(270e) Genome-Scale Analysis of Saccharomyces Cerevisiae Metabolism and Ethanol Production in Batch and Fed-Batch Culture

The availability of genome-scale stoichiometric models of cellular metabolism has enabled the development of computational algorithms for the analysis and design of complex metabolic networks. The most widely used approach is flux balance analysis (FBA), where a linear programming problem is posed to resolve the intracellular fluxes in an underdetermined metabolic network under the assumption that the cell utilizes available resources for growth rate maximization. FBA has been extended to allow the design of metabolic networks for the overproduction of desired metabolites through the deletion and addition of intracellular reactions. When combined with genome-scale models, these computational methods provide metabolic engineering targets that are experimentally testable. FBA methods assume time-invariant extracellular conditions and generate steady-state predictions consistent with continuous culture.

On the other hand, large-scale production of metabolic products is often achieved with batch and fed-batch culture. An important advantage of fed-batch culture is that nutrient levels can be transiently varied to achieve favorable conditions for cellular growth and product formation. Although batch culture is often used to evaluate FBA predictions, the results are not directly comparable. A possible solution to this dilemma is to perform metabolic network analysis and design using dynamic extensions of genome-scale stoichiometric models. Dynamic flux balance models are obtained by combining stoichiometric equations for intracellular metabolism with dynamic mass balances on key extracellular nutrients and products under the assumption of fast intracellular dynamics. The intracellular and extracellular descriptions are coupled through the cellular growth rate and nutrient uptake kinetics, which can be formulated to include key regulatory effects such as product inhibition of growth.

We previously developed a dynamic flux balance model of yeast metabolism using a genome-scale reconstruction of the intracellular reaction network (Hjersted et al., 2005). An efficient numerical solution method based on embedding the linear programming problem within the integration problem was developed to investigate the dynamics of wild-type Saccharomyces cerevisiae in batch and fed-batch culture. A specific gene knockout associated with oxidative phosphorylation (aac1) was investigated for ethanol overproduction in fed-batch culture.

In this presentation, we perform a detailed dynamic analysis of yeast mutants for ethanol overproduction in glucose and glucose/xylose media. A fully compartmentalized and charge balanced genome-scale reconstruction of the Saccharomyces cerevisiae reaction network (Duarte et al., 2004) is extended to include xylose metabolism. Our aac1 knockout and ten gene overexpression/insertion strategies reported in a recent steady-state FBA study (Bro et al., 2006) are evaluated with respect to their ethanol yield in batch and fed-batch culture. We also dynamically evaluate a mutant library of 538 independent gene deletions identified from steady-state FBA for their ethanol overproduction capabilities. The results demonstrate that dynamic FBA is a valuable tool for screening gene deletion/addition mutants for their metabolic capabilities in batch and fed-batch culture. We advocate the development of an integrated optimization framework that simultaneously identifies promising genetic manipulations and favorable dynamic process operating policies.

References

C. Bro, B. Regenberg, J. Forster and J. Nielsen, ?In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production,? Metabolic Engineering, 8, 102-111 (2006).

N.C. Duarte, M.J. Herrgard and B.O. Palsson, ?Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model,? Genome Research, 14, 1298-1309 (2004).

J.L. Hjersted, M.A. Henson and R. Mahadevan, ?Dynamic flux balance analysis of yeast metabolism in fed-batch culture,? AIChE Annual Meeting, Cincinnati, OH (2005).