Hjersted J L, Henson M A
Department of Chemical Engineering, University of Massachusetts, Amherst, MA 01003-9303, USA.
IET Syst Biol. 2009 May;3(3):167-79. doi: 10.1049/iet-syb.2008.0103.
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.
采用稳态和动态通量平衡分析（DFBA）来研究代谢模型复杂性和参数对野生型及工程化酿酒酵母乙醇产量预测的影响。研究了三种代谢网络模型，从代谢的单隔室表示到具有七个隔室和详细电荷平衡的基因组规模重建。稳态分析表明，这些模型对生物量和乙醇产量产生了相似的野生型预测，但对于十个工程菌株，七隔室模型产生的乙醇产量提高较小。将七隔室模型简化为两个细胞内隔室会提高乙醇产量，这表明反应定位对突变体表型预测有影响。使用七隔室模型进行的进一步分析表明，稳态预测可能对细胞内模型参数敏感，生物量产量对ATP利用参数和生物量组成表现出高敏感性。当氧气摄取率受到适当约束时，通过将与未表达基因相关的代谢反应归零来纳入基因表达数据，结果表明在稳态预测中产生的变化可忽略不计。通过添加葡萄糖和氧气摄取表达式以及瞬态细胞外平衡，开发了单隔室和七隔室模型的动态扩展。虽然动态模型对野生型的最佳分批乙醇生产率产生了相似的预测，但单隔室模型对四个可实施的基因插入产生了显著不同的预测。通过动态筛选十种代谢工程策略的多种组合，通过计算确定了具有提高乙醇生产率能力的组合缺失/过表达/插入突变体。作者得出结论，广泛的区室化和详细的电荷平衡对于可靠筛选依赖于全局氧化还原平衡修饰的代谢工程策略可能很重要，并且DFBA为识别用于分批和补料分批培养中提高代谢产物产量的新型突变体提供了潜力。
