Abstract
Steady-state and dynamic flux balance
analysis (DFBA) was used to investigate the effects of metabolic
model complexity and parameters on ethanol production predictions for
wild-type and engineered Saccharomyces cerevisiae. Three metabolic
network models ranging from a single compartment representation of
metabolism to a genome-scale reconstruction with seven compartments
and detailed charge balancing were studied. Steady-state analysis
showed that the models generated similar wild-type predictions for
the biomass and ethanol yields, but for ten engineered strains the
seven compartment model produced smaller ethanol yield enhancements.
Simplification of the seven compartment model to two intracellular
compartments produced increased ethanol yields, suggesting that reaction
localisation had an impact on mutant phenotype predictions. Further
analysis with the seven compartment model demonstrated that steady-state
predictions can be sensitive to intracellular model parameters, with
the biomass yield exhibiting high sensitivity to ATP utilisation
parameters and the biomass composition. The incorporation of gene
expression data through the zeroing of metabolic reactions associated
with unexpressed genes was shown to produce negligible changes in
steady-state predictions when the oxygen uptake rate was suitably
constrained. Dynamic extensions of the single and seven compartment models
were developed through the addition of glucose and oxygen uptake
expressions and transient extracellular balances. While the dynamic
models produced similar predictions of the optimal batch ethanol
productivity for the wild type, the single compartment model produced
significantly different predictions for four implementable gene insertions. A
combined deletion/overexpression/insertion mutant with improved ethanol
productivity capabilities was computationally identified by dynamically
screening multiple combinations of the ten Metab. Eng. strategies. The
authors concluded that extensive compartmentalisation and detailed
charge balancing can be important for reliably screening metabolic
engineering strategies that rely on modification of the global redox
balance and that DFBA offers the potential to identify novel mutants
for enhanced metabolite production in batch and fed-batch cultures.
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