Team:Aachen/TestBench

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<h1>Welcome!<br> We are iGEM Team Aachen 2014 </h1>
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<h1>
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Welcome to the Teamwiki of the iGEM Aachen in 2014!
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<h2>Cellock Holmes - a case of identity </h2>
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<img style="float: left; margin: 15px 15px 15px 15px;" src="https://static.igem.org/mediawiki/2014/a/ad/Aachen_Cellock_Rand.png" width="350" />
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As the team name suggests, we're a team participating in the iGEM 2014 competition from Aachen (Germany).
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Pathogens on solid surfaces in places where good hygiene is crucial pose a serious threat, since – even after cleaning – these can still be present in dangerous amounts. This is demonstrated by the high number of 3.2 million patients each year that have to be treated due to in the health sector acquired infections. And 37000 of those infections end deadly. The EU estimates that at least 20-30% of those cases would be preventable with an intensive hygiene program. However, for a more effective control these respective pathogens have to be identified.  
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If you're looking for information about our team, please visit the <a href="https://2014.igem.org/Team:Aachen/Team">team section</a>.
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If you want to support our team, please check out our <a href="https://2014.igem.org/Team:Aachen/Sponsors">sponsoring page</a>.
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We are developing a system that makes this possible. We are constructing a device with which pathogens can be detected easily, efficiently, effectively and less expensively by utilising genetically modified cells. We focus on a fast response time coupled with an automated analysis.
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Our project is not only applicable to the detection of pathogens but we are looking to develop it further into a platform for a general 2D detection of nearly any cell or substance. 
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Our biological approach
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<img style="float: left; margin: 15px 15px 15px 15px;" src="https://static.igem.org/mediawiki/2014/4/40/Aachen_Cellock_standingup.png" width="350" />
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In order to detect the pathogens fast, specifically and inexpensively we are building sensor cells to detect these pathogens. These sensor cells can identify pathogens in very low concentration by responsing to specific extracellular molecules either secreted by or displayed on the pathogens. These molecules trigger a fast fluorescence response by our immobilized sensor cells  which will be measured by our device.
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You can follow our molecular approach in more detail by checking out our
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<a href="https://2014.igem.org/Team:Aachen/Labbook">Biological Part/Labbook</a>
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The device
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Our device Cellock Holmes is designed to be an automated 2D fluorescence analyser. We aim to be able to quickly measure the fluorescence emitted by our sensor cells and automatically analyze the emitted images with our software Measurarty. This will enabel us to reliably detect the amount of CFU (colony forming units) of the pathogens present on the sample. The device will have different Filters for different wavelenghts included to be able to analyze different fluorescent proteins at the same time.
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To learn more about our device check out
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<a href="https://2014.igem.org/Team:Aachen/CellockHolmes">Cellock Holmes</a>.
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The software Measurarty
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The third part of our project is our software Measurarty. The software will allow us to analyze the fluorescence emitted by our sensor cells in a more advanced and better way than just using a simple Treshold. We will utilize a modern segmentation algorithm in combination with further, detailed image processing algorythms.
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For more information check out our
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<a href="https://2014.igem.org/Team:Aachen/Errorlog">Computational Part</a>.
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{{Team:Aachen/Footer}}
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Latest revision as of 13:51, 13 May 2014

Welcome to the Teamwiki of the iGEM Aachen in 2014!

Cellock Holmes - a case of identity

Pathogens on solid surfaces in places where good hygiene is crucial pose a serious threat, since – even after cleaning – these can still be present in dangerous amounts. This is demonstrated by the high number of 3.2 million patients each year that have to be treated due to in the health sector acquired infections. And 37000 of those infections end deadly. The EU estimates that at least 20-30% of those cases would be preventable with an intensive hygiene program. However, for a more effective control these respective pathogens have to be identified.

We are developing a system that makes this possible. We are constructing a device with which pathogens can be detected easily, efficiently, effectively and less expensively by utilising genetically modified cells. We focus on a fast response time coupled with an automated analysis.

Our project is not only applicable to the detection of pathogens but we are looking to develop it further into a platform for a general 2D detection of nearly any cell or substance.




Our biological approach

In order to detect the pathogens fast, specifically and inexpensively we are building sensor cells to detect these pathogens. These sensor cells can identify pathogens in very low concentration by responsing to specific extracellular molecules either secreted by or displayed on the pathogens. These molecules trigger a fast fluorescence response by our immobilized sensor cells which will be measured by our device.

You can follow our molecular approach in more detail by checking out our Biological Part/Labbook

The device

Our device Cellock Holmes is designed to be an automated 2D fluorescence analyser. We aim to be able to quickly measure the fluorescence emitted by our sensor cells and automatically analyze the emitted images with our software Measurarty. This will enabel us to reliably detect the amount of CFU (colony forming units) of the pathogens present on the sample. The device will have different Filters for different wavelenghts included to be able to analyze different fluorescent proteins at the same time.

To learn more about our device check out Cellock Holmes.

The software Measurarty

The third part of our project is our software Measurarty. The software will allow us to analyze the fluorescence emitted by our sensor cells in a more advanced and better way than just using a simple Treshold. We will utilize a modern segmentation algorithm in combination with further, detailed image processing algorythms.

For more information check out our Computational Part.

Contact Disclaimer

Retrieved from "http://2014.igem.org/Team:Aachen/TestBench"