Evidence map›Paper›PMID 38750569›Full record

ArticleMicrobial cell factories2024

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

Yutao Wang, Zhitao Mao, Jiacheng Dong, Peiji Zhang, Qiang Gao, Defei Liu, Chaoguang Tian, Hongwu Ma

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Article in Microbial cell factories, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Yutao Wang *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.
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 DongKey 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 ZhangBiodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
Qiang GaoKey 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.
Defei LiuHaihe Laboratory of Synthetic Biology, Tianjin, 300308, China. liudf@tib.cas.cn.
Chaoguang TianHaihe Laboratory of Synthetic Biology, Tianjin, 300308, China. tian_cg@tib.cas.cn.
Hongwu MaBiodesign Center, Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China. ma_hw@tib.cas.cn.

Funding

Guangxi Science and Technology Major Program Guike-AA22117013Innovation fund of Haihe Laboratory of Synthetic Biology 22HHSWSS00014Key research and development project of China National Tobacco Corporation 110202202004Project of Key Laboratory of Tobacco Processing of Zhengzhou Tobacco Research Institute of CNTC 202022AWCX02the Key Project of the Ministry of Science and Technology of China 2023YFC3403602 and 2018YFA0900500the National Natural Science Foundation of China 32071424, 32270100 and 32300529the Strategic Priority Research Program of the Chinese Academy of Sciences XDC0110301Tianjin Synthetic Biotechnology Innovation Capacity Improvement Project TSBICIPKJGG-015 and TSBICIP-PTJJ-007-12
6 · The paper itself

Abstract

backgroundGenome-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.

resultsInitially, the model iDL1450 underwent refinement and updates, resulting in a new version named iYW1475. These updates included adjustments to biomass components, correction of gene-protein-reaction (GPR) rules, and a consensus on metabolites. Subsequently, the first ecGEM for M. thermophila was constructed using machine learning-based k

conclusionsIn this study, the incorporation of enzyme constraint to iYW1475 not only improved prediction accuracy but also broadened the model's applicability. This research demonstrates the effectiveness of integrating of machine learning-based k

Indexed as

Machine LearningMetabolic Networks and PathwaysSordarialesBiomassGenome, FungalKineticsMetabolic EngineeringModels, BiologicalCarbon source hierarchy utilizationEnzyme-constrained modelMachine learningMetabolic engineeringMyceliophthora thermophila

Identifiers

PMID38750569
PMCPMC11558977

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