Team:Paris/Modeling/Implementation

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.
Every programs are written for Matlab.

Parameters Finder Programs

the data

The experimental data 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 data.

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 * ƒ_hill( [FlhDC], K₂, n₂ ) and
• ƒ5( 0, [FliA] ) = β₂₅ * ƒ_hill( [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 :