Vassily Hatzimanikatis

Abstract 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.

Abstract The synthesis of many important biochemicals involves complex molecules and numerous reactions. The design and optimization of whole-cell biocatalysts for the production of these molecules requires metabolic modeling to extract production pathways from biochemical databases and integrate them into genome-scale models of the host.

Abstract Generative machine learning methods that use neural networks to parameterize large-scale and near genome-scale kinetic models have delivered significant efficiency gains in model construction, paving the way toward high-throughput dynamic metabolism studies for biomedical and biotechnological applications.