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Tsouka S, Ataman M, Hameri T, et al. Constraint-based metabolic control analysis for rational strain engineering. Metab Eng. 2021;66:191-203doi: 10.1016/j.ymben.2021.03.003.
Tsouka, S., Ataman, M., Hameri, T., Miskovic, L., & Hatzimanikatis, V. (2021). Constraint-based metabolic control analysis for rational strain engineering. Metabolic engineering, 66191-203. https://doi.org/10.1016/j.ymben.2021.03.003
Tsouka, Sophia, et al. "Constraint-based metabolic control analysis for rational strain engineering." Metabolic engineering vol. 66 (2021): 191-203. doi: https://doi.org/10.1016/j.ymben.2021.03.003
Tsouka S, Ataman M, Hameri T, Miskovic L, Hatzimanikatis V. Constraint-based metabolic control analysis for rational strain engineering. Metab Eng. 2021 Jul;66:191-203. doi: 10.1016/j.ymben.2021.03.003. Epub 2021 Apr 22. PMID: 33895366.
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Metab Eng. 2021 Jul;66:191-203. doi: 10.1016/j.ymben.2021.03.003. Epub 2021 Apr 22.

Constraint-based metabolic control analysis for rational strain engineering.

Metabolic engineering

Sophia Tsouka, Meric Ataman, Tuure Hameri, Ljubisa Miskovic, Vassily Hatzimanikatis

Affiliations

  1. Laboratory of Computational Systems Biology (LCSB), EPFL, CH-1015, Lausanne, Switzerland.
  2. Laboratory of Computational Systems Biology (LCSB), EPFL, CH-1015, Lausanne, Switzerland. Electronic address: [email protected].

PMID: 33895366 DOI: 10.1016/j.ymben.2021.03.003

Abstract

The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolite concentrations, it does not consider the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework, Network Response Analysis (NRA), for rational genetic strain design. NRA is cast as a Mixed-Integer Linear Programming problem that integrates MCA, Thermodynamically-based Flux Analysis (TFA), biologically relevant constraints, as well as genome editing restrictions into a comprehensive platform for identifying metabolic engineering targets. We show that the NRA formulation and its core constraints are equivalent to the ones of Flux Balance Analysis (FBA) and TFA, which allows it to be used for a wide range of optimization criteria and with various physiological constraints. We also show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.

Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.

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