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

Author:

Ferreira Maurício Alexander de Moura1ORCID,Silveira Wendel Batista da1ORCID,Nikoloski Zoran23

Affiliation:

1. Department of Microbiology Federal University of Viçosa Viçosa Minas Gerais Brazil

2. Bioinformatics, Institute of Biochemistry and Biology University of Potsdam Potsdam Germany

3. Systems Biology and Mathematical Modeling Max Planck Institute of Molecular Plant Physiology Potsdam Germany

Abstract

AbstractGenome‐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.

Publisher

Wiley

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