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

Area of Science:

• Synthetic Biology
• Bioprocess Engineering
• Metabolic Engineering

Background:

• Mathematical models are crucial for bioprocess optimization but suffer from process-model mismatch (PMM).
• This discrepancy between model predictions and real-world performance limits optimization efficacy.
• Existing methods struggle to adequately address PMM in complex biological systems.

Purpose of the Study:

• To develop and evaluate a novel hybrid in silico/in-cell controller (HISICC) system.
• To address the challenge of process-model mismatch (PMM) in microbial bioprocesses.
• To enhance the optimization and reliability of isopropanol (IPA) production in engineered Escherichia coli.

Main Methods:

• Developed a hybrid control system integrating an in silico feedforward controller with in-cell feedback controllers.
• Utilized engineered Escherichia coli strains (TA1415 and TA2445) with synthetic genetic circuits (metabolic toggle switch, cell density detection).
• Constructed and validated mathematical models for optimizing inducer (IPTG) input, simulating PMM effects on IPA yield.

Main Results:

• The HISICC system, particularly with strain TA2445, demonstrated effective compensation for PMM.
• The in-cell feedback controller autonomously adjusted metabolic toggle switch activation timing.
• Simulations showed HISICC's robustness against varying magnitudes of PMM in cell growth rates, improving IPA yields.

Conclusions:

• The HISICC system offers a viable solution to the persistent PMM problem in bioprocess engineering.
• This approach enables more efficient and reliable optimization of microbial bioprocesses.
• HISICC paves the way for advanced control strategies in industrial biotechnology.