Emerging methods for genome-scale metabolic modeling of microbial communities.

genome-scale metabolic modeling metagenome microbial communities model reconstruction systems biology

Journal

Trends in endocrinology and metabolism: TEM
ISSN: 1879-3061
Titre abrégé: Trends Endocrinol Metab
Pays: United States
ID NLM: 9001516

Informations de publication

Date de publication:
03 Apr 2024
Historique:
received: 28 11 2023
revised: 28 02 2024
accepted: 29 02 2024
medline: 5 4 2024
pubmed: 5 4 2024
entrez: 4 4 2024
Statut: aheadofprint

Résumé

Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.

Identifiants

pubmed: 38575441
pii: S1043-2760(24)00062-6
doi: 10.1016/j.tem.2024.02.018
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests The authors declare no conflicts of interest.

Auteurs

Chaimaa Tarzi (C)

School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK.

Guido Zampieri (G)

Department of Biology, University of Padova, Padova, 35122, Veneto, Italy.

Neil Sullivan (N)

Complement Genomics Ltd, Station Rd, Lanchester, Durham, DH7 0EX, County Durham, UK.

Claudio Angione (C)

School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; Centre for Digital Innovation, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington, DL1 1HG, North Yorkshire, UK. Electronic address: c.angione@tees.ac.uk.

Classifications MeSH