Current advances for omics-guided process optimization of microbial manufacturing

Abstract

Currently, microbial manufacturing is widely used in various fields, such as food, medicine and energy, for its advantages of greenness and sustainable development. Process optimization is the committed step enabling the commercialization of microbial manufacturing products. However, the present optimization processes mainly rely on experience or trial-and-error method ignoring the intrinsic connection between cellular physiological requirement and production performance, so in many cases the productivity of microbial manufacturing could not been fully exploited at economically feasible cost. Recently, the rapid development of omics technologies facilitates the comprehensive analysis of microbial metabolism and fermentation performance from multi-levels of molecules, cells and microenvironment. The use of omics technologies makes the process optimization more explicit, boosting microbial manufacturing performance and bringing significant economic benefits and social value. In this paper, the traditional and omics technologies-guided process optimization of microbial manufacturing are systematically reviewed, and the future trend of process optimization is prospected.

Synergetic engineering of Escherichia coli for efficient production of l-tyrosine

“…At the end of this study, the feeding rate of the fermentation process was regulated, and a maximum l -tyrosine content of 92.8 g/L was achieved. Although the glucose conversion rate was decreased by 4.3 %, the l -tyrosine yield was improved by 12.3 g/L and the fermentation time was reduced by 10 h, which is of great significance for the industrial production of l -tyrosine [ 47 ].…”

Optimisation of surfactin yield inBacillususing active learning and high-throughput mass spectrometry

“…In this context, MS can be complemented with high-throughput multi-omics measurements. This idea has been intensively developed over the last years and they are an effective complement in the development of a bioprocess optimisation strategy that is at the same time metabolically informative [67]. These measurements are already contributing to synthetic biology and metabolic engineering [68], but more effort is needed to integrate this data in the algorithms and machine learning models used by active learning loops.…”

Optimisation of surfactin yield in Bacillus using data-efficient active learning and high-throughput mass spectrometry

“…In this context, MS can be complemented with high-throughput multi-omics measurements. This idea has been intensively developed over the last years and they are an effective complement in the development of a bioprocess optimisation strategy that is at the same time metabolically informative [68] . These measurements are already contributing to synthetic biology and metabolic engineering [69] , but more effort is needed to integrate this data in the algorithms and machine learning models used by active learning loops.…”