Team:Paris/Modeling/Implementation

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{{Paris/Header|Implementation}}
{{Paris/Header|Implementation}}
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{{Paris/Section_contents_characterization}}
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This section gives all the details about the informatical implementation of the "Characterization Approach". We show the method and we explain the program allowing, once we have our experimental data, to estimate our parameters. At the end, the final program aiming at beeing a "virtual predictive lab" is described.
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This section details all the computational implementations of the "Characterization Approach". We show our method and explain the algorithm allowing, once we have our experimental data, to estimate our parameters. At the end, the final program (coded in Matlab) aiming at a "virtual predictive lab", is described.
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<br>
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Every programs are written for '''Matlab'''.
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== Parameters Finder Programs ==
== Parameters Finder Programs ==
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=== the data ===
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=== The data ===
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The experimental data consist typically in two tables, '''X_data''' (various concentrations of the transcription factor) and '''Y_data''' (corresponding output values).  
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The experimental data consist typically of 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 '''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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* 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 achieve the targeted concentrations of the given transcription factor.
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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 data.
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* Extracting Y_data : the linear '''conversion''' between the fluorescence of GFP at maturation and its concentration gives us directly the expected data.
=== Parameters Finder for our Example ===
=== Parameters Finder for our Example ===
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We just show hereby the annoted program ''' find_FP.m ''' that is used to estimate, for instance, the parameters in :
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We show hereby the annotated program ''' find_FP.m ''' that is used to estimate, for instance, the parameters in :
* '''&#131;5( [''FlhDC''], 0 ) = ''&beta;<sub>24</sub> * &#131;<sub>hill</sub>''( [''FlhDC''], ''K<sub>2</sub>'', ''n<sub>2</sub>'' )''' and  
* '''&#131;5( [''FlhDC''], 0 ) = ''&beta;<sub>24</sub> * &#131;<sub>hill</sub>''( [''FlhDC''], ''K<sub>2</sub>'', ''n<sub>2</sub>'' )''' and  
* '''&#131;5( 0, [''FliA''] ) = ''&beta;<sub>25</sub> * &#131;<sub>hill</sub>''( [''FliA''], ''K<sub>8</sub>'', ''n<sub>8</sub>'' )'''
* '''&#131;5( 0, [''FliA''] ) = ''&beta;<sub>25</sub> * &#131;<sub>hill</sub>''( [''FliA''], ''K<sub>8</sub>'', ''n<sub>8</sub>'' )'''
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== All Algorithms ==
== All Algorithms ==
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We present here all the algorithms used in the whole "Characterization Approach".
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We present here all the algorithms used in our "Characterization Approach".
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First, the pages about the "prior characterization", needed to leads all the others :
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First, the "prior characterization", e.g. the inducible promoters controlling the master regulators (FlhDC, FliA...) :
<div style="text-align: center">
<div style="text-align: center">
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{{Paris/Toggle|Prior for Characterization|Team:Paris/Modeling/More_Algo_Prior|200px}}
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{{Paris/Toggle|Prior for Characterization|Team:Paris/Modeling/More_Algo_Prior|300px}}
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</div>
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Then, the pages with the programs of complete Characterization :
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Next, the algorithms representing the complete Characterizations, e.g. coupling the above to the downstream promoters activities :
<div style="text-align: center">
<div style="text-align: center">
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{{Paris/Toggle|Parameters Finders|Team:Paris/Modeling/More_Algo_Finder|200px}}
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{{Paris/Toggle|Parameters Finders|Team:Paris/Modeling/More_Algo_Finder|300px}}
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</div>
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Finally, all the others algorithms, that are involved in the previous programs, and the final simulating program :
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Finally, all the others auxiliary algorithms,as well as the final program code used for our simulations :
<div style="text-align: center">
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{{Paris/Toggle|The Global Model|Team:Paris/Modeling/More_Algo_Char|200px}}
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{{Paris/Toggle|The Global Model|Team:Paris/Modeling/More_Algo_Char|300px}}
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{{Paris/Navig|Team:Paris/Modeling/Workflow_Example}}
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[[Team:Paris/Modeling/Workflow_Example| <Back - to "Workflow on an Example"]]|
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</center>
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Latest revision as of 03:40, 30 October 2008

Implementation


This section details all the computational implementations of the "Characterization Approach". We show our method and explain the algorithm allowing, once we have our experimental data, to estimate our parameters. At the end, the final program (coded in Matlab) aiming at a "virtual predictive lab", is described.

Contents

Parameters Finder Programs

The data

The experimental data consist typically of 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 achieve the targeted concentrations of the given transcription factor.
  • Extracting Y_data : the linear conversion between the fluorescence of GFP at maturation and its concentration gives us directly the expected data.

Parameters Finder for our Example

We show hereby the annotated 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

We present here all the algorithms used in our "Characterization Approach".

First, the "prior characterization", e.g. the inducible promoters controlling the master regulators (FlhDC, FliA...) :

Next, the algorithms representing the complete Characterizations, e.g. coupling the above to the downstream promoters activities :

Finally, all the others auxiliary algorithms,as well as the final program code used for our simulations :



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