The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 kongens Lyngby, Denmark.
Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
Biotechnol J. 2019 Jan;14(1):e1800377. doi: 10.1002/biot.201800377. Epub 2018 Sep 9.
Systems biology approaches are increasingly applied to explore the potential of actinomycetes for the discovery and optimal production of antibiotics. In particular, genome-scale metabolic models (GEMs) of various actinomycetes are reconstructed at a faster rate in recent years, which has opened avenues to study interaction between primary and secondary metabolism at systems level, and to predict gene manipulation targets for overproduction of important antibiotics. Here, the status of actinomycetes' GEMs and their applications for designing antibiotics-overproducing strains are presented. Despite advances in the practice of GEM reconstruction, actinomycetes' GEMs still remain incomplete in describing a full set of biosynthetic pathways of secondary metabolites. As to the GEM-based strategies, various simulation methods are deployed to better describe secondary metabolism by introducing changes in constraints and/or objective function as well as by using omics data. Gene manipulation targeting algorithms developed for metabolic engineering of model organisms have also been actively applied to actinomycetes for the antibiotics production. Further consideration of computational resources dedicated to secondary metabolites in addition with automated GEM reconstruction tools will further upgrade GEMs of actinomycetes for antibiotics discovery and development.
系统生物学方法越来越多地被应用于探索放线菌在发现和优化抗生素生产方面的潜力。特别是,近年来各种放线菌的基因组规模代谢模型(GEM)以更快的速度被重建,这为研究初级代谢和次级代谢之间的相互作用以及预测基因操作靶点以实现重要抗生素的过量生产开辟了途径。本文介绍了放线菌 GEM 的现状及其在设计抗生素高产菌株中的应用。尽管在 GEM 重建实践方面取得了进展,但放线菌 GEM 在描述完整的次生代谢物生物合成途径方面仍然不完整。至于基于 GEM 的策略,各种模拟方法被部署用于通过引入约束和/或目标函数的变化以及使用组学数据来更好地描述次生代谢。针对模式生物代谢工程开发的基因操作靶向算法也被积极应用于放线菌的抗生素生产。进一步考虑专门用于次生代谢物的计算资源,并结合自动化 GEM 重建工具,将进一步升级用于抗生素发现和开发的放线菌 GEM。
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