Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks

Authors

  • Ali R. Zomorrodi,

    1. Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
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  • Jimmy G. Lafontaine Rivera,

    1. Department of Chemical and Biomolecular Engineering, University of California at Los Angeles, Los Angeles, CA, USA
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  • James C. Liao,

    1. Department of Chemical and Biomolecular Engineering, University of California at Los Angeles, Los Angeles, CA, USA
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  • Prof. Costas D. Maranas

    Corresponding author
    1. Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
    • Department of Chemical Engineering, 112A Fenske Lab, The Pennsylvania State University, University Park, PA 16802, USA

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Abstract

The ensemble modeling (EM) approach has shown promise in capturing kinetic and regulatory effects in the modeling of metabolic networks. Efficacy of the EM procedure relies on the identification of model parameterizations that adequately describe all observed metabolic phenotypes upon perturbation. In this study, we propose an optimization-based algorithm for the systematic identification of genetic/enzyme perturbations to maximally reduce the number of models retained in the ensemble after each round of model screening. The key premise here is to design perturbations that will maximally scatter the predicted steady-state fluxes over the ensemble parameterizations. We demonstrate the applicability of this procedure for an Escherichia coli metabolic model of central metabolism by successively identifying single, double, and triple enzyme perturbations that cause the maximum degree of flux separation between models in the ensemble. Results revealed that optimal perturbations are not always located close to reaction(s) whose fluxes are measured, especially when multiple perturbations are considered. In addition, there appears to be a maximum number of simultaneous perturbations beyond which no appreciable increase in the divergence of flux predictions is achieved. Overall, this study provides a systematic way of optimally designing genetic perturbations for populating the ensemble of models with relevant model parameterizations.

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