/ident

Perform identifiability analysis in small kinetic models for experimental design using steady state fluxes and concentrations.

Primary LanguageJupyter Notebook

Identifiability Analysis and Experimental Design:

Srinivasan, S., Cluett, W. R., Mahadevan, R., A scalable method for parameter identification in kinetic models of metabolism using steady state data, Bioinformatics, 2019.

Generating in silico data for identifiability analysis:

  • run create_experiment_data with desired options active to generate initial set of experimental data with mpirun -np 4 python create_experiment_data.py

  • all generated data files are stored in exp subdirectory

  • run ident_exp_data with desired options to generate data needed for identifiability analysis with python ident_exp_data.py

  • all generated data files are stored in exp subdirectory

Perform identifiability analysis:
  • run parallel_ident with desired functions to perform identifiability analysis on the small network
  • run the script with mpi to enable multithreaded operation
  • e.g., mpirun -np 4 python parallel_ident.py
  • all generated data files are stored in ident subdirectory
  • all generated figures are stored in the results subdirectory
Validate estimated parameters:
  • all estimated parameters from the identifiability analysis in the previous step can be validated
  • run parallel_validate with desired function to perform validation of identified and estimated parameters
  • run the script with mpi to enable multithreaded operation
  • e.g., mpirun -np 4 python parallel_validate.py

Prerequisites: