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

TL;DR

This study demonstrates the first experimental implementation of a model-based optogenetic control scheme to optimize ATPase expression in E. coli, enhancing bioprocess efficiency through simplified kinetic models supported by Gaussian processes.

Contribution

It introduces a simplified kinetic modeling approach combined with Gaussian processes for optogenetic ATPase regulation, enabling practical open-loop optimization in bioprocesses.

Findings

Successful experimental implementation in E. coli

Enhanced product formation through ATPase modulation

Reduced model complexity facilitates optimization

Abstract

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.