Generative approaches to kinetic parameter inference in metabolic networks via latent space exploration.

Dynamic (kinetic) models track time-varying metabolite concentrations, fluxes, and enzyme levels, quantifying responses to genetic and environmental perturbations. Yet building these models at scale is hindered by scarce enzyme kinetic parameters. Generative neural networks can rapidly parameterize near-genome-scale kinetic models, but their representations are hard to interpret and often require new training to move across species or physiological states. Here we introduce a latent-space exploration framework that repurposes a trained generative network to produce models with targeted dynamics in new regimes without additional training. We show in Escherichia coli that latent inputs tune aerobic response speed, identify rate-limiting enzymes, and retarget the generative network to anaerobic dynamics. We extend our approach to Saccharomyces cerevisiae, demonstrating robust control of metabolic dynamics across training stages and diverse latent inputs. Latent variables thus become practical control knobs for kinetic model behavior, accelerating cell-factory design and enabling personalized metabolic modeling.