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OptEnvelope:一种基于目标点的敲除方法,用于生长耦联生产。

OptEnvelope: A target point guided method for growth-coupled production using knockouts.

机构信息

Institute of Microbiology and Biotechnology, Computational Systems Biology Group, University of Latvia, Riga, Latvia.

出版信息

PLoS One. 2023 Nov 16;18(11):e0294313. doi: 10.1371/journal.pone.0294313. eCollection 2023.

Abstract

Finding the best knockout strategy for coupling biomass growth and production of a target metabolite using a mathematic model of metabolism is a challenge in biotechnology. In this research, a three-step method named OptEnvelope is presented based on finding minimal set of active reactions for a target point in the feasible solution space (envelope) using a mixed-integer linear programming formula. The method initially finds the reduced desirable solution space envelope in the product versus biomass plot by removing all inactive reactions. Then, with reinsertion of the deleted reactions, OptEnvelope attempts to reduce the number of knockouts so that the desirable production envelope is preserved. Additionally, OptEnvelope searches for envelopes with higher minimum production rates or fewer knockouts by evaluating different target points within the desired solution space. It is possible to limit the maximal number of knockouts. The method was implemented on metabolic models of E. coli and S. cerevisiae to test the method benchmarking the capability of these industrial microbes for overproduction of acetate and glycerol under aerobic conditions and succinate and ethanol under anaerobic conditions. The results illustrate that OptEnvelope is capable to find multiple strong coupled envelopes located in the desired solution space because of its novel target point oriented strategy of envelope search. The results indicate that E. coli is more appropriate to produce acetate and succinate while S. cerevisiae is a better host for glycerol production. Gene deletions for some of the proposed reaction knockouts have been previously reported to increase the production of these metabolites in experiments. Both organisms are suitable for ethanol production, however, more knockouts for the adaptation of E. coli are required. OptEnvelope is available at https://github.com/lv-csbg/optEnvelope.

摘要

利用代谢模型为生物量生长和目标代谢产物的生产找到最佳敲除策略是生物技术中的一个挑战。在这项研究中,提出了一种名为 OptEnvelope 的三步法,该方法基于使用混合整数线性规划公式在可行解空间(包络)中的目标点找到最小的一组活性反应。该方法最初通过从产物与生物量图中删除所有非活性反应来找到减少的理想解空间包络。然后,通过重新插入已删除的反应,OptEnvelope 尝试减少敲除的数量,以保留理想的生产包络。此外,OptEnvelope 通过在所需解空间内评估不同的目标点来搜索具有更高最小生产速率或更少敲除的包络。可以限制最大敲除数量。该方法已在大肠杆菌和酿酒酵母的代谢模型上实施,以测试该方法,基准测试这些工业微生物在有氧条件下生产醋酸盐和甘油以及在厌氧条件下生产琥珀酸和乙醇的能力。结果表明,由于 OptEnvelope 具有新颖的基于目标点的包络搜索策略,因此能够在所需解空间中找到多个强耦合包络。结果表明,大肠杆菌更适合生产醋酸盐和琥珀酸,而酿酒酵母则更适合生产甘油。先前已经报道了一些建议的反应敲除基因缺失可增加这些代谢物在实验中的产量。这两种生物都适合生产乙醇,但是需要对大肠杆菌进行更多的敲除以适应。OptEnvelope 可在 https://github.com/lv-csbg/optEnvelope 上获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5e5/10653430/5ed6ad9f4514/pone.0294313.g001.jpg

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