Team:Grenoble/Modeling/Introduction

From 2012.igem.org

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Because we know that the production of protein is not always turned on or turned off, this can lead to false positives/negatives. We could evaluate the false positives using a stochastic model.
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Because we know that the production of protein is not always turned on or turned off, this can lead to false positives/negatives. We also evaluated the false positives rate of our sensor using a stochastic model.  
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Revision as of 00:20, 27 September 2012

iGEM Grenoble 2012

Project

Introduction

To model the system, we divided it into two modules:

Signaling module

In this part we used a deterministic model to determine the sensitivity of the sensor. This analysis enabled us to know that the amplification module is required for the incoming signal to drive the subsequent modules.

Internal amplification module

We first used deterministic model to evaluate the sensitivity of the amplification loop and determine the response time. A steady state analysis was performed to understand how the system works.

External amplification and communication

Then, we studied the communication between the bacteria to evaluate the time collective response time of a bacterial population as a whole.

Because we know that the production of protein is not always turned on or turned off, this can lead to false positives/negatives. We also evaluated the false positives rate of our sensor using a stochastic model.