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Modelling cell metabolism : a review on constraint-based steady-state and kinetic approaches

Mohammadreza Yasemi and Mario Jolicoeur

Article (2021)

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Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.

Uncontrolled Keywords

constraint-based modelling approach; kinetic modelling; metabolic network; dynamic metabolic flux analysis; metabolic flux regulation; metabolic network structure; metabolic model parameterization; Gibbs free energy; thermodynamic constraints; metabolic control analysis

Subjects: 1800 Chemical engineering > 1800 Chemical engineering
1800 Chemical engineering > 1802 Biochemical engineering
1800 Chemical engineering > 1803 Thermodynamics
Department: Department of Chemical Engineering
Research Center: Other
Funders: GRSNG / NSERC - Discovery Grant
Grant number: RGPIN-2019-05050
PolyPublie URL: https://publications.polymtl.ca/9425/
Journal Title: Processes (vol. 9, no. 2)
Publisher: MDPI
DOI: 10.3390/pr9020322
Official URL: https://doi.org/10.3390/pr9020322
Date Deposited: 07 Sep 2023 10:09
Last Modified: 11 Apr 2024 07:51
Cite in APA 7: Yasemi, M., & Jolicoeur, M. (2021). Modelling cell metabolism : a review on constraint-based steady-state and kinetic approaches. Processes, 9(2), 38 pages. https://doi.org/10.3390/pr9020322


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