GENKI: A generative framework for scalable and robust metabolic kinetic modeling

GENKI (Generative ENsemble KPI-Informed) is a variational autoencoder-based framework for large-scale kinetic modeling of metabolism. Developed for metabolic engineering applications, GENKI is designed to improve the recovery of kinetically feasible models that reproduce experimentally observed phenotypes under genetic and environmental perturbations. The framework is trained on feasible kinetic model ensembles and uses phenotype-based key performance indicators (KPIs), derived from multi-omics and bioprocess data, to label and enrich models according to their agreement with mutant and condition-specific observations. This enables targeted generation of biologically relevant parameter sets with improved predictive performance. Crucially, GENKI recovers kinetic parameter sets that jointly reproduce wild-type and multiple perturbed physiologies within a single model. We apply GENKI to large-scale kinetic models of Escherichia coli and Saccharomyces cerevisiae under enzyme perturbations and oxygen shifts. In both systems, GENKI enriches kinetic ensembles with models that more accurately reproduce experimentally observed physiologies across multiple perturbations and conditions. GENKI therefore provides a practical framework for perturbation-aware kinetic model refinement within iterative Design-Build-Test-Learn workflows.