Steady-state and dynamic flux balance analysis of ethanol production by Saccharomyces cerevisiae

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 metabolic engineering 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.

Dynamic estimations of metabolic fluxes with constraint-based models and possibility theory

“…This means that the "finitewindow" approach may lead to less insight, although in no case it will lead to results deviated from those provided by the «whole-window» approach. Most of the works in the literature that account for extracellular dynamics follow approaches that can be seen as «finite-window» with windows of size one (Herwig, 2002;Takiguchi, 1997;Henry, 2007;Mahadevan, 2002;Hjersted, 2009). However, using windows of convenient size, the users could chose the tradeoff between computational efficiency and the more precise estimates of the «whole-window».…”

“…We consider two main approaches to this problem: (a) metabolic flux analysis (MFA), which considers experimental data to perform estimations (Heijden, 1994), and (b) flux balance analysis (FBA), which assumes an evolved optimality of cells for the same purpose . These predictions are typically static, aimed to study cells at a state of particular interest; however, several works taking extracellular dynamics into account can be found in the literature both in the context of MFA (Herwig, 2002;Takiguchi, 1997;Henry, 2007) and FBA (Mahadevan, 2002;Hjersted, 2009). …”

Interval and Possibilistic Methods for Constraint-Based Metabolic Models

“…This kind of "isolated" approaches, even imperfect, were indeed followed by the majority of works that can be found in the literature accounting for extracellular dynamics in the context of constraint-based modelling (Herwig, 2002;Takiguchi, 1997;Hjersted, 2009). …”

“…FBA is typically used to analyse cells at a particular state, but extracellular dynamics have been taken into account to predict fluxes and external metabolites during a cultivation Hjersted, 2009). The novelty of the approach described hereinafter is that optimality is defined in a gradual way using possibility theory: the optimal state is considered fully possible, and the more a state differs from it, the less possible such situation is considered.…”

“…Therefore extracellular dynamics are not considered and derivatives are replaced by constant uptake or production rates in (2b). However, several works accounting for extracellular dynamics can be found in the literature, both in the context of MFA (Herwig, 2002;Takiguchi, 1997; and FBA Hjersted, 2009).…”