Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China.
Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London SW72AZ, UK.
Curr Opin Biotechnol. 2022 Aug;76:102724. doi: 10.1016/j.copbio.2022.102724. Epub 2022 Apr 27.
In industrial bioprocesses, microbial metabolism dictates the product yields, and therefore, our capacity to control it has an enormous potential to help us move towards a bio-based economy. The rapid development of multiomics data has accelerated our systematic understanding of complex metabolic regulatory mechanisms, which allow us to develop tools to manipulate them. In the last few years, machine learning-based metabolic modeling, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) derived synthetic biology tools, and synthetic genetic circuits have been widely used to control the metabolism of microorganisms, manipulate gene expression, and build synthetic pathways for bioproduction. This review describes the latest developments for metabolic control, and focuses on the trends and challenges of metabolic engineering strategies.
在工业生物过程中，微生物代谢决定了产物的产量，因此，我们控制它的能力具有巨大的潜力，可以帮助我们迈向基于生物的经济。多组学数据的快速发展加速了我们对复杂代谢调控机制的系统理解，使我们能够开发工具来对其进行操作。在过去的几年中，基于机器学习的代谢建模、成簇规律间隔短回文重复（CRISPR）衍生的合成生物学工具以及合成遗传回路已被广泛用于控制微生物代谢、操纵基因表达和构建生物生产的合成途径。本综述描述了代谢控制的最新进展，并重点介绍了代谢工程策略的趋势和挑战。
