Department of Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany.
Department of Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany.
Bioinformatics. 2021 Jul 19;37(12):1717-1723. doi: 10.1093/bioinformatics/btaa996.
Large-scale metabolic models are widely used to design metabolic engineering strategies for diverse biotechnological applications. However, the existing computational approaches focus on alteration of reaction fluxes and often neglect the manipulations of gene expression to implement these strategies.
Here, we find that the association of genes with multiple reactions leads to infeasibility of engineering strategies at the flux level, since they require contradicting manipulations of gene expression. Moreover, we identify that all of the existing approaches to design gene knockout strategies do not ensure that the resulting design may also require other gene alterations, such as up- or downregulations, to match the desired flux distribution. To address these issues, we propose a constraint-based approach, termed GeneReg, that facilitates the design of feasible metabolic engineering strategies at the gene level and that is readily applicable to large-scale metabolic networks. We show that GeneReg can identify feasible strategies to overproduce ethanol in Escherichia coli and lactate in Saccharomyces cerevisiae, but overproduction of the TCA cycle intermediates is not feasible in five organisms used as cell factories under default growth conditions. Therefore, GeneReg points at the need to couple gene regulation and metabolism to design rational metabolic engineering strategies.
https://github.com/MonaRazaghi/GeneReg.
Supplementary data are available at Bioinformatics online.
大规模代谢模型被广泛用于设计各种生物技术应用的代谢工程策略。然而,现有的计算方法侧重于改变反应通量,往往忽略了基因表达的操纵,以实施这些策略。
在这里,我们发现基因与多个反应的关联导致在通量水平上进行工程策略的不可行性,因为它们需要对基因表达进行矛盾的操纵。此外,我们发现所有现有的基因敲除策略设计方法都不能确保所得到的设计也可能需要其他基因改变,如上调或下调,以匹配所需的通量分布。为了解决这些问题,我们提出了一种基于约束的方法,称为 GeneReg,它可以在基因水平上促进可行的代谢工程策略的设计,并且很容易适用于大规模代谢网络。我们表明,GeneReg 可以识别出在大肠杆菌中过量生产乙醇和在酿酒酵母中过量生产乳酸的可行策略,但在默认生长条件下用作细胞工厂的五个生物体中,TCA 循环中间产物的过量生产是不可行的。因此,GeneReg 指出需要将基因调控和代谢结合起来,以设计合理的代谢工程策略。
https://github.com/MonaRazaghi/GeneReg。
补充数据可在“Bioinformatics”在线获取。