Evidence map›Paper›PMID 27186840›Full record

ArticleJournal of cellular physiology2016

Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction.

Christopher S Henry, Hans C Bernstein, Pamela Weisenhorn, Ronald C Taylor, Joon-Yong Lee, Jeremy Zucker, Hyun-Seob Song

Abstract read
In one paragraph

Article in Journal of cellular physiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.

0numbers the graph read from it
0cells of the map it votes in
54citing 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

54 citing papers in PubMed.

  1. Article
  2. Review
  3. Constraint-Based Metabolic Modeling Approach for Microbial Communities.Methods in molecular biology (Clifton, N.J.) · 2026
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  9. NAR genomics and bioinformatics · 2024
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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

7 authors.

Christopher S HenryDivision of Mathematics and Computer Science, Argonne National Laboratory, Argonne, Illinois.
Hans C BernsteinBiodetection Sciences, National Security Directorate, Pacific Northwest National Laboratory, Richland, Washington.
Pamela WeisenhornDivision of Mathematics and Computer Science, Argonne National Laboratory, Argonne, Illinois.
Ronald C TaylorBiological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington.
Joon-Yong LeeBiological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington.
Jeremy ZuckerBiological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington.
Hyun-Seob SongBiological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic network modeling of microbial communities provides an in-depth understanding of community-wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high-quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community-level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph-heterotroph consortium that was used to provide data needed for a community-level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources. J. Cell. Physiol. 231: 2339-2345, 2016. © 2016 Wiley Periodicals, Inc.

Indexed as

Metabolic Networks and PathwaysMicrobial ConsortiaModels, BiologicalBacteriaGene Expression ProfilingGene Expression Regulation, BacterialGenome, Bacterial

Identifiers

PMID27186840
PMCPMC5132105

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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.