Suppr超能文献

增加蓝藻次生代谢产物产量的现状与未来策略

Current Status and Future Strategies to Increase Secondary Metabolite Production from Cyanobacteria.

作者信息

Jeong Yujin, Cho Sang-Hyeok, Lee Hookeun, Choi Hyung-Kyoon, Kim Dong-Myung, Lee Choul-Gyun, Cho Suhyung, Cho Byung-Kwan

机构信息

Department of Biological Sciences and KAIST Institutes for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea.

Institute of Pharmaceutical Research, College of Pharmacy, Gachon University, Incheon 21999, Korea.

出版信息

Microorganisms. 2020 Nov 24;8(12):1849. doi: 10.3390/microorganisms8121849.

Abstract

Cyanobacteria, given their ability to produce various secondary metabolites utilizing solar energy and carbon dioxide, are a potential platform for sustainable production of biochemicals. Until now, conventional metabolic engineering approaches have been applied to various cyanobacterial species for enhanced production of industrially valued compounds, including secondary metabolites and non-natural biochemicals. However, the shortage of understanding of cyanobacterial metabolic and regulatory networks for atmospheric carbon fixation to biochemical production and the lack of available engineering tools limit the potential of cyanobacteria for industrial applications. Recently, to overcome the limitations, synthetic biology tools and systems biology approaches such as genome-scale modeling based on diverse omics data have been applied to cyanobacteria. This review covers the synthetic and systems biology approaches for advanced metabolic engineering of cyanobacteria.

摘要

蓝藻细菌因其能够利用太阳能和二氧化碳产生各种次生代谢产物,是可持续生产生物化学品的潜在平台。到目前为止,传统的代谢工程方法已应用于各种蓝藻细菌物种,以提高包括次生代谢产物和非天然生物化学品在内的具有工业价值化合物的产量。然而,对于从大气碳固定到生物化学品生产的蓝藻细菌代谢和调控网络缺乏了解,以及可用工程工具的匮乏,限制了蓝藻细菌在工业应用中的潜力。最近,为了克服这些限制,合成生物学工具和系统生物学方法,如基于多样组学数据的基因组规模建模,已应用于蓝藻细菌。本综述涵盖了用于蓝藻细菌先进代谢工程的合成生物学和系统生物学方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a8c/7761380/5ceef9d58ed8/microorganisms-08-01849-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验