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Metabolic cybergenetics is a promising concept that interfaces geneexpression and cellular metabolism with computers for real-time dynamicmetabolic control. The focus is on control at the transcriptional level,serving as a means to modulate intracellular metabolic fluxes. Recentstrategies in this field have employed constraint-based dynamic models forprocess optimization, control, and estimation. However, this results in bileveldynamic optimization problems, which pose considerable numerical and conceptualchallenges. In this study, we present an alternative hybrid physics-informeddynamic modeling framework for metabolic cybergenetics, aimed at simplifyingoptimization, control, and estimation tasks. By utilizing machine-learningsurrogates, our approach effectively embeds the physics of metabolic networksinto the process rates of structurally simpler macro-kinetic models coupledwith gene expression. These surrogates, informed by flux balance analysis, linkthe domains of manipulatable intracellular enzymes to metabolic exchangefluxes. This ensures that critical knowledge captured by the system's metabolicnetwork is preserved. The resulting models can be integrated into metaboliccybergenetic schemes involving single-level optimizations. Additionally, thehybrid modeling approach maintains the number of system states at a necessaryminimum, easing the burden of process monitoring and estimation. Our hybridphysics-informed metabolic cybergenetic framework is demonstrated using acomputational case study on the optogenetically-assisted production ofitaconate by $\textit{Escherichia coli}$.

Hybrid physics-informed metabolic cybergenetics: process rates augmented with machine-learning surrogates informed by flux balance analysis