Local flux coordination and global gene expression regulation in metabolic modeling.


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
14 09 2023
Historique:
received: 05 11 2020
accepted: 04 09 2023
medline: 18 9 2023
pubmed: 15 9 2023
entrez: 14 9 2023
Statut: epublish

Résumé

Genome-scale metabolic networks (GSMs) are fundamental systems biology representations of a cell's entire set of stoichiometrically balanced reactions. However, such static GSMs do not incorporate the functional organization of metabolic genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; the global growth state often dynamically regulates many gene expression of metabolic reactions via global transcription factor regulators. Here, we develop a GSM reconstruction method, Decrem, by integrating locally coupled reactions and global transcriptional regulation of metabolism by cell state. Decrem produces predictions of flux and growth rates, which are highly correlated with those experimentally measured in both wild-type and mutants of three model microorganisms Escherichia coli, Saccharomyces cerevisiae, and Bacillus subtilis under various conditions. More importantly, Decrem can also explain the observed growth rates by capturing the experimentally measured flux changes between wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology, and microbial pathology.

Identifiants

pubmed: 37709734
doi: 10.1038/s41467-023-41392-6
pii: 10.1038/s41467-023-41392-6
pmc: PMC10502109
doi:

Substances chimiques

O-(glucuronic acid 2-sulfate)-(1--4)-O-(2,5)-anhydromannitol 6-sulfate 98632-68-9
Transcription Factors 0

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

5700

Informations de copyright

© 2023. Springer Nature Limited.

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Auteurs

Gaoyang Li (G)

Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.
Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.

Li Liu (L)

Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, 215316, China.

Wei Du (W)

Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China. weidu@jlu.edu.cn.

Huansheng Cao (H)

Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, 215316, China. hc284@duke.edu.

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