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

Abstract

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.

Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

“…Other abbreviations are as follows: AA, amino acid; ox., oxidative; transport p , transport per , and transport c , transport into plastid, peroxisome, and cytosol, respectively. a large number of kinetic parameters (Mahadevan et al, 2002;Hjersted and Henson, 2006;Oddone et al, 2009), in this study, SOA-based dFBA was computed by integrating a dynamic FPM. A similar approach has been presented by Feng et al (2012), who integrated FBA into a kinetic model to get insight into the dynamics of the metabolism of the unicellular Shewanella oneidensis.…”

Dynamic modeling and analyses of simultaneous saccharification and fermentation process to produce bio-ethanol from rice straw

“…5). A strategy to increase the productivity of the SSF process is to operate the batch process initially under aerobic condition to grow more of the biomass, and then to operate under anaerobic condition at the end of the batch in order to produce ethanol [25]. Figure 7 shows one of such batch run by operating the batch process aerobically with O = 7.5 mg l -1 for the first 3 h and then the air supply is stopped until the end of the batch.…”

Identification of Dynamic Metabolic Flux Balance Models Based on Parametric Sensitivity Analysis

“…These rate constraints are typically expressed with kinetic expressions such as Monod equation as a function of the time varying concentrations of substrates and products involved in the reaction associated with the constrained flux. The dynamic flux model offers a practical approach to develop a full metabolic network model when not enough kinetic data is available (Hjersted and Henson, 2006). This methodology is based on the assumption that the concentrations of metabolites equilibrate fast in response to disturbances.…”