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Multi-scale data-driven engineering for biosynthetic titer improvement
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DOI：10.1016/j.copbio.2020.04.002.png)
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摘要
En 中文
Industrial biosynthesis is a very complex process which depends on a range of different factors, from intracellular genes and metabolites, to extracellular culturing conditions and bioreactor engineering. The identification of species that improve the titer of some reaction is akin to the task of finding a needle in a haystack. This review aims to summarize state-of-the-art biosynthesis titer improvement on different scales separately, particularly regarding the advancement of metabolic pathway rewiring and data-driven process optimization and control. By integrating multi-scale data and establishing a mathematical replica of a real biosynthesis, more refined quantitative insights can be gained for achieving a higher titer than ever.
Keyword:
ITERATIVE LEARNING CONTROL
MODEL-PREDICTIVE CONTROL
NATURAL-PRODUCTS
AVERMECTINS
EXPRESSION
STRATEGIES
PROMOTERS
OPERATION
SEEKING
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