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In this study, we aim to optimize biotechnological production by manipulatingintracellular metabolic fluxes in microbial cell factories. Model-based dynamicoptimization is proposed to determine the optimal dynamic trajectories of themanipulatable intracellular fluxes. A challenge emerges as existing models areoften oversimplified, lacking insights into intracellular metabolism, or areexcessively complex, leading to numerical and implementation challenges inoptimal control (e.g., related to bilevel optimizations). We propose a solutioninvolving a machine-learning surrogate derived from steady-stateconstraint-based metabolic modeling. This surrogate bridges the gap betweenmanipulatable intracellular fluxes and process exchange rates. By integratingthe surrogate model with simple macro-kinetic dynamic models, we can develophybrid machine-learning-supported dynamic models. Conveniently, themanipulatable intracellular fluxes in these augmented models can be exploitedas dynamic optimization degrees of freedom. We apply this modeling andoptimization strategy to a representative metabolic network that showcasescommon challenges in dynamic metabolic control. We also present an example ofcybernetic control to counteract system uncertainties. Our approach facilitatesthe in silico evaluation of dynamic metabolic interventions and can aid in theselection of suitable control and actuation strategies.

Linking intra- and extra-cellular metabolic domains via neural-network surrogates for dynamic metabolic control