Enhancing genome-scale metabolic models with kinetic data: resolving growth and citramalate production trade-offs in

Summary: Metabolic models are valuable tools for analyzing and predicting cellular features such as growth, gene essentiality, and product formation. Among the various types of metabolic models, two prominent categories are constraint-based models and kinetic models. Constraint-based models typically represent a large subset of an organism's metabolic reactions and incorporate reaction stoichiometry, gene regulation, and constant flux bounds. However, their analyses are restricted to steady-state conditions, making it difficult to optimize competing objective functions. In contrast, kinetic models offer detailed kinetic information but are limited to a smaller subset of metabolic reactions, providing precise predictions for only a fraction of an organism's metabolism. To address these limitations, we proposed a hybrid approach that integrates these modeling frameworks by redefining the flux bounds in genome-scale constraint-based models using kinetic data. We applied this method to the constraint-based model of Availability and implementation: The Python code generated for this work is available at: https://github.com/jlazaroibanezz/citrabounds.