Reconstruction and application of genome-scale metabolic models (GEMs) have facilitated metabolic engineering by providing a platform on which systematic computational analysis of metabolic networks can be performed. In this study, a GEM of Escherichia coli NZN111 was employed by the analysis of production and growth coupling (APGC) algorithm to identify genetic strategies for the overproduction of succinate. Through in silico simulation and reaction expression analysis, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), phosphoglycerate kinase (PGK), triosephosphate isomerase (TPI), and phosphoenolpyruvate carboxylase (PPC), encoded by gapA, pgk, tpiA, and ppc, respectively, were selected for experimental overexpression. The results showed that overexpressing any of these could improve both growth and succinate production. Specifically, overexpression of GAPDH or PGK showed a significant effect with up to 24% increase on succinate production. These results indicate that the APGC algorithm can be effectively used to guide genetic manipulation for strain design by identifying genome-wide gene amplification targets.

中文翻译：

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模型指导的新型基因扩增靶标的鉴定，可提高大肠杆菌NZN111中琥珀酸的产生

基因组规模代谢模型（GEM）的重建和应用通过提供一个平台，可以在其上进行代谢网络的系统计算分析，促进了代谢工程。在这项研究中，通过分析生产和生长偶联（APGC）算法，采用了大肠杆菌NZN111的GEM来鉴定琥珀酸过量生产的遗传策略。通过计算机模拟和反应表达分析，分别由gapA，pgk，tpiA和ppc编码的3-磷酸甘油醛脱氢酶（GAPDH），磷酸甘油酸激酶（PGK），磷酸三糖异构酶（TPI）和磷酸烯醇丙酮酸羧化酶（PPC）。选择进行实验性过表达。结果表明，过表达其中任何一种都可以改善生长和琥珀酸盐的生产。具体来说，GAPDH或PGK的过表达表现出显着的影响，琥珀酸产量提高了24％。这些结果表明，通过鉴定全基因组基因扩增靶点，APGC算法可有效地用于指导菌株设计的遗传操作。