Metabolic engineering of Bacillus subtilis fueled by systems biology: Recent advances and future directions

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• 作者：Yanfeng Liu^a ; ^b ; Jianghua Li^a ; ^b ; Guocheng Du^a ; ^b ; Jian Chen^a ; ^b ; Long Liu^a ; ^b ; ^{longliu@jiangnan.edu.cn}
• 关键词：Systems biology ; Metabolic engineering ; Bacillus subtilis ; Global metabolism optimization
• 刊名：Biotechnology Advances
• 出版年：2017
• 出版时间：January-February 2017
• 年：2017
• 卷：35
• 期：1
• 页码：20-30
• 全文大小：933 K
• 卷排序：35

文摘

By combining advanced omics technology and computational modeling, systems biologists have identified and inferred thousands of regulatory events and system-wide interactions of the bacterium Bacillus subtilis, which is commonly used both in the laboratory and in industry. This dissection of the multiple layers of regulatory networks and their interactions has provided invaluable information for unraveling regulatory mechanisms and guiding metabolic engineering. In this review, we discuss recent advances in the systems biology and metabolic engineering of B. subtilis and highlight current gaps in our understanding of global metabolism and global pathway engineering in this organism. We also propose future perspectives in the systems biology of B. subtilis and suggest ways that this approach can be used to guide metabolic engineering. Specifically, although hundreds of regulatory events have been identified or inferred via systems biology approaches, systematic investigation of the functionality of these events in vivo has lagged, thereby preventing the elucidation of regulatory mechanisms and further rational pathway engineering. In metabolic engineering, ignoring the engineering of multilayer regulation hinders metabolic flux redistribution. Post-translational engineering, allosteric engineering, and dynamic pathway analyses and control will also contribute to the modulation and control of the metabolism of engineered B. subtilis, ultimately producing the desired cellular traits. We hope this review will aid metabolic engineers in making full use of available systems biology datasets and approaches for the design and perfection of microbial cell factories through global metabolism optimization.