A hybrid in silico/in-cell controller that handles process-model mismatches using intracellular biosensing

Abstract

Abstract The discrepancy between model predictions and actual processes, known as process-model mismatch (PMM), remains a substantial challenge in bioprocess optimization. We previously introduced a hybrid in silico/in-cell controller (HISICC) that combines model-based optimization with cell-based feedback to address this problem. Here, we extended this approach to regulate a key enzyme level using intracellular biosensing. The extended HISICC was implemented using an Escherichia coli strain engineered for fatty acid production (FA3). This strain contains a genetically encoded feedback controller that decelerates the expression of acetyl-CoA carboxylase (ACC) in response to malonyl-CoA synthesized through the enzymatic reaction. We modeled FA3 to allow the HISICC to optimize an inducer input that accelerates the enzyme expression. Simulations showed that the HISICC slowed the unexpectedly rapid accumulation of ACC resulting from PMMs before it reached cytotoxic levels, thereby improving fatty acid yields. These results highlight the potential of our approach, particularly in cases where monitoring intracellular biomolecules is required to handle PMMs.

Keywords

• Bioprocess control
• Genetic circuit engineering
• Model-based optimization
• Synthetic biology
• Negative feedback