Protein constraints in genome-scale metabolic models: Data integration, parameter estimation, and prediction of metabolic phenotypes.
Biotechnol Bioeng
; 121(3): 915-930, 2024 Mar.
Article
em En
| MEDLINE
| ID: mdl-38178617
ABSTRACT
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 (kcat ) 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.
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01-internacional
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MEDLINE
Assunto principal:
Fenótipo
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Modelos Biológicos
Tipo de estudo:
Prognostic_studies
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Risk_factors_studies
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Systematic_reviews
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article