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Team:Grenoble/Modeling/Introduction
From 2012.igem.org
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| - | To model the system, we divided it into two | + | To model the system, we divided it into two modules: |
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| - | In this part we did a classic deterministic model. The goal was to answer | + | In this part we did a classic deterministic model. The goal was to answer the question: what is the sensitivity of our detector? This analysis will enable us to know if our amplification module will be able to enhance the incoming signal enough to drive the other modules. We didn't do a stochastic model, as the number of false positives and negatives depend on the robustness of the biological system that we were not able to assess. |
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<ul><ul><img src="https://static.igem.org/mediawiki/2012/1/1e/2_mod.png" alt="" />The amplification module | <ul><ul><img src="https://static.igem.org/mediawiki/2012/1/1e/2_mod.png" alt="" />The amplification module | ||
Revision as of 15:19, 24 September 2012
Introduction
To model the system, we divided it into two modules:
The signaling module
In this part we did a classic deterministic model. The goal was to answer the question: what is the sensitivity of our detector? This analysis will enable us to know if our amplification module will be able to enhance the incoming signal enough to drive the other modules. We didn't do a stochastic model, as the number of false positives and negatives depend on the robustness of the biological system that we were not able to assess.
The amplification module
-We first did a classic deterministic model to be able to evaluate the sensitivity of the amplification loop. Then, we studied the temporal evolution to know how long we would have to wait for one bacterium to become green. Eventually, we did a study at the steady states to understand why our system would work.
-Then, we studied the communication between the bacteria to evaluate the time we would have to wait to actually be able to get the signal.
-Because we know that the production of protein is not always turned on or turned off, this can lead to false positives. We could evaluate the false positives by using a stochastic model.
