readme.md

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:

• Python 3.4
• mpi4py conda install mpi4py
• mpi4py master-slave library