Evidence map›Paper›PMID 30695998›Full record

ReviewMetabolites2019

Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview.

Partho Sen, Matej Orešič

Abstract readReview
In one paragraph

Review in Metabolites, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed
–field-weighted citation impact
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

40 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Unravelling three-way interactions betweenJournal of medical microbiology · 2025
    Review
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  14. Article
  15. Review
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  17. Article
  18. Article
  19. Article
  20. Modeling approaches for probing cross-feeding interactions in the human gut microbiome.Computational and structural biotechnology journal · 2022
    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

2 authors.

Partho SenTurku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland. partho.sen@utu.fi.ORCID 0000-0003-0475-2763
Matej OrešičTurku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland. matej.oresic@utu.fi.ORCID 0000-0002-2856-9165

Funding

Academy of Finland (Centre of Excellence in Molecular Systems Immunology and Physiology Research 2012-2017) Decision No. 250114, to M.O.Juvenile Diabetes Research Foundation (JDRF) 2-SRA-2014-159-Q-R to M.O.
6 · The paper itself

Abstract

There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet⁻microbiome, microbe⁻microbe and host⁻microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions.

Indexed as

constraint-based modelingflux balancegenome-scale metabolic modelinggut microbiomehost–microbiomemetabolic reconstructionsmetabolismmetabolomicsmetagenomicsmeta-omics

Identifiers

PMID30695998
PMCPMC6410263

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.