Evidence map›Paper›PMID 38243750›Full record

ReviewMicrobial biotechnology2024

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing.

Klara Cerk, Pablo Ugalde-Salas, Chabname Ghassemi Nedjad, Maxime Lecomte, Coralie Muller, David J Sherman, Falk Hildebrand, Simon Labarthe, Clémence Frioux

Open access · goldAbstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
4.7field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 20 citations in OpenAlex.

  1. Review
  2. Article
  3. SIMBA-GNN: mechanistic graph learning for microbiome prediction.NPJ systems biology and applications · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 2 countries.

Klara CerkQuadram Institute Bioscience, Norwich, UK.
Pablo Ugalde-SalasInria, University of Bordeaux, INRAE, Talence, France.
Chabname Ghassemi NedjadInria, University of Bordeaux, INRAE, Talence, France.
Maxime LecomteInria, University of Bordeaux, INRAE, Talence, France.
Coralie MullerInria, University of Bordeaux, INRAE, Talence, France.
David J ShermanInria, University of Bordeaux, INRAE, Talence, France.
Falk HildebrandQuadram Institute Bioscience, Norwich, UK.
Simon LabartheInria, University of Bordeaux, INRAE, Talence, France.
Clémence FriouxInria, University of Bordeaux, INRAE, Talence, France.ORCID 0000-0003-2114-0697
Institut national de recherche en sciences et technologies du numérique · FRQuadram Institute · GBCEA CESTA · FRCentre National de la Recherche Scientifique · FR

Funding

Biotechnology and Biological Sciences Research Council BB/X011054/1Medical Research Council
6 · The paper itself

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.

Indexed as

MetagenomeMicrobiotaComputational BiologyMetagenomicsSequence Analysis, DNA

Identifiers

PMID38243750
PMCPMC10832553
OpenAlexW4391051929

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.