Optimization of Fed‐Batch Saccharomyces cerevisiae Fermentation Using Dynamic Flux Balance Models

We developed a dynamic flux balance model for fed‐batch Saccharomyces cerevisiae fermentation that couples a detailed steady‐state description of primary carbon metabolism with dynamic mass balances on key extracellular species. Model‐based dynamic optimization is performed to determine fed‐batch operating policies that maximize ethanol productivity and/or ethanol yield on glucose. The initial volume and glucose concentrations, the feed flow rate and dissolved oxygen concentration profiles, and the final batch time are treated as decision variables in the dynamic optimization problem. Optimal solutions are generated to analyze the tradeoff between maximal productivity and yield objectives. We find that for both cases the prediction of a microaerobic region is significant. The optimization results are sensitive to network model parameters for the growth associated maintenance and P/O ratio. The results of our computational study motivate continued development of dynamic flux balance models and further exploration of their application to productivity optimization in biochemical reactors.