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Transcription Factor Engineering for High-Throughput Strain Evolution and Organic Acid Bioproduction: A Review.
Li, Jia-Wei; Zhang, Xiao-Yan; Wu, Hui; Bai, Yun-Peng.
Afiliação
  • Li JW; State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China.
  • Zhang XY; State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China.
  • Wu H; State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China.
  • Bai YP; State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China.
Article em En | MEDLINE | ID: mdl-32140463
Metabolic regulation of gene expression for the microbial production of fine chemicals, such as organic acids, is an important research topic in post-genomic metabolic engineering. In particular, the ability of transcription factors (TFs) to respond precisely in time and space to various small molecules, signals and stimuli from the internal and external environment is essential for metabolic pathway engineering and strain development. As a key component, TFs are used to construct many biosensors in vivo using synthetic biology methods, which can be used to monitor the concentration of intracellular metabolites in organic acid production that would otherwise remain "invisible" within the intracellular environment. TF-based biosensors also provide a high-throughput screening method for rapid strain evolution. Furthermore, TFs are important global regulators that control the expression levels of key enzymes in organic acid biosynthesis pathways, therefore determining the outcome of metabolic networks. Here we review recent advances in TF identification, engineering, and applications for metabolic engineering, with an emphasis on metabolite monitoring and high-throughput strain evolution for the organic acid bioproduction.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article