http://2014.igem.org/wiki/index.php?title=Special:Contributions/CorinnaO&feed=atom&limit=50&target=CorinnaO&year=&month=2014.igem.org - User contributions [en]2024-03-29T11:28:50ZFrom 2014.igem.orgMediaWiki 1.16.5http://2014.igem.org/Team:Oxford/teamTeam:Oxford/team2014-10-18T00:43:22Z<p>CorinnaO: </p>
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Glen Gowers</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>The guy who makes everything happen, the Fred to our iGEM "Mystery Machine". When Glen isn't sailing around the country, he's hard at work in the lab and at the desk.<br />
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<a href="http://uk.linkedin.com/pub/glen-gowers/84/1a2/bba/"><b>Glen's LinkedIn profile</b></a> <br />
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<div style="margin-left:310px;margin-right:10px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Jack Hoffman</font> <br><font style="color:grey;font-weight:500;">(3rd year Chemist, St John's College)</font><br><br>Though prodigious in his own field, Jack has tried his hand at many academic disciplines, and this curiosity and fervour for science is most welcome amongst the team.<br> He has been working closely with our engineers, designing mathematical frameworks for our modelling, synthesising <a href="https://2014.igem.org/Team:Oxford/biopolymer_containment#show2" />biopolymer capsules</a> and running diffusion experiments on them in the lab.<br><br><br />
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Jack leads the realisation part of the project and is in charge of our web development and graphics.<br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Corinna Oswald</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, St John's College)</font><br><br>Corinna is one of the founding members that organised Oxford’s first participation in iGEM, and was a key player in conceptualising our project 'DCMation'. <br><br />
In between perfecting the Viennese Waltz and denying that Austrians are always yodeling in the Alps (which we refuse to accept), she works reliably hard and efficient to ensure that both her and the entire team's work is completed to the highest standards. Her organisational and communication skills secure the smooth running of the project despite ever-increasing time pressure and the broadly interdisciplinary nature of our team.<br><br>contact: oswald.corinna@gmail.com <br> <a href="http://uk.linkedin.com/pub/corinna-oswald/67/b68/512"><b>Corinna's LinkedIn profile</b></a> </div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Andy Russell</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, The Queen's College)</font><br><br>Full-time biochemist, part-time fashionista; Andy fancies himself as the ''arbiter elegantarium'' of the team. Andy is now entering his third year as a biochemist at The Queen's College, where he is an accomplished sportsman.</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Sian Mcgibbon</font> <br><font style="color:grey;font-weight:500;">(3rd year Lawyer, University College)</font><br><br>Siân is the model lawyer along with everything that implies.<br><br />
She has led the Policy and Practices element of our project, with a particular emphasis on intellectual property policy and its role in shaping the developing field of synthetic biology. Sian has a particular interest in this area of law, and the key role it will play in determining the future of genetic technology and in many other branches of science.<br><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Tim Ang</font> <br><font style="color:grey;font-weight:500;">(3rd year Biologist, St John's College)</font><br><br>Tim's primary academic interests focus on evolutionary theory, particularly social evolution. A keen weightlifter, his frequent references to weights and protein shakes naturally make him the hub of enchanting conversation here at Oxford iGEM.<br><br><br />
Tim is in charge of our interlab study and poster.</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Emily Prichett</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>Emily has been the latest addition to our team and turned out to be absolutely crucial for our entire social media activities. She loves the E.coli chat and has been a well-renown expert beyond the iGEM teams for growth curves and methylotrophs.</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Philipp Lorenz</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>It was Phil's idea to bring iGEM to Oxford, and he continues to ensure our project runs as smoothly as a German train schedule. <br />
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<a href="http://de.linkedin.com/pub/philipp-lorenz/a1/92a/a49"><b>Phil's LinkedIn profile</b></a><br />
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<div id="franprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/5/5e/OxigemFran.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Fran Donellan</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, Oriel College)</font><br><br>Not just a member of the biochemist team but also leading our public engagement activities, no task is too big or small for Fran. This girl can provide sandwiches for 500 people at 24 hours' notice and look good doing it.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/1/1d/OxigemShahbano.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Shahbano Soomro</font> <br><font style="color:grey;font-weight:500;">(3rd year PPEist, University College)</font><br><br>A third year student in Politics and Economics at University College, Shahbano has a particular interest in Environmental policy and philosophy. As well as helping Fran with organising our public engagement and events, she will be watching over our project from afar during the summer.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/d/d8/OxigemMatt.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Matthew Booth</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, St John's College)</font><br><br><br />
Matt is an avid Rugby player, having recently achieved great success with the college team. His main contributions to iGEM have been developing the light detecting electrical circuit and the stochastic model for mCherry expression.<br />
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<img src="https://static.igem.org/mediawiki/2014/b/b7/OxigemOliver.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Oliver Vince</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, University College)</font><br><br><br />
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Oliver is our keenest team member - nothing can douse his motivation. In addition to taking charge of the maths and software, he feels responsible for our entertainment.<br />
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Oliver has been particularly involved with the modelling of the genetic circuits and the CAD/CAM side of the project. He has also played a leading role in the construction of the pages on the website.<br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Leroy Lim</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, St John's College)</font><br><br>During term, Leroy indulges in many of the quirks of Oxford life, namely rowing, bops and colourful trousers. During iGEM, he's our engineer and self-appointed social sec.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/3/34/George.jpg" style="z-index:6;position:absolute;margin-top:105%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:106%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Dr George Wadhams</font><br><br> <br />
George Wadhams’ research interests lie in how bacteria sense and integrate environmental information. His group focuses on understanding in a quantitative manner how multiple, homologous pathways operate in individual cells and how the components of these pathways can be used to create synthetic pathways.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/2/2f/OxigemCiaran.jpg" style="z-index:6;position:absolute;margin-top:126.5%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:127.5%;width:500px;color:black;right:15%;"><font style="font-size:large;">Dr Ciar&aacute;n Kelly</font><br><br> <br />
Ciarán Kelly obtained a PhD for his work in Frank Sargant’s group, constructing and characterising synthetic biohydrogen production pathways in E. coli. He is interested in the construction, characterisation, and re-engineering synthetic enzymes and pathways for the production of high-value chemicals. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/2/25/OxigemKarl.jpg" style="z-index:6;position:absolute;margin-top:148%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:149%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Mr Karl Brune</font><br><br> <br />
Karl Brune's research interests lie in elucidating metabolic pathways. His previous work at Imperial College London was focused on engineering microbial consortia to enhance biomining and bioremediation, as well as on the study of photoautotrophic organisms. </div><br />
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<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450; padding-left:15%; "><br><div style="opacity:0.7;">Advisors</div></div><br />
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<img src="https://static.igem.org/mediawiki/2014/d/da/Judy.jpg" style="z-index:6;position:absolute;margin-top:184.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px; " /><br />
<div style="z-index:6;position:absolute;margin-top:185.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Professor Judy Armitage</font><br><br> <br />
Judy Armitage is interested in the dynamics of bacterial sensory transduction and the control of bacterial motility. In particular, her research group focuses on the communication between the sensory and adaption mechanisms of the two pathways as a model for sensory network integration in general..</div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/05/OxigemAnthony.jpg" style="z-index:6;position:absolute;margin-top:206%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:207%;width:500px;color:black;right:15%;"><font style="font-size:large;">Professor Anthony Watts</font><br><br> <br />
Anthony Watts’ group is devising solid state NMR methods for determining high-resolution details of information-rich sites within membrane receptors. Recent focus has been on the neurotensin receptor (NTS1), which is now available highly purified and monodispersed in detergent as well as in a ligand-binding form. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/4/4f/OxigemAntonis.jpg" style="z-index:6;position:absolute;margin-top:227.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:228.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Professor Antonis Papachristodoulou</font><br><br> <br />
Antonis Papachristodoulou’s research interests include systems and synthetic biology, network systems, aerospace systems and flow control, and convex optimisation. Furthermore, he works on modern control theory, robust stability analysis and design, as well as nonlinear dynamical systems and Lyapunov stability. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/09/OxigemAndreas.jpg" style="z-index:6;position:absolute;margin-top:249%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:250%;width:500px;color:black;right:15%;"><font style="font-size:large;">Andreas Harris<br />
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Andreas Harris works on the design and implementation of gene regulatory networks harnessing feedback to increase robustness and tunability. The designs are based around transcriptional networks and attempt to translate well-understood control modules, such as proportional and integral controllers, to biological systems. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/a/aa/OxigemLucas.jpg" style="opacity:0.95;z-index:6;position:absolute;margin-top:270.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:271.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Dr Lucas Black</font><br><br> <br />
Lucas is an imaging specialist helping out with quantification of parts and to give general guidance throughout the project.<br />
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The team is very grateful for Lucas's excellent photography, including our team photos and the photograph of the Radcliffe Camera on our homepage. </div><br />
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<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450;padding-left:15%;"><br><div style="opacity:0.7;">Attributions</div></div><br />
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All molecular biology, biochemical and fluorescence work was performed by student team members: Glen; Philipp; Francesca; Corinna; Andrew; and Emily in the lab of Prof. Judith Armitage under supervision of Dr. George Wadhams, Dr. Ciaran Kelly, Karl Brune and Dr. Lucas Black. <br />
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Interlab measurement study was performed by Tim under supervision of Lucas. All modelling was performed by student team members: Oliver; Matthew; and Leroy under the supervision of Dr. Antonis Papachristodolou and Andreas Harris. <br><br>Human Practices was performed by Sian and Francesca.<br><br> Biobead synthesis was performed Jack under supervision of Dr. George Wadhams.<br><br> Wiki design and coding was done by Jack and Olliver, with debug help from Ashok Menon and Josef Patoprsty.<br />
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<font style="font-size:large;font-weight:500;font-style: italic;">With thanks:</font><br />
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<font style="font-size:15px;">Darragh Ennis</font><br><br />
For his 3D printing expertise.<br />
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<font style="font-size:15px;">Dr Frederic Coulon</font> (Department of Environmental Science and Technology, School of Applied Sciences, Cranfield University)<br><br />
For his information on DCM and water treatment.<br />
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<font style="font-size:15px;">Shaun Rowson</font> (Team Leader - Groundwater & Contaminated Land, Environment Agency)<br><br />
For his detailed information on the problem of chlorinated solvents and how they are currently disposed of.<br />
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<font style="font-size:15px;">Janet Stott and Sarah Lloyd</font><br><br />
For their kind advice and assistance in our public engagement events at the Oxford University Museum of Natural History.<br><br><br />
<font style="font-size:15px;">Carla Brown</font><br><br />
For generously donating several packs of her Bacteria Combat card game. <br><br><br />
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<a href="http://www.biochemistry.org" target="_blank"><img src="https://static.igem.org/mediawiki/2014/7/75/BS.png" style="position:absolute;width:25%;left:50.5%;top:40%;" /></a><br />
<a href="http://www.sgm.ac.uk" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/20/SGM.png" style="position:absolute;width:22%;left:24%;top:51%" /></a><br />
<a href="http://www.wellcome.ac.uk" target="_blank"><img src="https://static.igem.org/mediawiki/2014/4/4c/WT.png" style="position:absolute;width:22%;left:50.5%;top:52%;" /></a><br />
<a href="http://www.deskgen.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/6/65/Oxfordigem_deskgen.png" style="position:absolute;width:15%;left:27%;top:62%" /></a><br />
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<img id="fran" src="https://static.igem.org/mediawiki/2014/5/5e/OxigemFran.jpg" style="position:absolute;width:20%;left:59.5%;margin-top:29.5%;" /><br />
<a href="#"><img id="fran1" src="https://static.igem.org/mediawiki/2014/c/c6/OxigemFran1.jpg" style="position:absolute;width:20%;left:59.5%;margin-top:29.5%;display:none;" onClick="profile('fran')" /></a><br />
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<img id="glen" src="https://static.igem.org/mediawiki/2014/9/97/OxigemGlen.jpg" style="position:absolute;width:20%;left:20%;border-top-left-radius:15px;" /><br />
<a href="#"><img id="glen1" src="https://static.igem.org/mediawiki/2014/5/5e/OxigemGlen1.jpg" style="position:absolute;width:20%;left:20%;border-top-left-radius:15px;display:none;" onClick="profile('glen')" /></a><br />
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<img id="corinna" src="https://static.igem.org/mediawiki/2014/b/be/OxigemCorinna.jpg" style="position:absolute;width:20%;left:39.8%;" /><br />
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<img id="andy" src="https://static.igem.org/mediawiki/2014/f/ff/OxigemAndy.jpg" style="position:absolute;width:20%;left:59.5%;border-top-right-radius:15px;" /><br />
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<div id="glenprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/9/97/OxigemGlen.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Glen Gowers</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>The guy who makes everything happen, the Fred to our iGEM "Mystery Machine". When Glen isn't sailing around the country, he's hard at work in the lab and at the desk.<br />
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<a href="http://uk.linkedin.com/pub/glen-gowers/84/1a2/bba/"><b>Glen's LinkedIn profile</b></a> <br />
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<div id="jackprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/6/68/OxigemJack.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;margin-right:10px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Jack Hoffman</font> <br><font style="color:grey;font-weight:500;">(3rd year Chemist, St John's College)</font><br><br>Though prodigious in his own field, Jack has tried his hand at many academic disciplines, and this curiosity and fervour for science is most welcome amongst the team.<br> He has been working closely with our engineers, designing mathematical frameworks for our modelling, synthesising <a href="https://2014.igem.org/Team:Oxford/biopolymer_containment#show2" />biopolymer capsules</a> and running diffusion experiments on them in the lab.<br><br><br />
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Jack leads the realisation part of the project and is in charge of our web development and graphics.<br />
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<div id="corinnaprofile" style="color:black;padding-right:10px;display:none;"><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Corinna Oswald</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, St John's College)</font><br><br>Corinna is one of the founding members that organised Oxford’s first participation in iGEM, and was a key player in conceptualising our project 'DCMation'. <br><br />
In between perfecting the Viennese Waltz and denying that Austrians are always yodeling in the Alps (which we refuse to accept), she works reliably hard and efficient to ensure that both her and the entire team's work is completed to the highest standards. Her organisational and communication skills secure the smooth running of the project despite ever-increasing time pressure and the broadly interdisciplinary nature of our team.<br><br>contact: oswald.corinna@gmail.com </div><br />
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<div id="andyprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/OxigemAndy.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Andy Russell</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, The Queen's College)</font><br><br>Full-time biochemist, part-time fashionista; Andy fancies himself as the ''arbiter elegantarium'' of the team. Andy is now entering his third year as a biochemist at The Queen's College, where he is an accomplished sportsman.</div><br />
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<div id="sianprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/4/47/OxigemSian.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Sian Mcgibbon</font> <br><font style="color:grey;font-weight:500;">(3rd year Lawyer, University College)</font><br><br>Siân is the model lawyer along with everything that implies.<br><br />
She has led the Policy and Practices element of our project, with a particular emphasis on intellectual property policy and its role in shaping the developing field of synthetic biology. Sian has a particular interest in this area of law, and the key role it will play in determining the future of genetic technology and in many other branches of science.<br><br />
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<div id="timprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/2/21/OxigemTim.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Tim Ang</font> <br><font style="color:grey;font-weight:500;">(3rd year Biologist, St John's College)</font><br><br>Tim's primary academic interests focus on evolutionary theory, particularly social evolution. A keen weightlifter, his frequent references to weights and protein shakes naturally make him the hub of enchanting conversation here at Oxford iGEM.<br><br><br />
Tim is in charge of our interlab study and poster.</div><br />
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<div id="emilyprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/6/6e/OxigemEmily.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Emily Prichett</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>Emily has been the latest addition to our team and turned out to be absolutely crucial for our entire social media activities. She loves the E.coli chat and has been a well-renown expert beyond the iGEM teams for growth curves and methylotrophs.</div><br />
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<div id="philprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/8/8b/OxigemPhil.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Philipp Lorenz</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>It was Phil's idea to bring iGEM to Oxford, and he continues to ensure our project runs as smoothly as a German train schedule. <br />
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<a href="http://de.linkedin.com/pub/philipp-lorenz/a1/92a/a49"><b>Phil's LinkedIn profile</b></a><br />
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<div id="franprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/5/5e/OxigemFran.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Fran Donellan</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, Oriel College)</font><br><br>Not just a member of the biochemist team but also leading our public engagement activities, no task is too big or small for Fran. This girl can provide sandwiches for 500 people at 24 hours' notice and look good doing it.</div><br />
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<div id="shahbanoprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/1/1d/OxigemShahbano.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Shahbano Soomro</font> <br><font style="color:grey;font-weight:500;">(3rd year PPEist, University College)</font><br><br>A third year student in Politics and Economics at University College, Shahbano has a particular interest in Environmental policy and philosophy. As well as helping Fran with organising our public engagement and events, she will be watching over our project from afar during the summer.</div><br />
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<div id="mattprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/d/d8/OxigemMatt.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Matthew Booth</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, St John's College)</font><br><br><br />
Matt is an avid Rugby player, having recently achieved great success with the college team. His main contributions to iGEM have been developing the light detecting electrical circuit and the stochastic model for mCherry expression.<br />
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<div id="oliverprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/b/b7/OxigemOliver.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Oliver Vince</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, University College)</font><br><br><br />
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Oliver is our keenest team member - nothing can douse his motivation. In addition to taking charge of the maths and software, he feels responsible for our entertainment.<br />
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Oliver has been particularly involved with the modelling of the genetic circuits and the CAD/CAM side of the project. He has also played a leading role in the construction of the pages on the website.<br />
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<div id="leroyprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/9/91/OxigemLeroy.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Leroy Lim</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, St John's College)</font><br><br>During term, Leroy indulges in many of the quirks of Oxford life, namely rowing, bops and colourful trousers. During iGEM, he's our engineer and self-appointed social sec.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="z-index:5;position:absolute; width:100%;margin-top:91%;min-width:320px; border-radius:15px;"/><br />
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<div style="background-color:rgba(217, 217, 217, 0.7);position:absolute; z-index:5; Height:75px; width:100%;min-width:350px;margin-top:92%;font-size:65px;font-family:Helvetica;padding-top:5px; font-weight: 450;"><br />
<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450; padding-left:15%;"><br><div style="opacity:0.7;">Supervisors</div></div><br />
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<img src="https://static.igem.org/mediawiki/2014/3/34/George.jpg" style="z-index:6;position:absolute;margin-top:105%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:106%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Dr George Wadhams</font><br><br> <br />
George Wadhams’ research interests lie in how bacteria sense and integrate environmental information. His group focuses on understanding in a quantitative manner how multiple, homologous pathways operate in individual cells and how the components of these pathways can be used to create synthetic pathways.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/2/2f/OxigemCiaran.jpg" style="z-index:6;position:absolute;margin-top:126.5%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:127.5%;width:500px;color:black;right:15%;"><font style="font-size:large;">Dr Ciar&aacute;n Kelly</font><br><br> <br />
Ciarán Kelly obtained a PhD for his work in Frank Sargant’s group, constructing and characterising synthetic biohydrogen production pathways in E. coli. He is interested in the construction, characterisation, and re-engineering synthetic enzymes and pathways for the production of high-value chemicals. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/2/25/OxigemKarl.jpg" style="z-index:6;position:absolute;margin-top:148%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:149%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Mr Karl Brune</font><br><br> <br />
Karl Brune's research interests lie in elucidating metabolic pathways. His previous work at Imperial College London was focused on engineering microbial consortia to enhance biomining and bioremediation, as well as on the study of photoautotrophic organisms. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="position:absolute; z-index:5; width:100%;margin-top:170.5%;min-width:320px; border-radius:15px;border-bottom-left-radius:15px;"/><br />
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<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450; padding-left:15%; "><br><div style="opacity:0.7;">Advisors</div></div><br />
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<img src="https://static.igem.org/mediawiki/2014/d/da/Judy.jpg" style="z-index:6;position:absolute;margin-top:184.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px; " /><br />
<div style="z-index:6;position:absolute;margin-top:185.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Professor Judy Armitage</font><br><br> <br />
Judy Armitage is interested in the dynamics of bacterial sensory transduction and the control of bacterial motility. In particular, her research group focuses on the communication between the sensory and adaption mechanisms of the two pathways as a model for sensory network integration in general..</div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/05/OxigemAnthony.jpg" style="z-index:6;position:absolute;margin-top:206%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:207%;width:500px;color:black;right:15%;"><font style="font-size:large;">Professor Anthony Watts</font><br><br> <br />
Anthony Watts’ group is devising solid state NMR methods for determining high-resolution details of information-rich sites within membrane receptors. Recent focus has been on the neurotensin receptor (NTS1), which is now available highly purified and monodispersed in detergent as well as in a ligand-binding form. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/4/4f/OxigemAntonis.jpg" style="z-index:6;position:absolute;margin-top:227.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:228.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Professor Antonis Papachristodoulou</font><br><br> <br />
Antonis Papachristodoulou’s research interests include systems and synthetic biology, network systems, aerospace systems and flow control, and convex optimisation. Furthermore, he works on modern control theory, robust stability analysis and design, as well as nonlinear dynamical systems and Lyapunov stability. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/09/OxigemAndreas.jpg" style="z-index:6;position:absolute;margin-top:249%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:250%;width:500px;color:black;right:15%;"><font style="font-size:large;">Andreas Harris<br />
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Andreas Harris works on the design and implementation of gene regulatory networks harnessing feedback to increase robustness and tunability. The designs are based around transcriptional networks and attempt to translate well-understood control modules, such as proportional and integral controllers, to biological systems. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/a/aa/OxigemLucas.jpg" style="opacity:0.95;z-index:6;position:absolute;margin-top:270.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:271.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Dr Lucas Black</font><br><br> <br />
Lucas is an imaging specialist helping out with quantification of parts and to give general guidance throughout the project.<br />
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The team is very grateful for Lucas's excellent photography, including our team photos and the photograph of the Radcliffe Camera on our homepage. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="position:absolute; z-index:5; width:100%;margin-top:292.5%;min-width:320px; border-radius:15px;border-bottom-left-radius:15px;"/><br />
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<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450;padding-left:15%;"><br><div style="opacity:0.7;">Attributions</div></div><br />
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All molecular biology, biochemical and fluorescence work was performed by student team members: Glen; Philipp; Francesca; Corinna; Andrew; and Emily in the lab of Prof. Judith Armitage under supervision of Dr. George Wadhams, Dr. Ciaran Kelly, Karl Brune and Dr. Lucas Black. <br />
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Interlab measurement study was performed by Tim under supervision of Lucas. All modelling was performed by student team members: Oliver; Matthew; and Leroy under the supervision of Dr. Antonis Papachristodolou and Andreas Harris. <br><br>Human Practices was performed by Sian and Francesca.<br><br> Biobead synthesis was performed Jack under supervision of Dr. George Wadhams.<br><br> Wiki design and coding was done by Jack and Olliver, with debug help from Ashok Menon and Josef Patoprsty.<br />
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<font style="font-size:large;font-weight:500;font-style: italic;">With thanks:</font><br />
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<font style="font-size:15px;">Darragh Ennis</font><br><br />
For his 3D printing expertise.<br />
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<font style="font-size:15px;">Dr Frederic Coulon</font> (Department of Environmental Science and Technology, School of Applied Sciences, Cranfield University)<br><br />
For his information on DCM and water treatment.<br />
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<font style="font-size:15px;">Shaun Rowson</font> (Team Leader - Groundwater & Contaminated Land, Environment Agency)<br><br />
For his detailed information on the problem of chlorinated solvents and how they are currently disposed of.<br />
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<font style="font-size:15px;">Janet Stott and Sarah Lloyd</font><br><br />
For their kind advice and assistance in our public engagement events at the Oxford University Museum of Natural History.<br><br><br />
<font style="font-size:15px;">Carla Brown</font><br><br />
For generously donating several packs of her Bacteria Combat card game. <br><br><br />
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<a href="http://www.bbsrc.ac.uk/home/home.aspx" target="_blank"><img src="https://static.igem.org/mediawiki/2014/3/3e/BBSRC.png" style="position:absolute;width:19%;left:24%;top:40%;min-width:100px;" /></a><br />
<a href="http://www.biochemistry.org" target="_blank"><img src="https://static.igem.org/mediawiki/2014/7/75/BS.png" style="position:absolute;width:25%;left:50.5%;top:40%;" /></a><br />
<a href="http://www.sgm.ac.uk" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/20/SGM.png" style="position:absolute;width:22%;left:24%;top:51%" /></a><br />
<a href="http://www.wellcome.ac.uk" target="_blank"><img src="https://static.igem.org/mediawiki/2014/4/4c/WT.png" style="position:absolute;width:22%;left:50.5%;top:52%;" /></a><br />
<a href="http://www.deskgen.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/6/65/Oxfordigem_deskgen.png" style="position:absolute;width:15%;left:27%;top:62%" /></a><br />
<a href="http://www.bioch.ox.ac.uk/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/20/Oxfordigem_dept.png" style="position:absolute;width:22%;left:51%;top:63%;" /></a><br />
<a href="http://www.neb.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/b/b1/OxigemNeb_logo.png" style="position:absolute;width:18%;left:26%;top:75%" /></a><br />
<a href="http://www.snapgene.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/f/ff/Oxfordigem_snapgene.png" style="position:absolute;width:23%;left:49%;top:77%;" /></a><br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/safetyTeam:Oxford/safety2014-10-18T00:17:46Z<p>CorinnaO: </p>
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<img src="https://static.igem.org/mediawiki/2014/4/45/Oxigemlab.jpg" style="position:absolute; width:100%;z-index:-1; border-radius:15px;"/><br />
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<div style="background-color:#D9D9D9; opacity:0.7; z-index:5; Height:75px;margin-top:10px; width:100%;font-size:65px;font-family:Helvetica;padding-top:5px; font-weight: 450;"><br />
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<h1>Introduction</h1><br />
<br><br />
The Policy and Practices element of our project has asked how iGEM projects can grow from small lab studies into a commercial/industrial/medical product used by many people. An essential part of this research has been thinking about the safety of synthetic biology and specifically of the bioremediation technology we have developed, were it to be in widespread use. <br />
<br><br><br />
The new risks associated with the growth of our project are broadly similar to those related to synthetic biology generally: the possibility of spread of antibiotic resistance from synthetic organisms into the natural environment, and the possible escape of synthetic organisms into the natural environment. Specific risks associated with our project include the risk to lab workers from working with chlorinated solvents (although this is relatively easy to manage). <br />
There may be potential for the abuse of the agarose bacteria-containing beads being developed by our team as this technology will allow bacteria to survive in hostile environments. However, we consider that the potential benefits from the application of our design far outweigh any possible risks. <br />
<br><br> <br />
Our bacteria are contained within agarose beads that will control the concentration of DCM which they are exposed to. Eventually these agarose beads will begin to degrade naturally over time, exposing the bacteria to a higher DCM concentration of up to 200 mM. The bacteria will die when the concentration rises above the level they can tolerate, thus we have a kill switch inherently integrated into our design. In addition, the agarose beads will be trapped by a filter within the DCMation container in order to prevent them from being disposed down the sink. Our product will include instructions explaining to users how the beads can be disposed of safely (for example by sterilising with boiling water before disposal).<br />
<br />
<br><br><br />
<br />
<h1>Bacterial strains</h1><br />
<br />
Our team has been working with the following host strains. The table below indicates the risk group the organism belongs to - none of the organisms we worked with confer any risk to human health. <br />
<br />
<img src="https://static.igem.org/mediawiki/parts/0/0b/Saftey_Image1.png" style="float:left;position:relative; width:60%; margin-right:20%;margin-left:20%;margin-bottom:2%;" /><br />
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<br />
<br><br />
<br />
Oxford iGEM 2014<br />
<br><br />
<br><br />
<br />
<h1>Risks to the safety and health of team members, or other people working in the lab:</h1><br />
The E.coli, DM4 and Pseudomonas strains used do not present any risk to humans (ACDP hazard group 1), as indicated in the table above, and none of the sequences expressed are under the control of mammalian promoters in order to avoid their expression in human cells. Good microbiological practice including the wearing of gloves, labcoats and eye protection will be followed at all times.<br />
<br><br />
<br><br />
<br />
<h1>Risks to the safety and health of the general public (if any biological materials escaped from your lab) and risks to the environment:</h1><br />
We are not working with any pathogenic microorganisms or sequences that could introduce pathogenicity. The vectors we are using are designed for prokaryotes exclusively and none of the sequences expressed are under the control of mammalian promoters in order to avoid their expression in human cells.<br />
The above is also true regarding the risk to other animals and plants. The GMOs do not carry any additional risks for wild-type (unmodified) organisms.<br />
<br><br />
<br><br />
<br />
<h1>Risks to security through malicious mis-use by individuals, groups, or countries and measures we are taking to reduce this risk:</h1><br />
Our work will not introduce or increase any pathogenicity or virulence of any of the microorganisms we are working with. The safety system of the Department ensures that only members of the Department have access to its facilities and that the microorganisms and genetic parts we are using will not leave the laboratory by any means. <br />
We decided to work with microorganisms that belong to risk group 1 exclusively. We will be wearing gloves, safety glasses and lab coats throughout our entire lab work and will carry out sterile techniques in order to avoid any contamination. <br />
We have arranged safe containment of the microorganisms we are working with so that these cannot leave the laboratory.<br />
<br><br />
<br><br />
<br />
<h1>Features we have designed to minimise any risks:</h1><br />
Our bacteria are contained within <a href="https://2014.igem.org/Team:Oxford/biopolymer_containment#show2"><br />
<u>agarose beads</u></a><br />
which will control the concentration of DCM which they are exposed to. Eventually these agarose beads will begin to degrade naturally over time, exposing the bacteria to a higher DCM concentration of up to 200 millimolar. The bacteria will die when the concentration rises above the level they can tolerate, thus we have a kill switch inherently integrated into our design. In addition, the agarose beads will be trapped by a filter within the DCMation container in order to prevent them from being disposed down the sink. Instead of this happening, our product will include instructions explaining to users how the beads can be disposed of safely (for example by using virkon).<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/safetyTeam:Oxford/safety2014-10-18T00:17:22Z<p>CorinnaO: proofread</p>
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<h1>Introduction</h1><br />
<br><br />
The Policy and Practices element of our project has asked how iGEM projects can grow from small lab studies into a commercial/industrial/medical product used by many people. An essential part of this research has been thinking about the safety of synthetic biology and specifically of the bioremediation technology we have developed, were it to be in widespread use. <br />
<br><br><br />
The new risks associated with the growth of our project are broadly similar to those related to synthetic biology generally: the possibility of spread of antibiotic resistance from synthetic organisms into the natural environment, and the possible escape of synthetic organisms into the natural environment. Specific risks associated with our project include the risk to lab workers from working with chlorinated solvents (although this is relatively easy to manage). <br />
There may be potential for the abuse of the agarose bacteria-containing beads being developed by our team as this technology will allow bacteria to survive in hostile environments. However, we consider that the potential benefits from the application of our design far outweigh any possible risks. <br />
<br><br> <br />
Our bacteria are contained within agarose beads that will control the concentration of DCM which they are exposed to. Eventually these agarose beads will begin to degrade naturally over time, exposing the bacteria to a higher DCM concentration of up to 200 mM. The bacteria will die when the concentration rises above the level they can tolerate, thus we have a kill switch inherently integrated into our design. In addition, the agarose beads will be trapped by a filter within the DCMation container in order to prevent them from being disposed down the sink. Our product will include instructions explaining to users how the beads can be disposed of safely (for example by sterilising with boiling water before disposal).<br />
<br />
<br><br><br />
<br />
<h1>Bacterial strains</h1><br />
<br />
Our team has been working with the following host strains. The table below indicates the risk group the organism belongs to - none of the organisms we worked with confer any risk to human health. <br />
<br />
<img src="https://static.igem.org/mediawiki/parts/0/0b/Saftey_Image1.png" style="float:left;position:relative; width:60%; margin-right:20%;margin-left:20%;margin-bottom:2%;" /><br />
<br />
<br />
<br />
<br><br />
<br />
Oxford iGEM 2014<br />
<br><br />
<br><br />
<br />
<h1>Risks to the safety and health of team members, or other people working in the lab:</h1><br />
The E.coli, DM4 and Pseudomonas strains used do not present any risk to humans (ACDP hazard group 1), as indicated in the table above, and none of the sequences expressed are under the control of mammalian promoters in order to avoid their expression in human cells. Good microbiological practice including the wearing of gloves, labcoats and eye protection will be followed at all times.<br />
<br><br />
<br><br />
<br />
<h1>Risks to the safety and health of the general public (if any biological materials escaped from your lab) and risks to the environment:</h1><br />
We are not working with any pathogenic microorganisms or sequences that could introduce pathogenicity. The vectors we are using are designed for prokaryotes exclusively and none of the sequences expressed are under the control of mammalian promoters in order to avoid their expression in human cells.<br />
The above is also true regarding the risk to other animals and plants. The GMOs do not carry any additional risks for wild-type (unmodified) organisms.<br />
<br><br />
<br><br />
<br />
<h1>Risks to security through malicious mis-use by individuals, groups, or countries and measures we are taking to reduce this risk:</h1><br />
Our work will not introduce or increase any pathogenicity or virulence of any of the microorganisms we are working with. The safety system of the Department ensures that only members of the Department have access to its facilities and that the microorganisms and genetic parts we are using will not leave the laboratory by any means. <br />
We decided to work with microorganisms that belong to risk group 1 exclusively. We will be wearing gloves, safety glasses and lab coats throughout our entire lab work and will carry out sterile techniques in order to avoid any contamination. <br />
We have arranged safe containment of the microorganisms we are working with so that these cannot leave the laboratory.<br />
<br><br />
<br><br />
<br />
<h1>Features we have designed to minimise any risks:</h1><br />
Our bacteria are contained within <a href="https://2014.igem.org/Team:Oxford/biopolymer_containment#show2"><br />
<u>agarose beads</u></a><br />
which will control the concentration of DCM which they are exposed to. Eventually these agarose beads will begin to degrade naturally over time, exposing the bacteria to a higher DCM concentration of up to 200 millimolar. The bacteria will die when the concentration rises above the level they can tolerate, thus we have a kill switch inherently integrated into our design. In addition, the agarose beads will be trapped by a filter within the DCMation container in order to prevent them from being disposed down the sink. Instead of this happening, our product will include instructions explaining to users how the beads can be disposed of safely (for example by using virkon).<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/PartsTeam:Oxford/Parts2014-10-17T19:26:28Z<p>CorinnaO: proofread</p>
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Oxford iGEM has submitted three parts to the registry, which are described on the relevant registry pages. <br />
<br><br><br />
<a href="http://parts.igem.org/Part:BBa_K1446001"><br />
<u>Part no. 1 (BBa_K1446001)</u></a> is the gene coding for DcmA with an N-terminal pdu-microcompartment tag.<br />
<br><br><br />
<a href="http://parts.igem.org/Part:BBa_K1446002"><br />
<u>Part no. 2 (BBa_K1446002)</u></a> is a superfolder GFP-gene with the N-terminal pdu-microcompartment tag. We have included a ribosome binding site upstream of the coding region as well as a his-tag at the C-terminus. This part has been used for fluorescent image analysis to show that the targeting mechanism of the N-terminal microcompartment tag works as expected.<br />
<br><br><br />
<a href="http://parts.igem.org/Part:BBa_K1446003"><br />
<u>Part no. 3 (BBa_K1446003)</u></a> is the dcmR gene with a tetR promoter. We have used this part to get fluorescence data for our model described <a href="https://2014.igem.org/Team:Oxford/biosensor_characterisation#show2"><br />
<u>here</u></a> and showed that mCherry expression increased at higher ATC (anhydrous tetracycline) concentration, as expected. <br />
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<br><br<br />
Parts no. 1 and 2 belong to our bioremediation subproject and part 3 to our biosensor subproject.<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/safetyTeam:Oxford/safety2014-10-17T19:25:54Z<p>CorinnaO: proofread</p>
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<h1>Introduction</h1><br />
<br><br />
The Policy and Practices element of our project has asked how iGEM projects can grow from small lab studies into a commercial/industrial/medical product used by many people. An essential part of this research has been thinking about the safety of synthetic biology and specifically of the bioremediation technology we have developed, were it to be in widespread use. <br />
<br><br><br />
The new risks associated with the growth of our project are broadly similar to those related to synthetic biology generally: the possibility of spread of antibiotic resistance from synthetic organisms into the natural environment, and the possible escape of synthetic organisms into the natural environment. Specific risks associated with out project include the risk to lab workers from working with chlorinated solvents (although this is relatively easy to manage). <br />
There may be potential for the abuse of the BioBeads being developed by our team as this technology will allow bacteria to survive in hostile environments. However, we consider that the potential benefits from the application of our design far outweigh any possible risks. <br />
<br><br> <br />
Our bacteria are contained within agarose beads that will control the concentration of DCM which they are exposed to. Eventually these agarose beads will begin to degrade naturally over time, exposing the bacteria to a higher DCM concentration of up to 200 mM. The bacteria will die when the concentration rises above the level they can tolerate, thus we have a kill switch inherently integrated into our design. In addition, the agarose beads will be trapped by a filter within the DCMation container in order to prevent them from being disposed down the sink. Our product will include instructions explaining to users how the beads can be disposed of safely (for example by sterilising with boiling water before disposal).<br />
<br />
<br><br><br />
<br />
<h1>Bacterial strains</h1><br />
<br />
Our team has been working with the following host strains. The table below indicates the risk group the organism belongs to - none of the organisms we worked with confer any risk to human health. <br />
<br />
<img src="https://static.igem.org/mediawiki/parts/0/0b/Saftey_Image1.png" style="float:left;position:relative; width:60%; margin-right:20%;margin-left:20%;margin-bottom:2%;" /><br />
<br />
<br />
<br />
<br><br />
<br />
Oxford iGEM 2014<br />
<br><br />
<br><br />
<br />
<h1>Risks to the safety and health of team members, or other people working in the lab:</h1><br />
<br />
The E.coli, DM4 and Pseudomonas strains used do not present any risk to humans (ACDP hazard group 1), as indicated in the table above, and none of the sequences expressed are under the control of mammalian promoters in order to avoid their expression in human cells. Good microbiological practice including the wearing of gloves, labcoats and eye protection will be followed at all times.<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/InterlabResultsTeam:Oxford/InterlabResults2014-10-17T19:25:15Z<p>CorinnaO: </p>
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<center style="font-size:20px;font-weight:600;">View our interlab results pdf <a href="https://static.igem.org/mediawiki/2014/8/86/Oxigem_Interlab_results.pdf" target="_blank">here!</a></center><br />
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We have given each measurement here in arbitrary units - as provided by our laboratory's plate-reader and spectrophotometer.<br><br>Adjusted fluorescence is calculated as the mean fluo of each biological replicate minus the mean fluo for the DH5-a, giving an approximation for the fluorescence due to the devices only.<br><br>Cell counts were estimated based the OD, using the online calculator given by the link: www.genomics.agilent.com%2fbiocalculators%2fcalcODBacterial.jsp%3f_requestid%3d826255 <br><br> Results were given in triplicate with respect to biological replicates (meaning three separate colonies were taken from the same plates); and duplicate with respect to the technical replicates (two 200ul samples were taken from each culture to be read).<br><br>Although it is clear from visual inspection that there are significant differences in fluorescence depending on device, the fluorescence of each sample can be modelled as the following function:<br><br><br>FLUO = DEVICE + AUTO(CELLS) + AUTO(MEDIA) + ERROR(BIOLOGICAL) + ERROR(TECHNICAL).</div><br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/InterlabMeasurementTeam:Oxford/InterlabMeasurement2014-10-17T19:24:13Z<p>CorinnaO: proofread</p>
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<b>Time-course Protocol</b><br><br />
To understand the growth of the transformed DH5-a, we measured the Optical Density (OD), and corresponding Fluorescence (Fluo) for each device over the course of 40 cycles. This series of readings took over 13 hours, fully capturing the entire growth curve of the bacteria.<br />
3 cultures for each device were set-up overnight, taken from separate single colonies on the agar plate (biological replicates), as well as 3 cultures of non-transformed DH5-a (negative control). Of these 12 cultures, 3 repeated samples were taken to be read in our laboratory’s plate-reader. <br />
We used an Infinite m2000 plate reader, with a Greiner 96 Flatback plate across all protocol.<br />
The resulting growth curves showed no peculiarities in either measurement, leading us to then decide to take single-readings of each culture at, or close to, the end of the log-phase. <br><br><br />
<br />
<b>Single readings</b><br><br />
As before, 12 cultures were set up overnight. In the morning, 10ul were transferred from the overnight cultures into 5ml of LB broth, with appropriate amounts of antibiotic dependent on the device being used. These cultures were incubated and shaken at 37°C until their OD reached, or was close to, 0.6. This took approximately 3 hours.<br />
After this time, 1ml of each culture was centrifuged for 2 minutes, pipetting out all the supernatant. To re-suspend the pellet, we used 1X M9 salts so as to minimise autofluorescence contributing to our final readings. <br />
The amount of minimal media used for each culture was different dependent on the respective ODs. This was to standardise the amount of cells being used in the plate-reader. The specific volume of M9 salts was determined by (OD/0.6)ml. <br />
For each culture, 200ul were pipetted into the Greiner plate. This was repeated twice, giving two technical replicates and 24 wells occupied with samples. In addition, two wells were filled with M9 salts, as negative controls. Leaving the cover off, the plate was read once using the following settings:<br />
Excitation 485nm, Emission 510nm<br />
Shaking before – 30 seconds<br />
Gain 2000<br><br><br />
<br />
This protocol was repeated the following day, using new individual colonies from the original agar plates. <br><br><br />
<br />
We also took epifluorescent images of each cultures after the plate-readings, to visually inspect the variation in fluorescence per cell, and confirm that what we saw matched the data we were given by the plate-reader. For this we used a Nikon TE-200 microscope with GFP Filter set (Chroma), Hamamatsi EMCCD Camera and simple PCI software.<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/InterlabMeasurementTeam:Oxford/InterlabMeasurement2014-10-17T19:23:55Z<p>CorinnaO: </p>
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<br />
<b>Time-course Protocol</b><br><br />
To understand the growth of the transformed DH5-a, we measured the Optical Density (OD), and corresponding Fluorescence (Fluo) for each device over the course of 40 cycles. This series of readings took over 13 hours, fully capturing the entire growth curve of the bacteria.<br />
3 cultures for each device were set-up overnight, taken from separate single colonies on the agar plate (biological replicates), as well as 3 cultures of non-transformed DH5-a (negative control). Of these 12 cultures, 3 repeated samples were taken to be read in our laboratory’s plate-reader. <br />
We used an Infinite m2000 plate reader, with a Greiner 96 Flatback plate across all protocol.<br />
The resulting growth curves showed no peculiarities in either measurement, leading us to then decide to take single-readings of each culture at, or close to, the end of the log-phase. <br><br><br />
<br />
<b>Single-readings</b><br><br />
As before, 12 cultures were set up overnight. In the morning, 10ul were transferred from the overnight cultures into 5ml of LB broth, with appropriate amounts of antibiotic dependent on the device being used. These cultures were incubated and shaken at 37°C until their OD reached, or was close to, 0.6. This took approximately 3 hours.<br />
After this time, 1ml of each culture was centrifuged for 2 minutes, pipetting out all the supernatant. To re-suspend the pellet, we used 1X M9 salts so as to minimise autofluorescence contributing to our final readings. <br />
The amount of minimal media used for each culture was different dependent on the respective ODs. This was to standardise the amount of cells being used in the plate-reader. The specific volume of M9 salts was determined by (OD/0.6)ml. <br />
For each culture, 200ul were pipetted into the Greiner plate. This was repeated twice, giving two technical replicates and 24 wells occupied with samples. In addition, two wells were filled with M9 salts, as negative controls. Leaving the cover off, the plate was read once using the following settings:<br />
Excitation 485nm, Emission 510nm<br />
Shaking before – 30 seconds<br />
Gain 2000<br><br><br />
<br />
This protocol was repeated the following day, using new individual colonies from the original agar plates. <br><br><br />
<br />
We also took epifluorescent images of each cultures after the plate-readings, to visually inspect the variation in fluorescence per cell, and confirm that what we saw matched the data we were given by the plate-reader. For this we used a Nikon TE-200 microscope with GFP Filter set (Chroma), Hamamatsi EMCCD Camera and simple PCI software.<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/InterlabMeasurementTeam:Oxford/InterlabMeasurement2014-10-17T19:23:28Z<p>CorinnaO: proofread</p>
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<br><br />
<br />
<b>Time-course Protocol</b><br />
To understand the growth of the transformed DH5-a, we measured the Optical Density (OD), and corresponding Fluorescence (Fluo) for each device over the course of 40 cycles. This series of readings took over 13 hours, fully capturing the entire growth curve of the bacteria.<br />
3 cultures for each device were set-up overnight, taken from separate single colonies on the agar plate (biological replicates), as well as 3 cultures of non-transformed DH5-a (negative control). Of these 12 cultures, 3 repeated samples were taken to be read in our laboratory’s plate-reader. <br />
We used an Infinite m2000 plate reader, with a Greiner 96 Flatback plate across all protocol.<br />
The resulting growth curves showed no peculiarities in either measurement, leading us to then decide to take single-readings of each culture at, or close to, the end of the log-phase. <br><br><br><br />
<br />
<b>Single-readings</b><br />
As before, 12 cultures were set up overnight. In the morning, 10ul were transferred from the overnight cultures into 5ml of LB broth, with appropriate amounts of antibiotic dependent on the device being used. These cultures were incubated and shaken at 37°C until their OD reached, or was close to, 0.6. This took approximately 3 hours.<br />
After this time, 1ml of each culture was centrifuged for 2 minutes, pipetting out all the supernatant. To re-suspend the pellet, we used 1X M9 salts so as to minimise autofluorescence contributing to our final readings. <br />
The amount of minimal media used for each culture was different dependent on the respective ODs. This was to standardise the amount of cells being used in the plate-reader. The specific volume of M9 salts was determined by (OD/0.6)ml. <br />
For each culture, 200ul were pipetted into the Greiner plate. This was repeated twice, giving two technical replicates and 24 wells occupied with samples. In addition, two wells were filled with M9 salts, as negative controls. Leaving the cover off, the plate was read once using the following settings:<br />
Excitation 485nm, Emission 510nm<br />
Shaking before – 30 seconds<br />
Gain 2000<br><br><br />
<br />
This protocol was repeated the following day, using new individual colonies from the original agar plates. <br><br><br />
<br />
We also took epifluorescent images of each cultures after the plate-readings, to visually inspect the variation in fluorescence per cell, and confirm that what we saw matched the data we were given by the plate-reader. For this we used a Nikon TE-200 microscope with GFP Filter set (Chroma), Hamamatsi EMCCD Camera and simple PCI software.<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/InterlabDevicesTeam:Oxford/InterlabDevices2014-10-17T19:20:58Z<p>CorinnaO: proofread</p>
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<p><font style="font-weight:bold">I. Using the DNA distribution kit to extract devices 1-3:</font></p><br />
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[[File:Devices.png|centre]]<html><BR><br />
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We filled the 4 wells we needed with 10 uL of dH2O and let it sit for about 10 minutes to resuspend. We transformed the resuspended DNA into chemically competent <i>E.coli cells</i> (DH5) and plated them on an agar plate with kanamycin (plate for device 1) and chloramphenicol (plates for devices 2a, 2b/3b, and 3a) using the transformation into chemically competent E. coli cells protocol (however, we used 200 uL of competent cells per transformation rather than 100uL). <br />
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<p><font style="font-weight:bold">II. Liquid Cultures:</font></p><br />
We collected our plates from the 37°C incubator the following day. All plates had colonies growing on them, so we took one from each for generating liquid cultures.<br />
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<p><font style="font-weight:bold">III. MiniPrep Sequencing:</font></p><br />
Having let the cultures grow up overnight, we took samples from each culture and used the Miniprep kits to extract the plasmids. Using the Nanodrop to measure the DNA concentration we prepared them for sequencing by SourceBioscience. The 5uL sequencing samples were frozen overnight and then sent for sequencing the next day. The remaining 45uL were stored in the -20⁰c freezer.<br />
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<p><font style="font-weight:bold">IV. Sequencing Results:</font></p><br />
<p> We received sequencing results that confirmed the sequences of the parts we were using from the DNA-distribution kit, including the two point-mutations in part 3A. Shown below are the sequence alignments of the sequence we expected (top strand) and the sequencing results (bottom) of parts 2A (left) and 3A (right):</p><br />
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[[File:Sequence_Alignment_BBa_J23101.png|left|450px]][[File:Sequence_Alignment_BBa_J23115.png|right|450px]]<br />
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<p><font style="font-weight:bold">V. Restriction Digest:</font></p><br />
The first device is already built, but devices 2 and 3 have to be built. We decided to do a restriction digest of the plasmids. We opened the plasmid with part A (promoter) using SpeI and PstI and excised the B part of its vector using XbaI and PstI, which would facilitate the ligation of XbaI-site of the B-part to the SpeI site of the promoter region. The PstI sites of both parts A and B of course would ligate together. By retaining the promoter region within the backbone we allowed for easier detection of the larger region in DNA gel electrophoresis.<br />
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<p><font style="font-weight:bold">VI. Ligation:</font></p><br />
After running the gel, and clearly identifying the fragments of DNA we required, these were extracted and the appropriate combinations (2A and 2/3B; 3A and 2/3B) were ligated. These two solutions were placed under PCR and then left overnight. Measurements with the Nanodrop displayed confident concentrations of DNA (all >25ng/uL), as to be expected.<br><br><br />
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<p><font style="font-weight:bold">VII. Transformation and Liquid Cultures - 2:</font></p><br />
We transformed this DNA the following day into chemically competent <i>E. coli</i> cells, as in Step I. We also made 1/1000 dilutions of each plasmid (2 and 3), and spread onto plates, giving a total of four plates. The agar in this instance, in comparison to Step I, only contained chloramphenicol and not kanamycin. These revealed very positive growth after overnight incubation at 37°C, and we took the two largest colonies from each plate from which to make liquid cultures, as described in Step II. One of the colonies from the 2-dilution plate failed to continue to grow in liquid culture however.<br><br><br />
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<p><font style="font-weight:bold">VIII. MiniPrep and Sequencing - 2:</font></p><br />
As before, the plasmids were extracted following the MiniPrep protocol, from each of the 7 liquid cultures. Samples were tasted in the NanoDrop (concentrations ranged between 80-170 ng/uL). 5uL samples were prepared and sent off for sequencing, to ensure that our plasmids were correct in compositions.<br><br><br><br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/P%26P_intellectual_propertyTeam:Oxford/P&P intellectual property2014-10-17T19:08:19Z<p>CorinnaO: </p>
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="position:absolute; width:100%;min-width:100%;z-index:-1; border-radius:15px;"/><br />
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<div style="background-color:#D9D9D9; opacity:0.7; z-index:5; margin-right:auto;margin-left:auto; Height:75px; width:100%;min-width:300px;font-size:65px;font-family:Helvetica;padding-top:15px; font-weight: 450;"><br />
<div style="background-color:white; opacity:0.7; Height:75px; width:100%;margin-top:15px:margin-bottom:5px;min-width:300px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450;"><center><br>Intellectual Property</center></div><br />
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<img src="https://static.igem.org/mediawiki/2014/5/5c/OxiGEM_IP.jpg" style="float:right;position:relative; width:30%;" /> <br />
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<br />
Intellectual Property is an increasingly important and controversial aspect of scientific advance, and synthetic biology is perhaps the paradigmatic area illustrating the effects of growing legal influence. When thinking about how teams could turn their ideas from iGEM projects into viable real-world solutions, we realized that intellectual property is a crucial area to address.<br />
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Our team has produced a report exploring how teams can approach this task and how iGEM intellectual property policy can make the transition easier.<br />
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We begin with a brief overview of current intellectual property law (specifically relevant to the UK) before progressing to look at the challenges this poses for the iGEM competition. A number of different approaches which iGEM might choose to adopt towards intellectual property are discussed and the pros and cons of each are assessed. We then asked a range of interested groups, including iGEM students, professionals, and the public, for their views before concluding with recommendations for addressing intellectual property concerns in iGEM. We offer our conclusions in the form of advice to students, to the iGEM foundation, and briefly explore how a change in the law could have consequences for iGEM. This advice is purely based on our own views and our research which we hope will make interesting food for thought - it is not professional legal advice and should not be relied on as such!<br />
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<a href="https://static.igem.org/mediawiki/2014/8/86/IP_REPORTWEBVER.pdf"><br />
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Our policy research in relation to students, the iGEM foundation, and policy makers is summarized below, or download the full report to learn more about our work.<br />
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<h1blue2>Team Policy</h1blue2><br />
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Dealing with intellectual property is not only necessary on the level of the competition - each team must also make decisions as to how they wish to deal with the intellectual property they will acquire during the course of their project. Deciding whether to file a patent application can be a tricky decision - below are just some of the factors you might want to take into consideration…<br />
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<img src="https://static.igem.org/mediawiki/2014/8/84/OxigemIP_Flowchart2.jpg" style="float:left;position:relative; width:100%;margin-bottom:5%;" /><br />
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Oxford iGEM 2014<br />
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<h1blue2>Attitudes Survey</h1blue2><br />
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We conducted a survey of attitudes within iGEM teams to intellectual property. The results, illustrated below, are analysed in detail in our report. Broadly, we found a noticeable lack of understanding of IP issues (confirming the findings of University of British Columbia iGEM team 2013 - check out their great work on IP which we used as a starting point for our own research at https://2012.igem.org/Team:British_Columbia/Human_Practices/IP_FAQ), and a great deal of social-mindedness in the responses.<br />
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For a complete analysis of our results, take a look at our Intellectual Property Report...<br />
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The majority of teams (85%) appear to feel that there is at least the possibility that their project could be turned into a viable business or project – this makes sense given that many teams seek to use their project as an opportunity to use synthetic biology to address a problem. <br />
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Again, this chart shows how few teams believe their project is relevant to society. There was a split, slightly skewed towards commercial investment, in terms of the favoured means of funding, although it should be noted that many participants selected more than one option, suggesting mixed feelings and uncertainty on this question. Donation to the public domain was a popular option, indicating that many students support the BioBrick agreement and are keen to contribute their parts to it. <br />
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Responses to this question support the need for guidance for iGEM teams on IP issues. Possible explanations for this the larger than expected (29%) group selecting 'adequate' include: evidence of a Drunning-Kruger effect whereby people overestimate their level of knowledge; underestimating the significance and relevance of intellectual property law to synthetic biology; or a genuinely adequate understanding amongst students. <br />
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<div class="issuedate">Sangamo patents zinc finger nuclease technology</div><br />
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<p>Sangamo's patent, titled "Nucleic acid binding proteins (zinc finger proteins design rules)", ensures that any use or production of zinc fingers with attached nucleases is the intellectual property of Sangamo.</p><br />
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The graph indicates that profit is the least important considerations to students, but all others ranked more or less equally overall. The most important factor overall was benefit to society.<br />
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Oxford iGEM 2014<br />
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<h1blue2>Young Synthetic Biologists Intellectual Property Workshop</h1blue2><br />
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In the process of putting together our report and our guidance for iGEM teams, our team, in collaboration with the event organisers Philipp and Bethan of UCL iGEM, ran an intellectual property workshop at the Young Synthetic Biologists Conference 2.0 for other iGEMmers interested in the issue. The event was a great success; teams attending said 'I hadn't really appreciated just how big an impact this area of law could have on scientific development and particularly on synthetic biology - it's given me a lot to think about!'. We had a great time and there were some really interesting and insightful debates and comments - thanks especially to iGEM Cambridge for their passion on this topic!<br />
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Another team member stated, 'I don't think our team had really put much thought into the IP ownership of our project - it's definitely something we should consider though, as we would want to be able to stop anyone using our project for potentially harmful research'. 'I always thought IP was kind of a binary question - you either patent something, or you give it away. But there are so many options in between. It's a very nuanced area of law, I think that's the main thing I hadn't really appreciated, there are lots of different ways you can share or protect your ideas'.<br />
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<h1blue2>View the Oxford iGEM powerpoint presentation used as the basis for our workshop below!</h1blue2><br />
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Special thanks for the success of this event are owed to:<br />
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<br>Bethan Wolfenden and Philipp Boeing for organising and iGEM UCL for hosting the YSB event<br />
<br>Andrew Russell, Glen Gowers, and Philipp Lorenz<br />
<br>Dundee, Cambridge, Edinburgh, and all the other teams which attended and had such enthusiasm for the debate!<br />
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<h1blue2> iGEM policy </h1blue2><br />
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Given its ever increasing prominence in the field of synthetic biology and its unavoidable influence on the future of the field, iGEM must cannot remain neutral on the matter of intellectual property. In the words of iGEM start-up Morph Bioinformatics, <h1blue3>“iGEM must position itself and not only define its role in the world of biotech - but also how it sees the role of synbio”.</h1blue3> <br />
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iGEM’s approach to intellectual property will be intrinsically related to its overall position and self-definition in this exciting emerging field. <h1blue3>“It is time now to rationalize this 'big picture' and find a business model by treating the public, academia, biotech, pharma etc. as different units to optimize and synergize their outputs. And as with all business and communities the major factor of success is to follow one ultimate vision - and that should remain increasing the quality of life”.</h1blue3> <br />
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iGEM’s approach to intellectual property being critical for the future of the competition, its teams, and even the future of synthetic biology, our report analyses three alternative approaches to iGEM in dealing with this legal issue. <br />
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The benefits and disadvantages of each are summarised in the tables below.: maintaining the status quo; complete openness; confidentiality clauses; reach through licence agreements.<br />
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<h1blue2> Government Policy </h1blue2><br />
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Legislators have the difficult task of balancing a number of diverse and often conflicting intellectual property consideration. On the one hand, the government must incentivize innotvation - IP is an essential means of achieving this, as demonstrated by studies showing how patents can positively influence innovation by a margin of 15-25%<font style="vertical-align: super; font-size: 70%;">2</font> . <br />
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The flipside of this is the responsibility of the government to prevent the creation of monopoly and to ensure that ideas are shared so as to maximize productive research. Again, there is research indicating that intellectual property is crucial to maintaining this balance, as some studies have expressed concerns that patents on initial discoveries may 'delay, hamper, or deter' innovations building on the patented work. The transaction cost of working with patented material is unattractive to many researchers, particularly individuals and start-ups.<font style="vertical-align: super; font-size: 70%;">3</font>.<br />
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Based on our research and our experiences during iGEM and in the field of IP, we believe that one of the most important roles for the government is to lead a new, more imaginative line of thinking about intellectual property protection, and to move away from analyzing these issues within the traditional and deeply engrained innovation v access dichotomy. <br />
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Creating legal mechanisms to support this kind of innovative and flexible thinking about IP will be increasingly important to synthetic biology and to iGEM as the field grows increasingly complex and the dynamics between the many different interested parties continue to evolve. In order to successfully balance the demands of the public interest, investors, the environment, researchers, and inventors we will need to be more open minded when considering how to deal with IP in the future. It will not suffice to simply ask whether 'to patent or not to patent' and suppose that this is the extent of the available options. <br />
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A further issue which we believe needs addressing by a change in the law is the current incapacity of the law to provide protection for computer code and algorithms. This is an issue which extends far beyond iGEM. Counter-intuitively, the lack of protection for algorithms means that this information can justifiably be kept secret rather than being visible and accessible to the public and/or regulators. The danger of this situation was demonstrated only recently by Facebook's so called 'social experiment' during which the company controlled the newsfeed content of users in an attempt to manipulate their emotions. Jim Sheridan, a member of the Commons Media Select Committee, expressed his 'worries about the ability of Facebook and others to manipulate people's thoughts in politics or other areas', and stressed the need for legislation in this area. <br />
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Similarly, some form of protection for computer algorithms might allow models relevant to the iGEM competition (and to synthetic biology more broadly) to be shared in a similar way to BioBricks. Engineer Leroy Lim, responsible for some of the modeling aspect of the project commented that it would have been highly useful to have models from previous years available at the beginning of the project. 'People would be far more likely to share their code and collaborate on this if we thought we'd get credit for our work...with companies it's even worse, there's no option but to keep your code to yourself because there's nothing else stopping competitors from taking everything you've developed and taking away your business'.<br />
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<h1blue2> References </h1blue2><br />
<br><br>For full references see our Intellectual Property Report (downloadable above). <br />
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</html></div>CorinnaOhttp://2014.igem.org/Team:Oxford/biosensor_optimisationTeam:Oxford/biosensor optimisation2014-10-17T19:02:16Z<p>CorinnaO: proofread</p>
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<div style="width:100%;"><font style="font-size:15px;font-weight:500;">Show all:</font></div><br />
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<h2>Optimizing the biosensor design</h2><br />
An ideal biosensor would fulfil the following performance criteria:<br><br><br />
• <strong>Fast response</strong> to the presence/absence of DCM.<br><br />
• <strong>High amplitude of output signal</strong> – it must produce enough GFP to generate a distinct signal against background noise.<br><br />
• <strong>Sensitive</strong> - it must change significantly in low concentrations of DCM. This is vital in order to achieve a response that is as close to binary as possible. The ideal system will have a very sharp decline in fluorescence at a predefined, very low value of DCM. This will ensure that the sensor will clearly indicate when the DCM mixture can be safely disposed of. <br><br><br />
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• <strong>Robust</strong> - it must be able to cope with variations in ATC concentration without radically altering the behaviour of the system. This is crucial because we cannot ensure that ATC concentrations throughout all the cells will be uniform in the real system. <br><br><br />
By modelling the effects of parameters we are able to alter in the biological system, we were able to guide our design process to produce a biosensor that is as close to the ideal as possible without sacrificing any one criterion entirely.<br />
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<h1white>What can we alter in our biological system?</h1white><br />
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Our biosensor will not be able to meet all ideal criteria because <strong>1) We are limited by biology as to which parameters we can actually change</strong> and <strong>2) changing a parameter in a cellular system impacts more than one parameter. </strong><br><br />
However there are some things we can alter:<br><br><br />
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• <strong>The rate of GFP degradation</strong> - the cell will degrade GFP, but marking the protein with a degradation tag would increase the rate that this occurs.<br><br />
• <strong>The amount of GFP produced per mRNA transcribed</strong> – by altering the strength of the ribosome binding site we can alter the efficiency of translation.<br><br><br />
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By modelling the effects of these we can answer the following questions:<br><br><br />
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• <strong>Do we need to include a degradation tag on GFP, or is the turnover of GFP already adequate to give a fast 'off' rate?</strong><br><br />
• <strong>What RBS strength should we use to maximise output amplitude or reach a usable signal output?</strong><br><br />
• <strong>Will altering one of these to optimise one criterion negatively impact any other of our criteria?</strong><br><br />
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<h1white>What happens when we change the amount of each input added?</h1white><br />
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<h1white>What happens when we change the amount of each input added?</h1white></div></a><br />
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<h1>What are these graphs and where did they come from?</h1><br />
Using the bacterial fluorescence models we have built, we predicted the steady-state fluorescence levels of the system in varying levels of DCM and ATC by solving the system of differential equations we produced during the characterization section. The results are illustrated in the 3-dimensional surface plot below. <br />
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The two 2-dimensional graphs are slices taken from the 3-D plot. In each of these 'slices' we are effectively holding one variable constant (the amount of either DCM or ATC) while varying the other. <br />
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The 3-dimensional plot was produced by plotting the final fluorescence value from many different possible combinations of the two inputs (ATC and DCM). The top graph shows the variation in final fluorescence when DCM is held constant and ATC is varied, the second graph is vice versa.<br />
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It is important to understand that these graphs represent the expected steady state level of fluorescence of thousands of different simulations. From this we can select the DCM and ATC concentrations for a specific fluorescence response.<br />
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<h1>How much of each input should we use to test the biosensor?</h1><br />
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Our ideal biosensor must be robust. The top graph demonstrates this nicely. Beyond a certain threshold value of ATC, there is little change in the fluorescence response predicted - it saturates and maintains a constant level. Practically, this means we have to ensure that the ATC concentrations present in our final system must comfortably exceed this threshold ATC value.<br><br><br />
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From our initial system characterization, we have established that when DCM is not present in the system, there will be no fluorescence response aside from that due to the basal transcription rate. However, the model predicts that when even a small amount of DCM is added and the transient behaviour has stabilized, the fluorescence expressed in the system quickly reaches its saturation value. This corresponds to a highly sensitive biosensor which can effectively only express two fluorescence levels- zero or a predefined maximum. The transition from zero to the maximum saturation value occurs at very low concentrations of DCM. <br><br><br />
To summarise, we have established that the inputs to our biosensor should be a constant medium concentration of ATC and a varying concentration of DCM as it is degraded. We should note that the ATC concentration will not value without external influence because the system does not consume ATC and its rate of degradation is negligible. <br />
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<h1white>Modelling the biosensor to optimise ’ON’ and ‘OFF’ response</h1white><br />
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<h1white>Modelling the biosensor to optimise ’ON’ and ‘OFF’ response</h1white></div></a><br />
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As described above, the ideal biosensor is binary and its fluorescence response can only take two values. This relies on the system having two features- a fast response time to concentration changes and a large amplitude of response. Having previously established the ideal concentrations of DCM and ATC <u>(see above)</u> for the biosensor, our next task was to predict what combination of controllable variables would result in the ideal binary behaviour. This is a very important step in synthetic biology because it allows us to crudely optimise the design before construction even begins. To test the response of our biosensor, we used a step function of DCM the initial and sudden contact of DCM with our bacteria and then removing DCM through <u>spinning the cells(?)</u>. In the real system, the DCM input would be a step in the curve and then a gradual negative ramp as the DCM was degraded.<br />
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The two parameters that are most easily changed in the initial production of the bacteria are the RBS strength and the degradation rate. <br />
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Increasing the Ribosome Binding Site (RBS) strength can greatly increase the translation initiation rate, hence resulting in amplified fluorescence. <u>(HOW?) (CORRECT + DETAIL?)</u><br />
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The degradation rate of the fluorescent protein can also be changed by adding degradation tags. <u>(CORRECT + DETAIL?)</u><br />
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<h1white>Should we aim for high or low RBS strength?</h1white><br />
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<h1white>Should we aim for high or low RBS strength?</h1white></div></a><br />
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<img src="https://static.igem.org/mediawiki/2014/e/e8/Oxford_change_RBS_strength.png" style="margin-left:0%; float:right; margin-right:0%; position:relative; width:65%;" /><br />
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We ran the deterministic model whilst varying the activation rate (see <a href="https://static.igem.org/mediawiki/2014/b/be/Oxford_Equations_explained.png" target="_blank">'where did these equations come from?'</a>) of the sfGFP. The response is shown here:<br />
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<h1>What does this tell us?</h1><br />
As you can see from this graph, increasing the RBS strength only changes the amplitude of the systems response without affecting the response time of the system. This is highly beneficial for the system.<br />
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-->Therefore we will aim for as high an RBS strength as possible in our initial design.<br />
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<h1white>Should we aim for high or low degradation rate?</h1white><br />
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<h1white>Should we aim for high or low degradation rate?</h1white></div></a><br />
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<img src="https://static.igem.org/mediawiki/2014/6/67/Oxford_change_degradation_rate.png" style="margin-left:0%; float:right; margin-right:0%; position:relative; width:65%;" /><br />
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We ran the deterministic model whilst varying the degradation rate (see <a href="https://static.igem.org/mediawiki/2014/b/be/Oxford_Equations_explained.png" target="_blank">'where did these equations come from?'</a>) of the sfGFP. The response is shown here:<br />
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<h1>What does this tell us?</h1><br />
Changing the degradation rate of the protein is more of a trade-off. As you can see, a higher degradation rate gives a faster response but with a much lower steady state responses.<br />
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-->We should aim for a low degradation rate to begin with so that we can ensure a detectable level of fluorescence, and then gradually increase the degradation rate to get a faster response.<br />
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<h1white>How did this inform our design?</h1white><br />
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<h1white>How did this inform our design?</h1white></div></a><br />
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Based on the modelling, we could optimise each performance characteristic individually, but to create the best overall biosensor we needed to compromise with what we chose to implement:<br><br><br />
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<h1>RBS strength</h1> <br />
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<strong>Medium RBS strength</strong> – our modelling suggested we should use as high an RBS strength as possible. We have used a relatively high strength RBS to try and optimise our signal amplitude without stressing cellular metabolism too much.<br><br><br />
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<h1>GFP degradation</h1> <br />
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<strong>No degradation tag</strong> - in this instance the model showed that increasing degradation efficiency of GFP (and thus the speed of response) by utilising a degradation tag would also decrease the signal amplitude. In our first attempt at making a biosensor, we decided it was more important to increase the chance of generating a usable signal than to have a fast off rate. In the future, once our biosensor is made and if we have found it to have very high amplitude, we could add a degradation tag to improve the on/off dynamics at the expense of that excessive signal.<br />
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<h1>Modelling Summary</h1><br />
The above results demonstrate well the power of modelling genetic circuits. This approach has allowed us to develop our first construct intelligently and to have some trustworthy predictions on which to develop the rest of our system around. However, as always, there are limitations, especially in biological systems.<br />
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In an ideal world, we would like to have a very high expression rate (for a high steady state amplitude of fluorescence), a high degradation rate (for a fast responding biosensor) and a high copy number of the plasmid in each cell. Conversely though, optimising these parameters puts metabolic stress on the cells. This leads to the system not actually being as optimal as the model might have predicted. Here we identify the weakness in preliminary models. We will have to actually develop the bacteria and run the experiments in the lab before we will know if our biosensor will respond this well to the DCM. After this, we will work at creating secondary models which should be able to give more reliable predictions. Ideally we would be able to then make more bacteria and the Engineering-Biochemistry cycle would continue.<br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_realisation"><img src="https://static.igem.org/mediawiki/2014/4/4c/Oxford_Realisation.png" style="float:right;position:relative; width:23%;" /></a><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_construction"><img src="https://static.igem.org/mediawiki/2014/a/ae/Oxford_construction.png" style="float:left;position:relative; width:23%; margin-left: 2.66%" /></a><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_optimisation"><img src="https://static.igem.org/mediawiki/2014/6/6a/Oxford_Optimisation_dark.png" style="float:right;position:relative; width:23%; margin-right: 2.66%" /></a><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor"><img src="https://static.igem.org/mediawiki/2014/a/a7/Oxford_biosensor_link.png" style="float:left;position:relative;width:50%; margin-top:2%;margin-left:25%;margin-right:25%;" /></a><br />
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<a href="https://2014.igem.org/Team:Oxford/Modelling"><img src="https://static.igem.org/mediawiki/2014/6/6e/Oxford_modelling_homepage_link.png" style="float:left;position:relative; width:50%; margin-top:2%;margin-left:25%;margin-right:25%;" /></a><br />
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Oxford iGEM 2014<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/biosensor_optimisationTeam:Oxford/biosensor optimisation2014-10-17T18:58:52Z<p>CorinnaO: </p>
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<h2>Optimizing the biosensor design</h2><br />
An ideal biosensor would fulfil the following performance criteria:<br><br><br />
• <strong>Fast response</strong> to the presence/absence of DCM.<br><br />
• <strong>High amplitude of output signal</strong> – it must produce enough GFP to generate a distinct signal against background noise.<br><br />
• <strong>Sensitive</strong> - it must change significantly in low concentrations of DCM. This is vital in order to achieve a response that is as close to binary as possible. The ideal system will have a very sharp decline in fluorescence at a predefined, very low value of DCM. This will ensure that the sensor will clearly indicate when the DCM mixture can be safely disposed of. <br><br><br />
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• <strong>Robust</strong> - it must be able to cope with variations in ATC concentration without radically altering the behaviour of the system. This is crucial because we cannot ensure that ATC concentrations throughout all the cells will be uniform in the real system. <br><br><br />
By modelling the effects of parameters we are able to alter in the biological system, we were able to guide our design process to produce a biosensor that is as close to the ideal as possible without sacrificing any one criterion entirely.<br />
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<h1white>What can we alter in our biological system?</h1white><br />
<img src="https://static.igem.org/mediawiki/2014/4/4d/Oxford_plus-sign-clip-art.png" style="float:right;position:relative; width:2%;" /><br />
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<h1white>What can we alter in our biological system?</h1white></div></a><br />
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Our biosensor will not be able to meet all ideal criteria because <strong>1) We are limited by biology as to which parameters we can actually change</strong> and <strong>2) changing a parameter in a cellular system impacts more than one parameter. </strong><br><br />
However there are some things we can alter:<br><br><br />
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• <strong>The rate of GFP degradation</strong> - the cell will degrade GFP, but marking the protein with a degradation tag would increase the rate that this occurs.<br><br />
• <strong>The amount of GFP produced per mRNA transcribed</strong> – by altering the strength of the ribosome binding site we can alter the efficiency of translation.<br><br><br />
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By modelling the effects of these we can answer the following questions:<br><br><br />
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• <strong>Do we need to include a degradation tag on GFP, or is the turnover of GFP already adequate to give a fast 'off' rate?</strong><br><br />
• <strong>What RBS strength should we use to maximise output amplitude or reach a usable signal output?</strong><br><br />
• <strong>Will altering one of these to optimise one criterion negatively impact any other of our criteria?</strong><br><br />
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<h1white>What happens when we change the amount of each input added?</h1white><br />
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<h1white>What happens when we change the amount of each input added?</h1white></div></a><br />
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<img src="https://static.igem.org/mediawiki/2014/5/51/Oxford_varying_ATC_and_DCM.png" style="margin-left:0%; float:right; margin-right:0%; position:relative; width:45%;" /><br />
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<h1>What are these graphs and where did they come from?</h1><br />
Using the bacterial fluorescence models we have built, we predicted the steady-state fluorescence levels of the system in varying levels of DCM and ATC by solving the system of differential equations we produced during the characterization section. The results are illustrated in the 3-dimensional surface plot below. <br />
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The two 2-dimensional graphs are slices taken from the 3-D plot. In each of these 'slices' we are effectively holding one variable constant (the amount of either DCM or ATC) while varying the other. <br />
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The 3-dimensional plot was produced by plotting the final fluorescence value from many different possible combinations of the two inputs (ATC and DCM). The top graph shows the variation in final fluorescence when DCM is held constant and ATC is varied, the second graph is vice versa.<br />
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It is important to understand that these graphs represent the expected steady state level of fluorescence of thousands of different simulations. From this we can select the DCM and ATC concentrations for a specific fluorescence response.<br />
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<h1>How much of each input should we use to test the biosensor?</h1><br />
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Our ideal biosensor must be robust. The top graph demonstrates this nicely. Beyond a certain threshold value of ATC, there is little change in the fluorescence response predicted - it saturates and maintains a constant level. Practically, this means we have to ensure that the ATC concentrations present in our final system must comfortably exceed this threshold ATC value.<br><br><br />
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From our initial system characterization, we have established that when DCM is not present in the system, there will be no fluorescence response aside from that due to the basal transcription rate. However, the model predicts that when even a small amount of DCM is added and the transient behaviour has stabilized, the fluorescence expressed in the system quickly reaches its saturation value. This corresponds to a highly sensitive biosensor which can effectively only express two fluorescence levels- zero or a predefined maximum. The transition from zero to the maximum saturation value occurs at very low concentrations of DCM. <br><br><br />
To summarise, we have established that the inputs to our biosensor should be a constant medium concentration of ATC and a varying concentration of DCM as it is degraded. We should note that the ATC concentration will not value without external influence because the system does not consume ATC and its rate of degradation is negligible. <br />
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<h1white>Modelling the biosensor to optimise ’ON’ and ‘OFF’ response</h1white><br />
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<h1white>Modelling the biosensor to optimise ’ON’ and ‘OFF’ response</h1white></div></a><br />
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As described above, the ideal biosensor is binary and its fluorescence response can only take two values. This relies on the system having two features- a fast response time to concentration changes and a large amplitude of response. Having previously established the ideal concentrations of DCM and ATC <u>(see above)</u> for the biosensor, our next task was to predict what combination of controllable variables would result in the ideal binary behaviour. This is a very important step in synthetic biology because it allows us to crudely optimise the design before construction even begins. To test the response of our biosensor, we used a step function of DCM the initial and sudden contact of DCM with our bacteria and then removing DCM through <u>spinning the cells(?)</u>. In the real system, the DCM input would be a step in and then a gradual negative ramp as the DCM was degraded.<br />
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The two parameters that are most easily changed in the initial production of the bacteria are the RBS strength and the degradation rate. <br />
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Increasing the Ribosome Binding Site (RBS) strength can greatly increase the translation initiation rate, hence resulting in amplified fluorescence. <u>(HOW?) (CORRECT + DETAIL?)</u><br />
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The degradation rate of the fluorescent protein can also be changed by adding degradation tags. <u>(CORRECT + DETAIL?)</u><br />
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<h1white>Should we aim for high or low RBS strength?</h1white><br />
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<h1white>Should we aim for high or low RBS strength?</h1white></div></a><br />
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<img src="https://static.igem.org/mediawiki/2014/e/e8/Oxford_change_RBS_strength.png" style="margin-left:0%; float:right; margin-right:0%; position:relative; width:65%;" /><br />
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We ran the deterministic model whilst varying the activation rate (see <a href="https://static.igem.org/mediawiki/2014/b/be/Oxford_Equations_explained.png" target="_blank">'where did these equations come from?'</a>) of the sfGFP. The response is shown here:<br />
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<div class="white_news_block"><br />
<h1>What does this tell us?</h1><br />
As you can see from this graph, increasing the RBS strength only changes the amplitude of the systems response without affecting the response time of the system. This is highly beneficial for the system.<br />
<br><br> <br />
-->Therefore we will aim for as high an RBS strength as possible in our initial design.<br />
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<h1white>Should we aim for high or low degradation rate?</h1white><br />
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We ran the deterministic model whilst varying the degradation rate (see <a href="https://static.igem.org/mediawiki/2014/b/be/Oxford_Equations_explained.png" target="_blank">'where did these equations come from?'</a>) of the sfGFP. The response is shown here:<br />
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<h1>What does this tell us?</h1><br />
Changing the degradation rate of the protein is more of a trade-off. As you can see, a higher degradation rate gives a faster response but with a much lower steady state responses<br />
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-->We should aim for a low degradation rate to begin with so that we can ensure a detectable level of fluorescence, and then gradually increase the degradation rate to get a faster response.<br />
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<h1white>How did this inform our design?</h1white><br />
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<h1white>How did this inform our design?</h1white></div></a><br />
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Based on the modelling we could optimise each performance characteristic individually, but to create the best overall biosensor we needed to compromise with what we chose to implement:<br><br><br />
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<h1>RBS strength</h1> <br />
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<strong>Medium RBS strength</strong> – our modelling suggested we should use as high an RBS strength as possible. We have used a relatively high strength RBS to try and optimise our signal amplitude without over-stressing the cells.<br><br><br />
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<h1>GFP degradation</h1> <br />
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<strong>No degradation tag</strong> - in this instance the model showed that increasing degradation efficiency of GFP(and thus the speed of response) by utilising a degradation tag would also decrease the signal amplitude. In our first attempt at making a biosensor we decided it was more important to increase the chance of generating a usable signal than to have a fast off rate. In the future, once our biosensor is made and if we have found it to have very high amplitude we could add a degradation tag to improve the on/off dynamics at the expensive of that excessive signal.<br />
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<h1>Modelling Summary</h1><br />
The above results demonstrate well the power of modelling genetic circuits. This approach has allowed us to develop our first construct intelligently and to have some trustworthy predictions on which to develop the rest of our system around. However, as ever, there are limitations, especially in biological systems.<br />
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In an ideal world, we would like to have a very high expression rate (for a high steady state amplitude of fluorescence), a high degradation rate (for a fast responding biosensor) and a high copy number of the plasmid in each cell. Conversely though, optimising these parameters puts stress on the cells. This leads to the system not actually being as optimal as the model might have predicted. Here we identify the weakness in preliminary models. We will have to actually develop the bacteria and run the experiments in the lab before we will know if our biosensor will respond this well to the DCM. After this, we will work at creating secondary models which should be able to give more reliable predictions. Ideally we would be able to then make more bacteria and the Engineering-Biochemistry cycle would continue.<br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_characterisation"><img src="https://static.igem.org/mediawiki/2014/c/ca/Oxford_Characterisation.png" style="float:left;position:relative; width:23%;" /></a><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_realisation"><img src="https://static.igem.org/mediawiki/2014/4/4c/Oxford_Realisation.png" style="float:right;position:relative; width:23%;" /></a><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_construction"><img src="https://static.igem.org/mediawiki/2014/a/ae/Oxford_construction.png" style="float:left;position:relative; width:23%; margin-left: 2.66%" /></a><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor"><img src="https://static.igem.org/mediawiki/2014/a/a7/Oxford_biosensor_link.png" style="float:left;position:relative;width:50%; margin-top:2%;margin-left:25%;margin-right:25%;" /></a><br />
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<a href="https://2014.igem.org/Team:Oxford/Modelling"><img src="https://static.igem.org/mediawiki/2014/6/6e/Oxford_modelling_homepage_link.png" style="float:left;position:relative; width:50%; margin-top:2%;margin-left:25%;margin-right:25%;" /></a><br />
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Oxford iGEM 2014<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/biosensor_characterisationTeam:Oxford/biosensor characterisation2014-10-17T18:56:14Z<p>CorinnaO: </p>
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<div style="width:100%;"><font style="font-size:15px;font-weight:500;">Show all:</font></div><br />
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<h1white><font style="font-size:15px;font-weight:500;">Modelling</font></h1white></center><br />
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<h1>Introduction: what are we characterising?</h1><br />
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<font style="font-style: italic;">Methylobacterium Extorquens</font> DM4 in the presence of DCM expresses DcmA, a dichloromethane dehalogenase.<br />
Within 1.5kb upstream of <font style="font-style: italic;">dcmA</font> and in the opposite orientation is a second gene encoding DcmR, a regulatory protein that controls expression of DcmA:<br><br><br />
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In order to design and create a stable and sensitive system that responds to DCM we first need to characterise the regulatory nature of DcmR. Characterisation of this regulatory network has never been done before although it has been suggested to be a repressor [1]; we will be the first to fully characterise the mode of action of <font style="font-style: italic;">dcmR</font>. To do this we are testing the following hypotheses for DCM activating the transcription of <font style="font-style: italic;">dcmR</font>: either double repression or double activation. In other words, either DcmR represses <font style="font-style: italic;">dcmA</font> expression and DcmR is in negatively modulated by the presence of DCM; or expression of <font style="font-style: italic;">dcmA</font> requires DcmR as an activator, with DcmR in turn only activated in the presence of DCM.<br><br><br />
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<h1white>What is currently known about DcmR?</h1white><br />
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<h1white>What is currently known about DcmR?</h1white></div></a><br />
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<h1>DcmR and regulation of <font style="font-style: italic;">dcmA</font> expression</h1><br />
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Mutants with <font style="font-style: italic;">dcmA</font> and the intergenic region but without complete <font style="font-style: italic;">dcmR</font> express <font style="font-style: italic;">dcmA</font> constitutively. Re-integration of <font style="font-style: italic;">dcmR</font> restores regulation of <font style="font-style: italic;">dcmA</font> expression at the transcriptional level [1]. In addition, it has been shown that the region including <font style="font-style: italic;">dcmR</font>, the intergenic region and <font style="font-style: italic;">dcmA</font> is sufficient to confer a DCM dependent response in genetically engineered Methylobacterium extorquens DM4 [2]. <br><br><br />
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<h1>DcmR and DNA-binding</h1><br />
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DcmR is thought to be a DNA binding protein as structure predicting software indicates that there is a helix-turn-helix domain at the N-terminal of the protein. Since the region between the two promoters for <font style="font-style: italic;">dcmR</font> and <font style="font-style: italic;">dcmA</font> can be deleted without any effect on regulation it has been suggested that DcmR does not to a secondary regulatory site in between the genes but most likely acts directly on the <font style="font-style: italic;">dcmA</font> promoter itself [1]. In addition, regulated expression of <font style="font-style: italic;">dcmA</font> is not affected when the <font style="font-style: italic;">dcmR</font> and <font style="font-style: italic;">dcmA</font> transcriptional units are placed on separate replicons thereby suggesting that their topology is independent of the regulatory network. It is therefore suggested that DcmR binds the DNA in the intergenic region with the simplest model of its mode of action being as a trans-acting DNA-binding repressor; however this remains to be fully validated [1].<br><br><br />
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We have therefore proceeded on the assumption that DcmR is directly influenced by the presence or absence of DCM and furthermore that we can use <font style="font-style: italic;">dcmR</font>, the intergenic region and <font style="font-style: italic;">dcmA</font> alone to characterise the regulatory network. <br><br><br />
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[1] La Roche, S. D., and T. Leisinger. "Identification of <font style="font-style: italic;">dcmR</font>, the regulatory gene governing expression of dichloromethane dehalogenase in Methylobacterium sp. strain DM4." Journal of bacteriology 173.21 (1991): 6714-6721. <br><br />
[2] Lopes, N., et al “Detection of dichloromethane with a bioluminescent (lux) bacterial bioreporter” J Ind Microbiol Biotechnol (2012) 39:45–53<br />
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<h1>Characterising the DcmR - DCM - P_dcmA interaction</h1><br />
To find out whether the <font style="font-style: italic;">dcmR</font> gene acts as a repressor or an activator on the promoter of the <font style="font-style: italic;">dcmA</font> gene, we attempted to build the genetic circuit shown above on the right. Having <font style="font-style: italic;">dcmR</font> under inducible TetR expression should allow us to have very good control of the amount of DcmR present. Additionally a translational fusion with DcmR and a mCherry fluorescence tag will act as another confirmation to the amount of DcmR present.<br />
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We then extensively modelled the circuit to discover how the response of the system would differ if it was either of the two circuit systems. Click the modelling bubbles (pink) to find out exactly how we achieved this.<br />
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<h1white>Modelling the first half of the genetic circuit</h1white><br />
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<h1white>Modelling the first half of the genetic circuit</h1white></div></a><br />
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<h1>Predicting the mCherry fluorescence</h1><br />
We simplified the first double repression by modelling it as an activation of <font style="font-style: italic;">dcmR</font> by ATC, albeit parameterised by different constants. This assumption is justified by the fact that we are able to precisely control the addition of ATC and measure the fluorescence of the mCherry.<br />
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We modelled this first step using both deterministic and stochastic models.<br />
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<h1>Biochemical equations</h1><br />
The biochemical equations that describe the behaviour of the top half of the genetic circuit are:<br />
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<h1>Deterministic</h1><br />
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Deterministic models are very powerful tools for synthetic biology. They describe the behaviour of the bacteria at the population level and use Ordinary Differential Equations (ODEs) to relate each activation and repression. By constructing a cascade of differential equations one can build a realistic model of the average behaviour of the system.<br />
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The differential equation that describes this first step of the system is:<br />
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Solving this ODE in Matlab (with a zero basal transcription rate) predicts the following the response of the system:<br />
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This model works assuming that sufficient TetR is always present.<br />
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While the analysis of this circuit is not critical to the successful outcome of this part of the project, it will provide us with very good practice of both obtaining fluorescence time series data and accurately fitting the data to the model. It will also help us develop our methods of predicting future system behaviour. This is because this system is already well documented in the literature and so we should be able to test our methods and responses against well documented results from labs across the world.<br />
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As you can clearly see from the graph, the model predicts a large fluorescence increase as the input is added. This is the what we expect from the actual system and is the best approximation that is obtainable before we get experimental data.<br />
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In the graph above, the model is set to have a basal transcription rate of zero. This is why there is a zero fluorescence response before the input has been added - this corresponds to the tetO promoter not being leaky. This basal rate will be calibrated alongside all of the other parameters in the model.<br />
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<h1>Stochastic Modelling</h1><br />
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Stochastic modelling uses probability theory to predict the behaviour of a system. For our project, we used it to model the expression of GFP from bacteria. <br />
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We started with the Gillespie Algorithm, which considers the expression of GFP to be binary; a molecule of GFP is either produced or degraded. Before we determined which reaction happened, we had to work out when the reaction happened. Using the random number r (taken from a uniform distribution between 0 and 1), we produced another random number τ, which determined the time until the next reaction.<br />
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Where α_0 represents the probability that any reaction will happen, given by the following equation:<br />
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We modelled the probability of a molecule of GFP being created using the Michaelis-Menten model (α_1), incorporating a basal transcription rate (beta1). For the degradation, we assumed a simple proportional relationship: the more GFP you have, the more likely it is that a molecule degrades (δ_1). The constant of proportionality will be a function of the intrinsic life time of the protein in the cell. We considered there to be no DCM originally, then a large step in DCM at time=0. This is similar to placing the detector in a DCM polluted source, to make the model more realistic the level of DCM would go down as it is degraded but we had no time to obtain data for this rate.<br />
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To decide if GFP was produced, we looked at the percentage of “reactions” which were productive, and then we compared this to a second random number (again taken from a uniform distribution from 0 to 1). If the random number was lower, then a GFP was created. If it was higher, then a GFP was degraded. In this way we make a weighted random choice about whether GFP was created or degraded.<br />
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We only stored the time and amount of GFP when there was a reaction, to save on computation. However this made calculating the mean of realisation harder, but we got over the problem by….<br />
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Stochastic modelling is useful because it can show us the stochastic effects which are often observed in individual bacteria. By calculating the variation of the mean of multiple GFP producing bacteria, we can also work out the standard deviation. Then, if we assume that the system varies with respect to the normal distribution, we can produce error bounds for the production of GFP, such that we can say that 90% of the time we can expect the production of GFP from a single bacterium to be within these two curves. This could be useful for seeing if results are unexpected, or, if there are multiple outliers, that our model is incorrect. If we average an increasing number bacteria, then the mean curve tends towards the deterministic response. This is to be expected, as we are now looking at the system as a whole and fluctuations in the production from individual bacteria are averaged out. In terms of their use, when looking at small amounts of bacterium the stochastic model would be better, because real random fluctuations can be seen. For larger bacterial populations, the deterministic response models the growth very well. The stochastic model can also model large groups but requires large number of realisations which causes simulations to take a lot longer to run.<br />
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When running the models, we picked arbitrary constants to view the general response. If we had more time we would have attempted to work out the basal rate, transcription rate and degradation rate of the GFP from DCM.<br />
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<h1>Predicting the sfGFP fluorescence</h1><br />
<h1>Introduction</h1><br />
To allow us to characterize the second half of the genetic circuit, we needed to be able to predict the difference in response. To do this, we constructed models by cascading the differential equations according to the respective circuit structures thereby producing two different potential system responses.<br />
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We then set up the differential equations necessary to solve this problem in Matlab. The method and results are as detailed below:<br />
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<a href="https://static.igem.org/mediawiki/2014/b/be/Oxford_Equations_explained.png" target="_blank"><img src="https://static.igem.org/mediawiki/2014/4/41/Oxford_equations.png" style="float:left;position:relative; width:50%;margin-right:25%;margin-left:25%;" /></a><br />
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Oxford iGEM 2014<br />
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<h1>Conclusion</h1><br />
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The bottom graphs illustrate the predicted response of each system to a simultaneous step input of both DCM and ATC. As you can see, there is little difference in the predicted steady-state value of the fluorescence, however, providing the basal transcription rate of GFP is relatively low, there should be a clear difference in the level of fluorescence before either of these inputs are added. This very easily identifiable difference between the two systems will enable us to characterize the genetic circuit present in our particular system.<br />
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<h1>Calculating the parameters</h1><br />
Calculating the many parameters for this system will be undoubtedly challenging. <br />
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<a href="https://2014.igem.org/Team:Oxford/calculating_parameters" target="_blank">How are we calculating the parameters?</a><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_characterisation#show7">Go to the data section where we calculated parameters for this part of the circuit.<br />
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<a href="#show4" class="show wetlab-row" id="show4"><div class="wetlab"><br />
<h1white>ATC induction of mCherry expression</h1white><br />
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<h1white>ATC induction of mCherry expression</h1white></div></a><br />
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<h1>Wetlab data showing response in level of mCherry expressed with different concs of ATC</h1><br />
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By making a translational fusion of mCherry at the C terminus of the dcmR gene under the tet promoter and tet operator system (see our <a href="https://2014.igem.org/Team:Oxford/biosensor_construction">Construction page</a> for details) we could measure mCherry fluorescence to gain information about dcmR induction by ATC. Expression was induced with various amounts of ATC and the following fluorescence data acquired. Exposure time was 0.2 seconds. As no calibration data was obtained using purified mCherry, the results have been left in fluorescence arbitrary units. Images were analysed using imageJ software.<br><br />
mCherry fluorescence increases with amount of ATC used confirming that the dcmR gene was expressed under the control of the tet promoter and operator system.<br><br><br />
<img src="https://static.igem.org/mediawiki/parts/5/51/Oxford_DcmR-mCherry_expression_induced_by_0ng_ATC.png" <br />
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<img src="https://static.igem.org/mediawiki/parts/1/19/Oxford_DcmR-mCherry_expression_induced_by_10ng_ATC.png" style="float:right;position:relative; width:70%;" /><br />
<img src="https://static.igem.org/mediawiki/parts/6/63/Oxford_DcmR-mCherry_expression_induced_by_50ng_ATC.png" style="float:left;position:relative; width:70%;" /><br />
<img src="https://static.igem.org/mediawiki/parts/c/cb/Oxford_DcmR-mCherry_expression_induced_by_100ng_ATC.png" style="float:right;position:relative; width:70%;" /><br />
<img src="https://static.igem.org/mediawiki/parts/3/37/Oxford_DcmR-mCherry_expression_induced_by_200ng_ATC.png" style="float:centre;position:relative; width:70%;" /><br />
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<img src="https://static.igem.org/mediawiki/parts/e/e6/Oxford_Comparison_of_mean_flouresence_intensity_from_DcmR-mCherry_expression_induced_at_varying_amounts_of_ATC.png" style="float:right;position:relative; width:70%;margin-left:30%;" /><br />
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This data was then used to refine and test our models (see below).<br />
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<a href="#show5" class="show modelling-row" id="show5"><div class="modelling"><br />
<h1white>Adapting the model for the above data</h1white><br />
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<h1white>Adapting the model for the above data</h1white></div></a><br />
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<img src="https://static.igem.org/mediawiki/2014/4/4d/Oxford_Just_data.jpg" style="float:right;position:relative; width:50%;" /><br />
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<h1>Introduction</h1><br />
As you can see from the biochemistry bubble above, our team was only able to obtain fluorescence data for the first half of the genetic circuit (ATC-induced mCherry response). On top of this, the wet lab team were unable to obtain data that measured how the fluorescence of a single culture changed with time, again because of time constraints. This is slightly limiting because it means that we don’t have any dynamic data for any part of our system, and therefore can’t test the modelling predictions of the speed of the biosensor’s response.<br />
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The original data is shown on the right with error bars showing the standard error of the measurements.<br />
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Standard error is calculated as the average standard deviation divided by the square root of the total number of readings.<br />
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<h1>How we used the model</h1><br />
However, to demonstrate the power of the computer models that we’ve built, we made our model simulate the same graph (mean fluorescence against ATC concentration added). To build this, we started from the graph shown in the <br />
<a href="https://2014.igem.org/Team:Oxford/biosensor_characterisation#show2">first modelling bubble</a><br />
on this page, shown here with a small basal rate (see <br />
<a href="https://static.igem.org/mediawiki/2014/b/be/Oxford_Equations_explained.png" target="_blank">where did these equations come from?</a><br />
). This graph shows how the predicted fluorescence of the cells changes with time in response to an addition of ATC halfway along the time scale. At this stage, all input values, model parameters and therefore results are arbitrary.<br />
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<img src="https://static.igem.org/mediawiki/2014/4/46/Oxford_data2.png" style="float:right;position:relative; width:80%;margin-left:10%;margin-right:10%;" /><br />
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We then ran the model for the correct amount of time (2 hours 20mins incubation with ATC) and ran it for lots of different concentrations of ATC over the range that the wet-lab team tested. The parameters are still arbitrary at this point (the same as above) and the results of the graphs are therefore arbitrary are as well, but the input values are now correct. The graphic below shows how we used the existing model to obtain the same graphs as the wet-lab team had obtained.<br />
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The numerical inputs that were used to model this data set were therefore:<br />
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<img src="https://static.igem.org/mediawiki/2014/f/f6/Oxford_data3.png" style="float:right;position:relative; width:100%;margin-left:0%;margin-right:0%;" /><br />
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Oxford iGEM 2014<br />
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<h1white>Finding parameters</h1white><br />
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<h1white>Finding parameters</h1white></div></a><br />
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<h1>Parameters</h1><br />
As all results are arbitrary up to this point, it is now time to calculate the parameters that will make the model’s response match up with the wet-lab data. The purpose of doing this is that the model will be able to give relatively accurate predictions of the response of the bacteria to further testing, therefore making the development of the biosensor much more efficient. The amount of data here will not allow us to calculate the parameters to a high level of accuracy, but it should be able to give us some very good approximations of what we can expect.<br />
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<br><br><img src="https://static.igem.org/mediawiki/2014/f/ff/Oxford_mCherry_circuit2.png" style="float:right;position:relative; width:40%;" /><br />
The parameters that we need to calculate are the constants in the differential equation that governs the behaviour of the first half of the genetic circuit. This half of the system is shown again here to remind the reader which part we are considering.<br />
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These parameters are:<br />
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<li>α1 = expression rate constant of <font style="font-style: italic;">dcmR</font></li><br />
<li>k1 = Michaelis - Menten constant of <font style="font-style: italic;">dcmR</font></li><br />
<li>d1 = degradation constant of <font style="font-style: italic;">dcmR</font></li><br />
<li>β1 = Basal transcription rate of <font style="font-style: italic;">dcmR</font></li><br />
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Remember that because the mCherry gene is tagged (translational fusion) onto the end of the <font style="font-style: italic;">dcmR</font> gene, <font style="font-style: oblique;"> the mCherry fluorescence will be the same as the amount of DcmR protein present</font>. However, there is not very comprehensive data in the literature about the values that we can expect from the behaviour of the <font style="font-style: italic;">dcmR</font> gene and its stability in vivo.<br />
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<h1>Degradation constant</h1><br />
The initial steady state of the system (before ATC has been added) is determined by two constants in the model. These constants are the degradation constant of DcmR and the basal transcription rate of the system. Due to the lack of numerical information in the literature on the behaviour of the <font style="font-style: italic;">dcmR</font> gene, the way of calculating these two parameters is by using the single basal rate data point from the wet-lab data (fluorescence value when 0ng of ATC has been added).<br />
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If we assume that the half-life of the dcmR protein is 3 hours [1] (180 minutes), we can calculate the degradation constant for our model. The exponential protein decay is therefore described by:<br />
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<br><br><img src="https://static.igem.org/mediawiki/2014/0/0d/Oxford_data6.png" style="float:left;position:relative; width:25%;margin-right:75%;margin-bottom:2%;" /><br />
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<h1>Basal transcription rate</h1><br />
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At the basal steady state, the rate of change of DcmR (and therefore fluorescence) is zero. As no ATC has been added to the system yet, the value of [ATC] is also zero.<br />
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<img src="https://static.igem.org/mediawiki/2014/5/55/Oxford_data7.png" style="float:left;position:relative; width:70%;margin-right:30%;margin-bottom:2%;" /><br />
This simplifies the equation to:<br />
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<img src="https://static.igem.org/mediawiki/2014/e/e0/Oxford_data8.png" style="float:left;position:relative; width:70%;margin-right:30%;margin-bottom:2%;" /><br />
As we want our model to accurately predict the fluorescence, we will substitute the fluorescence value in place of the [DcmR] and rearrange:<br />
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<img src="https://static.igem.org/mediawiki/2014/f/f6/Oxford_data9.png" style="float:left;position:relative; width:70%;margin-right:30%;margin-bottom:2%;" /><br />
Substituting in the value for δ1 that we found above and the basal steady state fluorescence level from the data (471 to 3 s.f.) gives the basal transcription rate as:<br />
<img src="https://static.igem.org/mediawiki/2014/0/0a/Oxford_data10.png" style="float:left;position:relative; width:70%;margin-right:30%;margin-bottom:2%;" /><br />
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<h1>Expression rate constant and Michaelis - Menten constant</h1><br />
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After the ATC has been added to the system, the value of [ATC] becomes non-zero. This means that the expression constant and the Michaelis – Menten constant start to affect the system.<br />
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To find the parameters that make the model’s output match the data values, we turned to the code that we had developed for parameter calibration. <br />
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<a href="https://2014.igem.org/Team:Oxford/calculating_parameters" target="_blank">How are we calculating the parameters?</a><br />
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This code gave the parameters as:<br />
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<h1>α1 = expression rate constant of <font style="font-style: italic;">dcmR</font></li> = 16.5 (fluorescence/min)</h1><br />
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<h1>k1 = Michaelis - Menten constant of <font style="font-style: italic;">dcmR</font></li> = 0.015 (ml/ng)</h1><br />
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<h1white>Using the parameters and model analysis</h1white><br />
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<h1white>Using the parameters and model analysis</h1white></div></a><br />
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<h1>Entering the correct parameters</h1><br />
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When the parameters that had been calculated above were entered into the model: <br />
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<img src="https://static.igem.org/mediawiki/2014/d/d2/Oxford_data11.png" style="float:left;position:relative; width:60%;margin-right:20%;margin-bottom:2%;margin-left:20%;" /><br />
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alongside the correct inputs:<br />
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<img src="https://static.igem.org/mediawiki/2014/f/f8/Oxford_data12.png" style="float:left;position:relative; width:60%;margin-right:20%;margin-bottom:2%;margin-left:20%;" /><br />
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The graph below shows the model's predictions plotted in the same figure as the data points that the wet-lab team obtained for the system:<br />
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<img src="https://static.igem.org/mediawiki/2014/2/26/Final_mCherry_dots.jpg" style="float:left;position:relative; width:80%;margin-right:10%;margin-bottom:2%;margin-left:10%;" /><br />
Plotting the model's output as a by interpolating between the calculated values makes the graph clearer:<br />
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<img src="https://static.igem.org/mediawiki/2014/d/dc/Final_mCherry_line.jpg" style="float:left;position:relative; width:80%;margin-right:10%;margin-bottom:2%;margin-left:10%;" /><br />
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<h1>Sensitivity</h1><br />
An important part of building mathematical models is sensitivity analysis of the results. This can be basically explained as wiggling all of the input values and parameters to see how much variations in each of these values affects the system output. This is especially important for finding parameters to describe the system as it is important to know what level of accuracy the values need to be found to provide a reasonable degree of prediction accuracy.<br />
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On top of this, it is possible to find what range of values the system is especially sensitive to. An example of this analysis is shown with a simple example that is relevant to our system below:<br />
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<img src="https://static.igem.org/mediawiki/2014/1/1a/Oxford_data13.png" style="float:left;position:relative; width:80%;margin-right:10%;margin-bottom:2%;margin-left:10%;" /><br />
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<h1>Stability</h1><br />
In our Engineering studies we have learnt detailed control theory. Control theory is an interdisciplinary branch of engineering and mathematics that deals with the behaviour of dynamical systems with inputs, and how their behavior is modified by feedback. The usual objective of control theory is to control a system so that its output follows a desired control signal, called the reference, which may be a fixed or changing value. This important because many dynamic systems can go unstable if they are given an unsafe set of input values and/or operating conditions.<br />
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However, as there are no feedback loops in this synthetic circuit, control theory analysis of this system isn't necessary.<br />
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<h1>Future experiment ideas from an Engineering design perspective</h1><br />
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To see details about how the wet lab team then used this model to guide their work see our <a href="https://2014.igem.org/Team:Oxford/biosensor_optimisation">optimisation</a> page.<br />
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<h1>Reference</h1><br />
[1] Dr George Wadhams by personal communication (14/10/2014)<br />
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<h1white>Double repressor or double activator?</h1white><br />
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<h1white>Double repressor or double activator?</h1white></div></a><br />
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The first data readings that we managed to obtain from the completed genetic circuit were qualitative sfGFP fluorescence readings. The results of these are shown here:<br />
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<h1>First system tested</h1><br />
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<img src="https://static.igem.org/mediawiki/2014/c/c6/Oxford_bottom_line.png " style="float:left;position:relative; width:40%;margin-right:60%;margin-bottom:2%;margin-left:0%;" /><br />
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The first system that we tested is shown here. This is the bottom level of our synthetic circuit. Testing just this plasmid allowed us to obtain some important information to allow us to characterise the genetic system. <br />
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The GFP fluorescence of this system was high and this is the basal transcription rate of the PdcmA promoter.<br />
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<h1>Second system tested</h1><br />
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<img src="https://static.igem.org/mediawiki/2014/c/c1/Oxford_system2.png" style="float:left;position:relative; width:40%;margin-right:60%;margin-bottom:2%;margin-left:0%;" /><br />
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The second system that we tested was the whole synthetic system without any DCM added. This allowed us to analyse just the effect of DcmR on the PdcmA promoter when it was compared to the result from system 1. <br />
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The fluorescence was much lower than the basal transcription rate of the PdcmA. This strongly suggests that the DcmR protein acts as a <u>repressor</u> of the PdcmA. <br />
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The next step is to prove that adding DCM represses this repression and increases the sfGFP fluorescence of the system.<br />
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<h1>Third system tested</h1><br />
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<img src="https://static.igem.org/mediawiki/2014/4/40/Oxford_Whole_circuit.png" style="float:left;position:relative; width:40%;margin-right:60%;margin-bottom:2%;margin-left:0%;" /><br />
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<u> Therefore we know that DcmR represses PdcmA with DCM repressing this repression. This means that the system is a double repressor!</u><br />
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<h1white>Engineering analysis of the sfGFP data</h1white><br />
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<h1white>Engineering analysis of the sfGFP data</h1white></div></a><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_realisation"><img src="https://static.igem.org/mediawiki/2014/4/4c/Oxford_Realisation.png" style="float:right;position:relative; width:23%;" /></a><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_construction"><img src="https://static.igem.org/mediawiki/2014/a/ae/Oxford_construction.png" style="float:left;position:relative; width:23%; margin-left: 2.66%" /></a><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_optimisation"><img src="https://static.igem.org/mediawiki/2014/9/93/Oxford_Optimisation.png" style="float:right;position:relative; width:23%; margin-right: 2.66%" /></a><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor"><img src="https://static.igem.org/mediawiki/2014/a/a7/Oxford_biosensor_link.png" style="float:left;position:relative;width:50%; margin-top:2%;margin-left:25%;margin-right:25%;" /></a><br />
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<a href="https://2014.igem.org/Team:Oxford/Modelling"><img src="https://static.igem.org/mediawiki/2014/6/6e/Oxford_modelling_homepage_link.png" style="float:left;position:relative; width:50%; margin-top:2%;margin-left:25%;margin-right:25%;" /></a><br />
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Oxford iGEM 2014<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/biosensor_characterisationTeam:Oxford/biosensor characterisation2014-10-17T18:53:02Z<p>CorinnaO: </p>
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<img src="https://static.igem.org/mediawiki/2014/e/e6/Real_Biosensor.jpg" style="position:absolute; width:100%;z-index:-1; border-radius:15px;margin-top:-10px;"/><br />
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<div style="background-color:#D9D9D9; opacity:0.7; z-index:5; Height:75px; width:100%;font-size:65px;font-family:Helvetica;padding-top:5px; font-weight: 450;margin-top:10px;"><br />
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<a href="https://static.igem.org/mediawiki/2014/1/16/Oxigem_LAB_PROTOCOLS.pdf" target="_blank"><img src="https://static.igem.org/mediawiki/2014/a/a4/OxigemProtocols.png" style="position:absolute;width:6%;margin-left:91%;margin-top:-13%;z-index:10;"></a><br />
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<div style="width:100%;"><font style="font-size:15px;font-weight:500;">Show all:</font></div><br />
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<a href="#showmodelling"><div class="orange_news_block1 showmodelling" style="background: #F9A7B0;border-radius:15px;color:black;float:left;height:40%;width:40%;margin-left:6%;padding-top:10px;"><center><br />
<h1white><font style="font-size:15px;font-weight:500;">Modelling</font></h1white></center><br />
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<a href="#showwetlab"><div class="orange_news_block1 showwetlab" style="background: #ADD8E6;border-radius:15px;color:black;float:left;height:40%;width:40%;margin-left:3%;padding-top:10px;"><center><br />
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<h1>Introduction: what are we characterising?</h1><br />
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<font style="font-style: italic;">Methylobacterium Extorquens</font> DM4 in the presence of DCM expresses DcmA, a dichloromethane dehalogenase.<br />
Within 1.5kb upstream of <font style="font-style: italic;">dcmA</font> and in the opposite orientation is a second gene encoding DcmR, a regulatory protein that controls expression of DcmA:<br><br><br />
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<img src="https://static.igem.org/mediawiki/2014/7/7f/Oxford_charac3.png" style="float:right;position:relative; width:80%; margin-right:10%;margin-bottom:2%;margin-left:10%;" /><br><br><br><br><br><br><br><br><br><br />
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In order to design and create a stable and sensitive system that responds to DCM we first need to characterise the regulatory nature of DcmR. Characterisation of this regulatory network has never been done before although it has been suggested to be a repressor [1]; we will be the first to fully characterise the mode of action of <font style="font-style: italic;">dcmR</font>. To do this we are testing the following hypotheses for DCM activating the transcription of <font style="font-style: italic;">dcmR</font>: either double repression or double activation. In other words, either DcmR represses <font style="font-style: italic;">dcmA</font> expression and DcmR is in negatively modulated by the presence of DCM; or expression of <font style="font-style: italic;">dcmA</font> requires DcmR as an activator, with DcmR in turn only activated in the presence of DCM.<br><br><br />
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<h1white>What is currently known about DcmR?</h1white><br />
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<h1white>What is currently known about DcmR?</h1white></div></a><br />
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<h1>DcmR and regulation of <font style="font-style: italic;">dcmA</font> expression</h1><br />
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Mutants with <font style="font-style: italic;">dcmA</font> and the intergenic region but without complete <font style="font-style: italic;">dcmR</font> express <font style="font-style: italic;">dcmA</font> constitutively. Re-integration of <font style="font-style: italic;">dcmR</font> restores regulation of <font style="font-style: italic;">dcmA</font> expression at the transcriptional level [1]. In addition, it has been shown that the region including <font style="font-style: italic;">dcmR</font>, the intergenic region and <font style="font-style: italic;">dcmA</font> is sufficient to confer a DCM dependent response in genetically engineered Methylobacterium extorquens DM4 [2]. <br><br><br />
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<h1>DcmR and DNA-binding</h1><br />
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DcmR is thought to be a DNA binding protein as structure predicting software indicates that there is a helix-turn-helix domain at the N-terminal of the protein. Since the region between the two promoters for <font style="font-style: italic;">dcmR</font> and <font style="font-style: italic;">dcmA</font> can be deleted without any effect on regulation it has been suggested that DcmR does not to a secondary regulatory site in between the genes but most likely acts directly on the <font style="font-style: italic;">dcmA</font> promoter itself [1]. In addition, regulated expression of <font style="font-style: italic;">dcmA</font> is not affected when the <font style="font-style: italic;">dcmR</font> and <font style="font-style: italic;">dcmA</font> transcriptional units are placed on separate replicons thereby suggesting that their topology is independent of the regulatory network. It is therefore suggested that DcmR binds the DNA in the intergenic region with the simplest model of its mode of action being as a trans-acting DNA-binding repressor; however this remains to be fully validated [1].<br><br><br />
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We have therefore proceeded on the assumption that DcmR is directly influenced by the presence or absence of DCM and furthermore that we can use <font style="font-style: italic;">dcmR</font>, the intergenic region and <font style="font-style: italic;">dcmA</font> alone to characterise the regulatory network. <br><br><br />
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[1] La Roche, S. D., and T. Leisinger. "Identification of <font style="font-style: italic;">dcmR</font>, the regulatory gene governing expression of dichloromethane dehalogenase in Methylobacterium sp. strain DM4." Journal of bacteriology 173.21 (1991): 6714-6721. <br><br />
[2] Lopes, N., et al “Detection of dichloromethane with a bioluminescent (lux) bacterial bioreporter” J Ind Microbiol Biotechnol (2012) 39:45–53<br />
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<h1>Characterising the DcmR - DCM - P_dcmA interaction</h1><br />
To find out whether the <font style="font-style: italic;">dcmR</font> gene acts as a repressor or an activator on the promoter of the <font style="font-style: italic;">dcmA</font> gene, we attempted to build the genetic circuit shown above on the right. Having <font style="font-style: italic;">dcmR</font> under inducible TetR expression should allow us to have very good control of the amount of DcmR present. Additionally a translational fusion with DcmR and a mCherry fluorescence tag will act as another confirmation to the amount of DcmR present.<br />
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We then extensively modelled the circuit to discover how the response of the system would differ if it was either of the two circuit systems. Click the modelling bubbles (pink) to find out exactly how we achieved this.<br />
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<h1white>Modelling the first half of the genetic circuit</h1white><br />
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<h1white>Modelling the first half of the genetic circuit</h1white></div></a><br />
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<h1>Predicting the mCherry fluorescence</h1><br />
We simplified the first double repression by modelling it as an activation of <font style="font-style: italic;">dcmR</font> by ATC, albeit parameterised by different constants. This assumption is justified by the fact that we are able to precisely control the addition of ATC and measure the fluorescence of the mCherry.<br />
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We modelled this first step using both deterministic and stochastic models.<br />
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<h1>Biochemical equations</h1><br />
The biochemical equations that describe the behaviour of the top half of the genetic circuit are:<br />
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Oxford iGEM 2014<br />
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<h1>Deterministic</h1><br />
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<img src="https://static.igem.org/mediawiki/2014/2/2e/Oxford_DcmR_parameters.png" style="float:right;position:relative; height:8%; width:47%;" /><br />
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Deterministic models are very powerful tools for synthetic biology. They describe the behaviour of the bacteria at the population level and use Ordinary Differential Equations (ODEs) to relate each activation and repression. By constructing a cascade of differential equations one can build a realistic model of the average behaviour of the system.<br />
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The differential equation that describes this first step of the system is:<br />
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<a href="https://static.igem.org/mediawiki/2014/b/be/Oxford_Equations_explained.png" target="_blank"><img src="https://static.igem.org/mediawiki/2014/4/41/Oxford_equations.png" style="float:left;position:relative; width:40%;" /></a><br />
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Solving this ODE in Matlab (with a zero basal transcription rate) predicts the following the response of the system:<br />
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This model works assuming that sufficient TetR is always present.<br />
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<img src="https://static.igem.org/mediawiki/2014/e/e0/Oxford_MCherry_circuit.png" style="float:left;position:relative; width:40%;" /><br />
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<img src="https://static.igem.org/mediawiki/2014/9/92/MCherry_graph.png" style="float:right;position:relative; width:60%;margin-bottom:3%;" /><br />
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While the analysis of this circuit is not critical to the successful outcome of this part of the project, it will provide us with very good practice of both obtaining fluorescence time series data and accurately fitting the data to the model. It will also help us develop our methods of predicting future system behaviour. This is because this system is already well documented in the literature and so we should be able to test our methods and responses against well documented results from labs across the world.<br />
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As you can clearly see from the graph, the model predicts a large fluorescence increase as the input is added. This is the what we expect from the actual system and is the best approximation that is obtainable before we get experimental data.<br />
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In the graph above, the model is set to have a basal transcription rate of zero. This is why there is a zero fluorescence response before the input has been added - this corresponds to the tetO promoter not being leaky. This basal rate will be calibrated alongside all of the other parameters in the model.<br />
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<h1>Stochastic Modelling</h1><br />
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Stochastic modelling uses probability theory to predict the behaviour of a system. For our project, we used it to model the expression of GFP from bacteria. <br />
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We started with the Gillespie Algorithm, which considers the expression of GFP to be binary; a molecule of GFP is either produced or degraded. Before we determined which reaction happened, we had to work out when the reaction happened. Using the random number r (taken from a uniform distribution between 0 and 1), we produced another random number τ, which determined the time until the next reaction.<br />
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Where α_0 represents the probability that any reaction will happen, given by the following equation:<br />
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<img src="https://static.igem.org/mediawiki/2014/3/37/Oxford_Matt_equations_2.jpg" style="float:left;position:relative; height:4%; width:47%;" /><br />
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We modelled the probability of a molecule of GFP being created using the Michaelis-Menten model (α_1), incorporating a basal transcription rate (beta1). For the degradation, we assumed a simple proportional relationship; the more GFP you have the more likely it is that a molecule degrades (δ_1). The constant of proportionality will be a function of the intrinsic life time of the protein in the cell. We considered there to be no DCM originally, then a large step in DCM at time=0. This is similar to placing the detector in a DCM polluted source, to make the model more realistic the level of DCM would go down as it is degraded but we had no time to obtain data for this rate.<br />
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To decide if GFP was produced, we looked at the percentage of “reactions” which were productions, and then we compared this to a second random number (again taken from a uniform distribution from 0 to 1). If the random number was lower, then a GFP was created. If it was higher, then a GFP was degraded. In this way we make a weighted random choice about whether GFP was created or degraded.<br />
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We only stored the time and amount of GFP when there was a reaction, to save on computation. However this made calculating the mean of realisation harder, but we got over the problem by….<br />
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Stochastic modelling is useful because it can show us the stochastic effects which are often seen in real bacteria. By calculating the variation of the mean of multiple GFP producing bacteria, we can also work out the standard deviation. Then if we assume that the system varies with respect to the normal distribution, we can produce error bounds for the production of GFP. Such that we can say, 90% of the time we can expect the production of GFP from a single bacterium to be within these 2 curves. This could be useful for seeing if results are unexpected, or, if there are multiple outliers, that our model is incorrect. If we average more and more bacteria then the mean curve tend towards the deterministic response. This is to be expected as we are now looking at the system as a whole and fluctuations in the production from individual bacteria are averaged out. In terms of their use, when looking at small amounts of bacterium the stochastic model would be better, because real random fluctuations can be seen. For larger bacterium groups, the deterministic response models the growth very well. The stochastic model can also model large groups but requires large number of realisations which causes simulations to take a lot longer to run.<br />
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When running the models, we picked arbitrary constants to view the general response. If we had more time we would have attempted to work out the basal rate, transcription rate and degradation rate of the GFP from DCM.<br />
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<h1white>How can we tell the systems apart?</h1white><br />
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<h1white>How can we tell the systems apart?</h1white></div></a><br />
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<img src="https://static.igem.org/mediawiki/2014/6/6f/Oxford_Characterisation_question.png" style="float:right;position:relative; width:42%;" /><br />
<h1>Predicting the sfGFP fluorescence</h1><br />
<h1>Introduction</h1><br />
To allow us to characterize the second half of the genetic circuit, we needed to be able to predict the difference in response. To do this, we constructed models by cascading the differential equations according to the respective circuit structures thereby producing two different potential system responses.<br />
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We then set up the differential equations necessary to solve this problem in Matlab. The method and results are as detailed below:<br />
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Oxford iGEM 2014<br />
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<h1>Conclusion</h1><br />
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The bottom graphs illustrate the predicted response of each system to a simultaneous step input of both DCM and ATC. As you can see, there is little difference in the predicted steady-state value of the fluorescence, however, providing the basal transcription rate of GFP is relatively low, there should be a clear difference in the level of fluorescence before either of these inputs are added. This very easily identifiable difference between the two systems will enable us to characterize the genetic circuit present in our particular system.<br />
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<h1>Calculating the parameters</h1><br />
Calculating the many parameters for this system will be undoubtedly challenging. <br />
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<a href="https://2014.igem.org/Team:Oxford/calculating_parameters" target="_blank">How are we calculating the parameters?</a><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_characterisation#show7">Go to the data section where we calculated parameters for this part of the circuit.<br />
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<h1white>ATC induction of mCherry expression</h1white><br />
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<h1white>ATC induction of mCherry expression</h1white></div></a><br />
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<h1>Wetlab data showing response in level of mCherry expressed with different concs of ATC</h1><br />
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By making a translational fusion of mCherry at the C terminus of the dcmR gene under the tet promoter and tet operator system(see our <a href="https://2014.igem.org/Team:Oxford/biosensor_construction">Construction page</a> for details) we could measure mCherry fluorescence to gain information about dcmR induction by ATC. Expression was induced with various amounts of ATC and the following fluorescence data acquired. Exposure time was 0.2 seconds. As no calibration data was obtained using purified mCherry, the results have been left in fluorescence arbitrary units. Images were analysed using imageJ software.<br><br />
mCherry fluorescence increases with amount of ATC used confirming that the dcmR gene was expressed under the control of the tet promoter and operator system.<br><br><br />
<img src="https://static.igem.org/mediawiki/parts/5/51/Oxford_DcmR-mCherry_expression_induced_by_0ng_ATC.png" <br />
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This data was then used to refine and test our models (see below).<br />
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<h1white>Adapting the model for the above data</h1white><br />
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<h1>Introduction</h1><br />
As you can see from the biochemistry bubble above, our team was only able to obtain fluorescence data for the first half of the genetic circuit (ATC induced mCherry response). On top of this, the wet lab team were unable to obtain data that measured how the fluorescence of a single culture changed with time, again because of time constraints. This is slightly limiting because it means that we don’t have any dynamic data for any part of our system, and therefore can’t test the modelling predictions of the speed of the biosensor’s response.<br />
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The original data is shown on the right with error bars showing the standard error of the measurements.<br />
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Standard error is calculated as the average standard deviation divided by the square root of the total number of readings.<br />
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<h1>How we used the model</h1><br />
However, to demonstrate the power of the computer models that we’ve built, we made our model simulate the same graph (mean fluorescence against ATC concentration added). To build this, we started from the graph shown in the <br />
<a href="https://2014.igem.org/Team:Oxford/biosensor_characterisation#show2">first modelling bubble</a><br />
on this page, shown here with a small basal rate (see <br />
<a href="https://static.igem.org/mediawiki/2014/b/be/Oxford_Equations_explained.png" target="_blank">where did these equations come from?</a><br />
). This graph shows how the predicted fluorescence of the cells changes with time in response to an addition of ATC halfway along the time scale. At this stage, all input values, model parameters and therefore results are arbitrary.<br />
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<img src="https://static.igem.org/mediawiki/2014/4/46/Oxford_data2.png" style="float:right;position:relative; width:80%;margin-left:10%;margin-right:10%;" /><br />
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We then ran the model for the correct amount of time (2 hours 20mins incubation with ATC) and ran it for lots of different concentrations of ATC over the range that the wet-lab team tried. The parameters are still arbitrary at this point (the same as above) and the results of the graphs are therefore arbitrary are as well, but the input values are now correct. The graphic below shows how we used the existing model to obtain the same graphs as the wet-lab team had obtained.<br />
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The numerical inputs that were used to model this data set were therefore:<br />
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<img src="https://static.igem.org/mediawiki/2014/f/f6/Oxford_data3.png" style="float:right;position:relative; width:100%;margin-left:0%;margin-right:0%;" /><br />
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<h1white>Finding parameters</h1white><br />
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<h1>Parameters</h1><br />
As all results are arbitrary up to this point, it is now time to calculate the parameters that will make the model’s response match up with the wet-lab data. The purpose of doing this is that the model will be able to give relatively accurate predictions of the response of the bacteria to further testing, therefore making the development of the biosensor much more efficient. The amount of data here will not allow us to calculate the parameters to a high level of accuracy, but it should be able to give us some very good approximations of what we can expect.<br />
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<br><br><img src="https://static.igem.org/mediawiki/2014/f/ff/Oxford_mCherry_circuit2.png" style="float:right;position:relative; width:40%;" /><br />
The parameters that we need to calculate are the constants in the differential equation that governs the behaviour of the first half of the genetic circuit. This half of the system is shown again here to remind the reader which part we are considering.<br />
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These parameters are:<br />
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<li>α1 = expression rate constant of <font style="font-style: italic;">dcmR</font></li><br />
<li>k1 = Michaelis - Menten constant of <font style="font-style: italic;">dcmR</font></li><br />
<li>d1 = degradation constant of <font style="font-style: italic;">dcmR</font></li><br />
<li>β1 = Basal transcription rate of <font style="font-style: italic;">dcmR</font></li><br />
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Remember that because the mCherry gene is tagged (translational fusion) onto the end of the <font style="font-style: italic;">dcmR</font> gene, <font style="font-style: oblique;"> the mCherry fluorescence will be the same as the amount of DcmR protein present</font>. However, there is not very comprehensive data in the literature about the values that we can expect from the behaviour of the <font style="font-style: italic;">dcmR</font> gene and its stability in vivo.<br />
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<h1>Degradation constant</h1><br />
The initial steady state of the system (before ATC has been added) is determined by two constants in the model. These constants are the degradation constant of DcmR and the basal transcription rate of the system. Due to the lack of numerical information in the literature on the behaviour of the <font style="font-style: italic;">dcmR</font> gene, the way of calculating these two parameters is by using the single basal rate data point from the wet-lab data (fluorescence value when 0ng of ATC has been added).<br />
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If we assume that the half-life of the dcmR protein is 3 hours [1] (180 minutes), we can calculate the degradation constant for our model. The exponential protein decay is therefore described by:<br />
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<h1>Basal transcription rate</h1><br />
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At the basal steady state, the rate of change of DcmR (and therefore fluorescence) is zero. As no ATC has been added to the system yet, the value of [ATC] is also zero.<br />
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<img src="https://static.igem.org/mediawiki/2014/5/55/Oxford_data7.png" style="float:left;position:relative; width:70%;margin-right:30%;margin-bottom:2%;" /><br />
This simplifies the equation to:<br />
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<img src="https://static.igem.org/mediawiki/2014/e/e0/Oxford_data8.png" style="float:left;position:relative; width:70%;margin-right:30%;margin-bottom:2%;" /><br />
As we want our model to accurately predict the fluorescence, we will substitute the fluorescence value in place of the [DcmR] and rearrange:<br />
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<img src="https://static.igem.org/mediawiki/2014/f/f6/Oxford_data9.png" style="float:left;position:relative; width:70%;margin-right:30%;margin-bottom:2%;" /><br />
Substituting in the value for δ1 that we found above and the basal steady state fluorescence level from the data (471 to 3 s.f.) gives the basal transcription rate as:<br />
<img src="https://static.igem.org/mediawiki/2014/0/0a/Oxford_data10.png" style="float:left;position:relative; width:70%;margin-right:30%;margin-bottom:2%;" /><br />
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<h1>Expression rate constant and Michaelis - Menten constant</h1><br />
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After the ATC has been added to the system, the value of [ATC] becomes non-zero. This means that the expression constant and the Michaelis – Menten constant start to affect the system.<br />
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To find the parameters that make the model’s output match the data values, we turned to the code that we had developed for parameter calibration. <br />
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<a href="https://2014.igem.org/Team:Oxford/calculating_parameters" target="_blank">How are we calculating the parameters?</a><br />
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This code gave the parameters as:<br />
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<h1>α1 = expression rate constant of <font style="font-style: italic;">dcmR</font></li> = 16.5 (fluorescence/min)</h1><br />
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<h1>k1 = Michaelis - Menten constant of <font style="font-style: italic;">dcmR</font></li> = 0.015 (ml/ng)</h1><br />
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<h1white>Using the parameters and model analysis</h1white><br />
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<h1white>Using the parameters and model analysis</h1white></div></a><br />
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<h1>Entering the correct parameters</h1><br />
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When the parameters that had been calculated above were entered into the model: <br />
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<img src="https://static.igem.org/mediawiki/2014/d/d2/Oxford_data11.png" style="float:left;position:relative; width:60%;margin-right:20%;margin-bottom:2%;margin-left:20%;" /><br />
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alongside the correct inputs:<br />
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<img src="https://static.igem.org/mediawiki/2014/f/f8/Oxford_data12.png" style="float:left;position:relative; width:60%;margin-right:20%;margin-bottom:2%;margin-left:20%;" /><br />
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The graph below shows the model's predictions plotted in the same figure as the data points that the wet-lab team obtained for the system:<br />
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<img src="https://static.igem.org/mediawiki/2014/2/26/Final_mCherry_dots.jpg" style="float:left;position:relative; width:80%;margin-right:10%;margin-bottom:2%;margin-left:10%;" /><br />
Plotting the model's output as a by interpolating between the calculated values makes the graph clearer:<br />
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<img src="https://static.igem.org/mediawiki/2014/d/dc/Final_mCherry_line.jpg" style="float:left;position:relative; width:80%;margin-right:10%;margin-bottom:2%;margin-left:10%;" /><br />
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<h1>Sensitivity</h1><br />
An important part of building mathematical models is sensitivity analysis of the results. This can be basically explained as wiggling all of the input values and parameters to see how much variations in each of these values affects the system output. This is especially important for finding parameters to describe the system as it is important to know what level of accuracy the values need to be found to provide a reasonable degree of prediction accuracy.<br />
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On top of this, it is possible to find what range of values the system is especially sensitive to. An example of this analysis is shown with a simple example that is relevant to our system below:<br />
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<img src="https://static.igem.org/mediawiki/2014/1/1a/Oxford_data13.png" style="float:left;position:relative; width:80%;margin-right:10%;margin-bottom:2%;margin-left:10%;" /><br />
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<h1>Stability</h1><br />
In our Engineering studies we have learnt detailed control theory. Control theory is an interdisciplinary branch of engineering and mathematics that deals with the behaviour of dynamical systems with inputs, and how their behavior is modified by feedback. The usual objective of control theory is to control a system so that its output follows a desired control signal, called the reference, which may be a fixed or changing value. This important because many dynamic systems can go unstable if they are given an unsafe set of input values and/or operating conditions.<br />
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However, as there are no feedback loops in this synthetic circuit, control theory analysis of this system isn't necessary.<br />
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<h1>Future experiment ideas from an Engineering design perspective</h1><br />
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To see details about how the wet lab team then used this model to guide their work see our <a href="https://2014.igem.org/Team:Oxford/biosensor_optimisation">optimisation</a> page.<br />
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<h1>Reference</h1><br />
[1] Dr George Wadhams by personal communication (14/10/2014)<br />
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<h1white>Double repressor or double activator?</h1white><br />
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<h1white>Double repressor or double activator?</h1white></div></a><br />
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The first data readings that we managed to obtain from the completed genetic circuit were qualitative sfGFP fluorescence readings. The results of these are shown here:<br />
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<h1>First system tested</h1><br />
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<img src="https://static.igem.org/mediawiki/2014/c/c6/Oxford_bottom_line.png " style="float:left;position:relative; width:40%;margin-right:60%;margin-bottom:2%;margin-left:0%;" /><br />
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The first system that we tested is shown here. This is the bottom level of our synthetic circuit. Testing just this plasmid allowed us to obtain some important information to allow us to characterise the genetic system. <br />
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The GFP fluorescence of this system was high and this is the basal transcription rate of the PdcmA promoter.<br />
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<h1>Second system tested</h1><br />
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<img src="https://static.igem.org/mediawiki/2014/c/c1/Oxford_system2.png" style="float:left;position:relative; width:40%;margin-right:60%;margin-bottom:2%;margin-left:0%;" /><br />
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The second system that we tested was the whole synthetic system without any DCM added. This allowed us to analyse just the effect of DcmR on the PdcmA promoter when it was compared to the result from system 1. <br />
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The fluorescence was much lower than the basal transcription rate of the PdcmA. This strongly suggests that the DcmR protein acts as a <u>repressor</u> of the PdcmA. <br />
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The next step is to prove that adding DCM represses this repression and increases the sfGFP fluorescence of the system.<br />
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<h1>Third system tested</h1><br />
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<img src="https://static.igem.org/mediawiki/2014/4/40/Oxford_Whole_circuit.png" style="float:left;position:relative; width:40%;margin-right:60%;margin-bottom:2%;margin-left:0%;" /><br />
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<u> Therefore we know that DcmR represses PdcmA with DCM repressing this repression. This means that the system is a double repressor!</u><br />
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<h1white>Engineering analysis of the sfGFP data</h1white><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_realisation"><img src="https://static.igem.org/mediawiki/2014/4/4c/Oxford_Realisation.png" style="float:right;position:relative; width:23%;" /></a><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_construction"><img src="https://static.igem.org/mediawiki/2014/a/ae/Oxford_construction.png" style="float:left;position:relative; width:23%; margin-left: 2.66%" /></a><br />
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Oxford iGEM 2014<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/biosensor_characterisationTeam:Oxford/biosensor characterisation2014-10-17T18:51:36Z<p>CorinnaO: proofread</p>
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<h1>Introduction: what are we characterising?</h1><br />
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<font style="font-style: italic;">Methylobacterium Extorquens</font> DM4 in the presence of DCM expresses DcmA, a dichloromethane dehalogenase.<br />
Within 1.5kb upstream of <font style="font-style: italic;">dcmA</font> and in the opposite orientation is a second gene encoding DcmR, a regulatory protein that controls expression of DcmA:<br><br><br />
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<img src="https://static.igem.org/mediawiki/2014/7/7f/Oxford_charac3.png" style="float:right;position:relative; width:80%; margin-right:10%;margin-bottom:2%;margin-left:10%;" /><br><br><br><br><br><br><br><br><br><br />
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In order to design and create a stable and sensitive system that responds to DCM we first need to characterise the regulatory nature of DcmR. Characterisation of this regulatory network has never been done before although it has been suggested to be a repressor [1]; we will be the first to fully characterise the mode of action of <font style="font-style: italic;">dcmR</font>. To do this we assume the following hypotheses for DCM activating the transcription of <font style="font-style: italic;">dcmR</font>: either double repression or double activation. In other words, either DcmR represses <font style="font-style: italic;">dcmA</font> expression and DcmR is in negatively modulated by the presence of DCM; or expression of <font style="font-style: italic;">dcmA</font> requires DcmR as an activator, with DcmR in turn only activated in the presence of DCM.<br><br><br />
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<h1white>What is currently known about DcmR?</h1white><br />
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<h1white>What is currently known about DcmR?</h1white></div></a><br />
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<h1>DcmR and regulation of <font style="font-style: italic;">dcmA</font> expression</h1><br />
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Mutants with <font style="font-style: italic;">dcmA</font> and the intergenic region but without complete <font style="font-style: italic;">dcmR</font> express <font style="font-style: italic;">dcmA</font> constitutively. Re-integration of <font style="font-style: italic;">dcmR</font> restores regulation of <font style="font-style: italic;">dcmA</font> expression at the transcriptional level [1]. In addition, it has been shown that the region including <font style="font-style: italic;">dcmR</font>, the intergenic region and <font style="font-style: italic;">dcmA</font> is sufficient to confer a DCM dependent response in genetically engineered Methylobacterium extorquens DM4 [2]. <br><br><br />
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<h1>DcmR and DNA-binding</h1><br />
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DcmR is thought to be a DNA binding protein as structure predicting software indicates that there is a helix-turn-helix domain at the N-terminal of the protein. Since the region between the two promoters for <font style="font-style: italic;">dcmR</font> and <font style="font-style: italic;">dcmA</font> can be deleted without any effect on regulation it has been suggested that DcmR does not to a secondary regulatory site in between the genes but most likely acts directly on the <font style="font-style: italic;">dcmA</font> promoter itself [1]. In addition, regulated expression of <font style="font-style: italic;">dcmA</font> is not effected when the <font style="font-style: italic;">dcmR</font> and <font style="font-style: italic;">dcmA</font> transcriptional units are placed on separate replicons thereby suggesting that their topology is independent of the regulatory network. It is therefore suggested that DcmR binds the DNA in the intergenic region with the simplest model of its mode of action being as a trans-acting DNA-binding repressor; however this remains to be fully validated [1].<br><br><br />
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We have therefore proceeded on the assumption that DcmR is directly influenced by the presence or absence of DCM and furthermore that we can use <font style="font-style: italic;">dcmR</font>, the intergenic region and <font style="font-style: italic;">dcmA</font> alone to characterise the regulatory network. <br><br><br />
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[1] La Roche, S. D., and T. Leisinger. "Identification of <font style="font-style: italic;">dcmR</font>, the regulatory gene governing expression of dichloromethane dehalogenase in Methylobacterium sp. strain DM4." Journal of bacteriology 173.21 (1991): 6714-6721. <br><br />
[2] Lopes, N., et al “Detection of dichloromethane with a bioluminescent (lux) bacterial bioreporter” J Ind Microbiol Biotechnol (2012) 39:45–53<br />
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<h1>Characterising the DcmR - DCM - P_dcmA interaction</h1><br />
To find out whether the gene <font style="font-style: italic;">dcmR</font> acts as a repressor or an activator on the promoter of the <font style="font-style: italic;">dcmA</font> gene, we attempted to build the genetic circuit shown above on the right. Having <font style="font-style: italic;">dcmR</font> under inducible TetR expression should allow us to have very good control of the amount of DcmR present. Additionally a translational fusion with DcmR and a mCherry fluorescence tag will act as another confirmation to the amount of DcmR present.<br />
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We then extensively modelled the circuit to discover how the response of the system would differ if it was either of the two circuit systems. Click the modelling bubbles (pink) to find out exactly how we achieved this.<br />
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<h1white>Modelling the first half of the genetic circuit</h1white><br />
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<h1white>Modelling the first half of the genetic circuit</h1white></div></a><br />
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<h1>Predicting the mCherry fluorescence</h1><br />
We simplified the first double repression by modelling it as an activation of <font style="font-style: italic;">dcmR</font> by ATC, albeit parameterised by different constants. This assumption is justified by the fact that we are able to precisely control the addition of ATC and measure the fluorescence of the mCherry.<br />
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We modelled this first step using both deterministic and stochastic models.<br />
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<h1>Biochemical equations</h1><br />
The biochemical equations that describe the behaviour of the top half of the genetic circuit are:<br />
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<img src="https://static.igem.org/mediawiki/2014/f/ff/Oxford_mCherry_circuit2.png" style="float:right;position:relative; width:40%;margin-left:30%;margin-right:30%;" /><br />
<img src="https://static.igem.org/mediawiki/2014/1/1d/Oxford_biochem_equations.png" style="float:right;position:relative; width:80%;margin-left:10%;margin-right:10%;" /><br />
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<h1>Deterministic</h1><br />
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<img src="https://static.igem.org/mediawiki/2014/2/2e/Oxford_DcmR_parameters.png" style="float:right;position:relative; height:8%; width:47%;" /><br />
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Deterministic models are very powerful tools for synthetic biology. They describe the behaviour of the bacteria at the population level and use Ordinary Differential Equations (ODEs) to relate each activation and repression. By constructing a cascade of differential equations one can build a realistic model of the average behaviour of the system.<br />
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The differential equation that describes this first step of the system is:<br />
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<br><br><img src="https://static.igem.org/mediawiki/2014/e/ed/Oxford_DcmR_activation.png" style="float:left;position:relative; height:8%; width:47%;" /><br />
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Solving this ODE in Matlab (with a zero basal transcription rate) predicts the following the response of the system:<br />
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This model works assuming that sufficient TetR is always present.<br />
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<img src="https://static.igem.org/mediawiki/2014/e/e0/Oxford_MCherry_circuit.png" style="float:left;position:relative; width:40%;" /><br />
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<img src="https://static.igem.org/mediawiki/2014/9/92/MCherry_graph.png" style="float:right;position:relative; width:60%;margin-bottom:3%;" /><br />
Oxford iGEM 2014<br />
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While the analysis of this circuit is not critical to the successful outcome of this part of the project, it will provide us with very good practice of both obtaining fluorescence time series data and accurately fitting the data to the model. It will also help us develop our methods of predicting future system behaviour. This is because this system is already well documented in the literature and so we should be able to test our methods and responses against well documented results from labs across the world.<br />
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As you can clearly see from the graph, the model predicts a large fluorescence increase as the input is added. This is the what we expect from the actual system and is the best approximation that is obtainable before we get experimental data.<br />
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In the graph above, the model is set to have a basal transcription rate of zero. This is why there is a zero fluorescence response before the input has been added - this corresponds to the tetO promoter not being leaky at all. This basal rate will be calibrated alongside all of the other parameters in the model.<br />
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<h1>Stochastic Modelling</h1><br />
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Stochastic modelling uses probability theory to predict the behaviour of a system. For our project, we used it to model the expression of GFP from bacteria. <br />
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We started with the Gillespie Algorithm, which considers the expression of GFP to be binary; a molecule of GFP is either produced or degraded. Before we determined which reaction happened, we had to work out when the reaction happened. Using the random number r (taken from a uniform distribution between 0 and 1), we produced another random number τ, which determined the time until the next reaction.<br />
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<img src="https://static.igem.org/mediawiki/2014/8/89/Oxford_Matt_equations_1.jpg" style="float:left;position:relative; height:8%; width:20%;" /><br />
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Where α_0 represents the probability that any reaction will happen, given by the following equation:<br />
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<img src="https://static.igem.org/mediawiki/2014/3/37/Oxford_Matt_equations_2.jpg" style="float:left;position:relative; height:4%; width:47%;" /><br />
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We modelled the probability of a molecule of GFP being created using the Michaelis-Menten model (α_1), incorporating a basal transcription rate (beta1). For the degradation, we assumed a simple proportional relationship; the more GFP you have the more likely it is that a molecule degrades (δ_1). The constant of proportionality will be a function of the intrinsic life time of the protein in the cell. We considered there to be no DCM originally, then a large step in DCM at time=0. This is similar to placing the detector in a DCM polluted source, to make the model more realistic the level of DCM would go down as it is degraded but we had no time to obtain data for this rate.<br />
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To decide if GFP was produced, we looked at the percentage of “reactions” which were productions, and then we compared this to a second random number (again taken from a uniform distribution from 0 to 1). If the random number was lower, then a GFP was created. If it was higher, then a GFP was degraded. In this way we make a weighted random choice about whether GFP was created or degraded.<br />
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<img src="https://static.igem.org/mediawiki/2014/e/e1/Oxford_Matt_equations_3.jpg" style="float:left;position:relative; height:8%; width:30%;" /><br />
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We only stored the time and amount of GFP when there was a reaction, to save on computation. However this made calculating the mean of realisation harder, but we got over the problem by….<br />
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Stochastic modelling is useful because it can show us the stochastic effects which are often seen in real bacteria. By calculating the variation of the mean of multiple GFP producing bacteria, we can also work out the standard deviation. Then if we assume that the system varies with respect to the normal distribution, we can produce error bounds for the production of GFP. Such that we can say, 90% of the time we can expect the production of GFP from a single bacterium to be within these 2 curves. This could be useful for seeing if results are unexpected, or, if there are multiple outliers, that our model is incorrect. If we average more and more bacteria then the mean curve tend towards the deterministic response. This is to be expected as we are now looking at the system as a whole and fluctuations in the production from individual bacteria are averaged out. In terms of their use, when looking at small amounts of bacterium the stochastic model would be better, because real random fluctuations can be seen. For larger bacterium groups, the deterministic response models the growth very well. The stochastic model can also model large groups but requires large number of realisations which causes simulations to take a lot longer to run.<br />
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When running the models, we picked arbitrary constants to view the general response. If we had more time we would have attempted to work out the basal rate, transcription rate and degradation rate of the GFP from DCM.<br />
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<h1white>How can we tell the systems apart?</h1white><br />
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<a href="#hide3" class="hide" id="hide3"><div class="modelling"><br />
<h1white>How can we tell the systems apart?</h1white></div></a><br />
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<img src="https://static.igem.org/mediawiki/2014/6/6f/Oxford_Characterisation_question.png" style="float:right;position:relative; width:42%;" /><br />
<h1>Predicting the sfGFP fluorescence</h1><br />
<h1>Introduction</h1><br />
To allow us to characterize the second half of the genetic circuit, we needed to be able to predict the difference in response. To do this, we constructed models by cascading the differential equations according to the respective circuit structures thereby producing two different potential system responses.<br />
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We then set up the differential equations necessary to solve this problem in Matlab. The method and results are as detailed below:<br />
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<a href="https://static.igem.org/mediawiki/2014/b/be/Oxford_Equations_explained.png" target="_blank"><img src="https://static.igem.org/mediawiki/2014/4/41/Oxford_equations.png" style="float:left;position:relative; width:50%;margin-right:25%;margin-left:25%;" /></a><br />
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Oxford iGEM 2014<br />
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<h1>Conclusion</h1><br />
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The bottom graphs illustrate the predicted response of each system to a simultaneous step input of both DCM and ATC. As you can see, there is little difference in the predicted steady-state value of the fluorescence, however, providing the basal transcription rate of GFP is relatively low, there should be a clear difference in the level of fluorescence before either of these inputs are added. This very easily identifiable difference between the two systems will enable us to characterize the genetic circuit present in our particular system.<br />
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<h1>Calculating the parameters</h1><br />
Calculating the many parameters for this system will be undoubtedly challenging. <br />
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<a href="https://2014.igem.org/Team:Oxford/calculating_parameters" target="_blank">How are we calculating the parameters?</a><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_characterisation#show7">Go to the data section where we calculated parameters for this part of the circuit.<br />
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<h1white>ATC induction of mCherry expression</h1white><br />
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<h1white>ATC induction of mCherry expression</h1white></div></a><br />
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<h1>Wetlab data showing response in level of mCherry expressed with different concs of ATC</h1><br />
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By making a translational fusion of mCherry at the C terminus of the dcmR gene under the tet promoter and tet operator system(see our <a href="https://2014.igem.org/Team:Oxford/biosensor_construction">Construction page</a> for details) we could measure mCherry fluorescence to gain information about dcmR induction by ATC. Expression was induced with various amounts of ATC and the following fluorescence data acquired. Exposure time was 0.2 seconds. As no calibration data was obtained using purified mCherry, the results have been left in fluorescence arbitrary units. Images were analysed using imageJ software.<br><br />
mCherry fluorescence increases with amount of ATC used confirming that the dcmR gene was expressed under the control of the tet promoter and operator system.<br><br><br />
<img src="https://static.igem.org/mediawiki/parts/5/51/Oxford_DcmR-mCherry_expression_induced_by_0ng_ATC.png" <br />
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This data was then used to refine and test our models (see below).<br />
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<h1white>Adapting the model for the above data</h1white><br />
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<h1white>Adapting the model for the above data</h1white></div></a><br />
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<h1>Introduction</h1><br />
As you can see from the biochemistry bubble above, our team was only able to obtain fluorescence data for the first half of the genetic circuit (ATC induced mCherry response). On top of this, the wet lab team were unable to obtain data that measured how the fluorescence of a single culture changed with time, again because of time constraints. This is slightly limiting because it means that we don’t have any dynamic data for any part of our system, and therefore can’t test the modelling predictions of the speed of the biosensor’s response.<br />
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The original data is shown on the right with error bars showing the standard error of the measurements.<br />
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Standard error is calculated as the average standard deviation divided by the square root of the total number of readings.<br />
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<h1>How we used the model</h1><br />
However, to demonstrate the power of the computer models that we’ve built, we made our model simulate the same graph (mean fluorescence against ATC concentration added). To build this, we started from the graph shown in the <br />
<a href="https://2014.igem.org/Team:Oxford/biosensor_characterisation#show2">first modelling bubble</a><br />
on this page, shown here with a small basal rate (see <br />
<a href="https://static.igem.org/mediawiki/2014/b/be/Oxford_Equations_explained.png" target="_blank">where did these equations come from?</a><br />
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<img src="https://static.igem.org/mediawiki/2014/4/46/Oxford_data2.png" style="float:right;position:relative; width:80%;margin-left:10%;margin-right:10%;" /><br />
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We then ran the model for the correct amount of time (2 hours 20mins incubation with ATC) and ran it for lots of different concentrations of ATC over the range that the wet-lab team tried. The parameters are still arbitrary at this point (the same as above) and the results of the graphs are therefore arbitrary are as well, but the input values are now correct. The graphic below shows how we used the existing model to obtain the same graphs as the wet-lab team had obtained.<br />
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The numerical inputs that were used to model this data set were therefore:<br />
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<h1white>Finding parameters</h1white><br />
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<h1white>Finding parameters</h1white></div></a><br />
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<h1>Parameters</h1><br />
As all results are arbitrary up to this point, it is now time to calculate the parameters that will make the model’s response match up with the wet-lab data. The purpose of doing this is that the model will be able to give relatively accurate predictions of the response of the bacteria to further testing, therefore making the development of the biosensor much more efficient. The amount of data here will not allow us to calculate the parameters to a high level of accuracy, but it should be able to give us some very good approximations of what we can expect.<br />
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The parameters that we need to calculate are the constants in the differential equation that governs the behaviour of the first half of the genetic circuit. This half of the system is shown again here to remind the reader which part we are considering.<br />
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These parameters are:<br />
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<li>α1 = expression rate constant of <font style="font-style: italic;">dcmR</font></li><br />
<li>k1 = Michaelis - Menten constant of <font style="font-style: italic;">dcmR</font></li><br />
<li>d1 = degradation constant of <font style="font-style: italic;">dcmR</font></li><br />
<li>β1 = Basal transcription rate of <font style="font-style: italic;">dcmR</font></li><br />
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Remember that because the mCherry gene is tagged (translational fusion) onto the end of the <font style="font-style: italic;">dcmR</font> gene, <font style="font-style: oblique;"> the mCherry fluorescence will be the same as the amount of DcmR protein present</font>. However, there is not very comprehensive data in the literature about the values that we can expect from the behaviour of the <font style="font-style: italic;">dcmR</font> gene and its stability in vivo.<br />
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<h1>Degradation constant</h1><br />
The initial steady state of the system (before ATC has been added) is determined by two constants in the model. These constants are the degradation constant of DcmR and the basal transcription rate of the system. Due to the lack of numerical information in the literature on the behaviour of the <font style="font-style: italic;">dcmR</font> gene, the way of calculating these two parameters is by using the single basal rate data point from the wet-lab data (fluorescence value when 0ng of ATC has been added).<br />
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If we assume that the half-life of the dcmR protein is 3 hours [1] (180 minutes), we can calculate the degradation constant for our model. The exponential protein decay is therefore described by:<br />
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<br><br><img src="https://static.igem.org/mediawiki/2014/0/0d/Oxford_data6.png" style="float:left;position:relative; width:25%;margin-right:75%;margin-bottom:2%;" /><br />
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<h1>Basal transcription rate</h1><br />
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At the basal steady state, the rate of change of DcmR (and therefore fluorescence) is zero. As no ATC has been added to the system yet, the value of [ATC] is also zero.<br />
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<img src="https://static.igem.org/mediawiki/2014/5/55/Oxford_data7.png" style="float:left;position:relative; width:70%;margin-right:30%;margin-bottom:2%;" /><br />
This simplifies the equation to:<br />
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<img src="https://static.igem.org/mediawiki/2014/e/e0/Oxford_data8.png" style="float:left;position:relative; width:70%;margin-right:30%;margin-bottom:2%;" /><br />
As we want our model to accurately predict the fluorescence, we will substitute the fluorescence value in place of the [DcmR] and rearrange:<br />
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<img src="https://static.igem.org/mediawiki/2014/f/f6/Oxford_data9.png" style="float:left;position:relative; width:70%;margin-right:30%;margin-bottom:2%;" /><br />
Substituting in the value for δ1 that we found above and the basal steady state fluorescence level from the data (471 to 3 s.f.) gives the basal transcription rate as:<br />
<img src="https://static.igem.org/mediawiki/2014/0/0a/Oxford_data10.png" style="float:left;position:relative; width:70%;margin-right:30%;margin-bottom:2%;" /><br />
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<h1>Expression rate constant and Michaelis - Menten constant</h1><br />
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After the ATC has been added to the system, the value of [ATC] becomes non-zero. This means that the expression constant and the Michaelis – Menten constant start to affect the system.<br />
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To find the parameters that make the model’s output match the data values, we turned to the code that we had developed for parameter calibration. <br />
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<a href="https://2014.igem.org/Team:Oxford/calculating_parameters" target="_blank">How are we calculating the parameters?</a><br />
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This code gave the parameters as:<br />
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<h1>α1 = expression rate constant of <font style="font-style: italic;">dcmR</font></li> = 16.5 (fluorescence/min)</h1><br />
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<h1>k1 = Michaelis - Menten constant of <font style="font-style: italic;">dcmR</font></li> = 0.015 (ml/ng)</h1><br />
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<h1white>Using the parameters and model analysis</h1white><br />
<img src="https://static.igem.org/mediawiki/2014/4/4d/Oxford_plus-sign-clip-art.png" style="float:right;position:relative; width:2%;" /><br />
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<h1white>Using the parameters and model analysis</h1white></div></a><br />
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<h1>Entering the correct parameters</h1><br />
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When the parameters that had been calculated above were entered into the model: <br />
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<img src="https://static.igem.org/mediawiki/2014/d/d2/Oxford_data11.png" style="float:left;position:relative; width:60%;margin-right:20%;margin-bottom:2%;margin-left:20%;" /><br />
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alongside the correct inputs:<br />
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<img src="https://static.igem.org/mediawiki/2014/f/f8/Oxford_data12.png" style="float:left;position:relative; width:60%;margin-right:20%;margin-bottom:2%;margin-left:20%;" /><br />
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The graph below shows the model's predictions plotted in the same figure as the data points that the wet-lab team obtained for the system:<br />
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<img src="https://static.igem.org/mediawiki/2014/2/26/Final_mCherry_dots.jpg" style="float:left;position:relative; width:80%;margin-right:10%;margin-bottom:2%;margin-left:10%;" /><br />
Plotting the model's output as a by interpolating between the calculated values makes the graph clearer:<br />
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<img src="https://static.igem.org/mediawiki/2014/d/dc/Final_mCherry_line.jpg" style="float:left;position:relative; width:80%;margin-right:10%;margin-bottom:2%;margin-left:10%;" /><br />
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<h1>Sensitivity</h1><br />
An important part of building mathematical models is sensitivity analysis of the results. This can be basically explained as wiggling all of the input values and parameters to see how much variations in each of these values affects the system output. This is especially important for finding parameters to describe the system as it is important to know what level of accuracy the values need to be found to provide a reasonable degree of prediction accuracy.<br />
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On top of this, it is possible to find what range of values the system is especially sensitive to. An example of this analysis is shown with a simple example that is relevant to our system below:<br />
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<img src="https://static.igem.org/mediawiki/2014/1/1a/Oxford_data13.png" style="float:left;position:relative; width:80%;margin-right:10%;margin-bottom:2%;margin-left:10%;" /><br />
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<h1>Stability</h1><br />
In our Engineering studies we have learnt detailed control theory. Control theory is an interdisciplinary branch of engineering and mathematics that deals with the behaviour of dynamical systems with inputs, and how their behavior is modified by feedback. The usual objective of control theory is to control a system so that its output follows a desired control signal, called the reference, which may be a fixed or changing value. This important because many dynamic systems can go unstable if they are given an unsafe set of input values and/or operating conditions.<br />
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However, as there are no feedback loops in this synthetic circuit, control theory analysis of this system isn't necessary.<br />
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<h1>Future experiment ideas from an Engineering design perspective</h1><br />
<br />
To see details about how the wet lab team then used this model to guide their work see our <a href="https://2014.igem.org/Team:Oxford/biosensor_optimisation">optimisation</a> page.<br />
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<h1>Reference</h1><br />
[1] Dr George Wadhams by personal communication (14/10/2014)<br />
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<h1white>Double repressor or double activator?</h1white><br />
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<h1white>Double repressor or double activator?</h1white></div></a><br />
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The first data readings that we managed to obtain from the completed genetic circuit were qualitative sfGFP fluorescence readings. The results of these are shown here:<br />
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<h1>First system tested</h1><br />
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<img src="https://static.igem.org/mediawiki/2014/c/c6/Oxford_bottom_line.png " style="float:left;position:relative; width:40%;margin-right:60%;margin-bottom:2%;margin-left:0%;" /><br />
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The first system that we tested is shown here. This is the bottom level of our synthetic circuit. Testing just this plasmid allowed us to obtain some important information to allow us to characterise the genetic system. <br />
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The GFP fluorescence of this system was high and this is the basal transcription rate of the PdcmA promoter.<br />
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<h1>Second system tested</h1><br />
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<img src="https://static.igem.org/mediawiki/2014/c/c1/Oxford_system2.png" style="float:left;position:relative; width:40%;margin-right:60%;margin-bottom:2%;margin-left:0%;" /><br />
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The second system that we tested was the whole synthetic system without any DCM added. This allowed us to analyse just the effect of DcmR on the PdcmA promoter when it was compared to the result from system 1. <br />
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The fluorescence was much lower than the basal transcription rate of the PdcmA. This strongly suggests that the DcmR protein acts as a <u>repressor</u> of the PdcmA. <br />
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The next step is to prove that adding DCM represses this repression and increases the sfGFP fluorescence of the system.<br />
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<h1>Third system tested</h1><br />
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<img src="https://static.igem.org/mediawiki/2014/4/40/Oxford_Whole_circuit.png" style="float:left;position:relative; width:40%;margin-right:60%;margin-bottom:2%;margin-left:0%;" /><br />
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<u> Therefore we know that DcmR represses PdcmA with DCM repressing this repression. This means that the system is a double repressor!</u><br />
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<h1white>Engineering analysis of the sfGFP data</h1white><br />
<img src="https://static.igem.org/mediawiki/2014/4/4d/Oxford_plus-sign-clip-art.png" style="float:right;position:relative; width:2%;" /><br />
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<h1white>Engineering analysis of the sfGFP data</h1white></div></a><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_realisation"><img src="https://static.igem.org/mediawiki/2014/4/4c/Oxford_Realisation.png" style="float:right;position:relative; width:23%;" /></a><br />
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Oxford iGEM 2014<br />
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University of Oxford’s first iGEM team presents: DCMation, a novel bioremediation approach whose applications are limited only by the versatility of bacterial metabolism. OxiGEM are tackling environmental pollution by developing a user-friendly device for the detection & degradation of the hazardous yet indispensable solvent dichloromethane (DCM), to illustrate. <br />
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Inspired by the DCM degradation pathway of <font style="font-style: italic;">M. extorquens</font> DM4, our project is driven and refined by the dialogue between modelling simulations and experimental data. Bioremediation is optimised by expressing the DCM degrading system in host strains, along with microcompartments to accelerate the reaction and minimise toxic intermediates. Our biosensor is tuned to our characterisation and improvements of the catalytic efficiency of the system, while incorporation of the bacteria into novel diffusion-limiting biopolymeric beads ensures safe and rapid degradation.<br />
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This all-round modular design and scalability make DCMation ideal for extension to the disposal of many harmful substances. Explore our wiki for more!<br />
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<div class="Heading" style="position:fixed;bottom:3.8%; left: 7%; width:7%;max-height:6%;"><br />
<a href="http://www.bbsrc.ac.uk/home/home.aspx" target="_blank"><img src="https://static.igem.org/mediawiki/2014/3/3e/BBSRC.png"<br />
style="width: 110%;"></a><br />
</div><br />
<div class="Heading" style="position:fixed;bottom:3.8%; left: 16.5%; width:10%;max-height:6%;"><br />
<a href="http://www.biochemistry.org" target="_blank"><img src="https://static.igem.org/mediawiki/2014/7/75/BS.png"<br />
style="width: 110%;"></a><br />
</div><br />
<div class="Heading" style="position:fixed;bottom:3.4%; left: 29%; width:9%;max-height:6%;"><br />
<a href="http://www.sgm.ac.uk" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/20/SGM.png"<br />
style="width: 110%;"></a><br />
</div><br />
<div class="Heading" style="position:fixed;bottom:4.4%; left: 40.4%; width:10%;max-height:6%;"><br />
<a href="http://www.wellcome.ac.uk" target="_blank"><img src="https://static.igem.org/mediawiki/2014/4/4c/WT.png"<br />
style="width: 110%;"></a><br />
</div><br />
<div class="Heading" style="position:fixed;bottom:2.7%; left: 53.5%; width:6.5%;max-height:6%;"><br />
<a href="http://www.deskgen.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/6/65/Oxfordigem_deskgen.png"<br />
style="width: 110%;"></a><br />
</div><br />
<div class="Heading" style="position:fixed;bottom:2.5%; left: 62.7%; width:9.5%;max-height:6%;"><br />
<a href="http://www.bioch.ox.ac.uk/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/20/Oxfordigem_dept.png"<br />
style="width: 110%;"></a><br />
</div><br />
<div class="Heading" style="position:fixed;bottom:3%; left: 75%; width:7%;max-height:6%;"><br />
<a href="http://www.neb.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/c/c5/OxigemNeb_logo.jpg"<br />
style="width: 110%;"></a><br />
</div><br />
<div class="Heading" style="position:fixed;bottom:3%; left: 84%; width:8%;max-height:6%;"><br />
<a href="http://www.snapgene.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/f/ff/Oxfordigem_snapgene.png"<br />
style="width: 110%;"></a><br />
</div><br />
</div><br />
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</html></div>CorinnaOhttp://2014.igem.org/Team:Oxford/ResultsTeam:Oxford/Results2014-10-16T15:29:52Z<p>CorinnaO: </p>
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<br><br />
<font style=“font-weight:600;”>Wetlab Results:</font> <br><br><br />
For the bioremediation aspect of DCMation, we have managed to:<br><br><br />
1. purify the pUNI-ABTUNJK and pSB1C3-ABTUNJK plasmids, which encode the microcompartments<br><br />
2. verify expression of pUNI-ABTUNJK plasmids in E. coli using Western blotting<br><br />
3. insert the ABTUNJK vector into pSRKGm using Gibson assembly, for expression in P. putida <br><br />
4. insert the dcmA gene into the pCM66 backbone using Gibson assembly, since pCM66 is suitable for expression in Methylobacterium extorquens DM4 <br><br />
5. insert dcmA into the pRSFDuet, and transformed this into DH5α cells for hypermutagenic PCR on the dcmA gene <br><br />
6. fuse dcmA and sfGFP using PCR, followed by restriction and ligation to insert the dcmA-sfGFP transcriptional fusion into pRSFDuet. This was then transformed into DH5α cells and imaged using fluorescence microscopy <br><br />
7. insert microcompartment-tagged dcmA into pRSFDuet<br><br />
8. insert the microcompartment-tagged sfGFP into pME6010, and used fluorescence microscopy to image this<br><br />
<br />
<br><br><br />
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<font style=“font-weight:600;”>Realisation Results:</font> <br><br><br />
For the containment of our bacteria, we have managed to: <br><br><br />
1. synthesise novel agarose beads that have a polymeric coating which limits DCM diffusion into the beads. This allows optimum degradation by the bioremediation bacteria, while physically containing the bacteria for safety reasons <br><br />
2. verify the functioning of the biopolymeric beads by measuring diffusion using indigo dye<br />
3. use computer-aided modelling to design a prototype of the DCMation system, and physically constructed this container <br><br />
4. 3D print a cartridge to hold our biosensor bacteria, which can easily be replaced by the user<br><br />
5. construct a prototype circuit that lights up when the photodiodes detect light emission from our biosensing bacteria that are contained in the cartridge. This lets the user have a simple yes/no response to whether the contents of the container are safe for disposal.<br><br />
<br />
<br><br />
<br><br />
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</body><br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/ResultsTeam:Oxford/Results2014-10-16T15:23:59Z<p>CorinnaO: </p>
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<br><br />
Wetlab Results: <br><br><br />
For the bioremediation aspect of DCMation, we have managed to:<br><br><br />
1. purify the pUNI-ABTUNJK and pSB1C3-ABTUNJK plasmids, which encode the microcompartments<br><br />
2. verify expression of pUNI-ABTUNJK plasmids in E. coli using Western blotting<br><br />
3. insert the ABTUNJK vector into pSRKGm using Gibson assembly, for expression in P. putida <br><br />
4. insert the dcmA gene into the pCM66 backbone using Gibson assembly, since pCM66 is suitable for expression in Methylobacterium extorquens DM4 <br><br />
5. insert dcmA into the pRSFDuet, and transformed this into DH5α cells for hypermutagenic PCR on the dcmA gene <br><br />
6. fuse dcmA and sfGFP using PCR, followed by restriction and ligation to insert the dcmA-sfGFP transcriptional fusion into pRSFDuet. This was then transformed into DH5α cells and imaged using fluorescence microscopy <br><br />
7. insert microcompartment-tagged dcmA into pRSFDuet<br><br />
8. insert the microcompartment-tagged sfGFP into pME6010, and used fluorescence microscopy to image this<br><br />
<br />
<br><br><br />
<br />
Realisation Results: <br><br><br />
For the containment of our bacteria, we have managed to: <br><br><br />
1. synthesise novel agarose beads that have a polymeric coating which limits DCM diffusion into the beads. This allows optimum degradation by the bioremediation bacteria, while physically containing the bacteria for safety reasons <br><br />
2. use computer-aided modelling to design a prototype of the DCMation system, and physically constructed this container <br><br />
4. 3D print a cartridge to hold our biosensor bacteria, which can easily be replaced by the user<br><br />
5. construct a prototype circuit that lights up when the photodiodes detect light emission from our biosensing bacteria that are contained in the cartridge. This lets the user have a simple yes/no response to whether the contents of the container are safe for disposal.<br><br />
<br />
<br><br />
<br><br />
</div><br />
<br><br />
</body><br />
</html><br />
<br />
{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/ResultsTeam:Oxford/Results2014-10-16T15:23:17Z<p>CorinnaO: </p>
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<div style="background-color:white; opacity:0.9; Height:75px; width:100%;margin-top:5px:margin-bottom:5px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#00000; font-weight: 450;"><br><center><font style="opacity:0.6">Results</font></center></div><br />
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<br><br />
<p> Wetlab Results: <br><br><br />
For the bioremediation aspect of DCMation, we have managed to:<br><br><br />
1. purify the pUNI-ABTUNJK and pSB1C3-ABTUNJK plasmids, which encode the microcompartments<br><br />
2. verify expression of pUNI-ABTUNJK plasmids in E. coli using Western blotting<br><br />
3. insert the ABTUNJK vector into pSRKGm using Gibson assembly, for expression in P. putida <br><br />
4. insert the dcmA gene into the pCM66 backbone using Gibson assembly, since pCM66 is suitable for expression in Methylobacterium extorquens DM4 <br><br />
5. insert dcmA into the pRSFDuet, and transformed this into DH5α cells for hypermutagenic PCR on the dcmA gene <br><br />
6. fuse dcmA and sfGFP using PCR, followed by restriction and ligation to insert the dcmA-sfGFP transcriptional fusion into pRSFDuet. This was then transformed into DH5α cells and imaged using fluorescence microscopy <br><br />
7. insert microcompartment-tagged dcmA into pRSFDuet<br><br />
8. insert the microcompartment-tagged sfGFP into pME6010, and used fluorescence microscopy to image this<br><br />
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Realisation Results: <br><br><br />
For the containment of our bacteria, we have managed to: <br><br><br />
1. synthesise novel agarose beads that have a polymeric coating which limits DCM diffusion into the beads. This allows optimum degradation by the bioremediation bacteria, while physically containing the bacteria for safety reasons <br><br />
2. use computer-aided modelling to design a prototype of the DCMation system, and physically constructed this container <br><br />
4. 3D print a cartridge to hold our biosensor bacteria, which can easily be replaced by the user<br><br />
5. construct a prototype circuit that lights up when the photodiodes detect light emission from our biosensing bacteria that are contained in the cartridge. This lets the user have a simple yes/no response to whether the contents of the container are safe for disposal.<br><br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/ResultsTeam:Oxford/Results2014-10-16T15:14:39Z<p>CorinnaO: </p>
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For the font style="font-weight:bold">bioremediation</font> aspect of DCMation, we have managed to:<br><br><br />
1. purify the pUNI-ABTUNJK and pSB1C3-ABTUNJK plasmids, which encode the microcompartments<br><br />
2. verify expression of pUNI-ABTUNJK plasmids in E. coli using Western blotting<br><br />
3. insert the ABTUNJK vector into pSRKGm using Gibson assembly, for expression in P. putida <br><br />
4. insert the dcmA gene into the pCM66 backbone using Gibson assembly, since pCM66 is suitable for expression in Methylobacterium extorquens DM4 <br><br />
5. insert dcmA into the pRSFDuet, and transformed this into DH5α cells for hypermutagenic PCR on the dcmA gene <br><br />
6. fuse dcmA and sfGFP using PCR, followed by restriction and ligation to insert the dcmA-sfGFP transcriptional fusion into pRSFDuet. This was then transformed into DH5α cells and imaged using fluorescence microscopy <br><br />
7. insert microcompartment-tagged dcmA into pRSFDuet<br><br />
8. insert the microcompartment-tagged sfGFP into pME6010, and used fluorescence microscopy to image this<br><br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/ResultsTeam:Oxford/Results2014-10-16T15:13:37Z<p>CorinnaO: </p>
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Wetlab Results: <br><br />
For the bioremediation aspect of DCMation, we have managed to:<br><br />
1. purify the pUNI-ABTUNJK and pSB1C3-ABTUNJK plasmids, which encode the microcompartments<br><br />
2. verify expression of pUNI-ABTUNJK plasmids in E. coli using Western blotting<br><br />
3. insert the ABTUNJK vector into pSRKGm using Gibson assembly, for expression in P. putida <br><br />
4. insert the dcmA gene into the pCM66 backbone using Gibson assembly, since pCM66 is suitable for expression in Methylobacterium extorquens DM4 <br><br />
5. insert dcmA into the pRSFDuet, and transformed this into DH5α cells for hypermutagenic PCR on the dcmA gene <br><br />
6. fuse dcmA and sfGFP using PCR, followed by restriction and ligation to insert the dcmA-sfGFP transcriptional fusion into pRSFDuet. This was then transformed into DH5α cells and imaged using fluorescence microscopy <br><br />
7. insert microcompartment-tagged dcmA into pRSFDuet<br><br />
8. insert the microcompartment-tagged sfGFP into pME6010, and used fluorescence microscopy to image this<br><br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/P%26P_environmental_impactTeam:Oxford/P&P environmental impact2014-10-15T16:41:57Z<p>CorinnaO: </p>
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<h1blue2>Uses</h1blue2><br />
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Chlorinated solvents are organic solvents containing chlorine atoms in their molecular structure. They have a wide range of uses by individuals, professionals, and industry.<br />
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The chlorinated solvent DCM has been used in industry for over 80 years. Its unique combination of properties - low boiling point, high solvency power, relative inertness, low toxicity and non flammability - has led to its wide variety of applications. It is the most widely-used of the chlorinated solvents, particularly for pharmaceutical production, and is also used as an extraction medium/process solvent (Eurochlor). For these reasons, our team has chosen to use DCM as the case study chemical for which we will develop a bioremedication mechanism eventually applicable to all chlorinated solvents.<br />
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<h1blue2>Who produces chlorinated solvent waste, and why?</h1blue2><br />
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The worldwide production of DCM alone is estimated at around 570,000 tonnes, of which approximately 270,000 tonnes are thought to be produced in western Europe. Figures for chlorinated solvents generally are far higher but difficult to estimate with any accuracy due to the wide range of applications, which sometimes involve the use of only a small amount of solvent which individually be discounted but cumulatively these small scale uses are significant. <br />
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Eurochlor, the EU body responsible for the European Chlorinated Solvents Association) analyses the uses and impact of chlorinated solvents in three categories:<br><br />
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<li><h1black style="font-size: 100%;">Industrial</h1black><br />
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Chlorinated solvents have too many industrial applications to list. Amongst the most essential are food production, cleaning, the textile industry, manufacturing, foam blowing, fire extinguishers, and as an extraction solvent and functional fluid.</li><br />
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<li><h1black style="font-size: 100%;">Professional</h1black><br />
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These solvents are widely used in laboratories, as well as extensive use in dry cleaning, film cleaning and copying, aerosols, adhesives, and packaging.</li><br />
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<li><h1black style="font-size: 100%;">Consumer</h1black><br />
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Prominent uses include aerosols (despite controversies, chlorinated solvents remain present in a wide range of aerosol products including hairspray and deodorant), glue and other home decorating products such as paint and paint stripper. Chlorinated solvents are also common in home-use pest control sprays and are found in various washing and cleaning products. </li><br />
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<br><h1blue2>Environmental Impact</h1blue2><br />
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Current disposal methods for chlorinated solvents are inadequate on every level. Because individual users may use chlorinated solvents infrequently or in very small amounts, they make not think it worthwhile, may not have the means, or may lack the knowledge/understanding of chlorinated solvents to ensure waste is disposed of properly. Cumulatively, these small incidents amount to a large volume of chlorinated solvents which are simply poured down the drain or otherwise dangerously disposed of, leading to grave and often long-lasting environmental damage.<br />
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For worse is the impact of the much greater volumes of chlorinated solvents used professionally and in industry. Although disposal is regulated and there are high penalties for failure to deal properly chlorinated waste, contamination remains an issue. Meanwhile, even that waste which is disposed of in accordance with procedure causes environmental harm; currently chlorinated solvents which can no longer be recycled are simply incinerated, released damaging compounds into the atmosphere. <br />
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Click the images below to discover more about the damage caused by chlorinated solvent waste to the atmosphere, biosphere, ground and surface water, and to the marine environment.<br><br><br />
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<h1blue2><center>Atmosphere</center></h1blue2><br />
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The Environmental Protection Agency has expressed fears that even short lived halocarbons may have a significant detrimental effect on the global atmosphere, as well as concerns about our relative lack of understanding of the environmental effects of these compounds<font style="vertical-align: super; font-size: 70%;">2</font>. Several chlorinated solvents are listed by the U. S. Environmental Protection Agency (U.S. EPA) as a hazardous air pollutant (HAP) under the U.S. Clean Air Act. However, various environmental NGOs and organisations maintain that current regulation of chlorinated solvent disposal is inadequate - for example, chlorinated solvents are not regulated under the Montreal Protocol despite evidence that they may contribute to ozone depletion. <br />
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<h1blue2> Photochemical Smog</h1blue2><br />
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TCE and PBRC have both been linked to photochemical smog. Both these chemicals are used extensively in the textiles industry and current disposal is inadequate. Photochemical smog is a unique form of air pollution, caused by reactions between sunlight and pollutants. The products of these reactions are generally 'secondary' pollutants such as hydrocarbons or ozone (which in the lower atmosphere is not desirable as it causes irritation to the respiratory tract). <br />
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Photochemical smog is known to cause respiratory problems in humans and animals. Because the chemicals can travel on the wind, the problem can potentially affect all areas although it tends to be most serious in large cities. <br />
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<h1blue2>Global Warming</h1blue2><br />
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Dichloromethane has a Global Warming Potential (GWP) ten times greater than that of carbon dioxide, whilst trichloromethane has a GWP 30 times greater. At the time of writing, the GWPs of tetrachloroethene and trichloroethene are not known, but are expected to be comparable to those for DCM and TCM. <br />
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<h1blue2> Acid Rain</h1blue2><br />
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In the lower atmosphere degradation of chlorinated solvents is initiated by a reaction with the hydroxyl radical, and forms a variety of products including hydrochloric acid, formic acid, and phosgene (the colourless gas infamous for its use as a chemical weapon during World War One). These compounds dissolve in clouds and rain water, and are ultimately deposited from the atmosphere in acid the form of rain and snow.<br />
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Further, trichloroacetic acid (TCA) can be formed as a minor product in the atmospheric de-gradation of some chlorinated solvents. Studies have shown that TCA is broadly distributed in precipitation, surface water and soil on a global scale. Since the observed levels in soil in some areas have been found to exceed the accepted 'safe' levels (2.4 μg/kg for terrestrial organisms) the European Commission instructed producers of the relevant solvents to carry out extensive studies of the origin and fate of environmental TCA. Although the results of these studies suggest that TCA levels in soils could not be explained by precipitation alone, the European Union Risk Assessment nevertheless concluded that “it is considered unlikely that depo-sition of TCA from the atmosphere will by itself lead to levels of TCA in soil that pose a risk for ter-restrial organisms”.<br />
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<h1blue2><center>Biosphere</center></h1blue2> <br />
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<h1blue2>Humans</h1blue2><br />
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The Reference Dose (an estimate of the maximum level of continuous exposure to the human population which is unlikely to pose any significant risk of detrimental effects (excluding the risk of cancer of the course of a lifetime)) for DCM is 0.06 milligrams per kilogram of body weight per day. Worryingly, DCM has been found in some urban air and at some hazardous waste sites at average concentrations of 11 ppb of air, and the average daily intake of methylene chloride from outdoor air in three U.S. cities may reach 309 micrograms per day, suggesting it is entirely possible that intake may exceeds the reference dose in individual cases. <br />
The most frequent and dangerous exposure to chlorinated solvents generally occur in workplaces where the chemical is present. Workers are at risk of breathing in chlorinated solvents or accidently coming into skin contact with chemicals. Previous studies have shown concentrations of up to 1,000 ppm of DCM in air (note that 1 part per million is 1,000 times more than 1 part per billion) have been detected in general work areas, and even higher concentrations of up to 1,400 ppm have been detected in samples in the breathing zone of some workers. Such exposure levels far exceed the current recommended federal limits; The National Institute for Occupational Safety and Health (NIOSH) estimated that 1 million workers may be exposed to dangerous levels of dichloromethane, and for chlorinated solvents generally the figure is much higher.<br />
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DCM and other chlorinated solvents can have a devastating impact on human health. Case studies of DCM poisoning during paint stripping operations have shown that overexposure can be fatal to humans. Acute inhalation exposure can cause short term damage to the central nervous system including detriment to visual, auditory, and psychomotor functions, and irritation to the nose and throat. <br />
The major effects of chronic inhalation of DCM are also effects on the nervous system, including headaches, nausea, memory loss, and possibly dizziness. There is currently a lack of research indicating whether there may be developmental or reproductive effects in humans, although animal studies have previously shown that if DCM passes through the placental barrier there is a high risk of skeletal variations and/or lower fetal body weight. DCM is also considered to be a probable human carcinogen. Although research in this area is incomplete, animal studies have shown a sharp increase in liver and lung cancer and in mammary gland tumors following exposure to DCM. The US Environmental Protection Agency has concluded that, by a weight of evidence evaluation, 'dichloromethane is [and should be treated as] carcinogenic by a mutagenic mode of action'.<br />
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<h1blue2>Animals</h1blue2><br />
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Tests have shown that acute exposure to DCM causes moderate acute toxicity from oral/inhalation exposure in many animals. Chronic exposure can lead to problems with the liver, kidneys, nervous and cardiovascular systems of a variety of animals. <br />
There is also a risk, to humans as well as to animals, that DCM will be broken down by the body form carbon monoxide, which can cause respiratory problems and can ultimately be fatal.<br />
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<h1blue2>Plants</h1blue2><br />
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The No Observed Effect Concentration (NOEC) for the most sensitive species of plants was 46 μg/m. In some areas levels may exceed this, due to contamination of soil and groundwater. There is currently a worrying lack of understanding and research into the effects of chlorinated solvents on plant life. <br />
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<h1blue2><center>Ground & Surface Water</center></h1blue2><br />
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<h1blue2> Ground Water</h1blue2><br />
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TCE and TeCE are amongst the common contaminants and are particularly tricky to deal with due to the fact that their biodegradation pathways start off with reductive dechlorination to vinyl chloride, which in an anaerobic environment works fine. But then the process often gets stuck at vinyl chloride as that is typically oxidised in groundwater. With VC being far more carcinogenic than TCE and TeCE this is a problem.<br />
<br> TCE is probably the prevalent groundwater contaminant these days.<br />
In a public health statement, the Agency for Toxic Substances and Disease Registry (ASTDR) admitted that we do not know precisely how long chlorinated solvents may remain in the soil. What we do know, however, is that chlorinated solvents are a 'big deal' in groundwater - in fact, they are the most frequently detected groundwater contaminant in the USA. ASTDR also concedes that there is a possibility of contamination of drinking water by chlorinated solvents including dichloromethane1. <br />
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<h1blue2>Surface Water</h1blue2><br />
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Chlorinated solvent pollution also affect surface water - although these chemicals tend to volatilise, and are extensively diluted in big rivers, the environmental and drinking water quality standards are very low in comparison to their solubility. The figures are not trivial; according to the US Agency for Toxic Substances, averages of 68 ppb of methylene chloride in surface water and 98 ppb methylene chloride in groundwater have been found at some hazardous waste sites1 .<br />
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<h1blue2>Drinking Water</h1blue2><br />
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Conventional water treatment techniques (coagulation, sedimentation, filtration and chlorination) have been found to have a little or no effect in reducing concentrations of DCM in drinking water. Due to the volatile organic nature of DCM, there are two existing treatment technologies that public water systems can use: air stripping and granular activated carbon (GAC) adsorption The U.S. EPA recommends packed tower aeration (PTA) as a best available technology (BAT) for DCM removal in drinking water below the U.S. EPA Maximum Contaminant Level of 5 µg/L. However it should be noted that the selection of an appropriate treatment process for a specific water supply will depend on the characteristics of the raw water supply and the operational condition of the specific treatment method.<br />
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<h1blue2><center>Marine Environment</center></h1blue2><br />
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Chlorinated solvents are generally highly volatile and only sparingly soluble in water. Even if traces of solvents are briefly present in aqueous waste streams, they volatilise from rivers and lakes with a half-life of about a month or less, unless they are trapped in groundwater. Nevertheless, presence of chlorinated solvents is a concern due to its potential impact on marine life...<br />
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Oxford iGEM 2014<br />
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<h1blue2>Our Solution</h1blue2><br />
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We have developed a bioremediation system which degrades toxic chlorinated solvent pollution at its source - it can be used in chemistry laboratories, factories, and ultimately we hope to make the technology available to home users to dispose of any excess chlorinated solvents from home decorating and other processes.<br />
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We hope that this technology can have great benefits internationally. In countries such as the UK and US where chlorinated solvent pollution is strictly regulated, DCMation provides a straightforward, cheap, and easy to use method for compliance with these regulations. This is especially significant for small businesses, enterprises, and start-ups which may otherwise struggle to deal with the complexities and expenses of the disposal system. The availability and accessibility of bioremediation should also encourage increased compliance with environmental regulations, for the simple reason that environmentally responsible disposal will be as simple as pouring away the chlorinated solvent waste!<br />
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We also hope that in countries without such stringent environmental regulations, DCMation will enable individuals, companies, and researchers alike to act on their own initiative to dispose of chlorinated solvents in an environmentally responsible way. <br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Glen Gowers</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>The guy who makes everything happen, the Fred to our iGEM "Mystery Machine". When Glen isn't sailing around the country, he's hard at work in the lab and at the desk.<br />
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<a href="http://uk.linkedin.com/pub/glen-gowers/84/1a2/bba/"><b>Glen's LinkedIn profile</b></a> <br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Jack Hoffman</font> <br><font style="color:grey;font-weight:500;">(3rd year Chemist, St John's College)</font><br><br>Though prodigious in his own field, Jack has tried his hand at many academic disciplines, and this curiosity and fervour for science is most welcome amongst the team.<br> He has been working closely with our engineers, designing mathematical frameworks for our modelling, synthesising <a href="https://2014.igem.org/Team:Oxford/biopolymer_containment#show2" />biopolymer capsules</a> and on them running diffusion experiments in the lab.<br><br><br />
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Jack leads part 3 of the project and is in charge of our web development and graphics.<br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Corinna Oswald</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, St John's College)</font><br><br>Corinna is one of the founding members that organised Oxford’s first participation in iGEM, and was a key player in conceptualising our project 'DCMation'. <br><br />
In between perfecting the Viennese Waltz and denying that Austrians are always yodeling in the Alps (which we refuse to accept), she works reliably hard and efficient to ensure that both her and the entire team's work is completed to the highest standards. Her organisational and communication skills secure the smooth running of the project despite ever-increasing time pressure and the broadly interdisciplinary nature of our team.<br><br>contact: oswald.corinna@gmail.com </div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Andy Russell</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, The Queen's College)</font><br><br>Full-time biochemist, part-time fashionista; Andy fancies himself as the ''arbiter elegantarium'' of the team. Andy is now entering his third year as a biochemist at The Queen's College, where he is an accomplished sportsman.</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Sian Mcgibbon</font> <br><font style="color:grey;font-weight:500;">(3rd year Lawyer, University College)</font><br><br>Siân is the model lawyer along with everything that implies.<br><br />
She has led the Policy and Practices element of our project, with a particular emphasis on intellectual property policy and its role in shaping the developing field of synthetic biology.</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Tim Ang</font> <br><font style="color:grey;font-weight:500;">(3rd year Biologist, St John's College)</font><br><br>Tim's primary academic interests focus on evolutionary theory, particularly social evolution. A keen weightlifter, his frequent references to weights and protein shakes naturally make him the hub of enchanting conversation here at Oxford iGEM.<br><br><br />
Tim is in charge of our interlab study and poster.</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Emily Prichett</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>asfygia sdyasdyuf asdyu asydo yuasd yea sec asuif asgc asi gkuasd vyias hk;sbd ylgas d;v sdjog s;DC AGSLDC GAJSDG HAS DYG su sduigsuad g</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Philipp Lorenz</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>It was Phil's idea to bring iGEM to Oxford, and he continues to ensure our project runs as smoothly as a German train schedule.</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Fran Donellan</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, Oriel College)</font><br><br>Not just a member of the biochemist team but also leading our public engagement activities, no task is too big or small for Fran. This girl can provide sandwiches for 500 people at 24 hours' notice and look good doing it.</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Shahbano Soomro</font> <br><font style="color:grey;font-weight:500;">(3rd year PPEist, University College)</font><br><br>A third year student in Politics and Economics at University College, Shahbano has a particular interest in Environmental policy and philosophy. As well as helping Fran with organising our public engagement and events, she will be watching over our project from afar during the summer.</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Matt Booth</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, St John's College)</font><br><br>Matt is an avid Rugby player, having recently achieved great success with the college team, and adopts a no-nonsense approach to many of his university endeavours, including iGEM.</div><br />
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<div id="oliverprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/b/b7/OxigemOliver.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Oliver Vince</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, University College)</font><br><br><br />
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Oliver is our keenest team member - nothing can douse his motivation. In addition to taking charge of the maths and software, he feels responsible for our entertainment.<br />
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Oliver has been particularly involved with the modelling of the genetic circuits and the CAD/CAM side of the project. He has also played a leading role in the construction of the pages on the website.<br />
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<div id="leroyprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/9/91/OxigemLeroy.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Leroy Lim</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, St John's College)</font><br><br>During term, Leroy indulges in many of the quirks of Oxford life, namely rowing, bops and colourful trousers. During iGEM, he's our engineer and self-appointed social sec.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="z-index:5;position:absolute; width:100%;margin-top:91%;min-width:320px; border-radius:15px;"/><br />
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<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450; padding-left:15%;"><br><div style="opacity:0.7;">Supervisors</div></div><br />
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<img src="https://static.igem.org/mediawiki/2014/3/34/George.jpg" style="z-index:6;position:absolute;margin-top:105%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:106%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Dr George Wadhams</font><br><br> <br />
George Wadhams’ research interests lie in how bacteria sense and integrate environmental information. His group focuses on understanding in a quantitative manner how multiple, homologous pathways operate in individual cells and how the components of these pathways can be used to create synthetic pathways.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/2/2f/OxigemCiaran.jpg" style="z-index:6;position:absolute;margin-top:126.5%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:127.5%;width:500px;color:black;right:15%;"><font style="font-size:large;">Dr Ciar&aacute;n Kelly</font><br><br> <br />
Ciarán Kelly obtained a PhD for his work in Frank Sargant’s group, constructing and characterising synthetic biohydrogen production pathways in E. coli. He is interested in the construction, characterisation, and re-engineering synthetic enzymes and pathways for the production of high-value chemicals. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/2/25/OxigemKarl.jpg" style="z-index:6;position:absolute;margin-top:148%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:149%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Mr Karl Brune</font><br><br> <br />
Karl Brune's research interests lie in elucidating metabolic pathways. His previous work at Imperial College London was focused on engineering microbial consortia to enhance biomining and bioremediation, as well as on the study of photoautotrophic organisms. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="position:absolute; z-index:5; width:100%;margin-top:170.5%;min-width:320px; border-radius:15px;border-bottom-left-radius:15px;"/><br />
<div style="background-color:rgba(217, 217, 217, 0.7);position:absolute; z-index:5;Height:75px; width:100%;min-width:350px;margin-top:171.5%;font-size:65px;font-family:Helvetica;padding-top:5px; font-weight: 450;"><br />
<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450; padding-left:15%; "><br><div style="opacity:0.7;">Advisors</div></div><br />
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<img src="https://static.igem.org/mediawiki/2014/d/da/Judy.jpg" style="z-index:6;position:absolute;margin-top:184.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px; " /><br />
<div style="z-index:6;position:absolute;margin-top:185.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Professor Judy Armitage</font><br><br> <br />
Judy Armitage is interested in the dynamics of bacterial sensory transduction and the control of bacterial motility. In particular, her research group focuses on the communication between the sensory and adaption mechanisms of the two pathways as a model for sensory network integration in general..</div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/05/OxigemAnthony.jpg" style="z-index:6;position:absolute;margin-top:206%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:207%;width:500px;color:black;right:15%;"><font style="font-size:large;">Professor Anthony Watts</font><br><br> <br />
Anthony Watts’ group is devising solid state NMR methods for determining high-resolution details of information-rich sites within membrane receptors. Recent focus has been on the neurotensin receptor (NTS1), which is now available highly purified and monodispersed in detergent as well as in a ligand-binding form. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/4/4f/OxigemAntonis.jpg" style="z-index:6;position:absolute;margin-top:227.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:228.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Professor Antonis Papachristodoulou</font><br><br> <br />
Antonis Papachristodoulou’s research interests include systems and synthetic biology, network systems, aerospace systems and flow control, and convex optimisation. Furthermore, he works on modern control theory, robust stability analysis and design, as well as nonlinear dynamical systems and Lyapunov stability. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/09/OxigemAndreas.jpg" style="z-index:6;position:absolute;margin-top:249%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:250%;width:500px;color:black;right:15%;"><font style="font-size:large;">Andreas Harris<br />
</font><br><br> <br />
Andreas Harris works on the design and implementation of gene regulatory networks harnessing feedback to increase robustness and tunability. The designs are based around transcriptional networks and attempt to translate well-understood control modules, such as proportional and integral controllers, to biological systems. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/a/aa/OxigemLucas.jpg" style="opacity:0.95;z-index:6;position:absolute;margin-top:270.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:271.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Dr Lucas Black</font><br><br> <br />
Lucas is an imaging specialist helping out with quantification of parts and to give general guidance throughout the project.<br />
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The team is very grateful for Lucas's excellent photograpy, including our team photos and the photograph of the Radcliffe Camera on our homepage. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="position:absolute; z-index:5; width:100%;margin-top:292.5%;min-width:320px; border-radius:15px;border-bottom-left-radius:15px;"/><br />
<div style="background-color:rgba(217, 217, 217, 0.7);position:absolute; z-index:5; Height:75px; width:100%;min-width:600px;margin-top:293.5%;font-size:65px;font-family:Helvetica;padding-top:5px; font-weight: 450;"><br />
<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450;padding-left:15%;"><br><div style="opacity:0.7;">With thanks...</div></div><br />
<div style="background-color:rgba(235, 235, 235, 1);position:absolute; z-index:5;Height:200px; width:300%;margin-top:33px;margin-left:-30%;"></div><br />
</div><br />
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<div style="background-color:white;z-index:6;position:absolute;margin-top:306.8%;width:100%;color:black;border-top-right-radius:15px;border-bottom-left-radius:15px;padding:10px;margin-bottom:500px;"><font style="font-size:large;">Darragh Ennis</font><br><br />
For his 3D printing expertise.<br />
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<font style="font-size:large;">Josef Patoprsty</font><br><br />
For advising how to debug the website. <a href="http://josefnpat.com">http://josefnpat.com</a><br />
<br><br><br />
<font style="font-size:large;">Ashok Menon</font><br><br />
For his website advice and debugging expertise.<br />
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<font style="font-size:large;">Dr Frederic Coulon</font> (Department of Environmental Science and Technology, School of Applied Sciences, Cranfield University)<br><br />
For his information on DCM and water treatment.<br />
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<font style="font-size:large;">Shaun Rowson</font> (Team Leader - Groundwater & Contaminated Land, Environment Agency)<br><br />
For his detailed information on the problem of chlorinated solvents and how they are currently disposed of.<br />
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<font style="font-size:large;">Carla Brown</font><br><br />
For the generously donating several packs of her Bacteria Combat card game. <br><br><br />
<font style="font-size:large;">Janet Stott and Sarah Lloyd</font><br><br />
For their kind advice and assistance in our public engagement events at the Oxford University Museum of Natural History.<br />
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<br />
<a href="http://www.bbsrc.ac.uk/home/home.aspx" target="_blank"><img src="https://static.igem.org/mediawiki/2014/3/3e/BBSRC.png" style="position:absolute;width:19%;left:24%;top:40%;min-width:100px;" /></a><br />
<a href="http://www.biochemistry.org" target="_blank"><img src="https://static.igem.org/mediawiki/2014/7/75/BS.png" style="position:absolute;width:25%;left:50.5%;top:40%;" /></a><br />
<a href="http://www.sgm.ac.uk" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/20/SGM.png" style="position:absolute;width:22%;left:24%;top:51%" /></a><br />
<a href="http://www.wellcome.ac.uk" target="_blank"><img src="https://static.igem.org/mediawiki/2014/4/4c/WT.png" style="position:absolute;width:22%;left:50.5%;top:52%;" /></a><br />
<a href="http://www.deskgen.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/6/65/Oxfordigem_deskgen.png" style="position:absolute;width:15%;left:27%;top:62%" /></a><br />
<a href="http://www.bioch.ox.ac.uk/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/20/Oxfordigem_dept.png" style="position:absolute;width:22%;left:51%;top:63%;" /></a><br />
<a href="http://www.neb.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/b/b1/OxigemNeb_logo.png" style="position:absolute;width:18%;left:26%;top:75%" /></a><br />
<a href="http://www.snapgene.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/f/ff/Oxfordigem_snapgene.png" style="position:absolute;width:23%;left:49%;top:77%;" /></a><br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/teamTeam:Oxford/team2014-10-14T12:45:45Z<p>CorinnaO: </p>
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<img id="leroy" src="https://static.igem.org/mediawiki/2014/9/91/OxigemLeroy.jpg" style="position:absolute;width:20%;left:59.5%;margin-top:58.5%;border-bottom-right-radius:15px;" /><br />
<a href="#"><img id="leroy1" src="https://static.igem.org/mediawiki/2014/1/17/OxigemLeroy1.jpg" style="position:absolute;width:20%;left:59.5%;margin-top:58.5%;border-bottom-right-radius:15px;display:none;" onClick="profile('leroy')" /></a><br />
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<img id="tim" src="https://static.igem.org/mediawiki/2014/2/21/OxigemTim.jpg" style="position:absolute;width:20%;left:0.1%;margin-top:42%;border-bottom-left-radius:15px;" /><br />
<a href="#"><img id="tim1" src="https://static.igem.org/mediawiki/2014/3/33/OxigemTim1.jpg" style="position:absolute;width:20%;left:0.1%;margin-top:42%;border-bottom-left-radius:15px;display:none;" onClick="profile('tim')" /></a><br />
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<img id="jack" src="https://static.igem.org/mediawiki/2014/6/68/OxigemJack.jpg" style="position:absolute;width:20%;margin-top:13%;left:0.1%;border-top-left-radius:15px;" /><br />
<a href="#"><img id="jack1" src="https://static.igem.org/mediawiki/2014/e/ed/OxigemJack1.jpg" style="position:absolute;width:20%;margin-top:13%;left:0.1%;border-top-left-radius:15px;display:none;" onClick="profile('jack')" /></a><br />
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<img id="emily" src="https://static.igem.org/mediawiki/2014/6/6e/OxigemEmily.jpg" style="position:absolute;width:20%;left:20%;margin-top:29.5%;" /><br />
<a href="#"><img id="emily1" src="https://static.igem.org/mediawiki/2014/3/38/OxigemEmily1.jpg" style="position:absolute;width:20%;left:20%;margin-top:29.5%;display:none;" onClick="profile('emily')"/></a><br />
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<img id="phil" src="https://static.igem.org/mediawiki/2014/8/8b/OxigemPhil.jpg" style="position:absolute;width:20%;left:39.8%;margin-top:29.5%;" /><br />
<a href="#"><img id="phil1" src="https://static.igem.org/mediawiki/2014/9/90/OxigemPhil1.jpg" style="position:absolute;width:20%;left:39.8%;margin-top:29.5%;display:none;" onClick="profile('phil')" /></a><br />
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<img id="fran" src="https://static.igem.org/mediawiki/2014/5/5e/OxigemFran.jpg" style="position:absolute;width:20%;left:59.5%;margin-top:29.5%;" /><br />
<a href="#"><img id="fran1" src="https://static.igem.org/mediawiki/2014/c/c6/OxigemFran1.jpg" style="position:absolute;width:20%;left:59.5%;margin-top:29.5%;display:none;" onClick="profile('fran')" /></a><br />
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<img id="glen" src="https://static.igem.org/mediawiki/2014/9/97/OxigemGlen.jpg" style="position:absolute;width:20%;left:20%;border-top-left-radius:15px;" /><br />
<a href="#"><img id="glen1" src="https://static.igem.org/mediawiki/2014/5/5e/OxigemGlen1.jpg" style="position:absolute;width:20%;left:20%;border-top-left-radius:15px;display:none;" onClick="profile('glen')" /></a><br />
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<img id="corinna" src="https://static.igem.org/mediawiki/2014/b/be/OxigemCorinna.jpg" style="position:absolute;width:20%;left:39.8%;" /><br />
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<img id="andy" src="https://static.igem.org/mediawiki/2014/f/ff/OxigemAndy.jpg" style="position:absolute;width:20%;left:59.5%;border-top-right-radius:15px;" /><br />
<a href="#"><img id="andy1" src="https://static.igem.org/mediawiki/2014/e/e2/OxigemAndy1.jpg" style="position:absolute;width:20%;left:59.5%;border-top-right-radius:15px;display:none;" onClick="profile('andy')" /></a><br />
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<div id="profilewrap" onClick="profileHide()" style="background-color: rgba(0,0,0,0.7);z-index:11; position: fixed; top:0; left: 0; right: 0; bottom: 0;display:none;min-width:980px;"><br />
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<div id="glenprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/9/97/OxigemGlen.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Glen Gowers</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>The guy who makes everything happen, the Fred to our iGEM "Mystery Machine". When Glen isn't sailing around the country, he's hard at work in the lab and at the desk.<br />
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<a href="http://uk.linkedin.com/pub/glen-gowers/84/1a2/bba/"><b>Glen's LinkedIn profile</b></a> <br />
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<div id="jackprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/6/68/OxigemJack.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Jack Hoffman</font> <br><font style="color:grey;font-weight:500;">(3rd year Chemist, St John's College)</font><br><br>Though prodigious in his own field, Jack has tried his hand at many academic disciplines, and this curiosity and fervour for science is most welcome amongst the team.<br> He has been working closely with our engineers, designing mathematical frameworks for our modelling, synthesising <a href="https://2014.igem.org/Team:Oxford/biopolymer_containment#show2" />biopolymer capsules</a> and on them running diffusion experiments in the lab.<br><br><br />
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Jack leads part 3 of the project and is in charge of our web development and graphics.<br />
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<div id="corinnaprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/b/be/OxigemCorinna.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Corinna Oswald</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, St John's College)</font><br><br>Corinna is one of the founding members that organised Oxford’s first participation in iGEM, and was a key player in conceptualising our project 'DCMation'. <br><br />
In between perfecting the Viennese Waltz and denying that Austrians are always yodeling in the Alps (which we refuse to accept), she works reliably hard and efficient to ensure that both her and the entire team's work is completed to the highest standards. Her organisational and communication skills ensure the smooth running of the project despite ever-increasing time pressure and the broadly interdisciplinary nature of our team.<br><br>contact: oswald.corinna@gmail.com </div><br />
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<div id="andyprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/OxigemAndy.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Andy Russell</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, The Queen's College)</font><br><br>Full-time biochemist, part-time fashionista; Andy fancies himself as the ''arbiter elegantarium'' of the team. Andy is now entering his third year as a biochemist at The Queen's College, where he is an accomplished sportsman.</div><br />
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<div id="sianprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/4/47/OxigemSian.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Sian Mcgibbon</font> <br><font style="color:grey;font-weight:500;">(3rd year Lawyer, University College)</font><br><br>Siân is the model lawyer along with everything that implies.<br><br />
She has led the Policy and Practices element of our project, with a particular emphasis on intellectual property policy and its role in shaping the developing field of synthetic biology.</div><br />
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<div id="timprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/2/21/OxigemTim.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Tim Ang</font> <br><font style="color:grey;font-weight:500;">(3rd year Biologist, St John's College)</font><br><br>Tim's primary academic interests focus on evolutionary theory, particularly social evolution. A keen weightlifter, his frequent references to weights and protein shakes naturally make him the hub of enchanting conversation here at Oxford iGEM.<br><br><br />
Tim is in charge of our interlab study and poster.</div><br />
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<div id="emilyprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/6/6e/OxigemEmily.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Emily Prichett</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>asfygia sdyasdyuf asdyu asydo yuasd yea sec asuif asgc asi gkuasd vyias hk;sbd ylgas d;v sdjog s;DC AGSLDC GAJSDG HAS DYG su sduigsuad g</div><br />
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<div id="philprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/8/8b/OxigemPhil.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Philipp Lorenz</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>It was Phil's idea to bring iGEM to Oxford, and he continues to ensure our project runs as smoothly as a German train schedule.</div><br />
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<div id="franprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/5/5e/OxigemFran.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Fran Donellan</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, Oriel College)</font><br><br>Not just a member of the biochemist team but also leading our public engagement activities, no task is too big or small for Fran. This girl can provide sandwiches for 500 people at 24 hours' notice and look good doing it.</div><br />
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<div id="shahbanoprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/1/1d/OxigemShahbano.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Shahbano Soomro</font> <br><font style="color:grey;font-weight:500;">(3rd year PPEist, University College)</font><br><br>A third year student in Politics and Economics at University College, Shahbano has a particular interest in Environmental policy and philosophy. As well as helping Fran with organising our public engagement and events, she will be watching over our project from afar during the summer.</div><br />
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<div id="mattprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/d/d8/OxigemMatt.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Matt Booth</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, St John's College)</font><br><br>Matt is an avid Rugby player, having recently achieved great success with the college team, and adopts a no-nonsense approach to many of his university endeavours, including iGEM.</div><br />
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<div id="oliverprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/b/b7/OxigemOliver.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Oliver Vince</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, University College)</font><br><br><br />
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Oliver is our keenest team member - nothing can douse his motivation. In addition to taking charge of the maths and software, he feels responsible for our entertainment.<br />
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Oliver has been particularly involved with the modelling of the genetic circuits and the CAD/CAM side of the project. He has also played a leading role in the construction of the pages on the website.<br />
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<div id="leroyprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/9/91/OxigemLeroy.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Leroy Lim</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, St John's College)</font><br><br>During term, Leroy indulges in many of the quirks of Oxford life, namely rowing, bops and colourful trousers. During iGEM, he's our engineer and self-appointed social sec.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="z-index:5;position:absolute; width:100%;margin-top:91%;min-width:320px; border-radius:15px;"/><br />
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<div style="background-color:rgba(217, 217, 217, 0.7);position:absolute; z-index:5; Height:75px; width:100%;min-width:350px;margin-top:92%;font-size:65px;font-family:Helvetica;padding-top:5px; font-weight: 450;"><br />
<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450; padding-left:15%;"><br><div style="opacity:0.7;">Supervisors</div></div><br />
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<img src="https://static.igem.org/mediawiki/2014/3/34/George.jpg" style="z-index:6;position:absolute;margin-top:105%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:106%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Dr George Wadhams</font><br><br> <br />
George Wadhams’ research interests lie in how bacteria sense and integrate environmental information. His group focuses on understanding in a quantitative manner how multiple, homologous pathways operate in individual cells and how the components of these pathways can be used to create synthetic pathways.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/2/2f/OxigemCiaran.jpg" style="z-index:6;position:absolute;margin-top:126.5%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:127.5%;width:500px;color:black;right:15%;"><font style="font-size:large;">Dr Ciar&aacute;n Kelly</font><br><br> <br />
Ciarán Kelly obtained a PhD for his work in Frank Sargant’s group, constructing and characterising synthetic biohydrogen production pathways in E. coli. He is interested in the construction, characterisation, and re-engineering synthetic enzymes and pathways for the production of high-value chemicals. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/2/25/OxigemKarl.jpg" style="z-index:6;position:absolute;margin-top:148%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:149%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Mr Karl Brune</font><br><br> <br />
Karl Brune's research interests lie in elucidating metabolic pathways. His previous work at Imperial College London was focused on engineering microbial consortia to enhance biomining and bioremediation, as well as on the study of photoautotrophic organisms. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="position:absolute; z-index:5; width:100%;margin-top:170.5%;min-width:320px; border-radius:15px;border-bottom-left-radius:15px;"/><br />
<div style="background-color:rgba(217, 217, 217, 0.7);position:absolute; z-index:5;Height:75px; width:100%;min-width:350px;margin-top:171.5%;font-size:65px;font-family:Helvetica;padding-top:5px; font-weight: 450;"><br />
<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450; padding-left:15%; "><br><div style="opacity:0.7;">Advisors</div></div><br />
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<img src="https://static.igem.org/mediawiki/2014/d/da/Judy.jpg" style="z-index:6;position:absolute;margin-top:184.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px; " /><br />
<div style="z-index:6;position:absolute;margin-top:185.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Professor Judy Armitage</font><br><br> <br />
Judy Armitage is interested in the dynamics of bacterial sensory transduction and the control of bacterial motility. In particular, her research group focuses on the communication between the sensory and adaption mechanisms of the two pathways as a model for sensory network integration in general..</div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/05/OxigemAnthony.jpg" style="z-index:6;position:absolute;margin-top:206%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:207%;width:500px;color:black;right:15%;"><font style="font-size:large;">Professor Anthony Watts</font><br><br> <br />
Anthony Watts’ group is devising solid state NMR methods for determining high-resolution details of information-rich sites within membrane receptors. Recent focus has been on the neurotensin receptor (NTS1), which is now available highly purified and monodispersed in detergent as well as in a ligand-binding form. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/4/4f/OxigemAntonis.jpg" style="z-index:6;position:absolute;margin-top:227.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:228.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Professor Antonis Papachristodoulou</font><br><br> <br />
Antonis Papachristodoulou’s research interests include systems and synthetic biology, network systems, aerospace systems and flow control, and convex optimisation. Furthermore, he works on modern control theory, robust stability analysis and design, as well as nonlinear dynamical systems and Lyapunov stability. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/09/OxigemAndreas.jpg" style="z-index:6;position:absolute;margin-top:249%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:250%;width:500px;color:black;right:15%;"><font style="font-size:large;">Andreas Harris<br />
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Andreas Harris works on the design and implementation of gene regulatory networks harnessing feedback to increase robustness and tunability. The designs are based around transcriptional networks and attempt to translate well-understood control modules, such as proportional and integral controllers, to biological systems. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/a/aa/OxigemLucas.jpg" style="opacity:0.95;z-index:6;position:absolute;margin-top:270.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:271.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Dr Lucas Black</font><br><br> <br />
Lucas is an imaging specialist helping out with quantification of parts and to give general guidance throughout the project.<br />
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The team is very grateful for Lucas's excellent photograpy, including our team photos and the photograph of the Radcliffe Camera on our homepage. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="position:absolute; z-index:5; width:100%;margin-top:292.5%;min-width:320px; border-radius:15px;border-bottom-left-radius:15px;"/><br />
<div style="background-color:rgba(217, 217, 217, 0.7);position:absolute; z-index:5; Height:75px; width:100%;min-width:600px;margin-top:293.5%;font-size:65px;font-family:Helvetica;padding-top:5px; font-weight: 450;"><br />
<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450;padding-left:15%;"><br><div style="opacity:0.7;">With thanks...</div></div><br />
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<div style="background-color:white;z-index:6;position:absolute;margin-top:306.8%;width:100%;color:black;border-top-right-radius:15px;border-bottom-left-radius:15px;padding:10px;margin-bottom:500px;"><font style="font-size:large;">Darragh Ennis</font><br><br />
For his 3D printing expertise.<br />
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<font style="font-size:large;">Josef Patoprsty</font><br><br />
For advising how to debug the website. <a href="http://josefnpat.com">http://josefnpat.com</a><br />
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<font style="font-size:large;">Ashok Menon</font><br><br />
For his website advice and debugging expertise.<br />
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<font style="font-size:large;">Dr Frederic Coulon</font> (Department of Environmental Science and Technology, School of Applied Sciences, Cranfield University)<br><br />
For his information on DCM and water treatment.<br />
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<font style="font-size:large;">Shaun Rowson</font> (Team Leader - Groundwater & Contaminated Land, Environment Agency)<br><br />
For his detailed information on the problem of chlorinated solvents and how they are currently disposed of.<br />
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<font style="font-size:large;">Carla Brown</font><br><br />
For the generously donating several packs of her Bacteria Combat card game. <br><br><br />
<font style="font-size:large;">Janet Stott and Sarah Lloyd</font><br><br />
For their kind advice and assistance in our public engagement events at the Oxford University Museum of Natural History.<br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Glen Gowers</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>The guy who makes everything happen, the Fred to our iGEM "Mystery Machine". When Glen isn't sailing around the country, he's hard at work in the lab and at the desk.<br />
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<a href="http://uk.linkedin.com/pub/glen-gowers/84/1a2/bba/"><b>Glen's LinkedIn profile</b></a> <br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Jack Hoffman</font> <br><font style="color:grey;font-weight:500;">(3rd year Chemist, St John's College)</font><br><br>Though prodigious in his own field, Jack has tried his hand at many academic disciplines, and this curiosity and fervour for science is most welcome amongst the team.<br> He has been working closely with our engineers, designing mathematical frameworks for our modelling, synthesising <a href="https://2014.igem.org/Team:Oxford/biopolymer_containment#show2" />biopolymer capsules</a> and on them running diffusion experiments in the lab.<br><br><br />
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Jack leads part 3 of the project and is in charge of our web development and graphics.<br />
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<div id="corinnaprofile" style="color:black;padding-right:10px;display:none;"><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Corinna Oswald</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, St John's College)</font><br><br>Corinna is one of the founding members that organised Oxford’s first participation in iGEM.<br><br />
In between perfecting the Viennese Waltz and denying that Austrians are always yodeling in the Alps (which we refuse to accept), she works reliably hard and efficient to ensure that both her and the entire team's work is completed to the highest standards. Her organisational and communication skills ensure the smooth running of the project despite ever-increasing time pressure and the broadly interdisciplinary nature of our team.<br><br>contact: oswald.corinna@gmail.com </div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Sian Mcgibbon</font> <br><font style="color:grey;font-weight:500;">(3rd year Lawyer, University College)</font><br><br>Siân is the model lawyer along with everything that implies.<br><br />
She has led the Policy and Practices element of our project, with a particular emphasis on intellectual property policy and its role in shaping the developing field of synthetic biology.</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Tim Ang</font> <br><font style="color:grey;font-weight:500;">(3rd year Biologist, St John's College)</font><br><br>Tim's primary academic interests focus on evolutionary theory, particularly social evolution. A keen weightlifter, his frequent references to weights and protein shakes naturally make him the hub of enchanting conversation here at Oxford iGEM.<br><br><br />
Tim is in charge of our interlab study and poster.</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Emily Prichett</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>asfygia sdyasdyuf asdyu asydo yuasd yea sec asuif asgc asi gkuasd vyias hk;sbd ylgas d;v sdjog s;DC AGSLDC GAJSDG HAS DYG su sduigsuad g</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Philipp Lorenz</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>It was Phil's idea to bring iGEM to Oxford, and he continues to ensure our project runs as smoothly as a German train schedule.</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Fran Donellan</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, Oriel College)</font><br><br>Not just a member of the biochemist team but also leading our public engagement activities, no task is too big or small for Fran. This girl can provide sandwiches for 500 people at 24 hours' notice and look good doing it.</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Shahbano Soomro</font> <br><font style="color:grey;font-weight:500;">(3rd year PPEist, University College)</font><br><br>A third year student in Politics and Economics at University College, Shahbano has a particular interest in Environmental policy and philosophy. As well as helping Fran with organising our public engagement and events, she will be watching over our project from afar during the summer.</div><br />
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<div id="mattprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/d/d8/OxigemMatt.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Matt Booth</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, St John's College)</font><br><br>Matt is an avid Rugby player, having recently achieved great success with the college team, and adopts a no-nonsense approach to many of his university endeavours, including iGEM.</div><br />
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<div id="oliverprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/b/b7/OxigemOliver.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Oliver Vince</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, University College)</font><br><br><br />
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Oliver is our keenest team member - nothing can douse his motivation. In addition to taking charge of the maths and software, he feels responsible for our entertainment.<br />
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Oliver has been particularly involved with the modelling of the genetic circuits and the CAD/CAM side of the project. He has also played a leading role in the construction of the pages on the website.<br />
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<div id="leroyprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/9/91/OxigemLeroy.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Leroy Lim</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, St John's College)</font><br><br>During term, Leroy indulges in many of the quirks of Oxford life, namely rowing, bops and colourful trousers. During iGEM, he's our engineer and self-appointed social sec.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="z-index:5;position:absolute; width:100%;margin-top:91%;min-width:320px; border-radius:15px;"/><br />
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<div style="background-color:rgba(217, 217, 217, 0.7);position:absolute; z-index:5; Height:75px; width:100%;min-width:350px;margin-top:92%;font-size:65px;font-family:Helvetica;padding-top:5px; font-weight: 450;"><br />
<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450; padding-left:15%;"><br><div style="opacity:0.7;">Supervisors</div></div><br />
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<img src="https://static.igem.org/mediawiki/2014/3/34/George.jpg" style="z-index:6;position:absolute;margin-top:105%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:106%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Dr George Wadhams</font><br><br> <br />
George Wadhams’ research interests lie in how bacteria sense and integrate environmental information. His group focuses on understanding in a quantitative manner how multiple, homologous pathways operate in individual cells and how the components of these pathways can be used to create synthetic pathways.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/2/2f/OxigemCiaran.jpg" style="z-index:6;position:absolute;margin-top:126.5%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:127.5%;width:500px;color:black;right:15%;"><font style="font-size:large;">Dr Ciar&aacute;n Kelly</font><br><br> <br />
Ciarán Kelly obtained a PhD for his work in Frank Sargant’s group, constructing and characterising synthetic biohydrogen production pathways in E. coli. He is interested in the construction, characterisation, and re-engineering synthetic enzymes and pathways for the production of high-value chemicals. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/2/25/OxigemKarl.jpg" style="z-index:6;position:absolute;margin-top:148%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:149%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Mr Karl Brune</font><br><br> <br />
Karl Brune's research interests lie in elucidating metabolic pathways. His previous work at Imperial College London was focused on engineering microbial consortia to enhance biomining and bioremediation, as well as on the study of photoautotrophic organisms. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="position:absolute; z-index:5; width:100%;margin-top:170.5%;min-width:320px; border-radius:15px;border-bottom-left-radius:15px;"/><br />
<div style="background-color:rgba(217, 217, 217, 0.7);position:absolute; z-index:5;Height:75px; width:100%;min-width:350px;margin-top:171.5%;font-size:65px;font-family:Helvetica;padding-top:5px; font-weight: 450;"><br />
<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450; padding-left:15%; "><br><div style="opacity:0.7;">Advisors</div></div><br />
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<img src="https://static.igem.org/mediawiki/2014/d/da/Judy.jpg" style="z-index:6;position:absolute;margin-top:184.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px; " /><br />
<div style="z-index:6;position:absolute;margin-top:185.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Professor Judy Armitage</font><br><br> <br />
Judy Armitage is interested in the dynamics of bacterial sensory transduction and the control of bacterial motility. In particular, her research group focuses on the communication between the sensory and adaption mechanisms of the two pathways as a model for sensory network integration in general..</div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/05/OxigemAnthony.jpg" style="z-index:6;position:absolute;margin-top:206%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:207%;width:500px;color:black;right:15%;"><font style="font-size:large;">Professor Anthony Watts</font><br><br> <br />
Anthony Watts’ group is devising solid state NMR methods for determining high-resolution details of information-rich sites within membrane receptors. Recent focus has been on the neurotensin receptor (NTS1), which is now available highly purified and monodispersed in detergent as well as in a ligand-binding form. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/4/4f/OxigemAntonis.jpg" style="z-index:6;position:absolute;margin-top:227.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:228.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Professor Antonis Papachristodoulou</font><br><br> <br />
Antonis Papachristodoulou’s research interests include systems and synthetic biology, network systems, aerospace systems and flow control, and convex optimisation. Furthermore, he works on modern control theory, robust stability analysis and design, as well as nonlinear dynamical systems and Lyapunov stability. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/09/OxigemAndreas.jpg" style="z-index:6;position:absolute;margin-top:249%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:250%;width:500px;color:black;right:15%;"><font style="font-size:large;">Andreas Harris<br />
</font><br><br> <br />
Andreas Harris works on the design and implementation of gene regulatory networks harnessing feedback to increase robustness and tunability. The designs are based around transcriptional networks and attempt to translate well-understood control modules, such as proportional and integral controllers, to biological systems. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/a/aa/OxigemLucas.jpg" style="opacity:0.95;z-index:6;position:absolute;margin-top:270.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:271.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Dr Lucas Black</font><br><br> <br />
Lucas is an imaging specialist helping out with quantification of parts and to give general guidance throughout the project.<br />
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The team is very grateful for Lucas's excellent photograpy, including our team photos and the photograph of the Radcliffe Camera on our homepage. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="position:absolute; z-index:5; width:100%;margin-top:292.5%;min-width:320px; border-radius:15px;border-bottom-left-radius:15px;"/><br />
<div style="background-color:rgba(217, 217, 217, 0.7);position:absolute; z-index:5; Height:75px; width:100%;min-width:600px;margin-top:293.5%;font-size:65px;font-family:Helvetica;padding-top:5px; font-weight: 450;"><br />
<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450;padding-left:15%;"><br><div style="opacity:0.7;">With thanks...</div></div><br />
<div style="background-color:rgba(235, 235, 235, 1);position:absolute; z-index:5;Height:200px; width:300%;margin-top:33px;margin-left:-30%;"></div><br />
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<div style="background-color:white;z-index:6;position:absolute;margin-top:306.8%;width:100%;color:black;border-top-right-radius:15px;border-bottom-left-radius:15px;padding:10px;margin-bottom:500px;"><font style="font-size:large;">Darragh Ennis</font><br><br />
For his 3D printing expertise.<br />
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<font style="font-size:large;">Josef Patoprsty</font><br><br />
For advising how to debug the website. <a href="http://josefnpat.com">http://josefnpat.com</a><br />
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<font style="font-size:large;">Ashok Menon</font><br><br />
For his website advice and debugging expertise.<br />
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<font style="font-size:large;">Dr Frederic Coulon</font> (Department of Environmental Science and Technology, School of Applied Sciences, Cranfield University)<br><br />
For his information on DCM and water treatment.<br />
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<font style="font-size:large;">Shaun Rowson</font> (Team Leader - Groundwater & Contaminated Land, Environment Agency)<br><br />
For his detailed information on the problem of chlorinated solvents and how they are currently disposed of.<br />
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<font style="font-size:large;">Carla Brown</font><br><br />
For the generously donating several packs of her Bacteria Combat card game. <br><br><br />
<font style="font-size:large;">Janet Stott and Sarah Lloyd</font><br><br />
For their kind advice and assistance in our public engagement events at the Oxford University Museum of Natural History.<br />
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<div style="background-color:transparent;z-index:5;position:absolute;margin-top:306.8%;width:100%;height:470px;padding-top:290px;"><br />
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<a href="http://www.bbsrc.ac.uk/home/home.aspx" target="_blank"><img src="https://static.igem.org/mediawiki/2014/3/3e/BBSRC.png" style="position:absolute;width:19%;left:24%;min-width:100px;" /></a><br />
<a href="http://www.biochemistry.org" target="_blank"><img src="https://static.igem.org/mediawiki/2014/7/75/BS.png" style="position:absolute;width:25%;left:50.5%" /></a><br />
<a href="http://www.sgm.ac.uk" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/20/SGM.png" style="position:absolute;width:22%;left:24%;top:51%" /></a><br />
<a href="http://www.wellcome.ac.uk" target="_blank"><img src="https://static.igem.org/mediawiki/2014/4/4c/WT.png" style="position:absolute;width:22%;left:50.5%;top:52%;" /></a><br />
<a href="http://www.deskgen.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/6/65/Oxfordigem_deskgen.png" style="position:absolute;width:15%;left:27%;top:62%" /></a><br />
<a href="http://www.bioch.ox.ac.uk/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/20/Oxfordigem_dept.png" style="position:absolute;width:22%;left:51%;top:63%;" /></a><br />
<a href="http://www.neb.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/b/b1/OxigemNeb_logo.png" style="position:absolute;width:18%;left:26%;top:75%" /></a><br />
<a href="http://www.snapgene.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/f/ff/Oxfordigem_snapgene.png" style="position:absolute;width:23%;left:49%;top:77%;" /></a><br />
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<img id="oliver" src="https://static.igem.org/mediawiki/2014/b/b7/OxigemOliver.jpg" style="position:absolute;width:20%;left:39.8%;margin-top:58.5%;" /><br />
<a href="#"><img id="oliver1" src="https://static.igem.org/mediawiki/2014/6/6d/OxigemOliver1.jpg" style="position:absolute;width:20%;left:39.8%;margin-top:58.5%;display:none;" onClick="profile('oliver')" /></a><br />
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<img id="leroy" src="https://static.igem.org/mediawiki/2014/9/91/OxigemLeroy.jpg" style="position:absolute;width:20%;left:59.5%;margin-top:58.5%;border-bottom-right-radius:15px;" /><br />
<a href="#"><img id="leroy1" src="https://static.igem.org/mediawiki/2014/1/17/OxigemLeroy1.jpg" style="position:absolute;width:20%;left:59.5%;margin-top:58.5%;border-bottom-right-radius:15px;display:none;" onClick="profile('leroy')" /></a><br />
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<img id="tim" src="https://static.igem.org/mediawiki/2014/2/21/OxigemTim.jpg" style="position:absolute;width:20%;left:0.1%;margin-top:42%;border-bottom-left-radius:15px;" /><br />
<a href="#"><img id="tim1" src="https://static.igem.org/mediawiki/2014/3/33/OxigemTim1.jpg" style="position:absolute;width:20%;left:0.1%;margin-top:42%;border-bottom-left-radius:15px;display:none;" onClick="profile('tim')" /></a><br />
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<img id="jack" src="https://static.igem.org/mediawiki/2014/6/68/OxigemJack.jpg" style="position:absolute;width:20%;margin-top:13%;left:0.1%;border-top-left-radius:15px;" /><br />
<a href="#"><img id="jack1" src="https://static.igem.org/mediawiki/2014/e/ed/OxigemJack1.jpg" style="position:absolute;width:20%;margin-top:13%;left:0.1%;border-top-left-radius:15px;display:none;" onClick="profile('jack')" /></a><br />
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<img id="emily" src="https://static.igem.org/mediawiki/2014/6/6e/OxigemEmily.jpg" style="position:absolute;width:20%;left:20%;margin-top:29.5%;" /><br />
<a href="#"><img id="emily1" src="https://static.igem.org/mediawiki/2014/3/38/OxigemEmily1.jpg" style="position:absolute;width:20%;left:20%;margin-top:29.5%;display:none;" onClick="profile('emily')"/></a><br />
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<img id="phil" src="https://static.igem.org/mediawiki/2014/8/8b/OxigemPhil.jpg" style="position:absolute;width:20%;left:39.8%;margin-top:29.5%;" /><br />
<a href="#"><img id="phil1" src="https://static.igem.org/mediawiki/2014/9/90/OxigemPhil1.jpg" style="position:absolute;width:20%;left:39.8%;margin-top:29.5%;display:none;" onClick="profile('phil')" /></a><br />
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<img id="fran" src="https://static.igem.org/mediawiki/2014/5/5e/OxigemFran.jpg" style="position:absolute;width:20%;left:59.5%;margin-top:29.5%;" /><br />
<a href="#"><img id="fran1" src="https://static.igem.org/mediawiki/2014/c/c6/OxigemFran1.jpg" style="position:absolute;width:20%;left:59.5%;margin-top:29.5%;display:none;" onClick="profile('fran')" /></a><br />
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<img id="andy" src="https://static.igem.org/mediawiki/2014/f/ff/OxigemAndy.jpg" style="position:absolute;width:20%;left:59.5%;border-top-right-radius:15px;" /><br />
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<div id="glenprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/9/97/OxigemGlen.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Glen Gowers</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>The guy who makes everything happen, the Fred to our iGEM "Mystery Machine". When Glen isn't sailing around the country, he's hard at work in the lab and at the desk.<br />
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<a href="http://uk.linkedin.com/pub/glen-gowers/84/1a2/bba/"><b>Glen's LinkedIn profile</b></a> <br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Jack Hoffman</font> <br><font style="color:grey;font-weight:500;">(3rd year Chemist, St John's College)</font><br><br>Though prodigious in his own field, Jack has tried his hand at many academic disciplines, and this curiosity and fervour for science is most welcome amongst the team.<br> He has been working closely with our engineers, designing mathematical frameworks for our modelling, synthesising <a href="https://2014.igem.org/Team:Oxford/biopolymer_containment#show2" />biopolymer capsules</a> and on them running diffusion experiments in the lab.<br><br><br />
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Jack leads part 3 of the project and is in charge of our web development and graphics.<br />
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<div id="corinnaprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/b/be/OxigemCorinna.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Corinna Oswald</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, St John's College)</font><br><br>Corinna is one of the founding members that organised Oxford’s first participation in iGEM.<br><br />
In between perfecting the Viennese Waltz and denying that Austrians are always yodeling in the Alps (which we refuse to accept), she works reliably hard and efficient to ensure that both her and the entire team's work is completed to the highest standards. Her organisational and communication skills ensure the smooth running of the project despite ever-increasing time pressure and the broadly interdisciplinary nature of our team.</div><br />
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<div id="andyprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/f/ff/OxigemAndy.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Andy Russell</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, The Queen's College)</font><br><br>Full-time biochemist, part-time fashionista; Andy fancies himself as the ''arbiter elegantarium'' of the team. Andy is now entering his third year as a biochemist at The Queen's College, where he is an accomplished sportsman.</div><br />
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<div id="sianprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/4/47/OxigemSian.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Sian Mcgibbon</font> <br><font style="color:grey;font-weight:500;">(3rd year Lawyer, University College)</font><br><br>Siân is the model lawyer along with everything that implies.<br><br />
She has led the Policy and Practices element of our project, with a particular emphasis on intellectual property policy and its role in shaping the developing field of synthetic biology.</div><br />
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<div id="timprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/2/21/OxigemTim.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Tim Ang</font> <br><font style="color:grey;font-weight:500;">(3rd year Biologist, St John's College)</font><br><br>Tim's primary academic interests focus on evolutionary theory, particularly social evolution. A keen weightlifter, his frequent references to weights and protein shakes naturally make him the hub of enchanting conversation here at Oxford iGEM.<br><br><br />
Tim is in charge of our interlab study and poster.</div><br />
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<div id="emilyprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/6/6e/OxigemEmily.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Emily Prichett</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>asfygia sdyasdyuf asdyu asydo yuasd yea sec asuif asgc asi gkuasd vyias hk;sbd ylgas d;v sdjog s;DC AGSLDC GAJSDG HAS DYG su sduigsuad g</div><br />
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<div id="philprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/8/8b/OxigemPhil.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Philipp Lorenz</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>It was Phil's idea to bring iGEM to Oxford, and he continues to ensure our project runs as smoothly as a German train schedule.</div><br />
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<div id="franprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/5/5e/OxigemFran.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Fran Donellan</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, Oriel College)</font><br><br>Not just a member of the biochemist team but also leading our public engagement activities, no task is too big or small for Fran. This girl can provide sandwiches for 500 people at 24 hours' notice and look good doing it.</div><br />
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<div id="shahbanoprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/1/1d/OxigemShahbano.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Shahbano Soomro</font> <br><font style="color:grey;font-weight:500;">(3rd year PPEist, University College)</font><br><br>A third year student in Politics and Economics at University College, Shahbano has a particular interest in Environmental policy and philosophy. As well as helping Fran with organising our public engagement and events, she will be watching over our project from afar during the summer.</div><br />
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<div id="mattprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/d/d8/OxigemMatt.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Matt Booth</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, St John's College)</font><br><br>Matt is an avid Rugby player, having recently achieved great success with the college team, and adopts a no-nonsense approach to many of his university endeavours, including iGEM.</div><br />
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<div id="oliverprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/b/b7/OxigemOliver.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Oliver Vince</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, University College)</font><br><br><br />
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Oliver is our keenest team member - nothing can douse his motivation. In addition to taking charge of the maths and software, he feels responsible for our entertainment.<br />
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Oliver has been particularly involved with the modelling of the genetic circuits and the CAD/CAM side of the project. He has also played a leading role in the construction of the pages on the website.<br />
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<div id="leroyprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/9/91/OxigemLeroy.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Leroy Lim</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, St John's College)</font><br><br>During term, Leroy indulges in many of the quirks of Oxford life, namely rowing, bops and colourful trousers. During iGEM, he's our engineer and self-appointed social sec.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="z-index:5;position:absolute; width:100%;margin-top:91%;min-width:320px; border-radius:15px;"/><br />
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<div style="background-color:rgba(217, 217, 217, 0.7);position:absolute; z-index:5; Height:75px; width:100%;min-width:350px;margin-top:92%;font-size:65px;font-family:Helvetica;padding-top:5px; font-weight: 450;"><br />
<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450; padding-left:15%;"><br><div style="opacity:0.7;">Supervisors</div></div><br />
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<img src="https://static.igem.org/mediawiki/2014/3/34/George.jpg" style="z-index:6;position:absolute;margin-top:105%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:106%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Dr George Wadhams</font><br><br> <br />
George Wadhams’ research interests lie in how bacteria sense and integrate environmental information. His group focuses on understanding in a quantitative manner how multiple, homologous pathways operate in individual cells and how the components of these pathways can be used to create synthetic pathways.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/2/2f/OxigemCiaran.jpg" style="z-index:6;position:absolute;margin-top:126.5%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:127.5%;width:500px;color:black;right:15%;"><font style="font-size:large;">Dr Ciar&aacute;n Kelly</font><br><br> <br />
Ciarán Kelly obtained a PhD for his work in Frank Sargant’s group, constructing and characterising synthetic biohydrogen production pathways in E. coli. He is interested in the construction, characterisation, and re-engineering synthetic enzymes and pathways for the production of high-value chemicals. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/2/25/OxigemKarl.jpg" style="z-index:6;position:absolute;margin-top:148%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:149%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Mr Karl Brune</font><br><br> <br />
Karl Brune's research interests lie in elucidating metabolic pathways. His previous work at Imperial College London was focused on engineering microbial consortia to enhance biomining and bioremediation, as well as on the study of photoautotrophic organisms. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="position:absolute; z-index:5; width:100%;margin-top:170.5%;min-width:320px; border-radius:15px;border-bottom-left-radius:15px;"/><br />
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<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450; padding-left:15%; "><br><div style="opacity:0.7;">Advisors</div></div><br />
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<img src="https://static.igem.org/mediawiki/2014/d/da/Judy.jpg" style="z-index:6;position:absolute;margin-top:184.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px; " /><br />
<div style="z-index:6;position:absolute;margin-top:185.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Professor Judy Armitage</font><br><br> <br />
Judy Armitage is interested in the dynamics of bacterial sensory transduction and the control of bacterial motility. In particular, her research group focuses on the communication between the sensory and adaption mechanisms of the two pathways as a model for sensory network integration in general..</div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/05/OxigemAnthony.jpg" style="z-index:6;position:absolute;margin-top:206%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:207%;width:500px;color:black;right:15%;"><font style="font-size:large;">Professor Anthony Watts</font><br><br> <br />
Anthony Watts’ group is devising solid state NMR methods for determining high-resolution details of information-rich sites within membrane receptors. Recent focus has been on the neurotensin receptor (NTS1), which is now available highly purified and monodispersed in detergent as well as in a ligand-binding form. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/4/4f/OxigemAntonis.jpg" style="z-index:6;position:absolute;margin-top:227.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:228.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Professor Antonis Papachristodoulou</font><br><br> <br />
Antonis Papachristodoulou’s research interests include systems and synthetic biology, network systems, aerospace systems and flow control, and convex optimisation. Furthermore, he works on modern control theory, robust stability analysis and design, as well as nonlinear dynamical systems and Lyapunov stability. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/09/OxigemAndreas.jpg" style="z-index:6;position:absolute;margin-top:249%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:250%;width:500px;color:black;right:15%;"><font style="font-size:large;">Andreas Harris<br />
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Andreas Harris works on the design and implementation of gene regulatory networks harnessing feedback to increase robustness and tunability. The designs are based around transcriptional networks and attempt to translate well-understood control modules, such as proportional and integral controllers, to biological systems. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/a/aa/OxigemLucas.jpg" style="opacity:0.95;z-index:6;position:absolute;margin-top:270.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:271.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Dr Lucas Black</font><br><br> <br />
Lucas is an imaging specialist helping out with quantification of parts and to give general guidance throughout the project.<br />
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The team is very grateful for Lucas's excellent photograpy, including our team photos and the photograph of the Radcliffe Camera on our homepage. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="position:absolute; z-index:5; width:100%;margin-top:292.5%;min-width:320px; border-radius:15px;border-bottom-left-radius:15px;"/><br />
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<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450;padding-left:15%;"><br><div style="opacity:0.7;">With thanks...</div></div><br />
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<div style="background-color:white;z-index:6;position:absolute;margin-top:306.8%;width:100%;color:black;border-top-right-radius:15px;border-bottom-left-radius:15px;padding:10px;margin-bottom:500px;"><font style="font-size:large;">Darragh Ennis</font><br><br />
For his 3D printing expertise.<br />
<br><br><br />
<font style="font-size:large;">Josef Patoprsty</font><br><br />
For advising how to debug the website. <a href="http://josefnpat.com">http://josefnpat.com</a><br />
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<font style="font-size:large;">Ashok Menon</font><br><br />
For his website advice and debugging expertise.<br />
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<font style="font-size:large;">Dr Frederic Coulon</font> (Department of Environmental Science and Technology, School of Applied Sciences, Cranfield University)<br><br />
For his information on DCM and water treatment.<br />
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<font style="font-size:large;">Shaun Rowson</font> (Team Leader - Groundwater & Contaminated Land, Environment Agency)<br><br />
For his detailed information on the problem of chlorinated solvents and how they are currently disposed of.<br />
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<font style="font-size:large;">Carla Brown</font><br><br />
For the generously donating several packs of her Bacteria Combat card game. <br><br><br />
<font style="font-size:large;">Janet Stott and Sarah Lloyd</font><br><br />
For their kind advice and assistance in our public engagement events at the Oxford University Museum of Natural History.<br />
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<a href="http://www.biochemistry.org" target="_blank"><img src="https://static.igem.org/mediawiki/2014/7/75/BS.png" style="position:absolute;width:25%;left:50.5%" /></a><br />
<a href="http://www.sgm.ac.uk" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/20/SGM.png" style="position:absolute;width:22%;left:24%;top:51%" /></a><br />
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<a href="http://www.deskgen.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/6/65/Oxfordigem_deskgen.png" style="position:absolute;width:15%;left:27%;top:62%" /></a><br />
<a href="http://www.bioch.ox.ac.uk/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/20/Oxfordigem_dept.png" style="position:absolute;width:22%;left:51%;top:63%;" /></a><br />
<a href="http://www.neb.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/b/b1/OxigemNeb_logo.png" style="position:absolute;width:18%;left:26%;top:75%" /></a><br />
<a href="http://www.snapgene.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/f/ff/Oxfordigem_snapgene.png" style="position:absolute;width:23%;left:49%;top:77%;" /></a><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Glen Gowers</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>The guy who makes everything happen, the Fred to our iGEM "Mystery Machine". When Glen isn't sailing around the country, he's hard at work in the lab and at the desk.<br />
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<a href="http://uk.linkedin.com/pub/glen-gowers/84/1a2/bba/"><b>Glen's LinkedIn profile</b></a> <br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Jack Hoffman</font> <br><font style="color:grey;font-weight:500;">(3rd year Chemist, St John's College)</font><br><br>Though prodigious in his own field, Jack has tried his hand at many academic disciplines, and this curiosity and fervour for science is most welcome amongst the team.<br> He has been working closely with our engineers, designing mathematical frameworks for our modelling, synthesising <a href="https://2014.igem.org/Team:Oxford/biopolymer_containment#show2" />biopolymer capsules</a> and on them running diffusion experiments in the lab.<br><br><br />
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Jack leads part 3 of the project and is in charge of our web development and graphics.<br />
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<div id="corinnaprofile" style="color:black;padding-right:10px;display:none;"><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Corinna Oswald</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, St John's College)</font><br><br>Corinna is one of the founding members that organised Oxford’s first participation in iGEM.<br><br />
In between perfecting the Viennese Waltz and denying that Austrians are always yodeling in the Alps (which we refuse to accept), she works reliably hard and efficient to ensure that both her and the entire team's work is done to the highest standards. Her organisational and communication skills ensure the smooth running of the project despite the ever-increasing time pressure and broadly interdisciplinary nature of our team.</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Sian Mcgibbon</font> <br><font style="color:grey;font-weight:500;">(3rd year Lawyer, University College)</font><br><br>Siân is the model lawyer along with everything that implies.<br><br />
She has led the Policy and Practices element of our project, with a particular emphasis on intellectual property policy and its role in shaping the developing field of synthetic biology.</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Tim Ang</font> <br><font style="color:grey;font-weight:500;">(3rd year Biologist, St John's College)</font><br><br>Tim's primary academic interests focus on evolutionary theory, particularly social evolution. A keen weightlifter, his frequent references to weights and protein shakes naturally make him the hub of enchanting conversation here at Oxford iGEM.<br><br><br />
Tim is in charge of our interlab study and poster.</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Emily Prichett</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>asfygia sdyasdyuf asdyu asydo yuasd yea sec asuif asgc asi gkuasd vyias hk;sbd ylgas d;v sdjog s;DC AGSLDC GAJSDG HAS DYG su sduigsuad g</div><br />
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<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Philipp Lorenz</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, University College)</font><br><br>It was Phil's idea to bring iGEM to Oxford, and he continues to ensure our project runs as smoothly as a German train schedule.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/5/5e/OxigemFran.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Fran Donellan</font> <br><font style="color:grey;font-weight:500;">(3rd year Biochemist, Oriel College)</font><br><br>Not just a member of the biochemist team but also leading our public engagement activities, no task is too big or small for Fran. This girl can provide sandwiches for 500 people at 24 hours' notice and look good doing it.</div><br />
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<div id="shahbanoprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/1/1d/OxigemShahbano.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Shahbano Soomro</font> <br><font style="color:grey;font-weight:500;">(3rd year PPEist, University College)</font><br><br>A third year student in Politics and Economics at University College, Shahbano has a particular interest in Environmental policy and philosophy. As well as helping Fran with organising our public engagement and events, she will be watching over our project from afar during the summer.</div><br />
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<div id="mattprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/d/d8/OxigemMatt.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Matt Booth</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, St John's College)</font><br><br>Matt is an avid Rugby player, having recently achieved great success with the college team, and adopts a no-nonsense approach to many of his university endeavours, including iGEM.</div><br />
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<div id="oliverprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/b/b7/OxigemOliver.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Oliver Vince</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, University College)</font><br><br><br />
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Oliver is our keenest team member - nothing can douse his motivation. In addition to taking charge of the maths and software, he feels responsible for our entertainment.<br />
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Oliver has been particularly involved with the modelling of the genetic circuits and the CAD/CAM side of the project. He has also played a leading role in the construction of the pages on the website.<br />
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<div id="leroyprofile" style="color:black;padding-right:10px;display:none;"><br />
<img src="https://static.igem.org/mediawiki/2014/9/91/OxigemLeroy.jpg" style="position:absolute;height:100%;border-bottom-left-radius:15px;" /> <br />
<div style="margin-left:310px;"><font style="font-size:40px;font-weight:500;color:#5E5E5E;"><br>Leroy Lim</font> <br><font style="color:grey;font-weight:500;">(3rd year Engineer, St John's College)</font><br><br>During term, Leroy indulges in many of the quirks of Oxford life, namely rowing, bops and colourful trousers. During iGEM, he's our engineer and self-appointed social sec.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="z-index:5;position:absolute; width:100%;margin-top:91%;min-width:320px; border-radius:15px;"/><br />
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<div style="background-color:rgba(217, 217, 217, 0.7);position:absolute; z-index:5; Height:75px; width:100%;min-width:350px;margin-top:92%;font-size:65px;font-family:Helvetica;padding-top:5px; font-weight: 450;"><br />
<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450; padding-left:15%;"><br><div style="opacity:0.7;">Supervisors</div></div><br />
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<img src="https://static.igem.org/mediawiki/2014/3/34/George.jpg" style="z-index:6;position:absolute;margin-top:105%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:106%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Dr George Wadhams</font><br><br> <br />
George Wadhams’ research interests lie in how bacteria sense and integrate environmental information. His group focuses on understanding in a quantitative manner how multiple, homologous pathways operate in individual cells and how the components of these pathways can be used to create synthetic pathways.</div><br />
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<img src="https://static.igem.org/mediawiki/2014/2/2f/OxigemCiaran.jpg" style="z-index:6;position:absolute;margin-top:126.5%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:127.5%;width:500px;color:black;right:15%;"><font style="font-size:large;">Dr Ciar&aacute;n Kelly</font><br><br> <br />
Ciarán Kelly obtained a PhD for his work in Frank Sargant’s group, constructing and characterising synthetic biohydrogen production pathways in E. coli. He is interested in the construction, characterisation, and re-engineering synthetic enzymes and pathways for the production of high-value chemicals. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/2/25/OxigemKarl.jpg" style="z-index:6;position:absolute;margin-top:148%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:149%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Mr Karl Brune</font><br><br> <br />
Karl Brune's research interests lie in elucidating metabolic pathways. His previous work at Imperial College London was focused on engineering microbial consortia to enhance biomining and bioremediation, as well as on the study of photoautotrophic organisms. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="position:absolute; z-index:5; width:100%;margin-top:170.5%;min-width:320px; border-radius:15px;border-bottom-left-radius:15px;"/><br />
<div style="background-color:rgba(217, 217, 217, 0.7);position:absolute; z-index:5;Height:75px; width:100%;min-width:350px;margin-top:171.5%;font-size:65px;font-family:Helvetica;padding-top:5px; font-weight: 450;"><br />
<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450; padding-left:15%; "><br><div style="opacity:0.7;">Advisors</div></div><br />
<div style="background-color:rgba(235, 235, 235, 1);position:absolute; z-index:5;Height:200px; width:300%;margin-top:33px;margin-left:-30%;"></div><br />
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<img src="https://static.igem.org/mediawiki/2014/d/da/Judy.jpg" style="z-index:6;position:absolute;margin-top:184.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px; " /><br />
<div style="z-index:6;position:absolute;margin-top:185.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Professor Judy Armitage</font><br><br> <br />
Judy Armitage is interested in the dynamics of bacterial sensory transduction and the control of bacterial motility. In particular, her research group focuses on the communication between the sensory and adaption mechanisms of the two pathways as a model for sensory network integration in general..</div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/05/OxigemAnthony.jpg" style="z-index:6;position:absolute;margin-top:206%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:207%;width:500px;color:black;right:15%;"><font style="font-size:large;">Professor Anthony Watts</font><br><br> <br />
Anthony Watts’ group is devising solid state NMR methods for determining high-resolution details of information-rich sites within membrane receptors. Recent focus has been on the neurotensin receptor (NTS1), which is now available highly purified and monodispersed in detergent as well as in a ligand-binding form. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/4/4f/OxigemAntonis.jpg" style="z-index:6;position:absolute;margin-top:227.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:228.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Professor Antonis Papachristodoulou</font><br><br> <br />
Antonis Papachristodoulou’s research interests include systems and synthetic biology, network systems, aerospace systems and flow control, and convex optimisation. Furthermore, he works on modern control theory, robust stability analysis and design, as well as nonlinear dynamical systems and Lyapunov stability. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/09/OxigemAndreas.jpg" style="z-index:6;position:absolute;margin-top:249%;width:15%;right:0%;border-left: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:250%;width:500px;color:black;right:15%;"><font style="font-size:large;">Andreas Harris<br />
</font><br><br> <br />
Andreas Harris works on the design and implementation of gene regulatory networks harnessing feedback to increase robustness and tunability. The designs are based around transcriptional networks and attempt to translate well-understood control modules, such as proportional and integral controllers, to biological systems. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/a/aa/OxigemLucas.jpg" style="opacity:0.95;z-index:6;position:absolute;margin-top:270.5%;width:15%;border-right: 510px solid #f9f9f9; border-top-right-radius:15px;border-bottom-left-radius:15px;" /><br />
<div style="z-index:6;position:absolute;margin-top:271.5%;width:500px;color:black;margin-left:16%;"><font style="font-size:large;">Dr Lucas Black</font><br><br> <br />
Lucas is an imaging specialist helping out with quantification of parts and to give general guidance throughout the project.<br />
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The team is very grateful for Lucas's excellent photograpy, including our team photos and the photograph of the Radcliffe Camera on our homepage. </div><br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxigemradcamcrop.jpg" style="position:absolute; z-index:5; width:100%;margin-top:292.5%;min-width:320px; border-radius:15px;border-bottom-left-radius:15px;"/><br />
<div style="background-color:rgba(217, 217, 217, 0.7);position:absolute; z-index:5; Height:75px; width:100%;min-width:600px;margin-top:293.5%;font-size:65px;font-family:Helvetica;padding-top:5px; font-weight: 450;"><br />
<div style="background-color:rgba(255, 255, 255, 0.2); Height:75px; width:100%;margin-top:5px:margin-bottom:5px;min-width:350px;font-size:65px;font-family:Helvetica;padding-top:5px; color:#596C8A; font-weight: 450;padding-left:15%;"><br><div style="opacity:0.7;">With thanks...</div></div><br />
<div style="background-color:rgba(235, 235, 235, 1);position:absolute; z-index:5;Height:200px; width:300%;margin-top:33px;margin-left:-30%;"></div><br />
</div><br />
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<div style="background-color:white;z-index:6;position:absolute;margin-top:306.8%;width:100%;color:black;border-top-right-radius:15px;border-bottom-left-radius:15px;padding:10px;margin-bottom:500px;"><font style="font-size:large;">Darragh Ennis</font><br><br />
For his 3D printing expertise.<br />
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<font style="font-size:large;">Josef Patoprsty</font><br><br />
For advising how to debug the website. <a href="http://josefnpat.com">http://josefnpat.com</a><br />
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<font style="font-size:large;">Ashok Menon</font><br><br />
For his website advice and debugging expertise.<br />
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<font style="font-size:large;">Dr Frederic Coulon</font> (Department of Environmental Science and Technology, School of Applied Sciences, Cranfield University)<br><br />
For his information on DCM and water treatment.<br />
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<font style="font-size:large;">Shaun Rowson</font> (Team Leader - Groundwater & Contaminated Land, Environment Agency)<br><br />
For his detailed information on the problem of chlorinated solvents and how they are currently disposed of.<br />
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<font style="font-size:large;">Carla Brown</font><br><br />
For the generously donating several packs of her Bacteria Combat card game. <br><br><br />
<font style="font-size:large;">Janet Stott and Sarah Lloyd</font><br><br />
For their kind advice and assistance in our public engagement events at the Oxford University Museum of Natural History.<br />
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<a href="http://www.bbsrc.ac.uk/home/home.aspx" target="_blank"><img src="https://static.igem.org/mediawiki/2014/3/3e/BBSRC.png" style="position:absolute;width:19%;left:24%;min-width:100px;" /></a><br />
<a href="http://www.biochemistry.org" target="_blank"><img src="https://static.igem.org/mediawiki/2014/7/75/BS.png" style="position:absolute;width:25%;left:50.5%" /></a><br />
<a href="http://www.sgm.ac.uk" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/20/SGM.png" style="position:absolute;width:22%;left:24%;top:51%" /></a><br />
<a href="http://www.wellcome.ac.uk" target="_blank"><img src="https://static.igem.org/mediawiki/2014/4/4c/WT.png" style="position:absolute;width:22%;left:50.5%;top:52%;" /></a><br />
<a href="http://www.deskgen.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/6/65/Oxfordigem_deskgen.png" style="position:absolute;width:15%;left:27%;top:62%" /></a><br />
<a href="http://www.bioch.ox.ac.uk/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/2/20/Oxfordigem_dept.png" style="position:absolute;width:22%;left:51%;top:63%;" /></a><br />
<a href="http://www.neb.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/b/b1/OxigemNeb_logo.png" style="position:absolute;width:18%;left:26%;top:75%" /></a><br />
<a href="http://www.snapgene.com/" target="_blank"><img src="https://static.igem.org/mediawiki/2014/f/ff/Oxfordigem_snapgene.png" style="position:absolute;width:23%;left:49%;top:77%;" /></a><br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/biosensor_characterisationTeam:Oxford/biosensor characterisation2014-10-14T10:58:45Z<p>CorinnaO: proofread</p>
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<h1white><font style="font-size:15px;font-weight:500;">Modelling</font><br />
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<h1>Introduction: what are we characterising?</h1><br />
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<font style="font-style: italic;">Methylobacterium Extorquens</font> DM4 in the presence of DCM expresses DcmA, a dichloromethane dehalogenase.<br />
Within 1.5kb upstream of <font style="font-style: italic;">dcmA</font> and in the opposite orientation is a second gene encoding DcmR, a regulatory protein that controls expression of DcmA:<br><br><br />
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In order to design and create a stable and sensitive system that responds to DCM we first need to characterise the regulatory nature of DcmR. Characterisation of this regulatory network has never been done before although it has been suggested to be a repressor [1]; we will be the first to fully characterise the mode of action of <font style="font-style: italic;">dcmR</font>. To do this we suppose the following hypotheses for DCM activating the transcription of <font style="font-style: italic;">dcmR</font>: either double repression or double activation. In other words, either DcmR represses <font style="font-style: italic;">dcmA</font> expression and DcmR is in turn repressed by the presence of DCM; or expression of <font style="font-style: italic;">dcmA</font> requires DcmR as an activator, with DcmR in turn only activated in the presence of DCM.<br><br><br />
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<h1white>What is currently known about DcmR?</h1white><br />
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<h1white>What is currently known about DcmR?</h1white></div></a><br />
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<h1>DcmR and regulation of <font style="font-style: italic;">dcmA</font> expression</h1><br />
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Mutants with <font style="font-style: italic;">dcmA</font> and the intergenic region but without complete <font style="font-style: italic;">dcmR</font> express DcmA constitutively. Re-integration of <font style="font-style: italic;">dcmR</font> restores regulation of <font style="font-style: italic;">dcmA</font> expression at the transcriptional level [1]. In addition, it has been shown that the region including <font style="font-style: italic;">dcmR</font>, the intergenic region and <font style="font-style: italic;">dcmA</font> is sufficient to confer a DCM dependent response in genetically engineered Methylobacterium extorquens DM4 [2]. <br><br><br />
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<h1>DcmR and DNA-binding</h1><br />
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DcmR is thought to be a DNA binding protein as structure predicting software indicates that there is a helix-turn-helix domain at the N-terminal of the protein. Since the region between the two promoters for <font style="font-style: italic;">dcmR</font> and <font style="font-style: italic;">dcmA</font> can be deleted without any effect on regulation it has been suggested that DcmR does not to a secondary regulatory site in between the genes but most likely acts directly on the <font style="font-style: italic;">dcmA</font> promoter itself [1]. In addition, regulated expression of <font style="font-style: italic;">dcmA</font> is not effected when the <font style="font-style: italic;">dcmR</font> and <font style="font-style: italic;">dcmA</font> transcriptional units are placed on separate replicons thereby suggesting that their topology is independent of the regulatory network. It is therefore suggested that DcmR binds the DNA in the intergenic region with the simplest model of its mode of action being as a trans-acting DNA-binding repressor; however this remains to be fully validated [1].<br><br><br />
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We have therefore proceeded on the assumption that DcmR is directly influenced by the presence or absence of DCM and furthermore that we can use <font style="font-style: italic;">dcmR</font>, the intergenic region and <font style="font-style: italic;">dcmA</font> alone to characterise the regulatory network. <br><br><br />
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[1] La Roche, S. D., and T. Leisinger. "Identification of <font style="font-style: italic;">dcmR</font>, the regulatory gene governing expression of dichloromethane dehalogenase in Methylobacterium sp. strain DM4." Journal of bacteriology 173.21 (1991): 6714-6721. <br><br />
[2] Lopes, N., et al “Detection of dichloromethane with a bioluminescent (lux) bacterial bioreporter” J Ind Microbiol Biotechnol (2012) 39:45–53<br />
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<h1>Characterising the DcmR - DCM - P_dcmA interaction</h1><br />
To find out whether the gene <font style="font-style: italic;">dcmR</font> acts as a repressor or an activator on the promoter of the <font style="font-style: italic;">dcmA</font> gene, we attempted to build the genetic circuit shown above on the right. Having <font style="font-style: italic;">dcmR</font> under inducible TetR expression should allow us to have very good control of the amount of DcmR present. On top of this, attaching the mCherry fluorescence tag will act as another confirmation to the amount of DcmR present.<br />
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We then extensively modelled the circuit to discover how the response of the system would differ if it was either of the two circuit systems. Click the modelling bubbles (pink) to find out exactly how we achieved this.<br />
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<h1white>Modelling the first half of the genetic circuit</h1white><br />
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<h1white>Modelling the first half of the genetic circuit</h1white></div></a><br />
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<h1>Predicting the mCherry fluorescence</h1><br />
We simplified the first double repression by modelling it as an activation of <font style="font-style: italic;">dcmR</font> by ATC, albeit parameterised by different constants. This assumption is justified by the fact that we are able to precisely control the addition of ATC and measure the fluorescence of the mCherry.<br />
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We modelled this first step using both deterministic and stochastic models.<br />
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<h1>Biochemical equations</h1><br />
The biochemical equations that describe the behaviour of the top half of the genetic circuit are:<br />
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<img src="https://static.igem.org/mediawiki/2014/f/ff/Oxford_mCherry_circuit2.png" style="float:right;position:relative; width:40%;margin-left:30%;margin-right:30%;" /><br />
<img src="https://static.igem.org/mediawiki/2014/1/1d/Oxford_biochem_equations.png" style="float:right;position:relative; width:80%;margin-left:10%;margin-right:10%;" /><br />
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<h1>Deterministic</h1><br />
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<img src="https://static.igem.org/mediawiki/2014/2/2e/Oxford_DcmR_parameters.png" style="float:right;position:relative; height:8%; width:47%;" /><br />
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Deterministic models are very powerful tools for synthetic biology. They describe the behaviour of the bacteria at the population level and use Ordinary Differential Equations (ODEs) to relate each activation and repression. By constructing a cascade of differential equations one can build a realistic model of the average behaviour of the system.<br />
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The differential equation that descibes this first step of the system is:<br />
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<br><br><img src="https://static.igem.org/mediawiki/2014/e/ed/Oxford_DcmR_activation.png" style="float:left;position:relative; height:8%; width:47%;" /><br />
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Solving this ODE in Matlab <h1>(with zero basal transcription rate)</h1> predicts the following the response of the system:<br />
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This model works assuming that <h1>sufficient TetR</h1> is always present.<br />
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<img src="https://static.igem.org/mediawiki/2014/e/e0/Oxford_MCherry_circuit.png" style="float:left;position:relative; width:40%;" /><br />
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<img src="https://static.igem.org/mediawiki/2014/9/92/MCherry_graph.png" style="float:right;position:relative; width:60%;margin-bottom:3%;" /><br />
Oxford iGEM 2014<br />
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While the analysis of this circuit is not critical to the successful outcome of this part of the project, it will provide us with very good practice of both obtaining fluorescence time series data and accurately fitting the data to the model. It will also help us develop our methods of predicting future system behaviour. This is because this system is already well documented in the literature and so we should be able to test our methods and responses against well documented results from labs across the world.<br />
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As you can clearly see from the graph, the model predicts a large fluorescence increase as the input is added. This is the what we expect from the actual system and is the best approximation that is obtainable before we get experimental data.<br />
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In the graph above, the model is set to have a basal transcription rate of zero. This is why there is a zero fluorescence response before the input has been added - this corresponds to the tetO promoter not being leaky at all. This basal rate will be calibrated alongside all of the other parameters in the model.<br />
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<h1>Stochastic Modelling</h1><br />
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Stochastic modelling uses probability theory to predict the behaviour of a system. For our project, we used it to model the expression of GFP from bacteria. <br />
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We started with the Gillespie Algorithm, which considers the expression of GFP to be binary; a molecule of GFP is either produced or degraded. We modelled the probability of a molecule of GFP being created using the Michaelis-Menten model, incorporating a basal transcription rate. For the degradation, we assumed a simple proportional relationship; the more you have the more likely it is that a molecule degrades. The constant of proportionality will be a function of the intrinsic life time of the protein in the cell. Now at every increment in time we will not have a GFP reaction occurring, so before we decided what reaction occurs we had to work out if I a reaction occurred. We did this by writing an equation involving the probability of any reaction occurring with a random number generator. To work out which reaction occurred we compared the relative probability of a production to degradation, and used a random number to make a weighted choice. <br />
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We later changed this code so that a reaction occurred every time increment, but included a null reaction where no GFP was degraded or created. Although this made the code a lot more data heavy, it allowed for much easier calculation of the mean response of multiple realisations.<br />
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Stochastic modelling is useful because it can show us the stochastic effects which are often seen in real bacteria. By calculating the variation of the mean of multiple GFP producing bacteria, we can also work out the standard deviation. Then if we assume that the system varies with respect to the normal distribution, we can produce error bounds for the production of GFP. Such that we can say, 90% of the time we can expect the production of GFP from a single bacterium to be within these 2 curves. This could be useful for seeing if results are unexpected, or, if there are multiple outliers, that our model is incorrect. If we average more and more bacteria then the mean curve tend towards the deterministic response. This is to be expected as we are now looking at the system as a whole and fluctuations in the production from individual bacteria are averaged out. Sto for small Det for large<br />
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What is stochastic modelling? -- Yes<br />
How is it useful? Ads/Dis -- Getting there<br />
Tending to deterministic -- Yes<br />
Modelling activator repressor --?<br />
Parameter characterisation/Data matching -- Not yet<br />
Matlab graphs – Not yet<br />
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Improve Gillespie algorithm bit<br />
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<li>Matlab graphs</li><br />
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<a href="#show3" class="show" id="show3"><div class="modelling"><br />
<h1white>How can we tell the systems apart?</h1white><br />
<img src="https://static.igem.org/mediawiki/2014/4/4d/Oxford_plus-sign-clip-art.png" style="float:right;position:relative; width:2%;" /><br />
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<a href="#hide3" class="hide" id="hide3"><div class="modelling"><br />
<h1white>How can we tell the systems apart?</h1white></div></a><br />
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<div class="white_news_block2"><br />
<img src="https://static.igem.org/mediawiki/2014/6/6f/Oxford_Characterisation_question.png" style="float:right;position:relative; width:42%;" /><br />
<h1>Predicting the sfGFP fluorescence</h1><br />
<h1>Introduction</h1><br />
To allow us to characterize the second half of the genetic circuit, we needed to be able to predict the difference in response. To do this, we constructed models by cascading the differential equations according to the respective circuit structures thereby producing two different potential system responses.<br />
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To achieve this, we constructed simplified equivalent circuits that were linked by two potential activation-repression relationships.<br />
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It is important to understand that these simplified equivalent circuits will not give the correct mCherry response but they will give the correct GFP response after correct parameterisation.<br />
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We then set up the differential equations necessary to solve this problem in Matlab. The method and results are as detailed below:<br />
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<u>INSERT SENSITIVITY GRAPH HERE</u><br />
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<a href="https://static.igem.org/mediawiki/2014/b/be/Oxford_Equations_explained.png"><img src="https://static.igem.org/mediawiki/2014/4/41/Oxford_equations.png" style="float:left;position:relative; width:50%;margin-right:25%;margin-left:25%;" /></a><br />
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Oxford iGEM 2014<br />
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<h1>Conclusion</h1><br />
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The bottom graphs illustrate the predicted response of each system to a simultaneous step input of both DCM and ATC. As you can see, there is little difference in the predicted steady-state value of the fluorescence, however, providing the basal transcription rate of GFP is relatively low, there should be a clear difference in the level of fluorescence before either of these inputs are added. This very easily identifiable difference between the two systems will enable us to characterize the genetic circuit present in our particular system.<br />
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<h1>Calculating the parameters</h1><br />
Calculating the many parameters for this system will be undoubtedly challenging. <br />
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<a href="https://2014.igem.org/Team:Oxford/calculating_parameters">How are we calculating the parameters?</a><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_characterisation#show5">Go to the data section where we calculated parameters for this part of the circuit.<br />
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<a href="#show4" class="show" id="show4"><div class="wetlab"><br />
<h1white>ATC induction of mCherry expression</h1white><br />
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<h1white>ATC induction of mCherry expression</h1white></div></a><br />
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<h1>Wetlab data showing response in level of mCherry expressed with different concs of ATC</h1><br />
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By making a translational fusion of mCherry at the C terminus of the dcmR gene under the tet promoter and tet operator system(see our <a href="https://2014.igem.org/Team:Oxford/biosensor_construction">Construction page</a> for details) we could measure mCherry fluorescence to gain information about dcmR induction by ATC. Expression was induced with various amounts of ATC and the following fluorescence data acquired. Exposure time was 0.2 seconds. As no calibration data was obtained using purified mCherry, the results have been left in fluorescence arbitrary units. Images were analysed using imageJ software.<br><br />
mCherry fluorescence increases with amount of ATC used confirming that the dcmR gene was expressed under the control of the tet promoter and operator system.<br><br><br />
<img src="https://static.igem.org/mediawiki/parts/5/51/Oxford_DcmR-mCherry_expression_induced_by_0ng_ATC.png" <br />
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This data was then used to refine and test our models (see below).<br />
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<h1white>Fitting data and finding parameters</h1white><br />
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<h1white>Fitting data and finding parameters</h1white></div></a><br />
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<img src="https://static.igem.org/mediawiki/2014/f/f1/Oxford_data1.png" style="float:right;position:relative; width:50%;" /><br />
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<h1>Introduction</h1><br />
As you can see from the biochemistry bubble above, our team was only able to obtain fluorescence data for the first half of the genetic circuit (ATC induced mCherry response). On top of this, the wet lab team were unable to obtain data that measured how the fluorescence of a single culture changed with time, again because of time constraints. This is slightly limiting because it means that we don’t have any dynamic data for any part of our system, and therefore can’t test the modelling predictions of the speed of the biosensor’s response.<br />
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The original data is shown on the right with error bars showing the standard error of the measurements.<br />
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Standard error is calculated as the average standard deviation divided by the square root of the total number of readings.<br />
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<h1>How we used the model</h1><br />
However, to demonstrate the power of the computer models that we’ve built, we made our model simulate the same graph (mean fluorescence against ATC concentration added). To build this, we started from the graph shown in the <br />
<a href="https://2014.igem.org/Team:Oxford/biosensor_characterisation#show2">first modelling bubble</a><br />
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<a href="https://static.igem.org/mediawiki/2014/b/be/Oxford_Equations_explained.png">where did these equations come from?</a><br />
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<img src="https://static.igem.org/mediawiki/2014/4/46/Oxford_data2.png" style="float:right;position:relative; width:80%;margin-left:10%;margin-right:10%;" /><br />
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We then ran the model for the correct amount of time (2 hours 20mins incubation with ATC) and ran it for lots of different concentrations of ATC over the range that the wet-lab team tried. The parameters are still arbitrary at this point (the same as above) and the results of the graphs are therefore arbitrary are as well, but the input values are now correct. The graphic below shows how we used the existing model to obtain the same graphs as the wet-lab team had obtained.<br />
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The numerical inputs that were used to model this data set were therefore:<br />
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<img src="https://static.igem.org/mediawiki/2014/f/f6/Oxford_data3.png" style="float:right;position:relative; width:100%;margin-left:0%;margin-right:0%;" /><br />
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<h1>Parameters</h1><br />
As all results are arbitrary up to this point, it is now time to calculate the parameters that will make the model’s response match up with the wet-lab data. The purpose of doing this is that the model will be able to give relatively accurate predictions of the response of the bacteria to further testing, therefore making the development of the biosensor much more efficient. The amount of data here will not allow us to calculate the parameters to a high level of accuracy, but it should be able to give us some very good approximations of what we can expect.<br />
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The parameters that we need to calculate are the constants in the differential equation that governs the behaviour of the first half of the genetic circuit. This half of the system is shown again here to remind the reader which part we are considering.<br />
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These parameters are:<br />
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<li>α1 = expression rate constant of <font style="font-style: italic;">dcmR</font></li><br />
<li>k1 = Michaelis - Menten constant of <font style="font-style: italic;">dcmR</font></li><br />
<li>d1 = degradation constant of <font style="font-style: italic;">dcmR</font></li><br />
<li>β1 = Basal transcription rate of <font style="font-style: italic;">dcmR</font></li><br />
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Remember that because the mCherry gene is tagged (translational fusion) onto the end of the <font style="font-style: italic;">dcmR</font> gene, <font style="font-style: oblique;"> the mCherry fluorescence will be the same as the amount of DcmR protein present</font>. However, there is not very comprehensive data in the literature about the values that we can expect from the behaviour of the <font style="font-style: italic;">dcmR</font> gene and its stability in vivo.<br />
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<h1>Degradation constant</h1><br />
The initial steady state of the system (before ATC has been added) is determined by two constants in the model. These constants are the degradation constant of DcmR and the basal transcription rate of the system. Due to the lack of numerical information in the literature on the behaviour of the <font style="font-style: italic;">dcmR</font> gene, the way of calculating these two parameters is by using the single basal rate data point from the wet-lab data (fluorescence value when 0ng of ATC has been added).<br />
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If we assume that the half-life of the dcmR protein is 3 hours [1] (180 minutes), we can calculate the degradation constant for our model. The exponential protein decay is therefore described by:<br />
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<h1>Basal transcription rate</h1><br />
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<img src="https://static.igem.org/mediawiki/2014/5/55/Oxford_data7.png" style="float:left;position:relative; width:70%;margin-right:30%;" /><br />
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<h1>Expression rate constant and Michaelis - Menten constant</h1><br />
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<h1>Sensitivity</h1><br />
An important part of building mathematical models is sensitivity analysis of the results. This can be basically explained as wiggling all of the input values and parameters to see how much variations in each of these values affects the system output. This is especially important for finding parameters to describe the system as it is important to know what level of accuracy the values need to be found to provide a reasonable degree of prediction accuracy.<br />
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On top of this, it is possible to find what range of values the system is especially sensitive to. An example of this analysis is shown with a simple example that is relevant to our system below:<br />
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<h1>Stability</h1><br />
In our Engineering studies we have learnt detailed control theory. Control theory is an interdisciplinary branch of engineering and mathematics that deals with the behavior of dynamical systems with inputs, and how their behavior is modified by feedback. The usual objective of control theory is to control a system so that its output follows a desired control signal, called the reference, which may be a fixed or changing value. This important because many dynamic systems can go unstable if they are given an unsafe set of input values and/or operating conditions.<br />
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However, as there are no feedback loops in this synthetic circuit, control theory analysis of this system isn't necessary.<br />
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<h1>Future experiment ideas from an Engineering design perspective</h1><br />
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<h1>Reference</h1><br />
[1] Dr George Wadhams by personal communication (14/10/2014)<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/P%26P_public_engagementTeam:Oxford/P&P public engagement2014-10-14T08:45:15Z<p>CorinnaO: proofread 2</p>
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<h1blue2>Focus Groups</h1blue2><br />
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Our team hosted a group of volunteers composed of members of the public with no particular interest or prior experience of biotechnology. Our aim was to gain a deeper understanding of public perceptions and concerns regarding the advance of synthetic biology, and in particular to get an idea of how far these concerns are based on misinformation/lack of understanding, and how far they are legitimate, well-founded fears which need to be addressed by the scientific community as the field grows and develops. If synthetic biology is to become increasingly socially accepted, public participation in its growth will be essential.<br />
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<h1blue2>8th August 2014 - Round 1</h1blue2><br />
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Our team hosted a group of volunteers composed of members of the public with no particular interest or prior experience of biotechnology. Our aim was to gain a deeper understanding of public perceptions and concerns regarding the advance of synthetic biology, and in particular to get an idea of how far these concerns are based on misinformation/lack of understanding, and how far they are legitimate, well-founded fears which need to be addressed by the scientific community as the field grows and develops. <br />
Particular issues which appear to be recurring themes in this discussion include: <br />
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<h1>Cross-Contamination of 'engineered' genes between synthetic and natural organisms </h1><br />
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<h1>Cross-Contamination of 'engineered' genes between synthetic and natural organisms </h1><br />
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There is widespread concern that biological machines may evolve, proliferate, and produce unexpected interactions which might alter the ecosystem. <br />
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<h1>Use of Bacteria (such as E. coli and P. pseudomonas) </h1><br />
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<h1>Use of Bacteria (such as E. coli and P. pseudomonas)</h1><br />
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The notion of 'bacteria' generally, and particularly the strains our team plans to use, have strong medical associations and are believed by many to be hazardous to health. E coli is widely understood to cause diarrhea, nausea and vomiting, whilst pseudomonas is best known for causing infections including pneumonia and swimmer's ear. <br />
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<h1>Uncontrolled release of modified organisms into the environment </h1><br />
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People have concerns that these will have unpredictable effects on ecosystems and that once taken these actions are difficult if not impossible to reverse. For example, synthetic organisms may outcompete their ‘natural’ counterparts and permanently damage biodiversity. <br />
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<h1>Spread of Antibiotic Resistance from synthetic to natural bacteria </h1><br />
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Based on vague knowledge that bacteria used in research are generally given characteristics of antibiotic resistance and that these can spread between organisms. <br />
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<h1>Bioterrorism </h1><br />
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The public are fearful of the ability of synthetic biology to produce known/modified/new organisms designed to be harmful to humans (as demonstrated, for example, by the synthesis of viruses such as the polio virus and the pandemic Spanish Flu virus and nurtured by Hollywood dramas such as 'Outbreak'). <br />
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<h1>Creation of 'Artificial Life' </h1><br />
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Some of the group expressed fears about scientists 'playing God', explaining their philosophical and religious concerns about the process of creation and the nature of 'living' beings. There was significant confusion as to how exactly 'life' should be defined, where it begins and ends, and what the requisite level of complexity is. Some people vaguely expressed fears that synthetic biology will 'blur' the line between the 'artificial and natural worlds', although did not elaborate on what was meant by this nor why this would be such a negative development. <br />
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There is some concern that patenting could lead to creation of commercial monopolies and inhibit research. [e.g. BRCA 1 Gene monopoly causing increase in price of testing which could potentially save lives). <br />
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Some of the group expressed worries about the fact that much of the development of synthetic biology and resulting intellectual property is likely to take place in and by extension principally benefit rich, developed nations. Particular examples of this concern which were raised included the possibility of farmers in poor countries becoming dependent on modified crop seeds controlled by large corporations which could extract whatever price they wished for the product, and the possibility of cheap alternatives for manufacture of chemicals such as antimalarial medicine (artemisinin) ensuring that local prodiction of natural equivalent products would no longer be sustained. Generally, this fear is expressed as a concern that technologies which are socially accepted on the premise that they will improve quality of life in less developed countries (golden rice and GM mosquitoes being cited as examples of such projects) may in actual fact benefit only rich Western companies and have no or even a detrimental impact on the lives of those the project was intended to help. <br />
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There is great concern that the development of fast-growing fields such as synthetic biology is 'overtaking' the regulation which is in place to regulate its application and to balance the risks and potential benefits. The possibility of biological warfare programmes is a major worry. A further concern is that 'garbage biology' (DIY home synthetic biology) may become a more popular, widespread hobby in the future - and that increased accessibility of synthetic biology to the lay public would make it difficult to enforce current regulations in synthetic biology.<br />
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All members of the group agreed that scientists should. Some members also expressed scepticism as to the claims of synthetic biology, and suspected that the potential benefits of the technology had been 'overhyped' in many areas, creating unrealistic hopes. It is important <u>CONTINUE HERE</u><br />
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It was agreed that it is crucially important for scientists to recognise the importance of securing and maintaining public support and legitimacy. For this reason, scientific development must seek to earn public trust by not advancing too far ahead of public attitudes, and ensuring that potential applications of new technology offer clearly explained social benefits.<br />
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Many of the group felt that there remains a deficit of accessible, reliable, and impartial information. Independent sources of information are particularly significant: some members also expressed scepticism as to the claims of synthetic biology, and suspected that the potential benefits of the technology had been 'overhyped' in many areas, creating unrealistic hopes. Similarly, it was recognised that information concerning the risks of synthetic biology frequently comes from biased sources which may have a motive to overstate the dangers and seek to create excessive public anxiety. <br />
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There is great concern that the development of fast-growing fields such as synthetic biology is 'overtaking' the regulation which is in place to regulate its application and to balance the risks and potential benefits. Such concerns should in future be addressed by communicating with the public in terms of how the research and use of each synthetic biology project is restricted and controlled by existing regulations.<br />
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<h1blue2>15th August 2014 - Round 2</h1blue2><br />
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For the second focus group, we decided to produce a brief informative presentation giving an overview of synthetic biology, its pros and cons, and an outline of our project and aims. Also included were some 'mythbusters' directed at addressing the misconceptions we came across during the first focus group.<br />
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We hoped that comparison of the results from the group with the benefit of this information would help us to establish which concerns are legitimate and which are alleviated by greater understanding and communication. <br />
With this group, the focus of discussion shifted from the potential problems with biotechnology, to ways in which these could be addressed.<br />
With both groups, there appeared to be a very wide range in the level of understanding of synthetic biology. This is supported by the results of our survey, which also suggest a correlation between age and level of understanding (with younger generations tending to have increased knowledge). Further, it appears that many of the views of those in the focus group were based on media accounts of developments in the field - again, this is consistent with the feedback from our survey. Public concerns regarding synthetic biology arise from a combination of lack of understanding and legitimate worries. <br />
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<h1blue2>Attitudes Survey</h1blue2><br />
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Guided by the feedback we discovered during the focus group activities, we analysed the responses from over 100 members of the public aged 16-85, selected randomly and coming from all walks of life. Again, we split the respondents into two equal groups, one of which answered the questions after having had the benefit of the introductory presentation above (this time in the form of a leaflet). We compared the responses from these two groups, again to see whether responses were changed by a basic level of understanding (most of the respondents commented that they had had little understanding or had misunderstood many aspects of the topic). The results of our survey are illustrated below. <br />
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For a complete analysis of our results, take a look at our Intellectual Property Report...<br />
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<div class="issuedate">What is your primary source of information about synthetic biology?</div><br />
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The most significant source of information was formal education, which may be a cause for concern given the respondents ranged from 15-72 in age, suggesting that at least the older respondents are relying on outdated classroom teachings and are not being kept up to date with scientific development. The prevalence of media (21%) confirms the importance of having unbiased, factual reporting on synthetic biology issues. Nearly 1 in 4 respondents reported that independent research was their primary source of information about synthetic biology, which in most cases involved using the internet to seek information about latest developments. This, along with the results for 'word of mouth', suggest that synthetic biology is an area in which the public take an interest. <br />
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The respondents rated information highly for accuracy (although perhaps this is to be expected as it is unlikely one would continue to read information from a source which regularly got the facts wrong). Relevance also scored highly, showing that people feel synthetic biology has a sufficient impact on their lives that they ought to be kept informed about it. The high score for availability is likely due to the heavy reliance on online information; however, it is probable that this reliance also contributes to the relatively low scores for independence and accessibility. The sources accessed by the public online may well not be aimed at laymen but intended to be read by students or professionals and so may contain a high level of technical detail not accessible to the lay public.<br />
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More than 1 in 3 respondents 'would like to be more informed', confirming the demand for accessible information about synthetic biology. The lack of such a source may also account for the 10% of the population who claimed to be 'disinterested' in synbio as they not understand the ways in which the science is relevant to their lives. On the other hand, 38% feel 'very well' or 'sufficiently' informed. The broad range in reponses may be explained by the fact that the question is subjective (people may have differing perceptions as to what level of knowledge counts as 'sufficient').<br />
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This graph shows that the majority of people have strong views about the implications of synthetic biology. Only a tiny percentage of people had 'no view' on the impact of syntethetic biology, which is an indication of just how contentious the ethics surrounding this area of science are. Unfortunately, the majority of people are more concerned than optimistic about the implications of synbio - much of our work in public engagement involved trying to understand why this might be, and how the concerns of the public can be alleviated. <br />
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The vast majority of respondents felt that as their understanding increased and that their concerns were alleviated. It is the responsibility of synthetic biologists to ensure the public are kept informed about developments and their implications. This will be of critical importance in ensuring public acceptance of synthetic biology solutions to societal problems.<br />
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The graph shows an interesting correlation between perceived level of understanding and attitude - those at the extremes of understanding, i.e. those with very little or very high understanding, generally have positive attitudes, whereas those with a partial but incomplete understanding tend to be more concerned and have a more suspicious approach. This suggests indeed that 'a little learning is a dangerous thing, drink deep or taste not the Pierian Spring'.<br />
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<h1blue2>Public Engagement Events</h1blue2><br />
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Inspired by the results of the focus groups and survey we organised and developed activities to bring the world of synthetic biology to members of the public. Click here check out more on our <a href="https://2014.igem.org/Team:Oxford/Events"> Events</a> page!<br />
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<h1blue2>Youtube channel</h1blue2><br />
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Check out our <a href="https://www.youtube.com/channel/UCx1QJHqQcZ09aT97V2f4Xdw" style="background-image: none;">youtube channel</a> aiming to make iGEM, synthetic biology, and science generally accessible to a worldwide audience of all ages. Check out our videos on primer design, our light-detecting circuit and 'Primer Suspect' our teaser trailer for DCMation.<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/P%26P_public_engagementTeam:Oxford/P&P public engagement2014-10-14T08:40:14Z<p>CorinnaO: proofread</p>
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<h1blue2>Focus Groups</h1blue2><br />
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Our team hosted a group of volunteers composed of members of the public with no particular interest or prior experience of biotechnology. Our aim was to gain a deeper understanding of public perceptions and concerns regarding the advance of synthetic biology, and in particular to get an idea of how far these concerns are based on misinformation/lack of understanding, and how far they are legitimate, well-founded fears which need to be addressed by the scientific community as the field grows and develops. If synthetic biology is to become increasingly socially accepted, public participation in its growth will be essential.<br />
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<h1blue2>8th August 2014 - Round 1</h1blue2><br />
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Our team hosted a group of volunteers composed of members of the public with no particular interest or prior experience of biotechnology. Our aim was to gain a deeper understanding of public perceptions and concerns regarding the advance of synthetic biology, and in particular to get an idea of how far these concerns are based on misinformation/lack of understanding, and how far they are legitimate, well-founded fears which need to be addressed by the scientific community as the field grows and develops. <br />
Particular issues which appear to be recurring themes in this discussion include: <br />
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There is widespread concern that biological machines may evolve, proliferate, and produce unexpected interactions which might alter the ecosystem. <br />
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The notion of 'bacteria' generally, and particularly the strains our team plans to use, have strong medical associations and are believed by many to be hazardous to health. E coli is widely understood to cause diarrhea, nausea and vomiting, whilst pseudomonas is best known for causing infections including pneumonia and swimmer's ear. <br />
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People have concerns that these will have unpredictable effects on ecosystems and that once taken these actions are difficult if not impossible to reverse. For example, synthetic organisms may outcompete their ‘natural’ counterparts and permanently damage biodiversity. <br />
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Based on vague knowledge that bacteria used in research are generally given characteristics of antibiotic resistance and that these can spread between organisms. <br />
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The public are fearful of the ability of synthetic biology to produce known/modified/new organisms designed to be harmful to humans (as demonstrated, for example, by the synthesis of viruses such as the polio virus and the pandemic Spanish Flu virus and nurtured by Hollywood dramas such as 'Outbreak'). <br />
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Some of the group expressed fears about scientists 'playing God', explaining their philosophical and religious concerns about the process of creation and the nature of 'living' beings. There was significant confusion as to how exactly 'life' should be defined, where it begins and ends, and what the requisite level of complexity is. Some people vaguely expressed fears that synthetic biology will 'blur' the line between the 'artificial and natural worlds', although did not elaborate on what was meant by this nor why this would be such a negative development. <br />
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There is some concern that patenting could lead to creation of commercial monopolies and inhibit research. [e.g. BRCA 1 Gene monopoly causing increase in price of testing which could potentially save lives). <br />
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Some of the group expressed worries about the fact that much of the development of synthetic biology and resulting intellectual property is likely to take place in and by extension principally benefit rich, developed nations. Particular examples of this concern which were raised included the possibility of farmers in poor countries becoming dependent on modified crop seeds controlled by large corporations which could extract whatever price they wished for the product, and the possibility of cheap alternatives for manufacture of chemicals such as antimalarial medicine (artemisinin) ensuring that local prodiction of natural equivalent products would no longer be sustained. Generally, this fear is expressed as a concern that technologies which are socially accepted on the premise that they will improve quality of life in less developed countries (golden rice and GM mosquitoes being cited as examples of such projects) may in actual fact benefit only rich Western companies and have no or even a detrimental impact on the lives of those the project was intended to help. <br />
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There is great concern that the development of fast-growing fields such as synthetic biology is 'overtaking' the regulation which is in place to regulate its application and to balance the risks and potential benefits. The possibility of biological warfare programmes is a major worry. A further concern is that 'garbage biology' (DIY home synthetic biology) may become a more popular, widespread hobby in the future - and that increased accessibility of synthetic biology to the lay public would make it difficult to enforce current regulations in synthetic biology.<br />
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All members of the group agreed that scientists should. Some members also expressed scepticism as to the claims of synthetic biology, and suspected that the potential benefits of the technology had been 'overhyped' in many areas, creating unrealistic hopes. It is important <u>CONTINUE HERE</u><br />
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It was agreed that it is crucially important for scientists to recognise the importance of securing and maintaining public support and legitimacy. For this reason, scientific development must seek to earn public trust by not advancing too far ahead of public attitudes, and ensuring that potential applications of new technology offer clearly explained social benefits.<br />
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Many of the group felt that there remains a deficit of accessible, reliable, and impartial information. Independent sources of information are particularly significant: some members also expressed scepticism as to the claims of synthetic biology, and suspected that the potential benefits of the technology had been 'overhyped' in many areas, creating unrealistic hopes. Similarly, it was recognised that information concerning the risks of synthetic biology frequently comes from biased sources which may have a motive to overstate the dangers and seek to create excessive public anxiety. <br />
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There is great concern that the development of fast-growing fields such as synthetic biology is 'overtaking' the regulation which is in place to regulate its application and to balance the risks and potential benefits. Such concerns should in future be addressed by communicating with the public in terms of how the research and use of each synthetic biology project is restricted and controlled by existing regulations.<br />
We used the feedback from these focus groups to shape the direction of our survey questions.<br />
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We hoped that comparison of the results from the group with the benefit of this information would help us to establish which concerns are legitimate and which are alleviated by greater understanding and communication. <br />
With this group, the focus of discussion shifted from the potential problems with biotechnology, to ways in which these could be addressed.<br />
With both groups, there appeared to be a very wide range in the level of understanding of synthetic biology. This is supported by the results of our survey, which also suggest a correlation between age and level of understanding (with younger generations tending to have increased knowledge). Further, it appears that many of the views of those in the focus group were based on media accounts of developments in the field - again, this is consistent with the feedback from our survey. Public concerns regarding synthetic biology arise from a combination of lack of understanding and legitimate worries. <br />
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<h1blue2>15th August 2014 - Round 2</h1blue2><br />
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For the second focus group, we decided to produce a brief informative presentation giving an overview of synthetic biology, its pros and cons, and an outline of our project and aims. Also included were some 'mythbusters' directed at addressing the misconceptions we came across during the first focus group.<br />
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We hoped that comparison of the results from the group with the benefit of this information would help us to establish which concerns are legitimate and which are alleviated by greater understanding and communication. <br />
With this group, the focus of discussion shifted from the potential problems with biotechnology, to ways in which these could be addressed.<br />
With both groups, there appeared to be a very wide range in the level of understanding of synthetic biology. This is supported by the results of our survey, which also suggest a correlation between age and level of understanding (with younger generations tending to have increased knowledge). Further, it appears that many of the views of those in the focus group were based on media accounts of developments in the field - again, this is consistent with the feedback from our survey. Public concerns regarding synthetic biology arise from a combination of lack of understanding and legitimate worries. <br />
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<h1blue2>Attitudes Survey</h1blue2><br />
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Guided by the feedback we discovered during the focus group activities. We analysed the responses from over 100 members of the public aged 16-85, selected randomly and coming from all walks of life. Again, we split the respondents into two equal groups, one of which answered the questions after having had the benefit of the introductory presentation above (this time in the form of a leaflet). We compared the responses from these two groups, again to see whether responses were changed by a basic level of understanding (most of the respondents commented that they had had little understanding or had misunderstood many aspects of the topic). The results of our survey are illustrated below. <br />
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For a complete analysis of our results, take a look at our Intellectual Property Report...<br />
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<div class="issuedate">What is your primary source of information about synthetic biology?</div><br />
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The most significant source of information was formal education, which may be a cause for concern given the respondents ranged from 15-72 in age, suggesting that at least the older respondents are relying on outdated classroom teachings and are not being kept up to date with scientific development. The prevalence of media (21%) confirms the importance of having unbiased, factual reporting on synthetic biology issues. Nearly 1 in 4 respondents reported that independent research was their primary source of information about synthetic biology, which in most cases involved using the internet to seek information about latest developments. This, along with the results for 'word of mouth', suggest that synthetic biology is an area in which the public take an interest. <br />
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The respondents rated information highly for accuracy (although perhaps this is to be expected as it is unlikely one would continue to read information from a source which regularly got the facts wrong). Relevance also scored highly, showing that people feel synthetic biology has a sufficient impact on their lives that they ought to be kept informed about it. The high score for availability is likely due to the heavy reliance on online information, however it is probable that this reliance also contributes to the relatively low scores for independence and accessibility. The sources accessed by the public online may well not be aimed at laymen but intended to be read by students or professionals and so may contain a high level of technical detail not accessible to the lay public.<br />
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More than 1 in 3 respondents 'would like to be more informed', confirming the demand for accessible information about synthetic biology. The lack of such a source may also account for the 10% of the population who claimed to be 'disinterested' in synbio as they not understand the ways in which the science is relevant to their lives. On the other hand, 38% feel 'very well' or 'sufficiently' informed. The broad range in reponses may be explained by the fact that the question is subjective (people may have differing perceptions as to what level of knowledge counts as 'sufficient').<br />
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This graph shows that the majority of people have strong views about the implications of synthetic biology. Only a tiny percentage of people had 'no view' on the impact of syntethetic biology, which is an indication of just how contentious the ethics surrounding this area of science are. Unfortunately, the majority of people are more concerned than optimistic about the implications of synbio - much of our work in public engagement involved trying to understand why this might be, and how the concerns of the public can be alleviated. <br />
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The vast majority of respondents felt that as their understanding increased, their concerns were alleviated. It is the responsibility of synthetic biologists to ensure the public are kept informed about developments and their implications. This will be of critical importance in ensuring public acceptance of synthetic biology solutions to societal problems.<br />
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The graph shows an interesting correlation between perceived level of understanding and attitude - those at the extremes of understanding, i.e. those with very little or very high understanding, generally have positive attitudes, whereas those with a partial but incomplete understanding tend to be more concerned and have a more suspicious approach. This suggests that it is indeed that 'a little learning is a dangerous thing, drink deep or taste not the Pierian Spring'.<br />
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Inspired by the results of the focus groups and survey we organised and developed activities to bring the world of synthetic biology to members of the public. Click here check out more on our <a href="https://2014.igem.org/Team:Oxford/Events"> Events</a> page!<br />
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Check out our <a href="https://www.youtube.com/channel/UCx1QJHqQcZ09aT97V2f4Xdw" style="background-image: none;">youtube channel</a> aiming to make iGEM, synthetic biology, and science generally accessible to a worldwide audience of all ages. Check out our videos on primer design, our light-detecting circuit and 'Primer Suspect' our teaser trailer for DCMation.<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/EventsTeam:Oxford/Events2014-10-13T23:48:27Z<p>CorinnaO: proofread</p>
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<h2 class='modal-heading'>Click on a picture to find out about our events associated with the Oxford University Biochemistry Department, our public engagement events and what we got up to when we visited other iGEM teams.</h2><br />
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<h1 class="modal-heading">Oxford Biochemistry Alumni Event @ the Royal Society</h1><br />
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Team members Glen Gowers and Philipp Lorenz attended the 2014 London Biochemistry Alumni Event hosted at the Royal Society. The occasion attracted alumni from across a period of more than 50 years – bringing together those who completed their PhDs in the 1950s, with students graduating last year. Following an introduction by Head of Department Mark Sansom and talks from two Royal Institution Christmas Lecturers, our iGEM team members entertained guests with their presentation.<br><br><br />
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<h1 class='modal-heading'>Synthetic Biology: Short Past, Long Future</h1><br />
<h2 class='modal-heading'>Oxford iGEM meet up in collaboration with SynOX</h2><br />
On June 19th 2014, the Oxford iGEM team invited all of the teams in the UK and Ireland to the first British and Irish meet-up of 2014. Hosted in the Oxford University Biochemistry Department and co-organised with the Oxford Society for Synthetic Biology (SynOx), the event was attended by 15 iGEM teams as well as Oxford University undergraduates and postgraduates. <br><br><br />
The day included three talks about synthetic biology and iGEM:<br><br />
<strong>Dr Jarek Bryk</strong> (National Centre for Biotechnology Education, University of Reading – current mentor of Reading iGEM) spoke about what it takes to get synthetic biology incorporated into university teaching and about his dedicated efforts towards establishing the Reading University iGEM team.<br><br />
The iGEM supervisor’s perspective was presented to us by <strong>Dr Richard Kelwick</strong> (researcher at the EPSRC National Centre for Synthetic Biology and Innovation, Imperial College London) along with the lessons he has learned from being a mentor to the past three Imperial College teams. <br><br />
Last, but certainly not least, <strong>Randy Rettberg</strong> (Founder and President of the iGEM foundation) imparted to us his vision of synthetic biology as the next revolution in humanity’s technological development.<br><br><br />
The talks were filmed to enable a livestream feed of the event to be broadcast live to those unable to attend: even allowing them to be involved in the question and answer session. <br><br />
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Later in the afternoon came the first opportunity for the UK and Irish teams to get to know each other, initially by chatting over sandwiches before moving out of the department to the less formal setting of the pub. To facilitate this discussion the teams had been invited to provide a simple poster to display their main project themes and ideas.<br><br><br />
Our thanks to SynOx and to all those who attended! We would like to thank Randy Rettberg, Dr. Richard Kelwick and Dr. Jarek Bryk for their insightful and entertaining talks.<br><br><br />
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<h1 class='modal-heading'>Can you give our cells new instructions? Drop-in Activity</h1><br />
<h2 class='modal-heading'>Venue:</h2> A table in the centre of the University Museum of Natural History in Oxford, on a Saturday afternoon. Entrance to the museum is free and members of the public were could approach us and our activities on a drop-in basis.<br><br><br />
<h2 class='modal-heading'>Audience:</h2> Anyone of any age (babies to pensioners) with a whole range of previous biology experience from none to Biochemistry post docs!<br><br><br />
<h2 class='modal-heading'>How did we draw them in?</h2> Colourful table display, museum specimens of coral and insects that could be handled by the public, smells people were invited to smell. We also had our table under the T-rex which has a high footfall in the museum.<br><br><br />
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<h2 class='modal-heading'>Activities: </h2><br />
- <strong>Can you give our cell new instructions?</strong><br><br />
Activity inviting people to put ‘when’ and ‘what’ cards together to make ‘new instructions’ for a cell – e.g. putting together ‘LIGHT’ and ‘PINK’ cards to tell a cell express a pink chromoprotein when it senses light. The cards could then be turned over to reveal that this simple genetic circuit was made up of genes/parts of genes from very different kinds of living things e.g. Synechocystis (cyanobacterium), E.coli and Smooth cauliflower coral.<br><br><br />
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- <strong>Handling specimens and objects</strong><br><br />
Strong smells such as Wintergreen essential oil and vanilla essence invited discussion about using synthetic biology to produce naturally occurring odorants. Coral, firefly and meal worm specimens from the musuem’s collection started discussion about the possible applications of making colourful cells, electricity-free light and antifreeze proteins respectively. <br><br><br />
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- <strong>Bacteria Combat card game</strong> <br><br />
A Top Trumps style card game developed and generously donated by Carla Brown (Glasgow University). The simple game involves cards with information about many different kinds of pathogenic and beneficial bacteria.<br><br><br />
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<h2 class='modal-heading'>Challenge:</h2> A lot of concepts in synthetic biology rely on an understanding of other concepts i.e. DNA, genes and cells. As we had to cater for a range of levels of previous understanding we had to be able to explain these concepts to people (especially children) who had never heard the terms before. <strong>Our Solution: </strong><br><br />
1. Simplify the concept of synthetic biology to exclude a detailed knowledge of genetics – introduce it as moving ‘instructions’ from different living things into other living things in new combinations. E.g. ‘We can take the instruction that makes this coral pink and put it into completely different living thing, a bacterial cell. Now the bacterial cell, which is normally colourless, is the same colour as the coral.’ This could be extended to think about two kinds of instructions; ‘when’ i.e. regulatory components, and ‘what’ i.e. an instruction that tells a cell what to do or what to look like.<br><br />
2. Have ways of introducing these concepts e.g. describing what bacteria are using the Bacteria Combat card game.<br><br><br />
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<h2 class='modal-heading'>How were we informed by the survey and focus groups?</h2> <br />
- Addressing the concern raised in our focus group that the possibilities presented about synthetic biology are ‘over-hyped’ we specifically only used examples of ‘instructions’ that have been submitted as BioBricks to the iGEM registry. <br><br />
- As anyone could come up to our table we were able to engage older people, a group which our survey shows is more likely to have a lower level of previous knowledge about synthetic biology.<br><br><br />
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<h2 class='modal-heading'>What did we improve for our second go?</h2><br />
We found that people were more interested in the objects and having a discussion than necessarily the ‘Can you give our cells new instructions?’ activity. In fact, this became a good demonstration tool during discussion rather than a starting point for it. For our second event we were also publicised on the museum’s website.<br><br><br />
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<h1 class='modal-heading'>Synthetic Biology and Restriction Enzymes Talk @ Question of Taste Workshop</h1><br />
<h2 class='modal-heading'>Audience:</h2> 15-20 A2-level Biology students (17-18 years old) and their teachers.<br><br><br />
<h2 class='modal-heading'>The Brief:</h2> Give 20-25 minute presentation about synthetic biology focusing on molecular biology techniques during the Oxford University Museum of Natural History ‘A Question of Taste’ day-long workshop.<br><br><br />
<h2 class='modal-heading'>Content:</h2>In the course of the day the students undertook PCR, restriction enzyme digest and gel electrophoresis so we discussed how these techniques could be used in synthetic biology.<BR><BR><br />
PUT PRESENTATION HERE!<BR><BR><br />
We also developed a model of a plasmid and insert to demonstrate complementary ends and the problems with a one enzyme restriction digest and how this can be overcome by a double restriction enzyme digest.<br><br><br />
<h2 class='modal-heading'>How were we informed by the survey and focus groups?</h2><br />
- Addressing the concern raised in our focus group that the possibilities presented about synthetic biology are ‘over-hyped’ we only gave examples of synthetic biology projects that have been successfully undertaken. <br />
- Included discussion as to why we use antibiotic resistance genes (which in one session lead to a discussion of alternative selectable markers).<br><br><br />
<h2 class='modal-heading'>What did we improve for our second go?</h2><br />
The second time round added in an animation to show how restriction enzyme sites can be added to a fragment of DNA by PCR. <br><br><br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/biopolymer_containmentTeam:Oxford/biopolymer containment2014-10-13T23:44:15Z<p>CorinnaO: proofread</p>
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<h1>Introduction</h1><br />
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The ‘Realisation’ sections of our project aim to bridge the gap between laboratory research and industrial application by the development of a novel biotechnology. We are therefore synthesising bacteria-containing biopolymer beads that maximise the reaction rate while simultaneously acting as a substrate diffusion barrier. This diffusion barrier allows the capsules to be in direct contact with higher DCM concentrations, while ensuring that the rate of DCM diffusion into the beads is low enough to allow the bacteria to efficiently degrade DCM while not being exposed to toxic concentrations.<br />
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<h1white>Why is this necessary?</h1white><br />
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<h1white>Why is this necessary?</h1white></div></a><br />
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<h1blue2>1. Maximise rate.</h1blue2><br />
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A primary function of the beads is to maximise reaction rate per bead volume, since halving the radius of a sphere doubles its surface area:volume ratio. A large number of small, bacteria-embedded agarose beads (to a technical limit) are therefore optimal, as more bacteria will be closer to the surface of each bead and can metabolise DCM efficiently. Assuming brownian motion, substrate molecules are more likely to collide with and be broken down by ‘outer’ bacteria. Product molecules, additionally, have a shorter path length to the surface and are likely to diffuse out faster: <br />
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Assuming ρ (a coefficient of bacterial density), to be independent of r (distance from bead center) and R (bead radius), avg. bacterium-surface distance = <br />
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<h1blue2>2. Protect bacteria</h1blue2><br />
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For this system and others of its type, it is highly valuable to maximise local substrate concentration to the bacteria within the viable range of toxicity, especially since the viable concentration range to the strain remains a limitation to the breakdown rate (directly or indirectly).<br />
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In our case, the diffusion-limiting polymer chosen was cellulose acetate (as its synthesis from cellulose is straightforward and safe) for which we modeled diffusion data for variable polymer thickness (see below). Acylation stoichiometry or even polymer type entirely, polymer density and methods of bead coating are among many variables that can be further researched and optimised for desirable diffusion coefficients. This means our biopolymer beads can be adapted to restrict diffusion of a wide range of substrates.<br />
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Bacteria need direct access to water, yet DCM is only water-soluble up to ~200mM. Thus, for substrates that are not fully soluble in water, we propose suspending beads at the interface of a biphasic mixture of the two by exploiting differences in density. In such a system, the immediate substrate ‘reservoir’ is essentially maximised.<br />
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For the purposes of this project we opted to construct beads less dense than water, since the aqueous DCM concentration of the biphasic system is more reliable. Furthermore, we had yet to establish the robustness of the diffusion-limiting system to external fluctuations in DCM concentrations.<br />
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<h1blue2>3. Physically containing bacteria.</h1blue2><br />
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Surrounding bacteria-embedded agarose beads in a diffusion limiting polymer acts as a secondary safeguard to an envisioned genetic kill switch. Their physical confinement to the beads would ensure that, even if the beads are improperly disposed of, the bacteria have very little possibility of 'escaping into the wild'. This, together with considerations of practicality, is the reason we are using macroscopic beads that can contain ~10^7 bacteria.<br />
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<h1white>Proof of Principle</h1white><br />
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<h1white>Proof of Principle</h1white></div></a><br />
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Acylation of cellulose was carried out via Acetyl Chloride esterification, based on methodology by Org. Lett., 2005, 7, 1805-1808. 1 cm diameter agarose spheres were passed through a thin film of the polymer to coat. Thickness was then calculated by the difference in measured initial and final diameters (an average of 5 diameters, using 0.01 mm precision callipers).<br />
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The volatility and poor visible absorption of DCM posed a challenge in reliably measuring rates of diffusion though the polymer. We decided to base our modelling on the diffusion of indigo dye from within prepared beads, collecting the following spectrophotometric absorption data (calibrated to prepared concentration standards):<br />
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Alongside the experimental absorption data (red) we have plotted our theoretical lines of best fit. We predicted that system behaviour would be governed by Fick’s law, which states that:<br />
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i.e. that mass flux is proportional to a concentration gradient. Hence, we further predicted that the response of our system would follow the classic exponential asymptotic approach to a maximum value where the concentrations of dye both inside and outside the system were equal. <br />
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Thus our lines of best fit take the form:<br />
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<li>φ = average concentration outside bead (g/ml)</li><br />
<li>A = equilibrium concentration (g/ml)</li><br />
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The value of k in each system was obtained through our parameter fitting algorithm.<br />
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Our results are tabulated below:<br />
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<img src="https://static.igem.org/mediawiki/2014/0/08/Oxford_Leroy_table3.png" style="float:left;position:relative; width:80%;margin-left:10%;margin-right:10%;margin-bottom:2%;" /><br />
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These results demonstrate that the polymer coating is indeed diffusion limiting due two simultaneous effects. Firstly, the rate at which the system reaches equilibrium concentration i.e. defined by the variable k which is itself a function of bead surface area, polymer diffusivity and coating thickness, is reduced in each of the systems. Furthermore, the maximum concentration reachable at the equilibrium point is itself a function of the thickness of the coating and decreases as the polymer thickness increases. <br />
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<h1white>Further analysis of polymer coating</h1white><br />
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<h1white>Further analysis of polymer coating</h1white></div></a><br />
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<h1blue2>Further analysis of polymer coating</h1blue2><br />
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To further explore the relationship between coating thickness and diffusion rate, we used analogous relationships developed for heat diffusion. This is done because the fundamental laws governing mass and heat diffusion are of a similar form; they are both driven by concentration and temperature gradients, respectively:<br />
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Because the system involves two-phase diffusion, we used an equivalent form derived from two-phase heat transfer. <br />
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This yielded:<br />
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<img src="https://static.igem.org/mediawiki/2014/2/2e/Oxford_Leroy_eqn12.png" style="float:left;position:relative; width:30%;margin-left:35%;margin-right:35%;margin-bottom:2%;" /><br />
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Using this relationship alongside diffusion data for two given thicknesses, we can characterize the two phase system using two unknown diffusion constants: k and h. Because the system had not reached a steady state and the rate of change of concentration ̇was constantly varying, we used the conditions at the start of the diffusion process where C_0 = 0 and used the gradient at t = 0 as a starting value for C ̇. <br />
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Finding the mass transfer rate was done by matching the experimental data to an anticipated exponential response and calculating the initial gradient as described above.<br />
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<img src="https://static.igem.org/mediawiki/2014/a/a3/Oxford_Leroy_eqn13.png" style="float:left;position:relative; width:18%;margin-left:0%;margin-right:82%;margin-bottom:2%;" /><br />
Using this form, the initial gradient can be calculated:<br />
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<img src="https://static.igem.org/mediawiki/2014/3/33/Oxford_Leroy_eqn14.png" style="float:left;position:relative; width:27%;margin-left:0%;margin-right:73%;margin-bottom:2%;" /><br />
By using the data gathered from the 1mm and 5mm tests, we could then calculate the two diffusion constants and plot a theoretical predicted relationship for initial concentration flux rate against coating thickness:<br />
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<img src="https://static.igem.org/mediawiki/2014/5/5b/Oxford_polymer6.png" style="float:left;position:relative; width:80%;margin-left:10%;margin-right:10%;margin-bottom:2%;" /><br />
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<h1white>The next steps</h1white><br />
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Given more time, we would run the test for a range of other thicknesses and compare the data collected to the theoretical form established above. Once the accuracy of the above form could be established, the next step would then be to predict the theoretical maximum breakdown rate of DCM achievable by our bacterial systems and then calibrate the thickness of the biopolymer capsules such that the influx rate of DCM through the polymer is less than or equal to our breakdown rate. This would result in an approximate steady state [DCM], within the cells' limits of substrate toxicity.<br />
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<a href="https://2014.igem.org/Team:Oxford/realisation_bioremediation"><img src="https://static.igem.org/mediawiki/2014/a/a7/Oxford_Bioremediation_realisation.png" style="float:left;position:relative; width:20%; margin-top:2%;margin-left:10%;margin-right:10%;" /></a><br />
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<a href="https://2014.igem.org/Team:Oxford/biopolymer_containment"><img src="https://static.igem.org/mediawiki/2014/0/07/Oxford_Biopolymer_containment.png" style="float:left;position:relative; width:20%; margin-top:2%;margin-left:0%;margin-right:10%;" /></a><br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/biosensor_realisationTeam:Oxford/biosensor realisation2014-10-13T23:20:41Z<p>CorinnaO: proofread</p>
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<h1white><font style="font-size:15px;font-weight:500;">Modelling</font><br />
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<h1>Introduction</h1><br />
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One of the most important things we kept in mind throughout the entire project was how the system would be implemented in reality. This allowed us to keep our research constantly focussed on a user-friendly and practical end product.<br />
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As such, in addition to completely designing the system on SolidWorks, we have maximised use of facilities available to us and have 3D-printed the biosensor product, complete with our DCMation branding. On top of this, we designed and constructed very cheap electronics that are sensitive enough to detect even small amounts of GFP.<br />
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<h1white>Light Detecting Circuit Design</h1white><br />
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<h1white>Light Detecting Circuit Design </h1white></div></a><br />
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For our user-friendly kit we required a circuit which displayed a simple on/off output once a certain level of DCM had been reached. The biochemists attached super-folder green fluorescent protein (sfGFP) to the promoter region of dcmA, such that when DCM is present, the bacteria fluoresce.<br />
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After initially assuming that GFP fluoresces on its own, the biochemists informed me that it needed to be excited by a specific wavelength of light. For our project we chose to use sfGFP as it folds more robustly than regular GFP. sfGFP has an absorption peak at 485nm and an emission peak at 520nm. Whilst there was plenty of data for GFP, sfGFP absorption and emission spectra data could not be found. As such, we have used the spectra for eGFP which we believe to be very similar to that of sfGFP. As can be seen in the 'GFP Emission/Absorption' diagram, the absorption (blue) and emission (green) spectra cover quite a range of wavelengths with considerable overlap around the 500nm region. This overlap requires careful consideration in the design of the overall bio-detection kit as the excitation and emission light will mix and become indistinguishable to unfiltered sensors.<br />
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The configuration in our end product will follow that shown in the 'Component set up' diagram. We will use blue LEDs to excite our sfGFP, which are ideal because they emit light in the range 450-500nm and are very cheap. Photodiodes are highly sensitive to the direction of the incoming light, so we will manipulate the circuit such that the LEDs lie at right angles to the photodiodes to reduce the amount of incident blue light. <br />
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The mix of the blue excitation from the LED and the green emission light from the sfGFP will then be passed through a filter which has the absorption spectra shown in the 'Filter absorption spectra' diagram. This filter will absorb a large amount of the blue light and little of the green light, so that a larger portion of the incident light on the photodiode is the signal we want to measure. We can calibrate our photodiode to ignore the small amounts of blue light transmitted through the filter, by taking light measurements in the absence of sfGFP.<br />
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To simplify the electrical analysis, I will talk about the voltage levels at varying points throughout the circuit with two different light levels, light and dark.<br><br><br />
A. When light is incident on the photodiode, a small voltage is created across its terminals. This voltage is immediately amplified by 10 to get a larger voltage at node A. (Using the BPW21 photodiode on the fifth floor of the Thom building, Vlight=4V and Vdark=-6V.) The voltage produced at A is now a sizeable amount but it is offset from 0V, which will make further analysis harder.<br><br><br />
B. Using a voltage divider resistor network, we created a fixed voltage point equal to the dark voltage offset at A. We put an OP-AMP setup as a voltage follower between this and the next part of the circuit, to isolate the network and ensure that the voltage level created wasn’t affected by any circuitry downstream. This fixed voltage (-6V) can be measured at the node labelled OFFSET. We inputted the voltages A and OFFSET into a unity gain comparator. Which created a voltage at B which follows the equation B=OFFSET - A. By having the OFFSET equal to Vdark, it will cause the voltage for one of the light levels to be 0V. (At node B, Vlight=-10V and Vdark=0.)<br><br><br />
C. If necessary we can amplify the voltage at B again using the non-inverting amplifier which has a gain which follow the equation: Gain=1+R10/R9. With the resistor values seen in the circuit diagram, the gain=10. Although the maximum voltage we can produce is limited by the OP-AMP’s inability to provide voltages larger than the power rails which supply it (+/-15V). So if we are using the BPW21 no further amplification is required, so I removed R9 so that the gain was reduced down to Gain=1. (At node C, Vight=-10V and Vdark=0V).<br><br><br />
D. For the final stage, we created another fixed voltage point which will control the threshold light level (-5V). Since the last OP-AMP has no feedback, the gain is effectively infinite and the output will saturate to the power rails. When the voltage at C is larger than -5V (Vdark=0), the output of the OP-AMP will saturate positively causing the voltage at node D= +15V. This will cause the green LED to illuminate, indicating that it is safe to pour down the sink as there is no light being produced, as there is no SFGFP present because there is no DCM left. When the voltage at C is lower than -5V (Vlight=-10), the output of the OP-AMP will saturate negatively causing the voltage at node D=-15V. This will cause the red LED to illuminate, indicating that it is not safe to pour down the sink as light is being produced, since there is GFP present because some DCM is still present.<br />
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As desired, we have a simple binary output which shows the user when the liquid is safe to pour down the sink.<br />
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<h1white>Light Detecting Circuit Construction</h1white><br />
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<h1white>Light Detecting Circuit Construction </h1white></div></a><br />
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<h1>Light detecting circuit construction</h1><br />
After trying several different configurations for the initial photodiode setup, I settled for the one shown in the circuit diagram because it was the only one I could make work. After that the construction continued fairly easily, attaching the wires to circuit was probably the most time consuming thing.<br />
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In the beginning I was using a EPD-470 GaP photodiode, as it was the single remaining photodiode left in the department. Even though it had a desirable absorption spectrum, it was fairly old and very temperamental, I often had to move the diode about and switch it on and off several times to make it work in the same conditions. So after searching on the internet and looking and spectral data, I ordered some Bpw21 photodiodes instead, which had a better absorption spectrum and were much more reliable. I had to change some of the gains, by changing resistor values, as the initial voltage produced by the Bpw21 was much larger and caused saturation on the original circuit.<br />
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I chose the values for the voltages, OFFSET and THRESHOLD, by examining the range of voltages the circuit moved through, with changing light levels, and selecting appropriate values which lied in the middle of the range.<br />
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My first round of experiments was conducted on the 5th floor of the Thom Building with the photodiode facing out of the window; I changed the light levels by moving my hand over it. This setup was simple but unrealistic, as our final design would be inside a black box and I was I told that the light emitted from sfGFP would be a low level. I used this design to start with to ensure the circuitry worked correctly.<br />
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On my second experiment I moved the photodiode into a cardboard box and had it facing a green LED, I manually switched on and off the LED to get the varying light levels desired. I had to recalibrate all my resistor values as they were no longer appropriate. The light levels when ‘dark’ were much lower as the cardboard box blocked out a lot more light than my hand did. So too were the light levels for ‘light’ as the green LED emitted much less light than the sun coming into the building. Although the change in voltage between light and dark was a lot smaller for this setup, it was fixed and did not depend on what time of day, or what the weather was outside. It was more binary and so after recalculating values it was a lot more reliable. Adding a blue LED in the background produced a different light level again, but this was easily counteracted by changing the voltage OFFSET again. The background blue LED was used here to approximate some of the blue light which would leak through the filter in the real circuit.<br />
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Although I tried to get as close to our real system as possible, there were still some differences. I would have used multiple blue LEDs to illuminate the sfGFP to get as much green light as possible. A filter would have been put in place between the light emitters (blue LEDs and bacterium/green LEDs) and the light detectors (photodiodes) to reduce the amount of unwanted blue light. Finally, multiple photodiodes would have been used to increase the amplitude of the signal. These changes were not implemented simply because there was still a readable voltage signal from one LED and one photodiode. Whilst the geometry of the circuit on the bread board meant that inputting a filter would be impractical, manually offsetting the voltage had already rectified the blue LED problem.<br />
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<h1white>Building the biosensor - step 1</h1white><br />
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<h1white>Building the biosensor - step 1</h1white></div></a><br />
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<h1blue2>Step 1: Designing and visualising the biosensor</h1blue2><br />
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<h1>The replaceable cartridge</h1> <br />
This cartridge is where the reporting bacteria would be held. The basic design is a block of agarose containing the bacteria, surrounded by a thin film, encased in a solid plastic shell to make it more user friendly. This cartridge is therefore very cheap and simple as it would allow simple replacement once the reporting bacteria culture became unusable.<br><br><br />
The design shown here is an initial idea that is aesthetically pleasing.<br />
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<h1>The housing for the electronics</h1><br />
This housing is designed to contain the replaceable cartridge. The <br />
<a href="https://2014.igem.org/Team:Oxford/biosensor_realisation#show1">GFP sensing circuit</a> will be contained in the top half of the box shown here to allow the circuit (specifically the light sensing diodes and the blue LEDs (both shown in the 3D images below)) to have maximum exposure to the reporting bacteria. The design also allows the cartridge to have maximum exposure to the solution that it is sensing. <br />
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<h1white>Building the biosensor - step 2</h1white><br />
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<h1white>Building the biosensor - step 2</h1white></div></a><br />
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<h1blue2>Step 2: Constructing the components in <br />
<a href="http://www.123dapp.com/design">123D Design</a></h1blue2><br />
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To construct the complicated shape of the biosensor housing, we had to 3D print two halves of the biosensor individually so that we could have hollow sections. This is because 3D printers can’t easily construct anything that isn’t fully supported. This is the one major drawback of this type of rapid prototyping. The files of this construction are shown below.<br />
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Therefore, when we 3D printed the cartridge, we couldn’t go for the fancy designs that are shown above in SolidWorks. Instead we opted for a much more linear design. The files of this construction are shown below.<br />
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<img src="https://static.igem.org/mediawiki/2014/f/ff/Oxford_build4.png" style="float:left;position:relative; width:70%;margin-left:15%;margin-right:15%;margin-bottom:2%;" /><br />
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These files were then transferred into <br />
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<h1white>Building the biosensor - step 3</h1white><br />
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<h1white>Building the biosensor - step 3</h1white></div></a><br />
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<h1blue2>Step 3: Finished product</h1blue2><br />
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Pictures of the finished biosensor are shown below. The idea is that this biosensor can be used as a very cheap sensor that can give a basic binary answer to the question ‘is there a safe level of chlorinated solvents in the container?’ This safe level will ideally be the 5ppm, which is the maximum safe level for drinking water in the UK.<br />
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<img src="https://static.igem.org/mediawiki/2014/c/c0/Oxford_build5.png" style="float:left;position:relative; width:100%;margin-bottom:2%;" /> <br />
Oxford iGEM 2014<br />
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Oxford iGEM 2014<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/biosensor_constructionTeam:Oxford/biosensor construction2014-10-13T22:55:55Z<p>CorinnaO: proofread</p>
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<h1>Introduction: how we constructed our biosensor</h1><br />
In order to be able to use our model and to determine whether DcmR acts as a repressor or activator in the presence of DCM, we designed and constructed the following two-plasmid system. We primarily used Gibson assembly methods and sourced most of the necessary DNA from gblocks (synthesised oligonucleotides) we had designed based in the sequenced genome of Methylobacterium DM4. This system will also form the DCM biosensor and will be integrated with an electronic circuit to complement this genetic one:<br><br><br />
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<h1white>pOXON-2-dcmR-mCherry</h1white> -<h2> Production of the DCM-binding protein DcmR</h2white><br />
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Oxford iGEM 2014<br />
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<h1white>pSRK-Gm-pdcmAsfGFP and pJ404-pdcmAsfGFP</h1white> -<h2> The binding site for DcmR with expression-reporting GFP</h2white><br />
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Unfortunately, we were unable to assemble the pSRKGm pdcmAsfGFP construct even after multiple tries. Since we plan to prove that this system can work in E. coli, we re-designed this construct to use a different vector with an origin of replication that is compatible with our other construct pOXON-2 (containing dcmR). <br />
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We chose to use plasmid backbone pJ404 since it contains a pBR322 origin of replication which is compatible with p15A origin of replication present in pOXON-2. <br />
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Since DcmR is predicted to regulate expression of DcmA as well as auto-regulating its own expression, we decided to insert this promoter-containing intergenic region with GFP at both positions. These positions correspond to the equivalent position of dcmA (labelled as ‘forward’) or the equivalent position of dcmR (labelled as ‘reverse’). <br />
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Oxford iGEM 2014<br />
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<li>The two plasmids are partitioned during cell division by different systems, thus an equal proportion of each plasmid is maintained in each new daughter cell. </li><br> <li>Different antibiotic resistances will allow us to select for cells that have taken up both plasmids by application of both antibiotics.</li><br> <li>The replication origins compatible with E.coli and pseudomonas strains.</li><br> <li>We have used two plasmids so that we can test each part in isolation before transforming them both into the same cell.</li><br />
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<h1>Building pOXON-1</h1><br><br />
<li>The first task in the construction of the pOXON-2-dcmR-mcherry construct was the creation of pOXON-1; pME6010 with tetracycline resistance replaced by kanamycin resistance. (N.B. The KanR gene was amplified with an optimised RBS.)</li><br><br />
<li>pOXON-1 was produced using the Gibson assembly method.</li><br><br />
<h1>Building pOXON-2 and pOXON-2-dcmR</h1><br><br />
<li>pOXON-1 was then used as the vector for the insertion of the three gblock fragment constituting the inducible expression system of dcmR via Gibson assembly.</li><br><br />
<li>Upon sequencing of the product, it was determined that the version of the gblock containing the dcmR gene in the construct was actually truncated. This construct with the truncated dcmR is pOXON-2. A second Gibson assembly reaction was used to replace the truncated version with the full length gene also derived from the gblock. The resulting construct was named pOXON-2-dcmR.</li><br><br />
<h1>Adding in mCherry</h1><br><br />
<li>We then used pOXON-2-dcmR as the vector for the insertion of mCherry downstream of dcmR as a translational fusion by Gibson assembly.</li><br><br />
<li>We therefore have a system of expressing dcmR with (pOXON-2-dcmR-mCherry) and without (pOXON-2-dcmR) the mCherry fusion in order to test whether the addition of mCherry affects the action of DcmR. Both will be submitted as BioBricks in the standard pSB1C3 backbone.</li><br><br />
<li>All constructs were confirmed by sequencing.</li><br><br />
<h1>Building pSRK Gm construct</h1><br><br />
<li>We have attempted to make our second construct by inserting the pdcmAsfGFP gblock into the pSRK Gm vector by Gibson assembly. As this is proving difficult, the next approach will be to insert the two components separately and to source the DNA from sources other than the gblock. Firstly, pdcmA will be amplified from Methylobacterium extorquens DM4 genomic DNA and inserted into the pSRKGm vector. sfGFP will then be amplified from a plasmid already containing it, and added to the pSRKGm-pdcmA construct.</li><br><br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/calculating_parametersTeam:Oxford/calculating parameters2014-10-13T22:21:37Z<p>CorinnaO: proofread</p>
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<h1>Parameter calibration and data fitting</h1><br />
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Characterization of a biological system is an extremely important area of engineering in synthetic biology. Part of this characterization involves deducing the structure of the system, while another component of this involves matching experimental data to theoretical data and calculating the defining parameters from this. To do this accurately and quickly, we have developed a script which calculates the value of the given variable that gives the closest fit to the experimental data.<br />
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The programme requires a theoretical input form with one variable parameter to be compared with experimental data. After this, simulations are run for a range of parameter values and the programme finds the parameter value which maximizes the closeness of fit. This is done through the use of the Euclidean Norm of the vector calculated from the difference between the experimental data points and the hypothesized relationship. The Euclidean Norm is defined as:<br />
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<li>n = number of data points available</li><br />
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This code was very useful in a number of situations, for example in characterizing the bead diffusion parameters from given data. Displayed below is experimental diffusion data plotted alongside our fitted theoretical form.<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/biosensor_modelling_approximationsTeam:Oxford/biosensor modelling approximations2014-10-13T22:19:24Z<p>CorinnaO: </p>
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<h1>Explaining why we used the equivalent circuits</h1><br />
To model the complex interaction of activations and repressions in the genetic circuit that we were investigating, it was necessary to model a slightly modified versions of the original systems. We did this to save time and to keep the models comparatively simple. Modelling equivalent systems and making educated assumptions are well-established areas of engineering, and we therefore concluded that the equivalent circuits provided a very accurate representation of the real system. As the main initial goal of the modelling was to obtain parameters from the wet lab data that could predict the future response of the system, the quality of the end model has a much greater dependence on the accuracy to which we could determine the parameters than any assumptions the model made.<br />
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To summarise, as long as the we found the correct parameters for the equivalent model, we could get reliable predictions from this simulation.<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/biosensor_modelling_approximationsTeam:Oxford/biosensor modelling approximations2014-10-13T22:18:59Z<p>CorinnaO: proofread</p>
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<h1>Explaining why we used the equivalent circuits</h1><br />
To model the complex interaction of activations and repressions in the genetic circuit that we were investigating, it was necessary to model a slightly modified versions of the original systems. We did this to save time and to keep the models comparatively simple. Modelling equivalent systems and making educated assumptions are well-established areas of engineering, and we therefore concluded that the equivalent circuits provided a very accurate representation of the real system. As the main initial goal of the modelling was to obtain parameters from the wet lab data that could predict the future response of the system, the quality of the end model has a much greater dependence on the accuracy to which we could determine the parameters than any assumptions the model made.<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/biosensorTeam:Oxford/biosensor2014-10-13T22:10:02Z<p>CorinnaO: proofread</p>
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<h1>Introduction</h1><br />
To develop a cheap and user friendly way of detecting chlorinated solvents (focusing specifically on DCM) the engineering design team worked very closely with the biochemistry team to <br />
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See below for links to the wet lab work, modelling and physical realisation of our product!<br />
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<br><h1>What is a biosensor?</h1><br />
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Biological systems are very good at sensing the huge range of chemical and physical inputs in the world around them, often at very low levels. They need to do this in order to respond to and survive the constant changes in their environment. In many cases, this sensing results in a change at the transcriptional level in the organism. For example, Methylobacterium extorquens DM4 increases expression of DCM dehalogenase in the presence of DCM in order to exploit this carbon source. This means we can use these natural sensing systems to engineer novel genetic circuits that will respond to specific inputs with detectable outputs; in other words, to create a biosensor. <br><br> <br />
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<h1>Developing our biosensor</h1><br />
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The promoter of dcmA (DCM dehalogenase gene) is placed upstream of sfGFP; therefore we will get a fluorescent output instead of dcmA expression as in the native bacterium. Repression or activation of the dcmA promoter relies on the regulatory protein DcmR which responds to the [DCM]; in our genetic circuit, the dcmR gene is constitutively expressed.<br><br><br />
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Click on the characterisation link below to find out more about the genetic regulatory network we are characterising and engineering to produce our biosensor!<br><br><br />
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By having an electronic circuit, we can quickly adapt our DCMation kit to give the same user-friendly output (a green LED comes on on top of the bench-top kit to indicate the contents can be poured down the sink) depending on the result of our characterisation of the action of DcmR. This design meant that we could develop both the genetic circuit and the physical realisation of our product at the same time rather than sequentially - saving us time!<br><br><br />
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Click on the realisation link below to see more about our fluorescence detecting circuit!<br><br><br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/EventsTeam:Oxford/Events2014-10-12T10:12:11Z<p>CorinnaO: </p>
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<br><br />
<h1>Sheffield meet up</h1><br />
The Oxford iGEM team attended the meet up organised by the team at the University of Sheffield on Friday the 18th of July. The purpose of this visit was to continue building the relationships between teams that were started at the Oxford-based meet up last month. The team gave a short presentation on the project so far.<br />
<br><br><br />
<br />
<br />
<h1>Team members give talk at Royal Society</h1><br />
Team members Glen Gowers and Philipp Lorenz attended the 2014 London Biochemistry Alumni Event hosted at the Royal Society. The occasion attracted alumni from across a period of more than 50 years – bringing together those who completed their PhDs in the 1950s, with students graduating last year. Following an introduction by Head of Department Mark Sansom, two Royal Institution Christmas Lecturers and our iGEM team members entertained guests with their presentations.<br />
<br />
<br><br><br />
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<h1>Synthetic Biology - Short Past and Long Future</h1><br />
</html><br />
[[File:RR talking.jpg.png|250px|thumb|right|Randy giving his talk]]<br />
[[File:RR talking2.jpg|250px|thumb|right|The crowd gathers]]<br />
[[File:Rpunting.jpg|250px|thumb|right|Punting relaxation]]<br />
[[File:Teampunting.jpg|250px|thumb|right|Touring Magdalen College]]<br />
<html><br />
<h2>Introduction:</h2><br />
<br />
On June 19th 2014, the Oxford iGEM team invited all of the teams in the UK and Ireland to talks in the Oxford University Biochemistry Department, including a talk by the CEO of iGEM Randy Rettberg; 'Synthetic Biology - Short Past and Long Future'. The event was attended by 15 iGEM teams as well as Oxford University undergraduates and postgraduates. We would like to thank Randy Rettberg, Dr. Richard Kelwick and Dr. Jarek Bryk for their insightful and entertaining talks. Later in the afternoon came the first opportunity for the the UK and Irish teams to get to know each other, initially by chatting over sandwiches before moving out of the department to the less formal setting of the pub. Our thanks to all those who attended and we hope this is the start of many successful team collaborations to come! Thank you also to The Oxford Society for Synthetic Biology (SynOx) for their help in co-organising the event.<br><br><br />
<br />
<h2>'''Speakers'''</h2><br><br />
<br />
== Randy Rettberg ==<br><br />
<br />
<br />
President of iGEM - International Genetically Engineered Machine (iGEM) Foundation<br />
Randy Rettberg is the man behind iGEM, the global competition for undergraduates and high school students in designing brand new biological parts, or “genetically engineered machines”. An engineer by trade, he worked for years at internet pioneer Bolt, Beranek and Newman, at Apple, and Sun Microsystems, until he left the computer industry to work on Synthetic Biology at MIT. iGEM graduated from MIT two years ago and Randy is now President of the iGEM Foundation. iGEM also operates the Registry of Standard Biological Parts, a continuously growing library of genetic parts that can be mixed and matched to enable easier construction of synthetic biology devices and systems: it provides a resource of available genetic parts to iGEM teams and academic labs across the world, including the Oxford iGEM team.<br />
<br><br><br />
== Dr. Richard Kelwick ==<br><br />
<br />
Researcher at the EPSRC National Centre for Synthetic Biology and Innovation, Imperial College London<br />
Over five years of independent research experience, primarily in molecular biology, cancer biology and synthetic biology. Currently working on cell-free transcription and translation (TX-TL) systems to develop a high-throughput prototyping platform for biopart characterisation. Scientific advisor and project manager of three successful iGEM teams, 2011-2013. Most recently, Richard was the lead advisor for the iGEM team Plasticity, at Imperial College London, which came third out of over 200 teams at the world final, held at MIT.<br />
<br><br><br />
== Dr. Jarek Bryk ==<br><br />
National Centre for Biotechnology Education, University of Reading<br />
Jarek works at the National Centre for Biotechnology Education on a project to facilitate teaching of synthetic biology on an undergraduate level. He develops experimental kits that will be incorporated in synthetic biology curricula.He currently mentors the iGEM Reading team.<br />
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<br><br />
<h1>Sheffield meet up</h1><br />
The Oxford iGEM team attended the meet up organised by the team at the University of Sheffield on Friday the 18th of July. The purpose of this visit was to continue building the relationships between teams that were started at the Oxford-based meet up last month. The team gave a short presentation on the project so far.<br />
<br><br><br />
<br />
<br />
<h1>Team members give talk at Royal Society</h1><br />
Team members Glen Gowers and Philipp Lorenz attended the 2014 London Biochemistry Alumni Event hosted at the Royal Society. The occasion attracted alumni from across a period of more than 50 years – bringing together those who completed their PhDs in the 1950s, with students graduating last year. Following an introduction by Head of Department Mark Sansom, two Royal Institution Christmas Lecturers and our iGEM team members entertained guests with their presentations.<br />
<br />
<br><br><br />
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<h1>Synthetic Biology - Short Past and Long Future</h1><br />
</html><br />
[[File:RR talking.jpg.png|250px|thumb|right|Randy giving his talk]]<br />
[[File:RR talking2.jpg|250px|thumb|right|The crowd gathers]]<br />
[[File:Rpunting.jpg|250px|thumb|right|Punting relaxation]]<br />
[[File:Teampunting.jpg|250px|thumb|right|Touring Magdalen College]]<br />
<html><br />
<h2>Introduction:</h2><br />
<br />
On June 19th 2014, the Oxford iGEM team invited all of the teams in the UK and Ireland to talks in the Oxford University Biochemistry Department, including a talk by the CEO of iGEM Randy Rettberg; 'Synthetic Biology - Short Past and Long Future'. The event was attended by 15 iGEM teams as well as Oxford University undergraduates and postgraduates. We would like to thank Randy Rettberg, Dr. Richard Kelwick and Dr. Jarek Bryk for their insightful and entertaining talks. Later in the afternoon came the first opportunity for the the UK and Irish teams to get to know each other, initially by chatting over sandwiches before moving out of the department to the less formal setting of the pub. Our thanks to all those who attended and we hope this is the start of many successful team collaborations to come! Thank you also to The Oxford Society for Synthetic Biology (SynOx) for their help in co-organising the event.<br><br><br />
<br />
<h2>'''Speakers'''</h2><br />
<br />
== Randy Rettberg ==<br />
<br />
<br />
President of iGEM - International Genetically Engineered Machine (iGEM) Foundation<br />
Randy Rettberg is the man behind iGEM, the global competition for undergraduates and high school students in designing brand new biological parts, or “genetically engineered machines”. An engineer by trade, he worked for years at internet pioneer Bolt, Beranek and Newman, at Apple, and Sun Microsystems, until he left the computer industry to work on Synthetic Biology at MIT. iGEM graduated from MIT two years ago and Randy is now President of the iGEM Foundation. iGEM also operates the Registry of Standard Biological Parts, a continuously growing library of genetic parts that can be mixed and matched to enable easier construction of synthetic biology devices and systems: it provides a resource of available genetic parts to iGEM teams and academic labs across the world, including the Oxford iGEM team.<br />
<br><br />
== Dr. Richard Kelwick ==<br />
<br />
Researcher at the EPSRC National Centre for Synthetic Biology and Innovation, Imperial College London<br />
Over five years of independent research experience, primarily in molecular biology, cancer biology and synthetic biology. Currently working on cell-free transcription and translation (TX-TL) systems to develop a high-throughput prototyping platform for biopart characterisation. Scientific advisor and project manager of three successful iGEM teams, 2011-2013. Most recently, Richard was the lead advisor for the iGEM team Plasticity, at Imperial College London, which came third out of over 200 teams at the world final, held at MIT.<br />
<br><br />
== Dr. Jarek Bryk ==<br />
National Centre for Biotechnology Education, University of Reading<br />
Jarek works at the National Centre for Biotechnology Education on a project to facilitate teaching of synthetic biology on an undergraduate level. He develops experimental kits that will be incorporated in synthetic biology curricula.He currently mentors the iGEM Reading team.<br />
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<br><br />
<h1>Sheffield meet up</h1><br />
The Oxford iGEM team attended the meet up organised by the team at the University of Sheffield on Friday the 18th of July. The purpose of this visit was to continue building the relationships between teams that were started at the Oxford-based meet up last month. The team gave a short presentation on the project so far.<br />
<br><br><br />
<br />
<br />
<h1>Team members give talk at Royal Society</h1><br />
Team members Glen Gowers and Philipp Lorenz attended the 2014 London Biochemistry Alumni Event hosted at the Royal Society. The occasion attracted alumni from across a period of more than 50 years – bringing together those who completed their PhDs in the 1950s, with students graduating last year. Following an introduction by Head of Department Mark Sansom, two Royal Institution Christmas Lecturers and our iGEM team members entertained guests with their presentations.<br />
<br />
<br><br><br />
<br />
<h1>Synthetic Biology - Short Past and Long Future</h1><br />
</html><br />
[[File:RR talking.jpg.png|250px|thumb|right|Randy giving his talk]]<br />
[[File:RR talking2.jpg|250px|thumb|right|The crowd gathers]]<br />
[[File:Rpunting.jpg|250px|thumb|right|Punting relaxation]]<br />
[[File:Teampunting.jpg|250px|thumb|right|Touring Magdalen College]]<br />
<html><br />
<h2>Introduction</h2><br />
<br />
On June 19th 2014, the Oxford iGEM team invited all of the teams in the UK and Ireland to talks in the Oxford University Biochemistry Department, including a talk by the CEO of iGEM Randy Rettberg; 'Synthetic Biology - Short Past and Long Future'. The event was attended by 15 iGEM teams as well as Oxford University undergraduates and postgraduates. We would like to thank Randy Rettberg, Dr. Richard Kelwick and Dr. Jarek Bryk for their insightful and entertaining talks. Later in the afternoon came the first opportunity for the the UK and Irish teams to get to know each other, initially by chatting over sandwiches before moving out of the department to the less formal setting of the pub. Our thanks to all those who attended and we hope this is the start of many successful team collaborations to come! Thank you also to The Oxford Society for Synthetic Biology (SynOx) for their help in co-organising the event.<br />
<br />
<h2>'''Speakers'''</h2><br />
<br />
== Randy Rettberg ==<br />
<br />
<br><br />
President of iGEM - International Genetically Engineered Machine (iGEM) Foundation<br />
Randy Rettberg is the man behind iGEM, the global competition for undergraduates and high school students in designing brand new biological parts, or “genetically engineered machines”. An engineer by trade, he worked for years at internet pioneer Bolt, Beranek and Newman, at Apple, and Sun Microsystems, until he left the computer industry to work on Synthetic Biology at MIT. iGEM graduated from MIT two years ago and Randy is now President of the iGEM Foundation. iGEM also operates the Registry of Standard Biological Parts, a continuously growing library of genetic parts that can be mixed and matched to enable easier construction of synthetic biology devices and systems: it provides a resource of available genetic parts to iGEM teams and academic labs across the world, including the Oxford iGEM team.<br />
<br />
== Dr. Richard Kelwick ==<br />
<br><br />
<br>Researcher at the EPSRC National Centre for Synthetic Biology and Innovation, Imperial College London<br />
Over five years of independent research experience, primarily in molecular biology, cancer biology and synthetic biology. Currently working on cell-free transcription and translation (TX-TL) systems to develop a high-throughput prototyping platform for biopart characterisation. Scientific advisor and project manager of three successful iGEM teams, 2011-2013. Most recently, Richard was the lead advisor for the iGEM team Plasticity, at Imperial College London, which came third out of over 200 teams at the world final, held at MIT.<br />
<br><br />
== Dr. Jarek Bryk ==<br />
<br><br />
<br>National Centre for Biotechnology Education, University of Reading<br />
Jarek works at the National Centre for Biotechnology Education on a project to facilitate teaching of synthetic biology on an undergraduate level. He develops experimental kits that will be incorporated in synthetic biology curricula.He currently mentors the iGEM Reading team.<br />
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<br><br />
<h1>Sheffield meet up</h1><br />
The Oxford iGEM team attended the meet up organised by the team at the University of Sheffield on Friday the 18th of July. The purpose of this visit was to continue building the relationships between teams that were started at the Oxford-based meet up last month. The team gave a short presentation on the project so far.<br />
<br><br><br />
<br />
<br />
<h1>Team members give talk at Royal Society</h1><br />
Team members Glen Gowers and Philipp Lorenz attended the 2014 London Biochemistry Alumni Event hosted at the Royal Society. The occasion attracted alumni from across a period of more than 50 years – bringing together those who completed their PhDs in the 1950s, with students graduating last year. Following an introduction by Head of Department Mark Sansom, two Royal Institution Christmas Lecturers and our iGEM team members entertained guests with their presentations.<br />
<br />
<br><br><br />
<br />
<h1>Synthetic Biology - Short Past and Long Future</h1><br />
</html><br />
[[File:RR talking.jpg.png|250px|thumb|right|Randy giving his talk]]<br />
[[File:RR talking2.jpg|250px|thumb|right|The crowd gathers]]<br />
[[File:Rpunting.jpg|250px|thumb|right|Punting relaxation]]<br />
[[File:Teampunting.jpg|250px|thumb|right|Touring Magdalen College]]<br />
<html><br />
<h2>Introduction</h2><br />
<br />
<br>On June 19th 2014, the Oxford iGEM team invited all of the teams in the UK and Ireland to talks in the Oxford University Biochemistry Department, including a talk by the CEO of iGEM Randy Rettberg; 'Synthetic Biology - Short Past and Long Future'. The event was attended by 15 iGEM teams as well as Oxford University undergraduates and postgraduates. We would like to thank Randy Rettberg, Dr. Richard Kelwick and Dr. Jarek Bryk for their insightful and entertaining talks. Later in the afternoon came the first opportunity for the the UK and Irish teams to get to know each other, initially by chatting over sandwiches before moving out of the department to the less formal setting of the pub. Our thanks to all those who attended and we hope this is the start of many successful team collaborations to come! Thank you also to The Oxford Society for Synthetic Biology (SynOx) for their help in co-organising the event.<br />
<br />
<h2>'''Speakers'''</h2><br />
<br />
== Randy Rettberg ==<br />
<br />
<br><br />
President of iGEM - International Genetically Engineered Machine (iGEM) Foundation<br />
Randy Rettberg is the man behind iGEM, the global competition for undergraduates and high school students in designing brand new biological parts, or “genetically engineered machines”. An engineer by trade, he worked for years at internet pioneer Bolt, Beranek and Newman, at Apple, and Sun Microsystems, until he left the computer industry to work on Synthetic Biology at MIT. iGEM graduated from MIT two years ago and Randy is now President of the iGEM Foundation. iGEM also operates the Registry of Standard Biological Parts, a continuously growing library of genetic parts that can be mixed and matched to enable easier construction of synthetic biology devices and systems: it provides a resource of available genetic parts to iGEM teams and academic labs across the world, including the Oxford iGEM team.<br />
<br />
== Dr. Richard Kelwick ==<br />
<br><br />
<br>Researcher at the EPSRC National Centre for Synthetic Biology and Innovation, Imperial College London<br />
Over five years of independent research experience, primarily in molecular biology, cancer biology and synthetic biology. Currently working on cell-free transcription and translation (TX-TL) systems to develop a high-throughput prototyping platform for biopart characterisation. Scientific advisor and project manager of three successful iGEM teams, 2011-2013. Most recently, Richard was the lead advisor for the iGEM team Plasticity, at Imperial College London, which came third out of over 200 teams at the world final, held at MIT.<br />
<br><br />
== Dr. Jarek Bryk ==<br />
<br><br />
<br>National Centre for Biotechnology Education, University of Reading<br />
Jarek works at the National Centre for Biotechnology Education on a project to facilitate teaching of synthetic biology on an undergraduate level. He develops experimental kits that will be incorporated in synthetic biology curricula.He currently mentors the iGEM Reading team.<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/policy_and_practicesTeam:Oxford/policy and practices2014-10-11T18:33:30Z<p>CorinnaO: </p>
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In the emerging field of synthetic biology, iGEM is growing as a source of ambitious and imaginative ideas which have the potential to offer great benefits to human society and our environment. The number of successful iGEM start-ups clearly demonstrates that the competition gives teams the opportunity to get involved in much more than just a summer project; students have the chance to come up with a solution which has a positive impact in the real world. <br />
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In fact, many projects are conceived in the hope of doing just this, often with a specific unresolved social problem in mind. iGEM draws inspiration from the world and the challenges it faces, and contributes back potential solutions.<br />
<br><br><br />
<h1blue><br />
"Quote from EA about usefulness of our project/bioremediation/synthetic biology generally, the fact that it could actually help the problem etc…”<br />
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<h1blue2><br />
(British Environment Agency)<br />
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Our work with the Environment Agency, which inspired us to explore bioremediation as an option for dealing with the pressing environmental concern cause by chlorinated solvent pollution, brought home just how great an impact our own project might have on this issue. Chlorinated solvents are indispensable to many manufacturing, professional, and even everyday household activities, yet no environmentally acceptable method of disposal currently exists. Our research has led us to believe that bioremediation is a genuinely viable option for addressing this challenge, a position which the Environment Agency has supported. We realized that what began as an iGEM project may well be worth developing further than the months we had available to complete the competition.<br />
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With this in mind, for the policy and practices element of our project we decided to ask…<br />
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<center><font style="font-size:30px;font-weight:700;">HOW CAN AN IGEM PROJECT CHANGE THE WORLD?</font></center><br />
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For the policy and practices element of the competition, our team has researched how iGEM projects grow from ideas into real world solutions. What are the challenges facing teams who want to develop their projects beyond the jamboree? What can be done to help them realise the potential benefits of their ideas for society? And what should we be aiming to achieve by all this?<br />
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Our team identified what we believe are the key considerations for teams to take into account.<br />
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<h1blue3>Problem Solving….</h1blue3><br />
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<h2blue3>Many teams are inspired by their search for a synbio solution to a problem or challenge faced by the world. For our team, the need for a way of address the environmental problems caused by chlorinated solvents was clear. Find out more here...</h2blue3><br />
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<h1red>Practicality….</h1red><br />
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<h2red>How can the idea be implemented and delivered in the real world? Our engineers used design software and 3D printing to think about how we might realise DCMation and the environments in which the biosensor and bioremediation technique might be used.</h2red><br />
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<h1purple>Intellectual Property….</h1purple><br />
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<h2purple>Determining the ownership of the intellectual property of a project is crucial for any team hoping to develop their ideas beyond the Jamboree. Our report looks at how the iGEM community can navigate this controversial and difficult issue. </h2purple><br />
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<h1orange>Communication….</h1orange><br />
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<h2orange>Determining the ownership of the intellectual property of a project is crucial for any team hoping to develop their ideas beyond the Jamboree. Our report looks at how the iGEM community can navigate this controversial and difficult issue. </h2orange><br />
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<h1blue3>Public Participation….</h1blue3><br />
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Determining the ownership of the intellectual property of a project is crucial for any team hoping to develop their ideas beyond the Jamboree. Our report looks at how the iGEM community can navigate this controversial and difficult issue. <br />
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<h1green>The iGEM Competition….</h1green><br />
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iGEM has been steadily expanding since its beginnings in 2004, climbing from 5 to over 200 teams in the last 10 years. As the first ever Oxford team, we are more than a little late to the party! Our team has researched the growing contribution made by the rest of Europe to the competition, of which we hope to become a part from 2014 onwards!<br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/ModellingTeam:Oxford/Modelling2014-10-11T18:02:19Z<p>CorinnaO: proofread</p>
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<h1 style="color:#777777">Our team believes that the key to synthetic biology is to use engineering-based models and designs to improve the development of biochemical systems. Therefore, the engineers in our team have worked with the biochemists every step of the way to design initial project ideas, analyse in detail the expected response of the system, and interpret the results of the various types of experiments that we have run.<br />
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This means that, unlike iGEM teams in the past, our modelling is interspersed with our biochemistry information to give a feel of the real interactions that have taken place between the specialities in our team. To aid the viewer, all modelling sections have pink header bubbles, all of the biochemistry sections have light blue header bubbles.<br />
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We have also worked very closely with the human practices team and influential figures in industry to look at exactly how this project could be implemented in the real world. Included in this has been the 3D CAD (computer aided design) of the expected product and the 3D printing and circuit building of the biosensor unit.<br />
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Click on the links below to find out more!</h1><br />
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<h1blue3>Characterising a genetic network</h1blue3><br />
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<h2blue3>We used stochastic and deterministic genetic circuit modelling to help the wet-lab team in characterising a previously unknown genetic circuit. Find out more here...<br />
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<h1red>Optimising a genetic network</h1red><br />
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<h2red>We used these genetic circuit models to predict the fluoresence of the system as a response to thousands of different combinations of inputs. This allowed us to optimise the input levels and advise the biochemists on the construction of the system so that we could develop the best possible system in the amount of time available. See what we found out...</h2red><br />
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<h1purple>Analysing the native bacterium</h1purple><br />
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<h2purple>We constructed a model based on Michaelis-Menten kinetics that could inform us how much DCM the native bacterium would be able to degrade and also what the pH change of the system would be. This further convinced us to use synthetic biology to solve the problem of chlorinated waste disposal. See how we did it here...</h2purple><br />
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<h1orange>Analysing microcompartments mathematically</h1orange><br />
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<h2orange>We used spacial modelling to determine an estimate of various parameters to do with the microcompartments. We then gave this information to the biochemists to help them analyse their results with expressing microcompartments in E. coli and P. putida.</h2orange><br />
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<h1blue3>Analysing the benefits of microcompartments</h1blue3><br />
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On this page we explain in detail how our stochastic diffusion models work and then provide in-depth information on how we then used these carefully analyse of the benefits of microcompartments for our system.<br />
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We embarked on a major collaboration project with Melbourne iGEM team. Part of this collaboration involved extensively modelling the benefit of using their star peptide system in a bacterium and how that could control reaction rates. To do this we drew on the extensive knowledge that we've gained of stochastically modelling diffusion-driven systems.<br />
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<h1red>Realising the biosensor</h1red><br />
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<h2red>On the advice of industry experts, we produced concept designs of our whole system using CAD. We built the biosensor using the latest 3D printing technologies and we designed and built a very cheap circuit that can detect low levels of GFP fluorescence to go inside the biosensor. This part is really exciting...</h2red><br />
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<h1orange>Bioremediation realisation</h1orange><br />
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<h2orange> Discover how we envisage our project becoming a real world product and see the CAD models that have allowed us to demonstrate our idea to industry experts.</h2orange><br />
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<h1blue3>Biopolymer containment</h1blue3><br />
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<h2blue3> Find out how we modelled the processes that control the diffusion of DCM and reaction products through the biopolymer containment beads, and how this modelling played an integral part in calculating the optimum bead thickness for our system.</h2blue3><br />
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{{Team:Oxford/templates/footer}}</div>CorinnaOhttp://2014.igem.org/Team:Oxford/bioremediationTeam:Oxford/bioremediation2014-10-06T17:24:28Z<p>CorinnaO: </p>
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<h1>DCMation: decimating DCM pollution using bioremediation</h1><br />
Bioremediation is a form of clean waste management that relies on the ability of organisms to break down a wide range of pollutants and toxic wastes that would otherwise harm the environment and potentially pose a threat to human health. Dichloromethane (DCM) is such a toxic waste product, and we have made it our mission to explore and develop a system to safely degrade DCM using bacteria. <br><br><br />
The three 'DCMation' links below will guide you through our interdisciplinary approach of evaluating and improving the DCM-degrading capacity of the native bacterium, Methylobacterium extorquens DM4. Additionally, these pages explain how and why we have expressed the DCM degradation system in E. coli and P. putida, and how we are adapting them for DCMation.<br><br><br />
The three 'Microcompartment' pages will take you on a journey of how models of microcompartments designed by our engineers work hand-in-hand with the results that our biochemists obtained in wet-lab experiments. After explaining what microcompartments are and why we use them, we introduce our collaboration with the Melbourne iGEM team and their project, the Star Peptide, which is a possible alternative to the use of microcompartments. <br />
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</html></div>CorinnaOhttp://2014.igem.org/Team:Oxford/biosensor_optimisationTeam:Oxford/biosensor optimisation2014-10-05T10:51:18Z<p>CorinnaO: </p>
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<h1>Introduction: what are we optimising?</h1><br />
To develop the biosensor to the highest quality that we could reach in the short time period available for the project, it was very important to incorporate mathematical modelling into the design process.<br />
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Having already made the mathematical models <a href="https://2014.igem.org/Team:Oxford/biosensor_characterisation">(see the characterisation section)</a>, it was then important to:<br />
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• Analyse how varying the amount of each input added affected the response of the system.<br><br />
• Guide the biochemistry on parameter values to aim for when making the system.<br />
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Both of these helped us to save a lot of time and money. It fast tracked the development process because we didn’t then have to run lots of different variations of the tests and more importantly we didn’t have to build lots of different constructs containing different values of the parameters (for example, the degradation and expression rates).<br />
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<h1>Biochemistry...</h1><br />
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<h1white>What happens when we change the amount of each input added?</h1white><br />
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<h1>What are these graphs and where did they come from?</h1><br />
The 3D plot shown below shows what the model predicts the steady state fluorescence of the bacteria to be when varying amounts of ATC and DCM are added to the system. <u>(which system?)</u><br />
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The two graphs are slices of the overall 3D plot. In these we are analysing how the input added affects the steady state response whilst keeping the other input constant, we plotted this using a system of differential equations that we produced for the characterisation part. <u>(where was this?)</u> <br />
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To do this, we plotted the final fluorescence value from lots of different possible combinations of the two inputs (ATC and DCM). The top graph shows the variation in final fluorescence when DCM is held constant and ATC is varied, the second graph is vice versa.<br />
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It is important to understand that these graphs represent the expected steady state level of fluorescence of thousands of different simulations. From this we can select the DCM and ATC concentrations for a specific fluorescence response.<br />
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<h1>How much of each input should we use to test the biosensor?</h1><br />
For the biosensor, we need:<br />
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• The system to be robust to changes in ATC concentration as we cannot be sure that all of the cells will receive exactly the same amount of ATC in the real system. <br />
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The top graph shows that once you get above a certain threshold value of ATC input, the steady state fluorescence of the system doesn’t change. This means that to meet the above requirement, we simply have to use an ATC input value greater than the threshold value.<br />
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• The system needs to be very sensitive to changes when there is a low concentration of DCM. This is important because we want the output of our biosensor to change when there is only a very small amount of the DCM left, so that it is safe to be discarded.<br />
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We know that when no DCM is added to the system, there will be no fluorescence response aside from the basal rate. However, the model predicts that when even a small amount of DCM is added and the system is left for a while, the system fluoresces with the saturated level of fluorescence. Therefore, we have the potential to develop a very sensitive biosensor that senses the presence of DCM, fluorescing when DCM is present and only switching off when the amount of DCM reaches a very low level.<br />
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To summarise, we have established that the inputs to our biosensor should be a constant medium concentration of ATC (it isn’t degraded) and a varying concentration of DCM as it is degraded. <br />
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<h1white>Modelling the biosensor to optimise ’ON’ and ‘OFF’ response</h1white><br />
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<h1white>Modelling the biosensor to optimise ’ON’ and ‘OFF’ response</h1white><br />
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To be a good biosensor, we need to optimise the ‘ON’ and ‘OFF’ response. This relies on the system having two features; namely a fast response time to concentration changes and a large amplitude of response. Having previously established what inputs we need <u>(see above)</u> for the biosensor, we were then asked by the biochemists to analyse the effects of varying some of the parameters that we have control over were. This is a very important step in synthetic biology because it allows us to crudely optimise the design before construction even begins. This saves a lot of time and money to allow us to develop a useful system much faster. To test the response of our biosensor, we shall use a step function of DCM to simulate pouring DCM in and then removing DCM through <u>spinning the cells(?)</u>. In the real system, the DCM input would be a step in and then a gradual negative ramp as the DCM was degraded.<br />
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The two parameters that we can realistically change in the initial production of the bacteria are the RBS strength and the degradation rate. <br />
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Increasing the Ribosome Binding Site (RBS) strength can greatly increase the translation initiation rate, hence expressing more protein. <u>(HOW?) (CORRECT + DETAIL?)</u><br />
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We can change the degradation rate of the fluorescent protein by adding degradation tags. <u>(CORRECT + DETAIL?)</u><br />
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<h1white>Should we aim for high or low RBS strength?</h1white> <br />
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<h1white>Should we aim for high or low RBS strength?</h1white> </div></a><br />
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We ran the deterministic model whilst varying the activation rate (see <a href="https://2014.igem.org/Team:Oxford/biosensor_deterministic_equations">'where did these equations come from?'</a>) of the sfGFP. The response is shown here:<br />
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<h1>What does this tell us?</h1><br />
As you can see from this graph, increasing the RBS strength only changes the amplitude of the systems response without affecting the response time of the system. This is highly beneficial for the system.<br />
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-->Therefore we will aim for as high an RBS strength as possible in our initial design.<br />
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<h1white>Should we aim for high or low degradation rate?</h1white> <br />
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<h1white>Should we aim for high or low degradation rate?</h1white> </div></a><br />
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We ran the deterministic model whilst varying the degradation rate (see <a href="https://2014.igem.org/Team:Oxford/biosensor_deterministic_equations">'where did these equations come from?'</a>) of the sfGFP. The response is shown here:<br />
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<h1>What does this tell us?</h1><br />
Changing the degradation rate of the protein is more of a trade-off. As you can see, a higher degradation rate gives a faster response but with a much lower steady state responses<br />
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-->We should aim for a low degradation rate to begin with so that we can ensure a detectable level of fluorescence, and then gradually increase the degradation rate to get a faster response.<br />
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<h1>Modelling Summary</h1><br />
The above results demonstrate well the power of modelling genetic circuits. This approach has allowed us to develop our first construct intelligently and to have some trustworthy predictions on which to develop the rest of our system around. However, as ever, there are limitations, especially in biological systems.<br />
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In an ideal world, we would like to have a very high expression rate (for a high steady state amplitude of fluorescence), a high degradation rate (for a fast responding biosensor) and a high copy number of the plasmid in each cell. Conversely though, optimising these parameters puts stress on the cells. This leads to the system not actually being as optimal as the model might have predicted. Here we identify the weakness in preliminary models. We will have to actually develop the bacteria and run the experiments in the lab before we will know if our biosensor will respond this well to the DCM. After this, we will work at creating secondary models which should be able to give more reliable predictions. Ideally we would be able to then make more bacteria and the Engineering-Biochemistry cycle would continue.<br />
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</html></div>CorinnaOhttp://2014.igem.org/Team:Oxford/biosensor_optimisationTeam:Oxford/biosensor optimisation2014-10-05T10:49:41Z<p>CorinnaO: </p>
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<h1>Introduction: what are we optimising?</h1><br />
To develop the biosensor to the highest quality that we could reach in the short time period available for the project, it was very important to incorporate mathematical modelling into the design process.<br />
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Having already made the mathematical models <a href="https://2014.igem.org/Team:Oxford/biosensor_characterisation">(see the characterisation section)</a>, it was then important to:<br />
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• Analyse how varying the amount of each input added affected the response of the system.<br><br />
• Guide the biochemistry on parameter values to aim for when making the system.<br />
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Both of these helped us to save a lot of time and money. It fast tracked the development process because we didn’t then have to run lots of different variations of the tests and more importantly we didn’t have to build lots of different constructs containing different values of the parameters (for example, the degradation and expression rates).<br />
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<h1black>Insert biochem here?</h1black><br />
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<h1>Biochemistry...</h1><br />
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<h1white>What happens when we change the amount of each input added?</h1white><br />
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<h1white>What happens when we change the amount of each input added?</h1white></div></a><br />
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<h1>What are these graphs and where did they come from?</h1><br />
The 3D plot shown below shows what the model predicts the steady state fluorescence of the bacteria to be when varying amounts of ATC and DCM are added to the system. <u>(which system?)</u><br />
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The two graphs are slices of the overall 3D plot. In these we are analysing how the input added affects the steady state response whilst keeping the other input constant, we plotted this using a system of differential equations that we produced for the characterisation part. <u>(where was this?)</u> <br />
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To do this, we plotted the final fluorescence value from lots of different possible combinations of the two inputs (ATC and DCM). The top graph shows the variation in final fluorescence when DCM is held constant and ATC is varied, the second graph is vice versa.<br />
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It is important to understand that these graphs represent the expected steady state level of fluorescence of thousands of different simulations. From this we can select the DCM and ATC concentrations for a specific fluorescence response.<br />
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<h1>How much of each input should we use to test the biosensor?</h1><br />
For the biosensor, we need:<br />
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• The system to be robust to changes in ATC concentration as we cannot be sure that all of the cells will receive exactly the same amount of ATC in the real system. <br />
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The top graph shows that once you get above a certain threshold value of ATC input, the steady state fluorescence of the system doesn’t change. This means that to meet the above requirement, we simply have to use an ATC input value greater than the threshold value.<br />
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• The system needs to be very sensitive to changes when there is a low concentration of DCM. This is important because we want the output of our biosensor to change when there is only a very small amount of the DCM left, so that it is safe to be discarded.<br />
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We know that when no DCM is added to the system, there will be no fluorescence response aside from the basal rate. However, the model predicts that when even a small amount of DCM is added and the system is left for a while, the system fluoresces with the saturated level of fluorescence. Therefore, we have the potential to develop a very sensitive biosensor that senses the presence of DCM, fluorescing when DCM is present and only switching off when the amount of DCM reaches a very low level.<br />
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To summarise, we have established that the inputs to our biosensor should be a constant medium concentration of ATC (it isn’t degraded) and a varying concentration of DCM as it is degraded. <br />
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<h1white>Modelling the biosensor to optimise ’ON’ and ‘OFF’ response</h1white><br />
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<h1white>Modelling the biosensor to optimise ’ON’ and ‘OFF’ response</h1white><br />
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To be a good biosensor, we need to optimise the ‘ON’ and ‘OFF’ response. This relies on the system having two features; namely a fast response time to concentration changes and a large amplitude of response. Having previously established what inputs we need <u>(see above)</u> for the biosensor, we were then asked by the biochemists to analyse the effects of varying some of the parameters that we have control over were. This is a very important step in synthetic biology because it allows us to crudely optimise the design before construction even begins. This saves a lot of time and money to allow us to develop a useful system much faster. To test the response of our biosensor, we shall use a step function of DCM to simulate pouring DCM in and then removing DCM through <u>spinning the cells(?)</u>. In the real system, the DCM input would be a step in and then a gradual negative ramp as the DCM was degraded.<br />
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The two parameters that we can realistically change in the initial production of the bacteria are the RBS strength and the degradation rate. <br />
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Increasing the Ribosome Binding Site (RBS) strength can greatly increase the translation initiation rate, hence expressing more protein. <u>(HOW?) (CORRECT + DETAIL?)</u><br />
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We can change the degradation rate of the fluorescent protein by adding degradation tags. <u>(CORRECT + DETAIL?)</u><br />
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We ran the deterministic model whilst varying the activation rate (see <a href="https://2014.igem.org/Team:Oxford/biosensor_deterministic_equations">'where did these equations come from?'</a>) of the sfGFP. The response is shown here:<br />
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<h1>What does this tell us?</h1><br />
As you can see from this graph, increasing the RBS strength only changes the amplitude of the systems response without affecting the response time of the system. This is highly beneficial for the system.<br />
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-->Therefore we will aim for as high an RBS strength as possible in our initial design.<br />
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<h1white>Should we aim for high or low degradation rate?</h1white> <br />
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<h1white>Should we aim for high or low degradation rate?</h1white> </div></a><br />
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We ran the deterministic model whilst varying the degradation rate (see <a href="https://2014.igem.org/Team:Oxford/biosensor_deterministic_equations">'where did these equations come from?'</a>) of the sfGFP. The response is shown here:<br />
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<h1>What does this tell us?</h1><br />
Changing the degradation rate of the protein is more of a trade-off. As you can see, a higher degradation rate gives a faster response but with a much lower steady state responses<br />
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<h1>Modelling Summary</h1><br />
The above results demonstrate well the power of modelling genetic circuits. This approach has allowed us to develop our first construct intelligently and to have some trustworthy predictions on which to develop the rest of our system around. However, as ever, there are limitations, especially in biological systems.<br />
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In an ideal world, we would like to have a very high expression rate (for a high steady state amplitude of fluorescence), a high degradation rate (for a fast responding biosensor) and a high copy number of the plasmid in each cell. Conversely though, optimising these parameters puts stress on the cells. This leads to the system not actually being as optimal as the model might have predicted. Here we identify the weakness in preliminary models. We will have to actually develop the bacteria and run the experiments in the lab before we will know if our biosensor will respond this well to the DCM. After this, we will work at creating secondary models which should be able to give more reliable predictions. Ideally we would be able to then make more bacteria and the Engineering-Biochemistry cycle would continue.<br />
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</html></div>CorinnaOhttp://2014.igem.org/Team:Oxford/biosensor_optimisationTeam:Oxford/biosensor optimisation2014-10-05T10:47:52Z<p>CorinnaO: </p>
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<h1>Introduction: what are we optimising?</h1><br />
To develop the biosensor to the highest quality that we could reach in the short time period available for the project, it was very important to incorporate mathematical modelling into the design process.<br />
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Having already made the mathematical models <a href="https://2014.igem.org/Team:Oxford/biosensor_characterisation">(see the characterisation section)</a>, it was then important to:<br />
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• Analyse how varying the amount of each input added affected the response of the system.<br><br />
• Guide the biochemistry on parameter values to aim for when making the system.<br />
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Both of these helped us to save a lot of time and money. It fast tracked the development process because we didn’t then have to run lots of different variations of the tests and more importantly we didn’t have to build lots of different constructs containing different values of the parameters (for example, the degradation and expression rates).<br />
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<h1black>Insert biochem here?</h1black><br />
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<h1black>Insert biochem here?</h1black></div></a><br />
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<h1>Biochemistry...</h1><br />
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<h1white>What happens when we change the amount of each input added?</h1white><br />
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<h1white>What happens when we change the amount of each input added?</h1white></div></a><br />
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<img src="https://static.igem.org/mediawiki/2014/5/51/Oxford_varying_ATC_and_DCM.png" style="margin-left:0%; float:right; margin-right:0%; position:relative; width:45%;" /><br />
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<h1>What are these graphs and where did they come from?</h1><br />
The 3D plot shown below shows what the model predicts the steady state fluorescence of the bacteria to be when varying amounts of ATC and DCM are added to the system. <u>(which system?)</u><br />
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The two graphs are slices of the overall 3D plot. In these we are analysing how the input added affects the steady state response whilst keeping the other input constant, we plotted this using a system of differential equations that we produced for the characterisation part. <u>(where was this?)</u> <br />
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To do this, we plotted the final fluorescence value from lots of different possible combinations of the two inputs (ATC and DCM). The top graph shows the variation in final fluorescence when DCM is held constant and ATC is varied, the second graph is vice versa.<br />
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It is important to understand that these graphs represent the expected steady state level of fluorescence of thousands of different simulations. From this we can select the DCM and ATC concentrations for a specific fluorescence response.<br />
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<h1>How much of each input should we use to test the biosensor?</h1><br />
For the biosensor, we need:<br />
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• The system to be robust to changes in ATC concentration as we cannot be sure that all of the cells will receive exactly the same amount of ATC in the real system. <br />
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The top graph shows that once you get above a certain threshold value of ATC input, the steady state fluorescence of the system doesn’t change. This means that to meet the above requirement, we simply have to use an ATC input value greater than the threshold value.<br />
<br><br><br />
• The system needs to be very sensitive to changes when there is a low concentration of DCM. This is important because we want the output of our biosensor to change when there is only a very small amount of the DCM left, so that it is safe to be discarded.<br />
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We know that when no DCM is added to the system, there will be no fluorescence response aside from the basal rate. However, the model predicts that when even a small amount of DCM is added and the system is left for a while, the system fluoresces with the saturated level of fluorescence. Therefore, we have the potential to develop a very sensitive biosensor that senses the presence of DCM, fluorescing when DCM is present and only switching off when the amount of DCM reaches a very low level.<br />
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To summarise, we have established that the inputs to our biosensor should be a constant medium concentration of ATC (it isn’t degraded) and a varying concentration of DCM as it is degraded. <br />
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<h1white>Modelling the biosensor to optimise ’ON’ and ‘OFF’ response</h1white><br />
<img src="https://static.igem.org/mediawiki/2014/4/4d/Oxford_plus-sign-clip-art.png" style="float:right;position:relative; width:2%;" /><br />
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<h1white>Modelling the biosensor to optimise ’ON’ and ‘OFF’ response</h1white><br />
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To be a good biosensor, we need to optimise the ‘ON’ and ‘OFF’ response. This relies on the system having two features; namely a fast response time to concentration changes and a large amplitude of response. Having previously established what inputs we need <u>(see above)</u> for the biosensor, we were then asked by the biochemists to analyse the effects of varying some of the parameters that we have control over were. This is a very important step in synthetic biology because it allows us to crudely optimise the design before construction even begins. This saves a lot of time and money to allow us to develop a useful system much faster. To test the response of our biosensor, we shall use a step function of DCM to simulate pouring DCM in and then removing DCM through <u>spinning the cells(?)</u>. In the real system, the DCM input would be a step in and then a gradual negative ramp as the DCM was degraded.<br />
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The two parameters that we can realistically change in the initial production of the bacteria are the RBS strength and the degradation rate. <br />
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Increasing the Ribosome Binding Site (RBS) strength can greatly increase the translation initiation rate, hence expressing more protein. <u>(HOW?) (CORRECT + DETAIL?)</u><br />
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We can change the degradation rate of the fluorescent protein by adding degradation tags. <u>(CORRECT + DETAIL?)</u><br />
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<h1white>Should we aim for high or low RBS strength?</h1white> <br />
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<h1white>Should we aim for high or low RBS strength?</h1white> </div></a><br />
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We ran the deterministic model whilst varying the activation rate (see <a href="https://2014.igem.org/Team:Oxford/biosensor_deterministic_equations">'where did these equations come from?'</a>) of the sfGFP. The response is shown here:<br />
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<h1>What does this tell us?</h1><br />
As you can see from this graph, increasing the RBS strength only changes the amplitude of the systems response without affecting the response time of the system. This is highly beneficial for the system.<br />
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-->Therefore we will aim for as high an RBS strength as possible in our initial design.<br />
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<h1white>Should we aim for high or low degradation rate?</h1white> <br />
<img src="https://static.igem.org/mediawiki/2014/4/4d/Oxford_plus-sign-clip-art.png" style="float:right;position:relative; width:2%;" /><br />
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<h1white>Should we aim for high or low degradation rate?</h1white> </div></a><br />
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<img src="https://static.igem.org/mediawiki/2014/6/67/Oxford_change_degradation_rate.png" style="margin-left:0%; float:right; margin-right:0%; position:relative; width:65%;" /><br />
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We ran the deterministic model whilst varying the degradation rate (see <a href="https://2014.igem.org/Team:Oxford/biosensor_deterministic_equations">'where did these equations come from?'</a>) of the sfGFP. The response is shown here:<br />
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<h1>What does this tell us?</h1><br />
Changing the degradation rate of the protein is more of a trade-off. As you can see, a higher degradation rate gives a faster response but with a much lower steady state responses<br />
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-->We should aim for a low degradation rate to begin with so that we can ensure a detectable level of fluorescence, and then gradually increase the degradation rate to get a faster response.<br />
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<h1>Modelling Summary</h1><br />
The above results demonstrate well the power of modelling genetic circuits. This approach has allowed us to develop our first construct intelligently and to have some trustworthy predictions on which to develop the rest of our system around. However, as ever, there are limitations, especially in biological systems.<br />
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In an ideal world, we would like to have a very high expression rate (for a high steady state amplitude of fluorescence), a high degradation rate (for a fast responding biosensor) and a high copy number of the plasmid in each cell. Conversely though, optimising these parameters puts stress on the cells. This leads to the system not actually being as optimal as the model might have predicted. Here we identify the weakness in preliminary models. We will have to actually develop the bacteria and run the experiments in the lab before we will know if our biosensor will respond this well to the DCM. After this, we will work at creating secondary models which should be able to give more reliable predictions. Ideally we would be able to then make more bacteria and the Engineering-Biochemistry cycle would continue.<br />
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<h1>Biochemistry summary</h1><br />
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</html></div>CorinnaOhttp://2014.igem.org/Team:Oxford/biosensor_characterisationTeam:Oxford/biosensor characterisation2014-10-05T10:44:16Z<p>CorinnaO: </p>
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<h1>Introduction: what are we characterising?</h1><br />
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Methylobacterium extorquens DM4 in the presence of DCM expresses DcmA, a dichloromethane dehalogenase.<br />
Within 1.5kb upstream of dcmA and in the opposite orientation is a second gene encoding DcmR, a regulatory protein that controls expression of DcmA:<br><br><br />
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In order to design and create a stable and sensitive system that responds to DCM we first need to characterise the regulatory nature of DcmR. Characterisation of this regulatory network has never been done before although it has been suggested to be a repressor [1]; we will be the first to fully characterise the mode of action of dcmR. To do this we suppose the following hypotheses for DCM activating the transcription of dcmR: either double repression or double activation. In other words, either DcmR represses dcmA expression and DcmR is in turn repressed by the presence of DCM; or expression of dcmA requires DcmR as an activator, with DcmR in turn only activated in the presence of DCM.<br><br><br />
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<h1black>What is currently known about DcmR?</h1black><br />
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<h1black>What is currently known about DcmR?</h1black></div></a><br />
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<h1>DcmR and regulation of dcmA expression</h1><br />
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Mutants with dcmA and the intergenic region but without complete dcmR express DcmA constitutively. Re-integration of dcmR restores regulation of dcmA expression at the transcriptional level [1]. In addition, it has been shown that the region including dcmR, the intergenic region and dcmA is sufficient to confer a DCM dependent response in genetically engineered Methylobacterium extorquens DM4 [2]. <br><br><br />
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<h1>DcmR and DNA-binding</h1><br />
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DcmR is thought to be a DNA binding protein as structure predicting software indicates that there is a helix-turn-helix domain at the N-terminal of the protein. Since the region between the two promoters for dcmR and dcmA can be deleted without any effect on regulation it has been suggested that DcmR does not to a secondary regulatory site in between the genes but most likely acts directly on the dcmA promoter itself [1]. In addition, regulated expression of dcmA is not effected when the dcmR and dcmA transcriptional units are placed on separate replicons thereby suggesting that their topology is independent of the regulatory network. It is therefore suggested that DcmR binds the DNA in the intergenic region with the simplest model of its mode of action being as a trans-acting DNA-binding repressor; however this remains to be fully validated [1].<br><br><br />
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We have therefore proceeded on the assumption that DcmR is directly influenced by the presence or absence of DCM and furthermore that we can use dcmR, the intergenic region and dcmA alone to characterise the regulatory network. <br><br><br />
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[1] La Roche, S. D., and T. Leisinger. "Identification of dcmR, the regulatory gene governing expression of dichloromethane dehalogenase in Methylobacterium sp. strain DM4." Journal of bacteriology 173.21 (1991): 6714-6721. <br><br />
[2] Lopes, N., et al “Detection of dichloromethane with a bioluminescent (lux) bacterial bioreporter” J Ind Microbiol Biotechnol (2012) 39:45–53<br />
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<h1white>Modelling genetic circuits</h1white><br />
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<h1white>Modelling genetic circuits</h1white></div></a><br />
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<h1>Predicting the mCherry fluorescence</h1><br />
To model the first double repression, we took the fact that we won’t need to know the amount of tetR in the system and used the assumption that ATC is effectively activating the expression of dcmR, albeit parameterised by different constants. This assumption should be justified by the fact that we will be able to precisely control the addition of ATC and we will be able to measure the fluorescence of the mCherry.<br />
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We modelled this first step using both deterministic and stochastic models.<br />
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<h1>Deterministic</h1><br />
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<img src="https://static.igem.org/mediawiki/2014/2/2e/Oxford_DcmR_parameters.png" style="float:right;position:relative; height:8%; width:47%;" /><br />
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Deterministic models are very powerful tools in systems biology. They analyse the behaviour of the bacteria on a culture level and use ordinary differential equations (ODEs) to relate each activation and repression. By constructing a cascade of differential equations you can build a very robust model of the average behaviour of the gene circuit.<br />
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The differential equation that applies to this first step in the system is:<br />
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<br><br><img src="https://static.igem.org/mediawiki/2014/e/ed/Oxford_DcmR_activation.png" style="float:left;position:relative; height:8%; width:47%;" /><br />
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<a href="https://2014.igem.org/Team:Oxford/biosensor_deterministic_equations"><img src="https://static.igem.org/mediawiki/2014/4/41/Oxford_equations.png" style="float:left;position:relative; width:40%;" /></a><br />
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Solving this ODE in Matlab (with zero basal rate) gives the response of the system to be:<br />
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Oxford iGEM 2014<br />
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While the analysis of this circuit isn’t critical to the successful outcome of this part of the project, it will provide us with a very good practice of both obtaining fluorescence time data and accurately fitting the data to the model. It will also help us develop our methods of predicting future system behaviour. This is because this system is already well documented in literature and so we should be able to test our methods and responses against well documented results from labs across the world.<br />
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As you can clearly see from the graph, the model predicts a large fluorescence increase as the input is added. This is the expected response from the real response and is the best approximation that is obtainable before we get data from the biochemists.<br />
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In the graph above, the model is set to have a basal rate of zero. This is why there is a zero fluorescence response before the input has been added. In biochemical terms, this is the same as the tetO promoter not being leaky at all. This basal rate will be calibrated alongside all of the other parameters in the model.<br />
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<h1>Stochastic Modelling</h1><br />
Stochastic modelling uses probability to calculate what happens next in a system. For our project we used it to model the expression of genes from bacteria.<br />
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We started with the Gillespie Algorithm, which considers the expression of GFP to be binary; either a molecule of GFP was produced or it was degraded. We modelled the chance of a molecule of GFP being created using the Michaelis-Mentin equation and incorporating a basal rate. For the degradation, we assumed a simple proportional relationship, the reasoning being that if we have more GFP then we are more likely that a single GFP molecule will degrade. The constant of proportionality will be a function of the intrinsic life time of the protein in the cell. Now at every increment in time we will not have a GFP reaction occurring, so before we decided what reaction occurs we had to work out if I a reaction occurred. We did this by writing an equation involving the probability of any reaction occurring with a random number generator. To work out which reaction occurred we compared the relative probability of a production to degradation, and used a random number to make a weighted choice.<br />
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We later changed this code so that a reaction occurred every time increment, but included a null reaction where no GFP was degraded or created. Although this made the code a lot more data heavy, it allowed for much easier calculation of the mean response of multiple realisations.<br />
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Stochastic modelling is useful because it can show us the randomness which is often seen in real bacteria. Calculating the variation of the mean of multiple GFP producing bacteria we can also work out the standard deviation, and if we assume that the system varies with respect to the normal distribution, we can produce error bounds for the error growth. Such that we can say, 90% of the time we can expect the production of GFP from a single bacterium to be within these 2 curves. This could be useful for seeing if results are unexpected, or if there are multiple outliers, that our model is not correct. If we average more and more realisations of reactions then the mean curve tend towards the deterministic response. This is equivalent to looking at the mean of more and more bacteria until we are looking at the system as a whole and fluctuations in individual bacteria are averaged out.<br />
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<li>What is stochastic modelling?</li><br />
<li>How is it useful? Ads/Dis</li><br />
<li>Tending to deterministic</li> <br />
<li>Modelling activator repressor</li><br />
<li>Parameter characterisation/Data matching</li><br />
<li>Matlab graphs</li><br />
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<h1black>ATC induction of mCherry expression</h1black><br />
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<h1black>ATC induction of mCherry expression</h1black></div></a><br />
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<h1>Wetlab data showing response in level of mCHERRY expressed with different concs of ATC</h1><br />
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<h1white>How can we tell the systems apart?</h1white><br />
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<h1white>How can we tell the systems apart?</h1white></div></a><br />
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<h1>Predicting the sfGFP fluorescence</h1><br />
<h1>Introduction</h1><br />
To allow us to be able to identify which system was in the second half of the circuit, it was important to be able to predict the difference in response. To do this, we constructed models that involved cascading the differential equations in different formats to model the response.<br />
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To be able to do this, we had to construct simplified equivalent circuits that were made out of direct activations and repressions.<br />
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It is important to understand that these simplified equivalent circuits will not give the correct mCherry response but they will give the correct GFP response after correct parameterisation.<br />
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We then set up the differential equations necessary to solve this problem in Matlab. The method and results are as detailed below:<br />
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<h1>Conclusion</h1><br />
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The bottom graphs show how each hypothesised system would respond to a step input of both ATC and DCM at the same time. As you can see, there isn’t much difference in the predicted steady state value of the fluorescence. However, providing the basal rate of GFP is low enough, there should be a clear difference in the level of fluorescence before either of these inputs are added. Eventually, we plan to test which gene circuit is present in this system by using this method of differentiating between them.<br />
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Calculating the many parameters for this system will be tricky. <a href="https://2014.igem.org/Team:Oxford/calculating_parameters">How are we calculating the parameters?</a><br />
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Having made the models and understanding the assumptions that we’ve made, it is very important to understand where the limits of the predictions are and what range of inputs the model will give reliable information for. After all, no model is perfect. <u><-- BACK THIS UP?</u><br />
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<h1black>Conclusion: DcmR acts as a .....</h1black><br />
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<h1black>Conclusion: DcmR acts as a .....</h1black></div></a><br />
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<h1>Wetlab data showing whether it is a repressor or activator</h1><br />
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