Team:Paris/Modeling/Implementation

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(Difference between revisions)
(Parameters Finder for our Example)
(the datas)
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=== the datas ===
=== the datas ===
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The experimental datas consist typically in two tables, <span style="color:#0000FF;">X_data</span> (various concentrations of the transcription factor) and <span style="color:#0000FF;">Y_data</span> (corresponding output values).  
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The experimental datas consist typically in two tables, '''X_data''' (various concentrations of the transcription factor) and '''Y_data''' (corresponding output values).  
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* controlling X_data : thanks to the prior characterization of the inductible promoters that control the transcription factor concentrations, we can deduce from the <span style="color:#0000FF;">Inv_f1.m</span> and <span style="color:#0000FF;">Inv_f2.m</span> functions the necessary concentrations of ''aTc'' and ''arabinose'' to introduce in the medium to get the wanted concentrations of transcription factor.
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* controlling X_data : thanks to the prior characterization of the inductible promoters that control the transcription factor concentrations, we can deduce from the '''Inv_f1.m''' and '''Inv_f2.m''' functions the necessary concentrations of ''aTc'' and ''arabinose'' to introduce in the medium to get the wanted concentrations of transcription factor.
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* getting Y_data : the linear <span style="color:#0000FF;">conversion</span> between the fluorescence of GFP at maturation and its concentration gives us directly the expected datas.
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* getting Y_data : the linear '''conversion''' between the fluorescence of GFP at maturation and its concentration gives us directly the expected datas.
=== Parameters Finder for our Example ===
=== Parameters Finder for our Example ===

Revision as of 19:34, 29 October 2008

Implementation


We use Matlab for all implementations.

Contents

Parameters Finder Programs

the datas

The experimental datas consist typically in two tables, X_data (various concentrations of the transcription factor) and Y_data (corresponding output values).

  • controlling X_data : thanks to the prior characterization of the inductible promoters that control the transcription factor concentrations, we can deduce from the Inv_f1.m and Inv_f2.m functions the necessary concentrations of aTc and arabinose to introduce in the medium to get the wanted concentrations of transcription factor.
  • getting Y_data : the linear conversion between the fluorescence of GFP at maturation and its concentration gives us directly the expected datas.

Parameters Finder for our Example

We just show hereby the annoted program find_FP.m that is used to estimate, for instance, the parameters in :

  • ƒ5( [FlhDC], 0 ) = β24 * ƒhill( [FlhDC], K2, n2 ) and
  • ƒ5( 0, [FliA] ) = β25 * ƒhill( [FliA], K8, n8 )

All Algorithms

↓ Prior for Characterization ↑
↓ Parameters Finders ↑
↓ The Global Model ↑


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