Team:Paris/Modeling/Implementation
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Implementation
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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.
Parameters Finder ProgramsThe dataThe experimental data consist typically of two tables, X_data (various concentrations of the transcription factor) and Y_data (corresponding output values).
Parameters Finder for our ExampleWe show hereby the annotated program find_FP.m that is used to estimate, for instance, the parameters in :
All AlgorithmsWe 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...) : ↓ Prior for Characterization ↑
Next, the algorithms representing the complete Characterizations, e.g. coupling the above to the downstream promoters activities : ↓ Parameters Finders ↑
Finally, all the others auxiliary algorithms,as well as the final program code used for our simulations : ↓ The Global Model ↑
