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| - | <h1>Modelling Progress</h1>
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| - | <h2>Week 1 Day 3 - Major Breakthrough</h2>
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| - | <h2>Jack says:</h2>
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| - | <p>(Part A): Finished the thermodynamic model of solution-vapour equilibration to validate our approximation of [DCM] by estimating the significance of deviation due to this effect.</p>
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| - | <h2>Oliver says:</h2>
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| - | <p>Finished the first draft of the model, will leave it until we have real data to feed back into the system. The model is very robust and allows any user to input a large variety of parameters and scenarios that could be realistically expected in the laboratory results. The output of the model is the colour that you can expect over time (the outputs of the real system will be from a combination of mCherry and GFP).</p>
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| - | <p>The model reveals surprising results, including how even a small basal rate of gene expression (due to leakage of the promoters) can really change the results.</p>
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| - | <p>The way that I finally got the model to work was by returning to the ODE15s function in Matlab and not bothering with Laplace transforms. Information on how to use Matlab to model repressor and activator networks very easily, accurately and quickly will be uploaded to this wiki soon! If you want more details please don't hesitate to contact us.</p>
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| - | [[File:Model 1.png|900px|thumb|left|An example of the model's output]]
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| - | <h2>Week 1 Day 2 </h2>
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| - | <h2>Oliver says:</h2>
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| - | <p>Today was difficult. It was spent trying to write Matlab code to solve the differential equations. Having already written the code successfully for an autorepressor and an autoactivator using the built in function ode15s, I thought it would be relatively easy to use similar code to model a network. However, I ran into quite a lot of problems with transferring all of the required values back and forth between the function script and the data entry script.</p>
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| - | <p> In the afternoon, I tried to get the model to work using Laplace transforms and more specifically Matlab's incredible computing ability at calculating the inverse laplace transform of complex functions to allow solutions to be obtained. However, this presented more problems than the ode15s function due to vector sizes and things that quite quickly got quite messy.</p>
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| - | <p>Help with the autorepressor/autoactivator code will be up on the wiki shortly, please don't hesitate to contact us in the meantime for more info though.</p>
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| - | <h2>Week 1 Day 1 - Conceptualizing part B</h2>
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| - | <h2>Jack says:</h2>
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| - | <p>(Part A): day 1 modelling was spent setting up a kinetic 'map' of the tetR system as a biological repressor analogue to uncharacterised dcmR. Stochastic kinetic data was found2 and required coefficients approximated (relative orders of magnitude) from these data sets will be fed into Ollie's ODE Model. </p>
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| - | <h2>Oliver says:</h2>
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| - | <p>The morning was spent with Glen and Fran (who are working on part B) discussing exactly what network of activation and repression we were trying to categorize and turning it from Snapgene files (that the Biochemists understand) into a series of possible repression and activation scenarios.</p>
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| - | <p>Then, it was a matter of condensing the network of seemingly complex interactions into a set of differential equations with the relevant constants. This allows the response of the system to an external known input be accurately modeled.</p>
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| - | [[File:Repress and activate network.jpg.png|900px|centre|An example of the model's output]]
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