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. 2015 Jan 5;2:76. doi: 10.3389/fbioe.2014.00076

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Copyright © 2015 Khodayari, Chowdhury and Maranas.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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A schematic representation of the framework. (A) The reactions with kinetic descriptions are shown in blue. (B) The reactions are first decomposed into their elementary steps. (C) Elementary kinetic parameters are expressed as a function of reaction reversibilities and enzyme fractions. Reaction reversibilities and enzyme fractions are sampled to construct an ensemble of models, for any given reaction. (D) A genetic algorithm (GA) implementation identifies the optimal combination of the sampled parameters by minimizing the deviation from experimentally measured flux data for multiple mutant strains [see Methods of Khodayari et al. (2014)]. (E) The k-OptForce procedure identifies a minimal set of interventions that maximizes the yield of targeted product [see Methods of Chowdhury et al. (2014)].