(677a) Dynamic Flux Balance Models for Simulation and Optimization of Saccharomyces Cerevisiae Fed-Batch Cultures

We have previously developed dynamic flux balance models for
prediction of cellular growth and metabolic product formation rates
in Saccharomyces cerevisiae batch and fed-batch cell
cultures. These models couple steady-state stoichiometric balances
on intracellular metabolites with dynamic extracellular balances on
biomass, substrates, and metabolic byproducts through time-varying
substrate uptake rates. In this contribution, we present the results
of Saccharomyces cerevisiae fermentation experiments
designed to evaluate the dynamic flux balance model predictions. We
utilized a compartmentalized, genome-scale metabolic network to
describe intracellular metabolism and simple Michaelis-Menten
kinetics for the glucose and oxygen uptake rates. Kinetic parameters
for the substrate uptake and the intracellular stoichiometric
coefficients representing growth and non-growth associated energy
requirements were estimated by nonlinear least squares optimization.
A series of batch and fed-batch experiments demonstrated that the
dynamic flux balance model was able to produce accurate substrate,
biomass, and extracellular product concentration profile predictions
over a wide range of fermentation conditions. The model was
incorporated with a bi-level dynamic optimization scheme to compute
fed-batch operating policies for optimal ethanol production in batch
and fed-batch cultures. Experimental implementation of the optimal
policies showed good agreement with the in silico
predictions.