Team:Aachen

From 2014.igem.org

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Welcome to the Teamwiki of the iGEM Aachen in 2014!
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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Cellock Holmes - a case of identity
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<h2> Nosocomial infections - solving a real-world problem </h2>
<img style="float: left; margin: 15px 15px 15px 15px;" src="https://static.igem.org/mediawiki/2014/a/ad/Aachen_Cellock_Rand.png" width="250" />
<img style="float: left; margin: 15px 15px 15px 15px;" src="https://static.igem.org/mediawiki/2014/a/ad/Aachen_Cellock_Rand.png" width="250" />
<img style="float: right; margin: 15px 15px 15px 15px;" src="https://static.igem.org/mediawiki/2014/4/4f/Aachen_Cellock_lyingdown.png" width="350" />
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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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Existing sterilizing methods fail to completely eradicate harmful pathogens on solid surfaces, resulting in a high number of patients (~3.2 million in Europe1) to be treated annually due to such infections. The European Union estimated that at least 800,000 of these cases would be preventable by implementing an intensive hygiene program, raising the necessity for a real-time detection technique. Current techniques fail since they are not effective, economical and rapid2.
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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 iGEM Aachen team, consisting of life science, engineering as well as computer science students, aims to tackle these difficulties by developing a real-time pathogen detection technique, called ‘Cellock Holmes’, using SynBio and open hardware.
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Current techniques vs. Cellock Holmes
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Strategy and focus
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Revision as of 09:15, 3 June 2014

Welcome to the Teamwiki of the iGEM Aachen in 2014!

Cellock Holmes - a case of identity

Nosocomial infections - solving a real-world problem

Existing sterilizing methods fail to completely eradicate harmful pathogens on solid surfaces, resulting in a high number of patients (~3.2 million in Europe1) to be treated annually due to such infections. The European Union estimated that at least 800,000 of these cases would be preventable by implementing an intensive hygiene program, raising the necessity for a real-time detection technique. Current techniques fail since they are not effective, economical and rapid2.

Our iGEM Aachen team, consisting of life science, engineering as well as computer science students, aims to tackle these difficulties by developing a real-time pathogen detection technique, called ‘Cellock Holmes’, using SynBio and open hardware.

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.




Strategy and focus

Current techniques to detect pathogens on surfaces are very time consuming and require expensive equipment as well as trained personnel. We aim to make the detection not only easy to use and fast, but also inexpensive in both frequent use as well as device costs.

Additionally we aim to enhance the detection itself. The current methods have a high variability in their assays, especially in low concentrations. Our goal is to not only reduce the variability in the detection, but also reliably detect and quantify pathogen in the low concentration which are only required for these pathogens to be infectious.




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

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