Team:Aberdeen Scotland/Modeling

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<li class="curr"><a class="curr" href="http://2014.igem.org/Team:Aberdeen_Scotland/Modeling">Introduction</a></li>
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<li><a href="http://2014.igem.org/Team:Aberdeen_Scotland/Modeling/QS">Quorum Sensing</a></li>
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<li><a href="http://2014.igem.org/Team:Aberdeen_Scotland/Modeling/GFP">GFP Response</a></li>
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<h1>Modelling Aims</h1>
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<h3>Purpose</h3>
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<p></p>
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<p>In order to verify and characterize our diagnostic system we created mathematical models for each individual component. Modeling and simulations were essential
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techniques that guided our approach from design to completion. As the main point of interest in our system was inter-cellular communication and its optimization, we
 +
employed ODEs and PDEs as our main mathematical tools.</p>
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<img src="http://2014.igem.org/wiki/images/a/a7/AjkIkdjfmkJ.png">
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<h3>Quorum Sensing</h3>
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<p>We developed a spacial model for analyzing Quorum Sensing between cells and then studied it under our system desired conditions. This gave us insight into how
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best structure our assay.</p>
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<h3>GFP Response</h3>
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<p>By simulating GFP response we made sure our system will react in a predictable manner and in an appropriate amount of time. We ensured that simulations agreed with
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data so that we can rely on reproducibility.</p>
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<h3>Assay Sensitivity</h3>
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<p>As with any other test, sensitivity is a main factor. The test needed to be sensitive enough to detect HAT, but also tolerant to noise, so that false-positives
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were minimized. We explored the process of antibody binding to make sure we meet those criteria.</p>
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<p>If you choose to create a model during your project, please write about it here. Modeling is not an essential part of iGEM, but we encourage any and all teams to model some aspect of their project. See previous "Best Model" awards for more information.</p>
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Latest revision as of 02:20, 18 October 2014

Team:Aberdeen Scotland/Modelling - 2014.ogem.org



Modelling Aims


Purpose

In order to verify and characterize our diagnostic system we created mathematical models for each individual component. Modeling and simulations were essential techniques that guided our approach from design to completion. As the main point of interest in our system was inter-cellular communication and its optimization, we employed ODEs and PDEs as our main mathematical tools.

Quorum Sensing

We developed a spacial model for analyzing Quorum Sensing between cells and then studied it under our system desired conditions. This gave us insight into how best structure our assay.

GFP Response

By simulating GFP response we made sure our system will react in a predictable manner and in an appropriate amount of time. We ensured that simulations agreed with data so that we can rely on reproducibility.

Assay Sensitivity

As with any other test, sensitivity is a main factor. The test needed to be sensitive enough to detect HAT, but also tolerant to noise, so that false-positives were minimized. We explored the process of antibody binding to make sure we meet those criteria.