  Optogenetic modulation of adenosine triphosphatase (ATPase) expression
represents a novel approach to maximize bioprocess efficiency by leveraging
enforced adenosine triphosphate (ATP) turnover. In this study, we
experimentally implement a model-based open-loop optimization scheme for
optogenetic modulation of the expression of ATPase. Increasing the
intracellular concentration of ATPase, and thus the level of ATP turnover, in
bioprocesses with product synthesis coupled with ATP generation, can lead to
increased product formation and substrate uptake. Previous simulation studies
formulated optimal control problems using dynamic constraint-based models to
find optimal light inputs in fermentations with optogenetically mediated ATPase
expression. However, using these models poses challenges due to resulting
bilevel optimizations and complex parameterization. Here, we outline a
simplified unsegregated and quasi-unstructured kinetic modeling approach that
reduces the number of dynamic states and leads to single-level optimizations.
The models can be augmented with Gaussian processes to compensate for model
uncertainties. We implement optimal control constrained by knowledge-based and
hybrid 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-based
optimization of ATPase expression in bioprocesses.
