Multi-scale data-driven engineering for biosynthetic titer improvement.


Journal

Current opinion in biotechnology
ISSN: 1879-0429
Titre abrégé: Curr Opin Biotechnol
Pays: England
ID NLM: 9100492

Informations de publication

Date de publication:
10 2020
Historique:
received: 03 03 2020
revised: 18 03 2020
accepted: 17 04 2020
pubmed: 3 6 2020
medline: 2 3 2021
entrez: 3 6 2020
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 32485576
pii: S0958-1669(20)30054-9
doi: 10.1016/j.copbio.2020.04.002
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

205-212

Informations de copyright

Copyright © 2020. Published by Elsevier Ltd.

Auteurs

Zhixing Cao (Z)

State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China; MOE Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai 200237, China. Electronic address: zcao@ecust.edu.cn.

Jiaming Yu (J)

State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China.

Weishan Wang (W)

State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China; State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.

Hongzhong Lu (H)

Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96 Gothenburg, Sweden.

Xuekui Xia (X)

Key Biosensor Laboratory of Shandong Province, Biology Institute, QiluUniversity of Technology (Shandong Academy of Sciences), Jinan, 250013, China.

Hui Xu (H)

Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China.

Xiuliang Yang (X)

Shandong Jincheng Bio-Pharmaceutical Co., Ltd., No. 117 Qixing River Road, Zibo 255130, Shandong, China.

Lianqun Bao (L)

Shijiazhuang XingbaiBioengineering Co., Ltd, Shijiazhuang 050000, China.

Qing Zhang (Q)

Inner Mongolia New VeyongBiochemical Co., Ltd, Dalad Banner 014300, China.

Huifeng Wang (H)

MOE Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai 200237, China. Electronic address: whuifeng@ecust.edu.cn.

Siliang Zhang (S)

State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China.

Lixin Zhang (L)

State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China. Electronic address: lxzhang@ecust.edu.cn.

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Classifications MeSH