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Optogenetic modulation of adenosine triphosphatase (ATPase) expressionrepresents a novel approach to maximize bioprocess efficiency by leveragingenforced adenosine triphosphate (ATP) turnover. In this study, weexperimentally implement a model-based open-loop optimization scheme foroptogenetic modulation of the expression of ATPase. Increasing theintracellular concentration of ATPase, and thus the level of ATP turnover, inbioprocesses with product synthesis coupled with ATP generation, can lead toincreased product formation and substrate uptake. Previous simulation studiesformulated optimal control problems using dynamic constraint-based models tofind optimal light inputs in fermentations with optogenetically mediated ATPaseexpression. However, using these models poses challenges due to resultingbilevel optimizations and complex parameterization. Here, we outline asimplified unsegregated and quasi-unstructured kinetic modeling approach thatreduces the number of dynamic states and leads to single-level optimizations.The models can be augmented with Gaussian processes to compensate for modeluncertainties. We implement optimal control constrained by knowledge-based andhybrid models for optogenetic ATPase expression in $\textit{Escherichia coli}$with lactate as the main product. To do so, we genetically engineer $\textit{E.coli}$ to obtain optogenetic expression of ATPase using the CcaS/CcaR system.This represents the first experimental implementation of model-basedoptimization of ATPase expression in bioprocesses.

Experimentally implemented dynamic optogenetic optimization of ATPase expression using knowledge-based and Gaussian-process-supported models