A hybrid in silico/in-cell controller for microbial bioprocesses with process-model mismatch

Abstract

The optimization of bioprocess inputs using mathematical models is widely practiced. However, the mismatch between model prediction and the actual process [called process-model mismatch (PMM)] is problematic; when a large PMM exists, the process inputs optimized using the mathematical model in advance are no longer optimal for the actual process. In this study, we propose a hybrid control system that combines model-based optimization (in silico feedforward controller) and feedback controllers using synthetic genetic circuits integrated into cells (in-cell feedback controller) - which we named the hybrid in silico/in-cell controller (HISICC) - as a solution to this PMM issue. As a proof of concept for HISICC, we constructed a mathematical model of an engineered Escherichia coli strain for the isopropanol production process that was previously developed. This strain contains an in-cell feedback controller, and its combination with an in silico controller can be regarded as an example of HISICC. We demonstrated the robustness of HISICC against PMM by comparing the strain with another strain with no in-cell feedback controller in simulations assuming PMM of various magnitudes.