Team:PKU Beijing/Modeling/Parameters

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[[Team:PKU_Beijing/Modeling|Modeling]] > [[Team:PKU_Beijing/Modeling/Parameters|Parameters]]
[[Team:PKU_Beijing/Modeling|Modeling]] > [[Team:PKU_Beijing/Modeling/Parameters|Parameters]]
==='''Parameters'''===
==='''Parameters'''===
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Constructing [[Team:PKU_Bejing/Modeling/ODE|ODEs]] is only the first step of simulating our design. The parameters, actually, play a bigger role in the modeling process. Here's two sets of parameters(T3 RNA polymerase and P2) we used.
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We have done an systematic literature review to select parameters for our model. However, not every parameter can be found from existing works, which means we have to guess part of the parameters from trial and error. To check whether we have guessed correctly, we do the sensitivity test. The sensitivity test works like this: We first give both Sal(food) and AraC(bell) to make bistable turn to CI state(have memory). After a period of time, we give the ''E. coli'' AraC stimulus and GFP output will raise after a short while. We select the highest concentration point of GFP in the second procedure. The sensitivity of a parameter is calculated by using the following equations. The closer the sensitivity is to zero, the more reasonable the parameter is.
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<math>Sensitivity=\frac{c_{max}^{105%}-c_{max}^{95%)}{(105%-95%)*c_{max}^{100%}}</math>
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==='''Assumptions'''===
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Our model consists of 53 parameters, which makes the modeling process very difficult. To reduce the amount of work without losing quality, we proposed the following assumptions.
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*'''Transcription'''
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The average transcription speed in '''E.coli''' is 70nt/s. Assume all the transcription in our circuit works in such speed,
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{{PKU_Beijing/Foot}}
{{PKU_Beijing/Foot}}

Revision as of 21:30, 16 October 2009
 
Modeling > Parameters

Parameters

Constructing ODEs is only the first step of simulating our design. The parameters, actually, play a bigger role in the modeling process. Here's two sets of parameters(T3 RNA polymerase and P2) we used.

We have done an systematic literature review to select parameters for our model. However, not every parameter can be found from existing works, which means we have to guess part of the parameters from trial and error. To check whether we have guessed correctly, we do the sensitivity test. The sensitivity test works like this: We first give both Sal(food) and AraC(bell) to make bistable turn to CI state(have memory). After a period of time, we give the E. coli AraC stimulus and GFP output will raise after a short while. We select the highest concentration point of GFP in the second procedure. The sensitivity of a parameter is calculated by using the following equations. The closer the sensitivity is to zero, the more reasonable the parameter is. <math>Sensitivity=\frac{c_{max}^{105%}-c_{max}^{95%)}{(105%-95%)*c_{max}^{100%}}</math>

Assumptions

Our model consists of 53 parameters, which makes the modeling process very difficult. To reduce the amount of work without losing quality, we proposed the following assumptions.

  • Transcription

The average transcription speed in E.coli is 70nt/s. Assume all the transcription in our circuit works in such speed,




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