Team:Oxford/modelling biosensor

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<br><img src="https://static.igem.org/mediawiki/2014/e/ed/Oxford_DcmR_activation.png" style="float:left;position:relative; height:8%; width:50%;" />
<br><img src="https://static.igem.org/mediawiki/2014/e/ed/Oxford_DcmR_activation.png" style="float:left;position:relative; height:8%; width:50%;" />
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Where did this equation come from?
Where did this equation come from?
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Solving this ODE in Matlab (with zero basal rate) gives the response of the system to be:
Solving this ODE in Matlab (with zero basal rate) gives the response of the system to be:
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Revision as of 11:12, 9 September 2014


Characterisation


Modelling genetic circuits

Predicting the mCherry fluorescence

To model the first double repression, we took the fact that we won’t need to know the amount of tetR in the system and used the assumption that ATC is effectively activating the expression of dcmR, albeit parameterised by different constants. This assumption should be justified by the fact that we will be able to precisely control the addition of ATC and we will be able to measure the fluorescence of the mCherry.

We modelled this first step using both deterministic and stochastic models.

Deterministic

Deterministic models are very powerful tools in systems biology. They analyse the behaviour of the bacteria on a culture level and use ordinary differential equations (ODEs) to relate each activation and repression. By constructing a cascade of differential equations you can build a very robust model of the average behaviour of the gene circuit.
The differential equation that applies to this first step in the system is:




Where did this equation come from?

Solving this ODE in Matlab (with zero basal rate) gives the response of the system to be: