Evidence map›Paper›PMID 34904863›Full record

ArticlemSystems2021

Enhancing Microbiome Research through Genome-Scale Metabolic Modeling.

Nana Y D Ankrah, David B Bernstein, Matthew Biggs, Maureen Carey, Melinda Engevik, Beatriz García-Jiménez, Meiyappan Lakshmanan, Alan R Pacheco, Snorre Sulheim, Gregory L Medlock

Abstract read
In one paragraph

Article in mSystems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. Metabolic Engineering for Gibberellic Acid Production inMolecules (Basel, Switzerland) · 2026
    Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Review
  8. Article
  9. mSystems · 2025
    Article
  10. The role of bacterial metabolism in human gut colonization.International microbiology : the official journal of the Spanish Society for Microbiology · 2025
    Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Review
  20. 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

10 authors.

Nana Y D AnkrahState University of New York at Plattsburgh, Plattsburgh, New York, USA.ORCID 0000-0003-0492-545X
David B BernsteinUniversity of California, Berkeley, California, USA.
Matthew BiggsSestina Bio, Pleasanton, California, USA.
Maureen CareyUniversity of Virginia, Charlottesville, Virginia, USA.
Melinda EngevikMedical University of South Carolina, Charleston, South Carolina, USA.
Beatriz García-JiménezBiome Makers, Inc., West Sacramento, California, USA.
Meiyappan LakshmananBioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore.
Alan R PachecoETH Zurich, Zurich, Switzerland.
Snorre SulheimSINTEF Industry, Trondheim, Norway.ORCID 0000-0002-3967-1683
Gregory L MedlockVedanta Biosciences, Cambridge, Massachusetts, USA.ORCID 0000-0002-1571-0801

Funding

Bill and Melinda Gates Foundation (BMGF) OPP1211869
6 · The paper itself

Abstract

Construction and analysis of genome-scale metabolic models (GEMs) is a well-established systems biology approach that can be used to predict metabolic and growth phenotypes. The ability of GEMs to produce mechanistic insight into microbial ecological processes makes them appealing tools that can open a range of exciting opportunities in microbiome research. Here, we briefly outline these opportunities, present current rate-limiting challenges for the trustworthy application of GEMs to microbiome research, and suggest approaches for moving the field forward.

Indexed as

metabolic modelingmetabolismmicrobiome

Identifiers

PMID34904863
PMCPMC8670372

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.