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This repository provides all the code and notebooks used in this paper, published at the Biochemical Engineering Journal.
Net Stoichiometric Core Model of C. autoethanogenum: model_core.mat
Experimental_Data: MATLAB data file containing experimental data used for screening kinetic ensemble
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To generate and screen ensemble of kinetic models: Open TEST_SCRIPT and input elementary model, reference state, ensemble size, and screening parameters. Example values have been provided.
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Final 18 parameter set solution stored in 'Clostridium autoethanogenum kinetic ensemble solution' folder Final 18 parameter sets stored as 'Final_Kinetic_Ensemble.mat' Edit 'TEST_SCRIPT_perturb_final_ensemble.m' file to perturb final ensemble. You can select enzyme to perturb and if you want to predict overexpression, underexpression, or a knockout by setting parameter defined in the top of the script. You can also change gas uptake flux values to generate kinetic parameter predictions for differnt uptake ratios.
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Folder descriptions: a. 'Clostridium autoethanogenum kinetic ensemble solution' - final kinitic ensemble model (18 unique kinetic parameter sets) and scripts for interrogating them
b. 'Ensemble Modeling Scripts' - scripts for generating initial ensemble and screening kinetic parameter sets
c. 'Reference Conditions' - MATLAB data files containing FBA generated flux solutions for each biomass concentration reference state
Portions of ensemble modeling code was adapted from MATLAB scripts provided by the Liao and Maranas groups:
- Rizk, M. L., and J. C. Liao. 2009. Ensemble modeling and related mathematical modeling of metabolic networks. Journal of the Taiwan Institute of Chemical Engineers 40:595-601.
- Tan, Y., J. G. Rivera, C. A. Contador, J. A. Asenjo, and J. C. Liao. 2011. Reducing the allowable kinetic space by constructing ensemble of dynamic models with the same steady-state flux. Metab. Eng. 13:60-75.
- Khodayari, A., A. R. Zomorrodi, J. C. Liao, and C. D. Maranas. 2014. A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data. Metab. Eng. 25:50-62.
- Greene, J. L., Wäechter, A., Tyo, K. E., & Broadbelt, L. J. (2017). Acceleration Strategies to Enhance Metabolic Ensemble Modeling Performance. Biophysical Journal, 113(5), 1150-1162.