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OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production.
Jiang, Shouyong; Otero-Muras, Irene; Banga, Julio R; Wang, Yong; Kaiser, Marcus; Krasnogor, Natalio.
Afiliação
  • Jiang S; Department of Computing Science, University of Aberdeen, Aberdeen AB24 3FX, U.K.
  • Otero-Muras I; Institute for Integrative Systems Biology, UV-CSIC, Valencia 46980, Spain.
  • Banga JR; Computational Biology Lab, MBG-CSIC, Pontevedra 36143, Spain.
  • Wang Y; School of Automation, Central South University, Changsha 410083, China.
  • Kaiser M; School of Medicine, University of Nottingham, Nottingham NG7 2RD, U.K.
  • Krasnogor N; School of Computing, Newcastle University, Tyne NE4 5TG, U.K.
ACS Synth Biol ; 11(4): 1531-1541, 2022 04 15.
Article em En | MEDLINE | ID: mdl-35389631
ABSTRACT
Computational tools have been widely adopted for strain optimization in metabolic engineering, contributing to numerous success stories of producing industrially relevant biochemicals. However, most of these tools focus on single metabolic intervention strategies (either gene/reaction knockout or amplification alone) and rely on hypothetical optimality principles (e.g., maximization of growth) and precise gene expression (e.g., fold changes) for phenotype prediction. This paper introduces OptDesign, a new two-step strain design strategy. In the first step, OptDesign selects regulation candidates that have a noticeable flux difference between the wild type and production strains. In the second step, it computes optimal design strategies with limited manipulations (combining regulation and knockout), leading to high biochemical production. The usefulness and capabilities of OptDesign are demonstrated for the production of three biochemicals in Escherichia coli using the latest genome-scale metabolic model iML1515, showing highly consistent results with previous studies while suggesting new manipulations to boost strain performance. The source code is available at https//github.com/chang88ye/OptDesign.
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Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 3_ND Base de dados: MEDLINE Assunto principal: Escherichia coli / Engenharia Metabólica Tipo de estudo: Prognostic_studies Idioma: En Revista: ACS Synth Biol Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 3_ND Base de dados: MEDLINE Assunto principal: Escherichia coli / Engenharia Metabólica Tipo de estudo: Prognostic_studies Idioma: En Revista: ACS Synth Biol Ano de publicação: 2022 Tipo de documento: Article