自动化
考试(生物学)
计算机科学
生化工程
计算生物学
生物
数据科学
工程类
生态学
机械工程
作者
Nicolás Gurdo,Daniel C. Volke,Pablo I. Nikel
标识
DOI:10.1016/j.tibtech.2022.03.004
摘要
Major advances toward a bio-based industry enabled cost-efficient bioproduction of multiple chemicals, yet successful industrial processes are relatively scarce and limited to the use of few workhorse microbes as hosts. An in-depth understanding of the physiology and metabolism of nontraditional microorganisms is key to unleash their biotechnological potential. The inception of biofoundries multiplied the capacity of constructing and testing a large number of microbial strains tailored for bioproduction – and we argue that automation workflows therein can be adapted to gain fundamental knowledge of nontraditional hosts. Here, we propose a ‘metabolism-centric’ approach to the design–build–test–learn cycle of synthetic biology, supported by multi-omic analyses, to facilitate the deployment of microbial cell factories designed for bioproduction beyond the typical landscape of target products.
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