Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

Large omics datasets are nowadays routinely generated to provide insights into cellular processes. Nevertheless, making sense of omics data and determining intracellular metabolic states remains challenging. Kinetic models of metabolism are crucial for integrating and consolidating omics data because they explicitly link metabolite concentrations, metabolic fluxes, and enzyme levels. However, the difficulties in determining kinetic parameters that govern cellular physiology prevent the broader adoption of these models by the research community. We present RENAISSANCE (REconstruction of dyNAmIc models through Stratified Sampling using Artificial Neural networks and Concepts of Evolution strategies), a generative machine learning framework for efficiently parameterizing large-scale kinetic models with dynamic properties matching experimental observations. We showcase RENAISSANCE’s capabilities through three applications: generation of kinetic models of E. coli metabolism, characterization of intracellular metabolic states, and assimilation and reconciliation of experimental kinetic data. We provide the open-access code to facilitate experimentalists and modelers applying this framework to diverse metabolic systems and integrating a broad range of available data. We anticipate that the proposed framework will be invaluable for researchers who seek to analyze metabolic variations involving changes in metabolite and enzyme levels and enzyme activity in health and biotechnological studies.