ReviewMicrobial biotechnology2024
Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing.
Review in Microbial biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 20 citations in OpenAlex.
- Foundation Models for Microbiome Research: From Sequence Semantics to Community Dynamics and Multimodal World Models.Advanced genetics (Hoboken, N.J.) · 2026Review
- Integrating metagenome-scale metabolic models and metabolomics to explore candidate biochemical interactions in cultivatedISME communications · 2026Article
- SIMBA-GNN: mechanistic graph learning for microbiome prediction.NPJ systems biology and applications · 2025Article
- Modeling the emergent metabolic potential of soil microbiomes in Atacama landscapes.Environmental microbiome · 2025Article
- microbetag: simplifying microbial network interpretation through annotation, enrichment tests, and metabolic complementarity analysis.Genome biology · 2025Article
- Article
- Mapler: a pipeline for assessing assembly quality in taxonomically rich metagenomes sequenced with HiFi reads.Bioinformatics (Oxford, England) · 2025Article
- Metagenomic analyses of gut microbiome composition and function with age in a wild bird; little change, except increased transposase gene abundance.ISME communications · 2025Article
- Panera: An innovative framework for surmounting uncertainty in microbial community modeling using pan-genera metabolic models.iScience · 2024Article
- Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing.Microbial biotechnology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.