Construction of an enzyme-constrained metabolic network model for Myceliophthora thermophila using machine learning-based k data.

Journal: Microbial cell factories
PMID: 38750569

Abstract

BACKGROUND: Genome-scale metabolic models (GEMs) serve as effective tools for understanding cellular phenotypes and predicting engineering targets in the development of industrial strain. Enzyme-constrained genome-scale metabolic models (ecGEMs) have emerged as a valuable advancement, providing more accurate predictions and unveiling new engineering targets compared to models lacking enzyme constraints. In 2022, a stoichiometric GEM, iDL1450, was reconstructed for the industrially significant fungus Myceliophthora thermophila. To enhance the GEM's performance, an ecGEM was developed for M. thermophila in this study.

Authors

  • Yutao Wang
    Affiliated Hospital of Medical School, Ningbo University, Ningbo 315020, China.
  • Zhitao Mao
    Biodesign Center, Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
  • Jiacheng Dong
    Key Laboratory of Industrial Fermentation Microbiology of the Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, College of Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457, China.
  • Peiji Zhang
    Biodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
  • Qiang Gao
    Faculty of Material Science and Chemistry, China University of Geosciences, Wuhan 430074, PR China.
  • Defei Liu
    Haihe Laboratory of Synthetic Biology, Tianjin, 300308, China. liudf@tib.cas.cn.
  • Chaoguang Tian
    Haihe Laboratory of Synthetic Biology, Tianjin, 300308, China. tian_cg@tib.cas.cn.
  • Hongwu Ma
    Biodesign Centre, Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China. Electronic address: ma_hw@tib.cas.cn.

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