a>), the web-based nature of this program creates some limitations. Firstly, the SIB provides computational resources for the calculation, resulting in slower turnaround for the user (searches take about 15 minutes). Secondly, this program will only search for proteins already in the Swiss-Prot / TrEMBL databases. (At last check, only the smaller Swiss-Prot search was functional.) If an organism is being newly studied and has just been sequenced, its predicted proteome will not be in these databases. Thirdly, due to limited resources, only the top matches to a given search are provided. This does not allow for statistical comparison to the "typical" protein within a given proteome. Fourthly, for very-high-security tasks, uploading data to a third party may be undesirable.<br />
</h6><br />
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<!-- ====== CompositionSearch: Solution ====== --><br />
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<h5><center>Solution</h5><br />
<h6>CompositionSearch is a software tool we have created to address these issues by allowing an individual to rank all proteins in a proteome by similarity (minimum Euclidian distance) to a reference amino acid distribution locally on one's own computer. This ranking can be generated in a matter of seconds, rather than taking several minutes. Because it ranks all proteins in a proteome, CompositionSearch can also generate a figure for the significance of the similarity of a given protein to a given amino acid distribution, using the similarity of the rest of the proteome as a statistical distribution function. <br />
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<!-- ====== CompositionSearch: Availability and Usage ====== --><br />
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<h5><center>Availability and Usage</h5><br />
<h6>CompositionSearch may be downloaded <a href= "http://drive.google.com/a/brown.edu/file/d/0B6Q5Eo65G4cPbnpQYlNlcU1QUE0/view?usp=sharing"><u>here</u></a>. <br></br><br />
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CompositionSearch is a command line utility, provided as a Java jar file. It can be invoked from command line on any system with Java 7 or later installed, with the following syntax:<br></br><br />
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<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv <br></br> </div><br />
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where "prot.fasta" is a FASTA-formatted proteome, and "freqs.txt" is a file containing the desired amino distribution to match. It should be formatted as plain text, according to the following example template:<br><br />
<br />
<div class="sub6"><br />
A 0.134 <br></br> <br />
D 0.044 <br></br> <br />
E 0.04 <br></br><br />
G 0.228 <br></br> <br />
I 0.033 <br></br> <br />
K 0.021<br></br><br />
L 0.04 <br></br><br />
P 0.08 <br></br><br />
R 0.009<br></br><br />
S 0.151<br></br><br />
T 0.038<br></br><br />
V 0.083<br></br><br />
</div> <br />
<br />
Note that this may contain as many or as few amino acids as desired. The frequencies, however, are interpreted as absolute, so if all amino acids are represented, they should add 1. (See below for discussion of how the optional -N and -X flags affect this interpretation). <br></br> <br />
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"out.csv" in the above example line is the destination path to store the results of the calculation. The output will be a spreadsheet in csv format, which may be imported into your favorite desktop spreadsheet application (i.e. Microsoft Excel, LibreOffice Calc, etc.).<br></br><br />
<br />
After execution, out.csv will contain a spreadsheet showing the reference distribution, and, in order by similarity to the reference distribution, the amino acid distributions of all proteins in the proteome. Protein name, similarity ranking, similarity score (Euclidian distance between amino acid distributions; lower is better), and similarity p-value (on the curve of other proteins in the proteome) will be listed for each protein. <br></br><br />
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The following optional flags may be used to change the program's behavior: <br></br><br />
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<div><h5><ul><li>-N</ul></h5></div><br />
<br />
This will cause the program to ignore amino acids in the proteins that are not in the distribution list. In other words, this means that the frequencies given refer to frequencies relative to only the the other amino acids listed, instead of all amino acids. To clarify:<br></br><br />
<br />
Without the -N flag, the line in the above example distribution list: <br><br />
<div class="sub6"> P 0.08 <br></br> </div><br />
means that 8% of all amino acid residues in the matching protein are expected to be proline residues. <br></br><br />
<br />
With the -N flag, the line in the above example distribution list:<br><br />
<div class="sub6"> P 0.08 <br></br> </div> <br />
means that 8% of amino acid residues that belong in the set {ADEGIKLPRSTV} (the amino acids with defined frequences) in the matching protein are expected to be proline residues. <br></br><br />
<br />
Example:<br><br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv -N <br></br><br />
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</div><br />
<div><h5><ul><li>-X$$</ul></h5></div><br />
This will cause the program to completely disregard certain amino acid residue symbols in the proteome, regardless of use of the N flag.<br></br><br />
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The default value of this set of characters is 'X,' often used to represent unknown amino acid residues. Therefore the flag -XX is equivalent to normal behavior. Note that the set of characters being ignored is replaced by the -X flag, so it is always advisable to list X when using this flag.<br></br><br />
<br />
Example:<br><br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv -XX*-<br></br> </div><br />
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This will cause the symbols X,*, and - to be ignored in the proteome. <br />
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<!-- ====== Cellulose Modeling ====== --><br />
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Cellulose Pathway Modeling </h5><br />
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<!-- ====== Flux Balance Analysis ====== --><br />
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<h6><center>Flux Balance Analysis</h6><br><br />
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<h6><br />
Modeling allows for efficient experimental design by optimizing specific conditions before testing with in vitro methods. Flux Balance Analysis (FBA) is used to optimize the growth medium for the bioengineered gluconacetobacter hansenii to yield bacterial cellulose to be used as biomaterial for a biodegradable drone. FBA is a mathematical method used to examine how metabolites relate to each network and makes predictions for the growth of an organism and product output. It is a direct application of linear programming to biological systems that used the stoichiometric coefficient for each reaction in the system as the set of constraints for the optimization. (add glycolysis stoichiometric picture here)<br />
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</br></br>Flux Balance Analysis is perfomed under steady state conditions and requires information about the stoichiometry of metabolic pathways, metabolic demands, and strain specific parameters. At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption (3).<br />
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</br></br>FBA will be used to optimize the growth conditions of g. hansenii in order to maximize the product output of cellulose. The exchange reactions determine the metabolites that are beneficial to the medium. Changing the composition will allow us to be able to determine the effect the composition has on the efficiency of the production media.<br />
</h6><br><br><br />
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<!-- ====== Model SEED- ModelView ====== --><br />
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<h5><center>Model SEED- ModelView</h5><br />
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<h6>Model SEED is an online analysis software with its own genome and media formulation database. An analysis of the G. hansenii is done with the curated model including a biomass reaction set for optimal organismal growth and a control of complete media. The biomass reaction describes the rate, which all the biomass precursors are made in correct proportions.(2) Biomass reactions include the information listed in Figure 1.(Biomass reaction picture) A biomass reactions requires a knowledge of the composition of a cell and the energy requirements that allow for the cell to grow. The biomass equation consists of three levels : macromolecular level (RNA, protein), intermediate level (biosynthetic energy), advanced level (vitamins, elements, and cofactors). The biomass equation also list to stochiometric coefficients for each part of the biomass equation. Using this biomass equation the growth rate was determined to be 66.48 gm per biomass CDW/hr was obtained. Since the flux is positive this explains that the flux of the analysis is indeed towards growth a negative or zero flux would have explained that the pathway is not used or it is impossible. <br />
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<!-- ====== Future Directions/Modifications ====== --><br />
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<h5><center>Future Directions/Modifications</h5><br />
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<h6>Although a growth rate is obtained using Model SEED, this rate is for an environment where a complete medium is used. The growth medium being used contains dextrose, peptone, yeast extract, Na2HPO4, citric acid, agar and water. Model SEED does not have the capabilities to create a media file, so all further analysis will be done using KBase because it has the ability to customize the media used for analysis. Kbase is a software that allows for online analysis of models, where models and the corresponding media for organismal growth can be optimized. The genome database contains all genomes including the G. hansenii genome (as a JSON file). A full database for medias that exist and capabilities to create the media being used in experimentation through the IRIS interface, which is the command line environment used by Kbase. Using IRIS and the subsequent FBA tutorial, a flux balance analysis was attempted but several issue were observed because of command line issues. Kbase will allow for the majority of modifications to be done in order to obtain the correct model and medium, further help will be sought out in order to conquer the error messages seen. Once the error messages are fixed, further literature searches and consultations will be done in order to change the biomass reaction to one that will target the product growth of cellulose instead of organismal growth of G. hansenii. Again once an accurate growth rate with the proper medium is found, it will then be possible to plot the organism growth rate vs. product growth rate. Plotting this data will determine if there is a direct or inverse relationship between the two quantities.<br />
</h6><br><br><br />
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<!-- ====== References ====== --><br />
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<h5><center>References</h5><br />
<br />
<h6>1. Covert, M. W. and B. O. Palsson. Constraints-based models: regulation of gene expression reduces the steady-state solution space. J Theor Biol [Online] 2003 221(3), 309-325.</br></br><br />
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2. Feist, Adam M, and Bernhard O Palsson. The biomass objective function. Current Opinion in Microbiology. [Online] 2010,13.3, 344-349. </br></br><br />
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3. Orth, Jeffrey D, Ines Thiele, and Bernhard Ø Palsson. What is flux balance analysis? Nature Biotechnology [Online] 2010, 28.3, 245-248.</br></br><br />
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4. Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nature Protocols [Online], 2011, 6:1290-1307.<br />
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</html></div>Eliblockhttp://2014.igem.org/Team:StanfordBrownSpelman/ModellingTeam:StanfordBrownSpelman/Modelling2014-10-17T04:21:20Z<p>Eliblock: </p>
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Modelling">Bioinformatics & Modeling</a><br />
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<h7><center><a href="#" id="data">DoubleOptimizer</a> ● <a href="#" id="methods">CompositionSearch</a><br><a href="#" id="pics">Flux Balance Analysis</a></h7></div></center><br />
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<h6 id="int"> While working on our synthetic biology projects for this year's iGEM competition, we found ourselves in need of some computational tools for synthetic biology that did not yet exist. Therefore, we developed our own software packages to meet these needs. In particular, we have developed two pieces of software, specifically designed for the needs of synthetic biologists:<b></b><br />
<ul><li>DoubleOptimizer: a tool that facilitates synthesis of repetitive genes by optimizing codon usage to both match a codon usage distribution for a desired organism, and to reduce and avoid repetitive nucleotide sequences, allowing for easier synthesis.<b></b><li>CompositionSearch: a tool that quickly ranks all proteins in a proteome by their similarity to a given amino acid distribution.</ul><br />
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DoubleOptimizer </h5><br />
<h6><center>A utility for simultaneous codon and <br>gene synthesis optimization<br></h6><br />
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<h6><br />
Gene synthesis as a tool for biological engineering presents both opportunities and challenges. One opportunity presented is the ability to optimize codon usage in a gene to match that of a host organism. Compared to traditional cloning methods, this can increase protein yields in the host organism by several fold.[1] However, while there exist a large number of freely-usable programs that perform codon optimization, there is no guarantee that the sequences these programs provide will be able to be synthesized. Specifically, in the case of genes with repetitive amino acid sequences, these programs will often generate outputs that contain too many repeated short DNA sequences to be synthesized commercially. <br></br><br />
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As an example, the hypothetical protein X777_06170 from the ant species <i>Cerapachys biroi</i> has an amino acid sequence that appears to be somewhat repetitive: <br><br><br />
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<div class="sub6">001 mklfkclvpv vvlllikdss arpglirdfv ggtvgsilep fqilkpkdsy adanshasah <br></br><br />
061 nlggtfslgp vslggglssa sasssasang gglasasska daqaggygyg gsnanaqasa <br></br><br />
121 sanaqgggyg nggihgiypg qqgvhggnpf lggagsnana naiananaqa naggnngglg<br></br><br />
181 syggyqqggn ypidsstgpi gnnpflsggh gdgnanaaan anagasaign gggpidvnnp<br></br><br />
241 flhggaansg agginyqpgn aggiilsekp lglptiypgq hppayldsig spgansnaga<br></br><br />
301 napcsecgss gatilgyegq glggikesgs sgatilgyeg qglggikesg ssgatilgye<br></br><br />
361 gqglggikes gssgatilgs ydgqgpsgat ilgdyngqgl ggikessgvt vlgdyegqgl<br></br><br />
421 ggisgphggh gqaganagan ananagatvg ssggvlggvg dhggyhgyng hdgssglnlg<br></br><br />
481 gygggsnana qassnalass ggsssatsda lsnahssggs alanssskas angsgsanan<br></br><br />
541 ahassnassg shglgsktsa ssqasasadt rdmlifs[2]</div><br><br />
</h6><br />
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<h6> Note that this sequence is not simply the same sequence repeated multiple times, but instead contains several motifs on the order of 10 - 20 amino acids in length that occur several times. When this sequence was run through the codon optimization program for expression in ''E. coli'' provided by a major DNA synthesis firm, the resulting output could not be synthesized by the very same firm: the "optimized" DNA sequence contained too many recurring short (> 8 nucleotide) DNA sequences to allow for synthesis.<br><br><br />
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Manually correcting for repeats in the codon-optimized DNA sequence is a sub-optimal solution: not only is this process time-consuming, but it has the tendency to undo the codon-optimization: if a sequence of amino acids occurs several times, one may be forced to use all possible codon-combinations to represent this sequence to avoid nucleotide-sequence repetition. Unless corrected for by skewing codon usage elsewhere in the sequence, this will tend to make the codon usage more uniform than is optimal for the expression vector. Additionally, any changes made in either correcting for repeats or re-correcting for codon usage may in turn introduce additional repeats.<br />
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<h5><center>Solution</h5><br />
<h6>DoubleOptimizer is a software tool we have created to optimize codon usage in a gene both to match a given codon usage distribution and to avoid repetition of nucleotide sequences. Given a DNA or amino acid sequence and a desired codon distribution, DoubleOptimizer will produce, within a matter of minutes, an equivalent sequence that has substantially reduced DNA sequence repetition, while also closely matching the desired codon usage.<br />
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<h5><center>Availability and Usage</h5><br />
<h6>DoubleOptimizer may be downloaded <a href = "http://drive.google.com/a/brown.edu/file/d/0B6Q5Eo65G4cPZC1SZWEzbUtrYUU/view?usp=sharing"><u>here</u></a>. DoubleOptimizer is a command line utility, provided as a Java jar file. It can be invoked from command line on any system with Java 7 or later installed, with the following syntax:<br></br><br />
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<div class="sub6">java -jar DoubleOptimizer.jar seq.txt codons.txt <br></br> </div><br />
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where "seq.txt" is a DNA sequence, stored as a plain text file, and "codons.txt" is a file containing the desired codon distribution to match. It should be formatted as plain text, according to the following example template: <br><br><br />
<br />
<div class="sub6"><br />
A<br></br><br />
GCG .36<br></br><br />
GCC .27<br></br><br />
GCA .21<br></br><br />
GCT .16<br></br><br />
R<br></br><br />
CGC .40<br></br><br />
CGT .38<br></br><br />
CGG .10<br></br><br />
CGA .06<br></br><br />
AGA .04<br></br><br />
AGG .02<br></br><br />
N<br></br><br />
AAC .55<br></br><br />
AAT .45<br></br><br />
D<br></br><br />
GAT .63<br></br><br />
GAC .37<br></br><br />
C<br></br><br />
TGC .55<br></br><br />
TGT .45<br></br><br />
E<br></br><br />
GAA .69<br></br><br />
GAG .31<br></br><br />
Q<br></br><br />
CAG .65<br></br><br />
CAA .35<br></br><br />
G<br></br><br />
GGC .40<br></br><br />
GGT .34<br></br><br />
GGG .15<br></br><br />
GGA .11<br></br><br />
H<br></br><br />
CAT .57<br></br><br />
CAC .43<br></br><br />
I<br></br><br />
ATT .51<br></br><br />
ATC .42<br></br><br />
ATA .07<br></br><br />
L<br></br><br />
CTG .50<br></br><br />
TTG .13<br></br><br />
TTA .13<br></br><br />
CTT .10<br></br><br />
CTC .10<br></br><br />
CTA .04<br></br><br />
K<br></br><br />
AAA .77<br></br><br />
AAG .23<br></br><br />
M<br></br><br />
ATG 1<br></br><br />
F<br></br><br />
TTT .57<br></br><br />
TTC .43<br></br><br />
P<br></br><br />
CCG .52<br></br><br />
CCA .19<br></br><br />
CCT .16<br></br><br />
CCC .12<br></br><br />
S<br></br><br />
AGC .28<br></br><br />
AGT .15<br></br><br />
TCG .15<br></br><br />
TCT .15<br></br><br />
TCC .15<br></br><br />
TCA .12<br></br><br />
*<br></br><br />
TAA .64<br></br><br />
TGA .29<br></br><br />
TAG .07<br></br><br />
T<br></br><br />
ACC .44<br></br><br />
ACG .27<br></br><br />
ACT .17<br></br><br />
ACA .13<br></br><br />
W<br></br><br />
TGG 1<br></br><br />
Y<br></br><br />
TAT .57<br></br><br />
TAC .43<br></br><br />
V<br></br><br />
GTG .37<br></br><br />
GTT .26<br></br><br />
GTC .22<br></br><br />
GTA .15<br></br><br />
</div><br />
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(Note that the above example is actually the codon usage distribution of <i>E. coli</i>.[3]) <br></br><br />
<br />
DoubleOptimizer supports non-canonical codon assignments: the amino acid-codon groupings can by specified in whatever way the user wants in the codon distribution file.<br></br><br />
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When executed, DoubleOptimizer will first display the input sequence with repetitive regions highlighted. It will also give the fraction of the sequence that initially consists of repetitive regions (defined by default as regions of 8 nucleotides or more that occur more than once in the sequence, including as their reverse complement), and a chi-squared value for the goodness-of-fit to the desired codon distribution (lower is better).<br></br><br />
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DoubleOptimizer will then compute and display the optimized sequence (By default, it will produce the best sequence it can find after 10 seconds of computation time). Again, repetitive regions will be highlighted, and the same measurements of repetitiveness and codon fit will be given.<br></br><br />
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The following optional flags may be used to change the program's behavior:<br></br><br />
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<div><h5><ul><li>-A</ul></h5></div><br />
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This allows for an amino-acid sequence, specified in single-letter code, to be used as input instead of a DNA sequence. The initial sequence statistics displayed will be for a uniform random reverse translation of the given amino acid sequence. <br></br><br />
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Example: <br><br />
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<div class="sub6">java -jar DoubleOptimizer.jar aaseq.txt codons.txt -A <br></br><br />
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</div><br />
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<div><h5><ul><li>-T##</ul></h5></div><br />
This allows the user to specify, in seconds, a different run-time for the program other than the default 10 seconds. While 10 seconds should be sufficient to produce a well-optimized result for most genes of moderate length on modern desktop computers, longer times may produce better-optimized results on slower machines or on longer sequences. <br></br><br />
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Example:<br><br />
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<div class="sub6">java -jar DoubleOptimizer.jar aaseq.txt codons.txt -A -T30 <br></br><br />
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<div><h5><ul><li>-L##</ul></h5></div><br />
This allows the user to specify a different minimum length for what is considered a repeat, other than the default 8 nucleotides.<br></br><br />
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Example:<br><br />
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<div class="sub6"> java -jar DoubleOptimizer.jar seq.txt codons.txt -L7 -T15 <br></br><br />
*-S##, -E##<br></br> <br />
</div><br />
<br />
These allow the user to specify the starting and ending nucleotide, respectively, of the coding region in a construct sequence to be sythesized. Nucleotides outside this frame will be ignored for codon usage optimization, and will never be modified. This option is useful for preventing repetitions, within the coding region, of fixed sequences that occur at the ends of a construct to be sythesized, outside of the coding region. The default values are the beginning of the sequence, and the end of the last complete codon. These options may be used together or independently. Values are one-indexed. If used with ''-A'', these will be interpreted as amino acid indices.<br></br><br />
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Example:<br><br />
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<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -R1000 -D100<br />
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</div><br />
<div><h5><ul><li>-D##</ul></h5></div><br />
This option will make the program periodically display the current best sequence, and associated statistics, as it runs. The number given is the number of optoimization cycles the program will perform between each round of displaying the sequence. This provides a continuous measure of progress on long optimization runs. Note that, when given 10 seconds, the program may execute several thousand cycles of optimization, so an argument on the order of 100 may be reasonable.<br></br> <br />
<br />
<br />
Example:<br><br />
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<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -R1000 -D100<br />
<br />
</div><br />
<div><h5><ul><li>-R##</ul></h5></div><br />
This option will make the program halt optimization after a certain number of optimization cycles. This may be used with a large value of -T to standardize optimization quality between computers of different speeds. Because this option is mostly only useful for testing the efficiency of this program itself, it may be removed from future releases.<br></br><br />
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Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -T1000 -R1000<br />
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<h5><center>Examples of Use</h5><br />
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<h6>As an example of the normal operation of the program on an amino acid sequence, consider the above-listed example of a repetitive amino acid sequence, hypothetical protein X777_06170 from the ant species <i>Cerapachys biroi</i>. At one point in our waterproofing project, this protein was considered for sythesis as a possible homologue of the waterproofing <i>Polistes</i> protein. DoubleOptimizer was originally written to overcome difficulties in synthesising this protein for expression in <i> E. coli</i>. (Due to further advances in the <i>Polistes</i> project, this gene was never synthesized). <b></b><br />
A screenshot of a run of DoubleOptimizer on the amino acid sequence for this protein (listed above), with the <i> E. coli</i> codon usage table also listed above, with default settings for 10 seconds on a laptop computer (2011 model Macbook Pro) is shown below: <b></b></h6></div><br />
<div><br><center><img src="https://static.igem.org/mediawiki/2014/8/85/AADoubleOptExample.png" width="1300" height="800"><br><br />
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<b></b><br />
Note that the number of repeated sequences is significantly reduced, and the closeness-of-fit to the desired codon sequence is much improved, compared to the initial random reverse-translation.<br />
<b></b><b></b><br />
As an example of DoubleOptimizer run on a DNA sequence, a somewhat-repetitive <i>Homo sapiens</i> gene was arbitrarily selected from the NCBI database. Specifically, the exon sequence for <a href="http://www.ncbi.nlm.nih.gov/nuccore/18028443"><u>Ca+2-binding protein CBP86 form VII</u></a> was optimized for synthesis, again in <i> E. coli</i>. The same computer setup was used, and DoubleOptimizer was run for the default 10 seconds: <b></b><br />
</h6></div><br />
<div><br><center><img src="https://static.igem.org/mediawiki/2014/0/09/DNADoubleOptExample.png" width="1300" height="800"><br><br />
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<br />
<br />
<b></b><br />
In this case, the sequence went from being over 47% composed of repetitive sequences 8 nucleotides or more in length to being completely non-repetitive. Also, the codon usage distribution is now a near-perfect match to the <i> E. coli</i> codon usage distribution.<b><br />
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<h5><center>Algorithm</h5><br />
<h6>DoubleOptimizer uses a genetic optimization algorithm.[4] This algorithm mimics natural selection by taking the original gene sequence and generating several variants by copying the sequence and creating random silent point mutations in each copy. Each variant is then assigned a fitness score based on its repetitiveness and closeness-of-fit to the desired codon usage distribution, and the most fit variants are kept. These are then re-copied, re-mutated and re-scored, and again only the most fit variants are kept, through several thousand rounds of selection. When optimization time runs out, the most fit member of the population after the final round of selection is displayed. <br></br> <br />
One challenge in implementing this algorithm efficiently is that scoring for repetitiveness is a time-consuming process (the time belongs to O(n<sup>2</sup>) in the length of the sequence). Because many thousands of sequences are scored in a single optimization, finding a way to reduce the computational burden of this step was a task of paramount importance. Our solution was to store for each sequence a matrix representing the locations in the sequence where repetitions are already known to occur, and to only attempt to add or remove repetitions that could by affected by the point mutations that were made. This significantly improved run-time, compared to the naive technique of independently re-scoring each modified sequence.<br />
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<h5><center>References</h5><br />
<h6>1. Several references exist that establish the link between codon usage and expression. To give some highly-cited examples:<br> <br />
<br />
Ikemura, T. Codon usage and tRNA content in unicellular and multicellular organisms. Mol Biol Evol (1985) 2 (1): 13-34 <br></br> <br />
Gouy, M. and Gautier, C. Codon usage in bacteria: correlation with gene expressivity Nucl. Acids Res. (1982) 10 (22): 7055-7074 doi:10.1093/nar/10.22.7055 <br></br> <br />
Sharp, Paul M. and Li, Wen-Hsiung. The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications Nucl. Acids Res. (1987) 15 (3): 1281-1295 doi:10.1093/nar/15.3.1281 <br></br> <br />
2. http://www.ncbi.nlm.nih.gov/protein/607359946. Original paper: Oxley PR, Ji L, Fetter-Pruneda I, McKenzie SK, Li C, Hu H, Zhang G, Kronauer DJ. The genome of the clonal raider ant Cerapachys biroi. Curr Biol. 2014 Feb 17;24(4):451-8. doi: 10.1016/j.cub.2014.01.018. Epub 2014 Feb 6. PubMed PMID: 24508170; PubMed Central PMCID: PMC3961065. <br></br> <br />
3. Kazuna DNA Research Institute. Codon Usage Database. http://www.kazusa.or.jp/codon/cgi-bin/showcodon.cgi?species=316407. <br></br> <br />
4. For a good introduction to genetic algorithms, see:<br></br> <br />
Goldberg, David E. Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, Mass: Addison-Wesley Pub. Co, 1989. <br />
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<!-- ====== COMPOSITION SEARCH ====== --><br />
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<h5 id="process"><center> CompositionSearch</h5><br />
<h6><center>A fast, local search of protein sequences<br>against a specified amino acid distribution</h6><br> <br />
<h6>When the relative proportions of amino acids in an unknown protein product have been chemically determined, it is often useful to search a proteome for proteins that have similar amino acid distributions, in order to identify this protein product. While at least one online utility for performing this task already exists (provided by the <a href = "http://web.expasy.org/aacompident/aacompfree.html"> <u>Swiss Institute of Bioinformatics</u> </a>), the web-based nature of this program creates some limitations. Firstly, the SIB provides computational resources for the calculation, resulting in slower turnaround for the user (searches take about 15 minutes). Secondly, this program will only search for proteins already in the Swiss-Prot / TrEMBL databases. (At last check, only the smaller Swiss-Prot search was functional.) If an organism is being newly studied and has just been sequenced, its predicted proteome will not be in these databases. Thirdly, due to limited resources, only the top matches to a given search are provided. This does not allow for statistical comparison to the "typical" protein within a given proteome. Fourthly, for very-high-security tasks, uploading data to a third party may be undesirable.<br />
</h6><br />
</div><br />
</div><br />
<br />
<!-- ====== CompositionSearch: Solution ====== --><br />
<br />
<div class="row"><br />
<div id="subheader" class="small-8 small-centered columns"><br />
<h5><center>Solution</h5><br />
<h6>CompositionSearch is a software tool we have created to address these issues by allowing an individual to rank all proteins in a proteome by similarity (minimum Euclidian distance) to a reference amino acid distribution locally on one's own computer. This ranking can be generated in a matter of seconds, rather than taking several minutes. Because it ranks all proteins in a proteome, CompositionSearch can also generate a figure for the significance of the similarity of a given protein to a given amino acid distribution, using the similarity of the rest of the proteome as a statistical distribution function. <br />
</h6><br />
</div><br />
</div><br />
<br />
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<!-- ====== CompositionSearch: Availability and Usage ====== --><br />
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<div class="row"><br />
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<h5><center>Availability and Usage</h5><br />
<h6>CompositionSearch may be downloaded <a href= "http://drive.google.com/a/brown.edu/file/d/0B6Q5Eo65G4cPbnpQYlNlcU1QUE0/view?usp=sharing"><u>here</u></a>. <br></br><br />
<br />
CompositionSearch is a command line utility, provided as a Java jar file. It can be invoked from command line on any system with Java 7 or later installed, with the following syntax:<br></br><br />
<br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv <br></br> </div><br />
<br />
where "prot.fasta" is a FASTA-formatted proteome, and "freqs.txt" is a file containing the desired amino distribution to match. It should be formatted as plain text, according to the following example template:<br><br />
<br />
<div class="sub6"><br />
A 0.134 <br></br> <br />
D 0.044 <br></br> <br />
E 0.04 <br></br><br />
G 0.228 <br></br> <br />
I 0.033 <br></br> <br />
K 0.021<br></br><br />
L 0.04 <br></br><br />
P 0.08 <br></br><br />
R 0.009<br></br><br />
S 0.151<br></br><br />
T 0.038<br></br><br />
V 0.083<br></br><br />
</div> <br />
<br />
Note that this may contain as many or as few amino acids as desired. The frequencies, however, are interpreted as absolute, so if all amino acids are represented, they should add 1. (See below for discussion of how the optional -N and -X flags affect this interpretation). <br></br> <br />
<br />
"out.csv" in the above example line is the destination path to store the results of the calculation. The output will be a spreadsheet in csv format, which may be imported into your favorite desktop spreadsheet application (i.e. Microsoft Excel, LibreOffice Calc, etc.).<br></br><br />
<br />
After execution, out.csv will contain a spreadsheet showing the reference distribution, and, in order by similarity to the reference distribution, the amino acid distributions of all proteins in the proteome. Protein name, similarity ranking, similarity score (Euclidian distance between amino acid distributions; lower is better), and similarity p-value (on the curve of other proteins in the proteome) will be listed for each protein. <br></br><br />
<br />
The following optional flags may be used to change the program's behavior: <br></br><br />
<br />
<div><h5><ul><li>-N</ul></h5></div><br />
<br />
This will cause the program to ignore amino acids in the proteins that are not in the distribution list. In other words, this means that the frequencies given refer to frequencies relative to only the the other amino acids listed, instead of all amino acids. To clarify:<br></br><br />
<br />
Without the -N flag, the line in the above example distribution list: <br><br />
<div class="sub6"> P 0.08 <br></br> </div><br />
means that 8% of all amino acid residues in the matching protein are expected to be proline residues. <br></br><br />
<br />
With the -N flag, the line in the above example distribution list:<br><br />
<div class="sub6"> P 0.08 <br></br> </div> <br />
means that 8% of amino acid residues that belong in the set {ADEGIKLPRSTV} (the amino acids with defined frequences) in the matching protein are expected to be proline residues. <br></br><br />
<br />
Example:<br><br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv -N <br></br><br />
<br />
</div><br />
<div><h5><ul><li>-X$$</ul></h5></div><br />
This will cause the program to completely disregard certain amino acid residue symbols in the proteome, regardless of use of the N flag.<br></br><br />
<br />
The default value of this set of characters is 'X,' often used to represent unknown amino acid residues. Therefore the flag -XX is equivalent to normal behavior. Note that the set of characters being ignored is replaced by the -X flag, so it is always advisable to list X when using this flag.<br></br><br />
<br />
Example:<br><br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv -XX*-<br></br> </div><br />
<br />
This will cause the symbols X,*, and - to be ignored in the proteome. <br />
<br />
<br></br><br />
</h6><br />
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<!-- ====== Cellulose Modeling ====== --><br />
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<h5 id="results"><center><br />
Cellulose Pathway Modeling </h5><br />
<br />
<!-- ====== Flux Balance Analysis ====== --><br />
<br />
<h6><center>Flux Balance Analysis</h6><br><br />
<br />
<h6><br />
Modeling allows for efficient experimental design by optimizing specific conditions before testing with in vitro methods. Flux Balance Analysis (FBA) is used to optimize the growth medium for the bioengineered gluconacetobacter hansenii to yield bacterial cellulose to be used as biomaterial for a biodegradable drone. FBA is a mathematical method used to examine how metabolites relate to each network and makes predictions for the growth of an organism and product output. It is a direct application of linear programming to biological systems that used the stoichiometric coefficient for each reaction in the system as the set of constraints for the optimization. (add glycolysis stoichiometric picture here)<br />
<br />
</br></br>Flux Balance Analysis is perfomed under steady state conditions and requires information about the stoichiometry of metabolic pathways, metabolic demands, and strain specific parameters. At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption (3).<br />
<br />
</br></br>FBA will be used to optimize the growth conditions of g. hansenii in order to maximize the product output of cellulose. The exchange reactions determine the metabolites that are beneficial to the medium. Changing the composition will allow us to be able to determine the effect the composition has on the efficiency of the production media.<br />
</h6><br><br><br />
<br />
<!-- ====== Model SEED- ModelView ====== --><br />
<br />
<h5><center>Model SEED- ModelView</h5><br />
<br />
<h6>Model SEED is an online analysis software with its own genome and media formulation database. An analysis of the G. hansenii is done with the curated model including a biomass reaction set for optimal organismal growth and a control of complete media. The biomass reaction describes the rate, which all the biomass precursors are made in correct proportions.(2) Biomass reactions include the information listed in Figure 1.(Biomass reaction picture) A biomass reactions requires a knowledge of the composition of a cell and the energy requirements that allow for the cell to grow. The biomass equation consists of three levels : macromolecular level (RNA, protein), intermediate level (biosynthetic energy), advanced level (vitamins, elements, and cofactors). The biomass equation also list to stochiometric coefficients for each part of the biomass equation. Using this biomass equation the growth rate was determined to be 66.48 gm per biomass CDW/hr was obtained. Since the flux is positive this explains that the flux of the analysis is indeed towards growth a negative or zero flux would have explained that the pathway is not used or it is impossible. <br />
</h6><br><br><br />
<br />
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<!-- ====== Future Directions/Modifications ====== --><br />
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<h5><center>Future Directions/Modifications</h5><br />
<br />
<h6>Although a growth rate is obtained using Model SEED, this rate is for an environment where a complete medium is used. The growth medium being used contains dextrose, peptone, yeast extract, Na2HPO4, citric acid, agar and water. Model SEED does not have the capabilities to create a media file, so all further analysis will be done using KBase because it has the ability to customize the media used for analysis. Kbase is a software that allows for online analysis of models, where models and the corresponding media for organismal growth can be optimized. The genome database contains all genomes including the G. hansenii genome (as a JSON file). A full database for medias that exist and capabilities to create the media being used in experimentation through the IRIS interface, which is the command line environment used by Kbase. Using IRIS and the subsequent FBA tutorial, a flux balance analysis was attempted but several issue were observed because of command line issues. Kbase will allow for the majority of modifications to be done in order to obtain the correct model and medium, further help will be sought out in order to conquer the error messages seen. Once the error messages are fixed, further literature searches and consultations will be done in order to change the biomass reaction to one that will target the product growth of cellulose instead of organismal growth of G. hansenii. Again once an accurate growth rate with the proper medium is found, it will then be possible to plot the organism growth rate vs. product growth rate. Plotting this data will determine if there is a direct or inverse relationship between the two quantities.<br />
</h6><br><br><br />
<br />
<br />
<!-- ====== References ====== --><br />
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<h5><center>References</h5><br />
<br />
<h6>1. Covert, M. W. and B. O. Palsson. Constraints-based models: regulation of gene expression reduces the steady-state solution space. J Theor Biol [Online] 2003 221(3), 309-325.</br></br><br />
<br />
2. Feist, Adam M, and Bernhard O Palsson. The biomass objective function. Current Opinion in Microbiology. [Online] 2010,13.3, 344-349. </br></br><br />
<br />
3. Orth, Jeffrey D, Ines Thiele, and Bernhard Ø Palsson. What is flux balance analysis? Nature Biotechnology [Online] 2010, 28.3, 245-248.</br></br><br />
<br />
4. Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nature Protocols [Online], 2011, 6:1290-1307.<br />
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<h3><center><a href="https://2014.igem.org/Team:StanfordBrownSpelman/Modelling">Bioinformatics & Modeling</a><br />
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<h7><center><a href="#" id="data">DoubleOptimizer</a> ● <a href="#" id="methods">CompositionSearch</a><br><a href="#" id="pics">Flux Balance Analysis</a></h7></div></center><br />
<br />
<h6 id="int"> While working on our synthetic biology projects for this year's iGEM competition, we found ourselves in need of some computational tools for synthetic biology that did not yet exist. Therefore, we developed our own software packages to meet these needs. In particular, we have developed two pieces of software, specifically designed for the needs of synthetic biologists:<b></b><br />
<ul><li>DoubleOptimizer: a tool that facilitates synthesis of repetitive genes by optimizing codon usage to both match a codon usage distribution for a desired organism, and to reduce and avoid repetitive nucleotide sequences, allowing for easier synthesis.<b></b><li>CompositionSearch: a tool that quickly ranks all proteins in a proteome by their similarity to a given amino acid distribution.</ul><br />
</h6><br />
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<!-- ====== DOUBLE OPTIMIZER ====== --><br />
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<div id="subheader" class="small-8 small-centered columns"><br />
<h5 id="results"><center><br />
DoubleOptimizer </h5><br />
<h6><center>A utility for simultaneous codon and <br>gene synthesis optimization<br></h6><br />
</div></div><br />
<div class="small-7 small-centered columns"><br><center><img src="https://static.igem.org/mediawiki/2014/0/0b/Double_optimizer_graphic.jpg"><br />
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<h6><br />
<center><iframe width="420" height="315" src="//www.youtube.com/embed/sO1qd3eTzRo" frameborder="0" allowfullscreen></iframe><br><br><br />
<h6><br />
Gene synthesis as a tool for biological engineering presents both opportunities and challenges. One opportunity presented is the ability to optimize codon usage in a gene to match that of a host organism. Compared to traditional cloning methods, this can increase protein yields in the host organism by several fold.[1] However, while there exist a large number of freely-usable programs that perform codon optimization, there is no guarantee that the sequences these programs provide will be able to be synthesized. Specifically, in the case of genes with repetitive amino acid sequences, these programs will often generate outputs that contain too many repeated short DNA sequences to be synthesized commercially. <br></br><br />
<br />
<br />
As an example, the hypothetical protein X777_06170 from the ant species <i>Cerapachys biroi</i> has an amino acid sequence that appears to be somewhat repetitive: <br><br><br />
<br />
<div class="sub6">001 mklfkclvpv vvlllikdss arpglirdfv ggtvgsilep fqilkpkdsy adanshasah <br></br><br />
061 nlggtfslgp vslggglssa sasssasang gglasasska daqaggygyg gsnanaqasa <br></br><br />
121 sanaqgggyg nggihgiypg qqgvhggnpf lggagsnana naiananaqa naggnngglg<br></br><br />
181 syggyqqggn ypidsstgpi gnnpflsggh gdgnanaaan anagasaign gggpidvnnp<br></br><br />
241 flhggaansg agginyqpgn aggiilsekp lglptiypgq hppayldsig spgansnaga<br></br><br />
301 napcsecgss gatilgyegq glggikesgs sgatilgyeg qglggikesg ssgatilgye<br></br><br />
361 gqglggikes gssgatilgs ydgqgpsgat ilgdyngqgl ggikessgvt vlgdyegqgl<br></br><br />
421 ggisgphggh gqaganagan ananagatvg ssggvlggvg dhggyhgyng hdgssglnlg<br></br><br />
481 gygggsnana qassnalass ggsssatsda lsnahssggs alanssskas angsgsanan<br></br><br />
541 ahassnassg shglgsktsa ssqasasadt rdmlifs[2]</div><br><br />
</h6><br />
<br />
<h6> Note that this sequence is not simply the same sequence repeated multiple times, but instead contains several motifs on the order of 10 - 20 amino acids in length that occur several times. When this sequence was run through the codon optimization program for expression in ''E. coli'' provided by a major DNA synthesis firm, the resulting output could not be synthesized by the very same firm: the "optimized" DNA sequence contained too many recurring short (> 8 nucleotide) DNA sequences to allow for synthesis.<br><br><br />
<br />
Manually correcting for repeats in the codon-optimized DNA sequence is a sub-optimal solution: not only is this process time-consuming, but it has the tendency to undo the codon-optimization: if a sequence of amino acids occurs several times, one may be forced to use all possible codon-combinations to represent this sequence to avoid nucleotide-sequence repetition. Unless corrected for by skewing codon usage elsewhere in the sequence, this will tend to make the codon usage more uniform than is optimal for the expression vector. Additionally, any changes made in either correcting for repeats or re-correcting for codon usage may in turn introduce additional repeats.<br />
</h6><br />
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<!-- ====== Solution: DoubleOptimizer ====== --><br />
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<div id="subheader" class="small-8 small-centered columns"><br />
<h5><center>Solution</h5><br />
<h6>DoubleOptimizer is a software tool we have created to optimize codon usage in a gene both to match a given codon usage distribution and to avoid repetition of nucleotide sequences. Given a DNA or amino acid sequence and a desired codon distribution, DoubleOptimizer will produce, within a matter of minutes, an equivalent sequence that has substantially reduced DNA sequence repetition, while also closely matching the desired codon usage.<br />
</h6><br />
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<!-- ====== Availability and Usage ====== --><br />
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<h5><center>Availability and Usage</h5><br />
<h6>DoubleOptimizer may be downloaded <a href = "http://drive.google.com/a/brown.edu/file/d/0B6Q5Eo65G4cPZC1SZWEzbUtrYUU/view?usp=sharing"><u>here</u></a>. DoubleOptimizer is a command line utility, provided as a Java jar file. It can be invoked from command line on any system with Java 7 or later installed, with the following syntax:<br></br><br />
<br />
<div class="sub6">java -jar DoubleOptimizer.jar seq.txt codons.txt <br></br> </div><br />
<br />
where "seq.txt" is a DNA sequence, stored as a plain text file, and "codons.txt" is a file containing the desired codon distribution to match. It should be formatted as plain text, according to the following example template: <br><br><br />
<br />
<div class="sub6"><br />
A<br></br><br />
GCG .36<br></br><br />
GCC .27<br></br><br />
GCA .21<br></br><br />
GCT .16<br></br><br />
R<br></br><br />
CGC .40<br></br><br />
CGT .38<br></br><br />
CGG .10<br></br><br />
CGA .06<br></br><br />
AGA .04<br></br><br />
AGG .02<br></br><br />
N<br></br><br />
AAC .55<br></br><br />
AAT .45<br></br><br />
D<br></br><br />
GAT .63<br></br><br />
GAC .37<br></br><br />
C<br></br><br />
TGC .55<br></br><br />
TGT .45<br></br><br />
E<br></br><br />
GAA .69<br></br><br />
GAG .31<br></br><br />
Q<br></br><br />
CAG .65<br></br><br />
CAA .35<br></br><br />
G<br></br><br />
GGC .40<br></br><br />
GGT .34<br></br><br />
GGG .15<br></br><br />
GGA .11<br></br><br />
H<br></br><br />
CAT .57<br></br><br />
CAC .43<br></br><br />
I<br></br><br />
ATT .51<br></br><br />
ATC .42<br></br><br />
ATA .07<br></br><br />
L<br></br><br />
CTG .50<br></br><br />
TTG .13<br></br><br />
TTA .13<br></br><br />
CTT .10<br></br><br />
CTC .10<br></br><br />
CTA .04<br></br><br />
K<br></br><br />
AAA .77<br></br><br />
AAG .23<br></br><br />
M<br></br><br />
ATG 1<br></br><br />
F<br></br><br />
TTT .57<br></br><br />
TTC .43<br></br><br />
P<br></br><br />
CCG .52<br></br><br />
CCA .19<br></br><br />
CCT .16<br></br><br />
CCC .12<br></br><br />
S<br></br><br />
AGC .28<br></br><br />
AGT .15<br></br><br />
TCG .15<br></br><br />
TCT .15<br></br><br />
TCC .15<br></br><br />
TCA .12<br></br><br />
*<br></br><br />
TAA .64<br></br><br />
TGA .29<br></br><br />
TAG .07<br></br><br />
T<br></br><br />
ACC .44<br></br><br />
ACG .27<br></br><br />
ACT .17<br></br><br />
ACA .13<br></br><br />
W<br></br><br />
TGG 1<br></br><br />
Y<br></br><br />
TAT .57<br></br><br />
TAC .43<br></br><br />
V<br></br><br />
GTG .37<br></br><br />
GTT .26<br></br><br />
GTC .22<br></br><br />
GTA .15<br></br><br />
</div><br />
<br />
(Note that the above example is actually the codon usage distribution of <i>E. coli</i>.[3]) <br></br><br />
<br />
DoubleOptimizer supports non-canonical codon assignments: the amino acid-codon groupings can by specified in whatever way the user wants in the codon distribution file.<br></br><br />
<br />
When executed, DoubleOptimizer will first display the input sequence with repetitive regions highlighted. It will also give the fraction of the sequence that initially consists of repetitive regions (defined by default as regions of 8 nucleotides or more that occur more than once in the sequence, including as their reverse complement), and a chi-squared value for the goodness-of-fit to the desired codon distribution (lower is better).<br></br><br />
<br />
DoubleOptimizer will then compute and display the optimized sequence (By default, it will produce the best sequence it can find after 10 seconds of computation time). Again, repetitive regions will be highlighted, and the same measurements of repetitiveness and codon fit will be given.<br></br><br />
<br />
The following optional flags may be used to change the program's behavior:<br></br><br />
<br />
<div><h5><ul><li>-A</ul></h5></div><br />
<br />
This allows for an amino-acid sequence, specified in single-letter code, to be used as input instead of a DNA sequence. The initial sequence statistics displayed will be for a uniform random reverse translation of the given amino acid sequence. <br></br><br />
<br />
Example: <br><br />
<br />
<div class="sub6">java -jar DoubleOptimizer.jar aaseq.txt codons.txt -A <br></br><br />
<br />
</div><br />
<br />
<div><h5><ul><li>-T##</ul></h5></div><br />
This allows the user to specify, in seconds, a different run-time for the program other than the default 10 seconds. While 10 seconds should be sufficient to produce a well-optimized result for most genes of moderate length on modern desktop computers, longer times may produce better-optimized results on slower machines or on longer sequences. <br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6">java -jar DoubleOptimizer.jar aaseq.txt codons.txt -A -T30 <br></br><br />
<br />
</div><br />
<br />
<div><h5><ul><li>-L##</ul></h5></div><br />
This allows the user to specify a different minimum length for what is considered a repeat, other than the default 8 nucleotides.<br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.txt codons.txt -L7 -T15 <br></br><br />
*-S##, -E##<br></br> <br />
</div><br />
<br />
These allow the user to specify the starting and ending nucleotide, respectively, of the coding region in a construct sequence to be sythesized. Nucleotides outside this frame will be ignored for codon usage optimization, and will never be modified. This option is useful for preventing repetitions, within the coding region, of fixed sequences that occur at the ends of a construct to be sythesized, outside of the coding region. The default values are the beginning of the sequence, and the end of the last complete codon. These options may be used together or independently. Values are one-indexed. If used with ''-A'', these will be interpreted as amino acid indices.<br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -R1000 -D100<br />
<br />
</div><br />
<div><h5><ul><li>-D##</ul></h5></div><br />
This option will make the program periodically display the current best sequence, and associated statistics, as it runs. The number given is the number of optoimization cycles the program will perform between each round of displaying the sequence. This provides a continuous measure of progress on long optimization runs. Note that, when given 10 seconds, the program may execute several thousand cycles of optimization, so an argument on the order of 100 may be reasonable.<br></br> <br />
<br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -R1000 -D100<br />
<br />
</div><br />
<div><h5><ul><li>-R##</ul></h5></div><br />
This option will make the program halt optimization after a certain number of optimization cycles. This may be used with a large value of -T to standardize optimization quality between computers of different speeds. Because this option is mostly only useful for testing the efficiency of this program itself, it may be removed from future releases.<br></br><br />
<br />
Example:<br><br />
<br />
<div class="sub6"> java -jar DoubleOptimizer.jar seq.test codons.txt -S121 -E1853 -T1000 -R1000<br />
</div><br />
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<!-- ====== Examples of Use ====== --><br />
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<h5><center>Examples of Use</h5><br />
<br />
<h6>As an example of the normal operation of the program on an amino acid sequence, consider the above-listed example of a repetitive amino acid sequence, hypothetical protein X777_06170 from the ant species <i>Cerapachys biroi</i>. At one point in our waterproofing project, this protein was considered for sythesis as a possible homologue of the waterproofing <i>Polistes</i> protein. DoubleOptimizer was originally written to overcome difficulties in synthesising this protein for expression in <i> E. coli</i>. (Due to further advances in the <i>Polistes</i> project, this gene was never synthesized). <b></b><br />
A screenshot of a run of DoubleOptimizer on the amino acid sequence for this protein (listed above), with the <i> E. coli</i> codon usage table also listed above, with default settings for 10 seconds on a laptop computer (2011 model Macbook Pro) is shown below: <b></b></h6></div><br />
<div><br><center><img src="https://static.igem.org/mediawiki/2014/8/85/AADoubleOptExample.png" width="1300" height="800"><br><br />
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<b></b><br />
Note that the number of repeated sequences is significantly reduced, and the closeness-of-fit to the desired codon sequence is much improved, compared to the initial random reverse-translation.<br />
<b></b><b></b><br />
As an example of DoubleOptimizer run on a DNA sequence, a somewhat-repetitive <i>Homo sapiens</i> gene was arbitrarily selected from the NCBI database. Specifically, the exon sequence for <a href="http://www.ncbi.nlm.nih.gov/nuccore/18028443"><u>Ca+2-binding protein CBP86 form VII</u></a> was optimized for synthesis, again in <i> E. coli</i>. The same computer setup was used, and DoubleOptimizer was run for the default 10 seconds: <b></b><br />
</h6></div><br />
<div><br><center><img src="https://static.igem.org/mediawiki/2014/0/09/DNADoubleOptExample.png" width="1300" height="800"><br><br />
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<h6><br />
<br />
<br />
<b></b><br />
In this case, the sequence went from being over 47% composed of repetitive sequences 8 nucleotides or more in length to being completely non-repetitive. Also, the codon usage distribution is now a near-perfect match to the <i> E. coli</i> codon usage distribution.<b><br />
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</h6><br />
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<h5><center>Algorithm</h5><br />
<h6>DoubleOptimizer uses a genetic optimization algorithm.[4] This algorithm mimics natural selection by taking the original gene sequence and generating several variants by copying the sequence and creating random silent point mutations in each copy. Each variant is then assigned a fitness score based on its repetitiveness and closeness-of-fit to the desired codon usage distribution, and the most fit variants are kept. These are then re-copied, re-mutated and re-scored, and again only the most fit variants are kept, through several thousand rounds of selection. When optimization time runs out, the most fit member of the population after the final round of selection is displayed. <br></br> <br />
One challenge in implementing this algorithm efficiently is that scoring for repetitiveness is a time-consuming process (the time belongs to O(n<sup>2</sup>) in the length of the sequence). Because many thousands of sequences are scored in a single optimization, finding a way to reduce the computational burden of this step was a task of paramount importance. Our solution was to store for each sequence a matrix representing the locations in the sequence where repetitions are already known to occur, and to only attempt to add or remove repetitions that could by affected by the point mutations that were made. This significantly improved run-time, compared to the naive technique of independently re-scoring each modified sequence.<br />
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<!-- ====== References ====== --><br />
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<h5><center>References</h5><br />
<h6>1. Several references exist that establish the link between codon usage and expression. To give some highly-cited examples:<br> <br />
<br />
Ikemura, T. Codon usage and tRNA content in unicellular and multicellular organisms. Mol Biol Evol (1985) 2 (1): 13-34 <br></br> <br />
Gouy, M. and Gautier, C. Codon usage in bacteria: correlation with gene expressivity Nucl. Acids Res. (1982) 10 (22): 7055-7074 doi:10.1093/nar/10.22.7055 <br></br> <br />
Sharp, Paul M. and Li, Wen-Hsiung. The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications Nucl. Acids Res. (1987) 15 (3): 1281-1295 doi:10.1093/nar/15.3.1281 <br></br> <br />
2. http://www.ncbi.nlm.nih.gov/protein/607359946. Original paper: Oxley PR, Ji L, Fetter-Pruneda I, McKenzie SK, Li C, Hu H, Zhang G, Kronauer DJ. The genome of the clonal raider ant Cerapachys biroi. Curr Biol. 2014 Feb 17;24(4):451-8. doi: 10.1016/j.cub.2014.01.018. Epub 2014 Feb 6. PubMed PMID: 24508170; PubMed Central PMCID: PMC3961065. <br></br> <br />
3. Kazuna DNA Research Institute. Codon Usage Database. http://www.kazusa.or.jp/codon/cgi-bin/showcodon.cgi?species=316407. <br></br> <br />
4. For a good introduction to genetic algorithms, see:<br></br> <br />
Goldberg, David E. Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, Mass: Addison-Wesley Pub. Co, 1989. <br />
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<!-- ====== COMPOSITION SEARCH ====== --><br />
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<h5 id="process"><center> CompositionSearch</h5><br />
<h6><center>A fast, local search of protein sequences<br>against a specified amino acid distribution</h6><br> <br />
<h6>When the relative proportions of amino acids in an unknown protein product have been chemically determined, it is often useful to search a proteome for proteins that have similar amino acid distributions, in order to identify this protein product. While at least one online utility for performing this task already exists (provided by the <a href = "http://web.expasy.org/aacompident/aacompfree.html"> <u>Swiss Institute of Bioinformatics</u> </a>), the web-based nature of this program creates some limitations. Firstly, the SIB provides computational resources for the calculation, resulting in slower turnaround for the user (searches take about 15 minutes). Secondly, this program will only search for proteins already in the Swiss-Prot / TrEMBL databases. (At last check, only the smaller Swiss-Prot search was functional.) If an organism is being newly studied and has just been sequenced, its predicted proteome will not be in these databases. Thirdly, due to limited resources, only the top matches to a given search are provided. This does not allow for statistical comparison to the "typical" protein within a given proteome. Fourthly, for very-high-security tasks, uploading data to a third party may be undesirable.<br />
</h6><br />
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<!-- ====== CompositionSearch: Solution ====== --><br />
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<h5><center>Solution</h5><br />
<h6>CompositionSearch is a software tool we have created to address these issues by allowing an individual to rank all proteins in a proteome by similarity (minimum Euclidian distance) to a reference amino acid distribution locally on one's own computer. This ranking can be generated in a matter of seconds, rather than taking several minutes. Because it ranks all proteins in a proteome, CompositionSearch can also generate a figure for the significance of the similarity of a given protein to a given amino acid distribution, using the similarity of the rest of the proteome as a statistical distribution function. <br />
</h6><br />
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<!-- ====== CompositionSearch: Availability and Usage ====== --><br />
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<h5><center>Availability and Usage</h5><br />
<h6>CompositionSearch may be downloaded <a href= "http://drive.google.com/a/brown.edu/file/d/0B6Q5Eo65G4cPbnpQYlNlcU1QUE0/view?usp=sharing"><u>here</u></a>. <br></br><br />
<br />
CompositionSearch is a command line utility, provided as a Java jar file. It can be invoked from command line on any system with Java 7 or later installed, with the following syntax:<br></br><br />
<br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv <br></br> </div><br />
<br />
where "prot.fasta" is a FASTA-formatted proteome, and "freqs.txt" is a file containing the desired amino distribution to match. It should be formatted as plain text, according to the following example template:<br><br />
<br />
<div class="sub6"><br />
A 0.134 <br></br> <br />
D 0.044 <br></br> <br />
E 0.04 <br></br><br />
G 0.228 <br></br> <br />
I 0.033 <br></br> <br />
K 0.021<br></br><br />
L 0.04 <br></br><br />
P 0.08 <br></br><br />
R 0.009<br></br><br />
S 0.151<br></br><br />
T 0.038<br></br><br />
V 0.083<br></br><br />
</div> <br />
<br />
Note that this may contain as many or as few amino acids as desired. The frequencies, however, are interpreted as absolute, so if all amino acids are represented, they should add 1. (See below for discussion of how the optional -N and -X flags affect this interpretation). <br></br> <br />
<br />
"out.csv" in the above example line is the destination path to store the results of the calculation. The output will be a spreadsheet in csv format, which may be imported into your favorite desktop spreadsheet application (i.e. Microsoft Excel, LibreOffice Calc, etc.).<br></br><br />
<br />
After execution, out.csv will contain a spreadsheet showing the reference distribution, and, in order by similarity to the reference distribution, the amino acid distributions of all proteins in the proteome. Protein name, similarity ranking, similarity score (Euclidian distance between amino acid distributions; lower is better), and similarity p-value (on the curve of other proteins in the proteome) will be listed for each protein. <br></br><br />
<br />
The following optional flags may be used to change the program's behavior: <br></br><br />
<br />
<div><h5><ul><li>-N</ul></h5></div><br />
<br />
This will cause the program to ignore amino acids in the proteins that are not in the distribution list. In other words, this means that the frequencies given refer to frequencies relative to only the the other amino acids listed, instead of all amino acids. To clarify:<br></br><br />
<br />
Without the -N flag, the line in the above example distribution list: <br><br />
<div class="sub6"> P 0.08 <br></br> </div><br />
means that 8% of all amino acid residues in the matching protein are expected to be proline residues. <br></br><br />
<br />
With the -N flag, the line in the above example distribution list:<br><br />
<div class="sub6"> P 0.08 <br></br> </div> <br />
means that 8% of amino acid residues that belong in the set {ADEGIKLPRSTV} (the amino acids with defined frequences) in the matching protein are expected to be proline residues. <br></br><br />
<br />
Example:<br><br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv -N <br></br><br />
<br />
</div><br />
<div><h5><ul><li>-X$$</ul></h5></div><br />
This will cause the program to completely disregard certain amino acid residue symbols in the proteome, regardless of use of the N flag.<br></br><br />
<br />
The default value of this set of characters is 'X,' often used to represent unknown amino acid residues. Therefore the flag -XX is equivalent to normal behavior. Note that the set of characters being ignored is replaced by the -X flag, so it is always advisable to list X when using this flag.<br></br><br />
<br />
Example:<br><br />
<div class="sub6"> java -jar CompositionSearch.jar prot.fasta freqs.txt out.csv -XX*-<br></br> </div><br />
<br />
This will cause the symbols X,*, and - to be ignored in the proteome. <br />
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Cellulose Pathway Modeling </h5><br />
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<!-- ====== Flux Balance Analysis ====== --><br />
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<h6><center>Flux Balance Analysis</h6><br><br />
<br />
<h6><br />
Modeling allows for efficient experimental design by optimizing specific conditions before testing with in vitro methods. Flux Balance Analysis (FBA) is used to optimize the growth medium for the bioengineered gluconacetobacter hansenii to yield bacterial cellulose to be used as biomaterial for a biodegradable drone. FBA is a mathematical method used to examine how metabolites relate to each network and makes predictions for the growth of an organism and product output. It is a direct application of linear programming to biological systems that used the stoichiometric coefficient for each reaction in the system as the set of constraints for the optimization. (add glycolysis stoichiometric picture here)<br />
<br />
</br></br>Flux Balance Analysis is perfomed under steady state conditions and requires information about the stoichiometry of metabolic pathways, metabolic demands, and strain specific parameters. At steady state, there is no accumulation or depletion of metabolites in a metabolic network, so the production rate of each metabolite in the network must equal its rate of consumption (3).<br />
<br />
</br></br>FBA will be used to optimize the growth conditions of g. hansenii in order to maximize the product output of cellulose. The exchange reactions determine the metabolites that are beneficial to the medium. Changing the composition will allow us to be able to determine the effect the composition has on the efficiency of the production media.<br />
</h6><br><br><br />
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<!-- ====== Model SEED- ModelView ====== --><br />
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<h5><center>Model SEED- ModelView</h5><br />
<br />
<h6>Model SEED is an online analysis software with its own genome and media formulation database. An analysis of the G. hansenii is done with the curated model including a biomass reaction set for optimal organismal growth and a control of complete media. The biomass reaction describes the rate, which all the biomass precursors are made in correct proportions.(2) Biomass reactions include the information listed in Figure 1.(Biomass reaction picture) A biomass reactions requires a knowledge of the composition of a cell and the energy requirements that allow for the cell to grow. The biomass equation consists of three levels : macromolecular level (RNA, protein), intermediate level (biosynthetic energy), advanced level (vitamins, elements, and cofactors). The biomass equation also list to stochiometric coefficients for each part of the biomass equation. Using this biomass equation the growth rate was determined to be 66.48 gm per biomass CDW/hr was obtained. Since the flux is positive this explains that the flux of the analysis is indeed towards growth a negative or zero flux would have explained that the pathway is not used or it is impossible. <br />
</h6><br><br><br />
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<!-- ====== Future Directions/Modifications ====== --><br />
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<h5><center>Future Directions/Modifications</h5><br />
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<h6>Although a growth rate is obtained using Model SEED, this rate is for an environment where a complete medium is used. The growth medium being used contains dextrose, peptone, yeast extract, Na2HPO4, citric acid, agar and water. Model SEED does not have the capabilities to create a media file, so all further analysis will be done using KBase because it has the ability to customize the media used for analysis. Kbase is a software that allows for online analysis of models, where models and the corresponding media for organismal growth can be optimized. The genome database contains all genomes including the G. hansenii genome (as a JSON file). A full database for medias that exist and capabilities to create the media being used in experimentation through the IRIS interface, which is the command line environment used by Kbase. Using IRIS and the subsequent FBA tutorial, a flux balance analysis was attempted but several issue were observed because of command line issues. Kbase will allow for the majority of modifications to be done in order to obtain the correct model and medium, further help will be sought out in order to conquer the error messages seen. Once the error messages are fixed, further literature searches and consultations will be done in order to change the biomass reaction to one that will target the product growth of cellulose instead of organismal growth of G. hansenii. Again once an accurate growth rate with the proper medium is found, it will then be possible to plot the organism growth rate vs. product growth rate. Plotting this data will determine if there is a direct or inverse relationship between the two quantities.<br />
</h6><br><br><br />
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<!-- ====== References ====== --><br />
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<h5><center>References</h5><br />
<br />
<h6>1. Covert, M. W. and B. O. Palsson. Constraints-based models: regulation of gene expression reduces the steady-state solution space. J Theor Biol [Online] 2003 221(3), 309-325.</br></br><br />
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2. Feist, Adam M, and Bernhard O Palsson. The biomass objective function. Current Opinion in Microbiology. [Online] 2010,13.3, 344-349. </br></br><br />
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3. Orth, Jeffrey D, Ines Thiele, and Bernhard Ø Palsson. What is flux balance analysis? Nature Biotechnology [Online] 2010, 28.3, 245-248.</br></br><br />
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4. Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nature Protocols [Online], 2011, 6:1290-1307.<br />
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