Multi-scale data-driven engineering for biosynthetic titer improvement
| Autor: | Weishan Wang, Zhixing Cao, Huifeng Wang, Qing Zhang, Hongzhong Lu, Lianqun Bao, Hui Xu, Xiuliang Yang, Xuekui Xia, Lixin Zhang, Siliang Zhang, Jiaming Yu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
0303 health sciences Computer science Process (engineering) Scale (chemistry) Biomedical Engineering Bioengineering Computational biology 01 natural sciences Biosynthetic Pathways Metabolic engineering 03 medical and health sciences Metabolic pathway Titer Bioreactors Metabolic Engineering 010608 biotechnology Fermentation Bioreactor Process optimization Haystack Metabolic Networks and Pathways 030304 developmental biology Biotechnology |
| Zdroj: | Current opinion in biotechnology. 65 |
| ISSN: | 1879-0429 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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