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

• Published in: Biotechnology and bioengineering 2024-03, Vol.121 (3), p.915-930
• Main Authors: Ferreira, Maurício Alexander de Moura, Silveira, Wendel Batista da, Nikoloski, Zoran
• Format: Article
• Language: English
• Subjects: catalytic rates; Cell culture; Constraint modelling; Data integration; enzyme concentration; flux balance analysis; Genomes; machine learning; Models, Biological; Parameter estimation; parameterization; Performance prediction; Phenotype; Phenotypes; Physiology; Predictions; Proteins; Proteins - genetics; Proteins - metabolism
• ISSN: 0006-3592; 1097-0290; 1097-0290
• DOI: 10.1002/bit.28650

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.