Protein constraints in genome-scale metabolic models: Data integration, parameter estimation, and prediction of metabolic phenotypes.

Autor: Ferreira MAM; Department of Microbiology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil., Silveira WBD; Department of Microbiology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil., Nikoloski Z; Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.; Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
Jazyk: angličtina
Zdroj: Biotechnology and bioengineering [Biotechnol Bioeng] 2024 Mar; Vol. 121 (3), pp. 915-930. Date of Electronic Publication: 2024 Jan 04.
DOI: 10.1002/bit.28650
Abstrakt: Genome-scale metabolic models provide a valuable resource to study metabolism and cell physiology. These models are employed with approaches from the constraint-based modeling framework to predict metabolic and physiological phenotypes. The prediction performance of genome-scale metabolic models can be improved by including protein constraints. The resulting protein-constrained models consider data on turnover numbers (k cat ) and facilitate the integration of protein abundances. In this systematic review, we present and discuss the current state-of-the-art regarding the estimation of kinetic parameters used in protein-constrained models. We also highlight how data-driven and constraint-based approaches can aid the estimation of turnover numbers and their usage in improving predictions of cellular phenotypes. Finally, we identify standing challenges in protein-constrained metabolic models and provide a perspective regarding future approaches to improve the predictive performance.
(© 2024 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals LLC.)
Databáze: MEDLINE
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