Evidence map›Paper›PMID 30412640›Full record

ArticlePloS one2018

GutLogo: Agent-based modeling framework to investigate spatial and temporal dynamics in the gut microbiome.

Charlie Lin, Joshua Culver, Bronson Weston, Evan Underhill, Jonathan Gorky, Prasad Dhurjati

Abstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Control of medical digital twins with artificial neural networks.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025
    Article
  6. Review
  7. Article
  8. Feasibility of engineeredBioengineering & translational medicine · 2024
    Article
  9. Article
  10. Article
  11. Review
  12. Modeling approaches for probing cross-feeding interactions in the human gut microbiome.Computational and structural biotechnology journal · 2022
    Review
  13. Review
  14. Article
  15. Review
  16. Review
  17. Agent-based models of inflammation in translational systems biology: A decade later.Wiley interdisciplinary reviews. Systems biology and medicine · 2019
    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

6 authors.

Charlie LinChemical and Biomolecular Engineering, University of Delaware, Newark, DE, United States of America.
Joshua CulverChemical and Biomolecular Engineering, University of Delaware, Newark, DE, United States of America.
Bronson WestonChemical and Biomolecular Engineering, University of Delaware, Newark, DE, United States of America.
Evan UnderhillChemical and Biomolecular Engineering, University of Delaware, Newark, DE, United States of America.
Jonathan GorkyChemical and Biomolecular Engineering, University of Delaware, Newark, DE, United States of America.
Prasad DhurjatiChemical and Biomolecular Engineering, University of Delaware, Newark, DE, United States of America.ORCID 0000-0002-0056-4776

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Knowledge of the spatial and temporal dynamics of the gut microbiome is essential to understanding the state of human health, as over a hundred diseases have been correlated with changes in microbial populations. Unfortunately, due to the complexity of the microbiome and the limitations of in vivo and in vitro experiments, studying spatial and temporal dynamics of gut bacteria in a biological setting is extremely challenging. Thus, in silico experiments present an excellent alternative for studying such systems. In consideration of these issues, we have developed a user-friendly agent-based model, GutLogo, that captures the spatial and temporal development of four representative bacterial genera populations in the ileum. We demonstrate the utility of this model by simulating population responses to perturbations in flow rate, nutrition, and probiotics. While our model predicts distinct changes in population levels due to these perturbations, most of the simulations suggest that the gut populations will return to their original steady states once the disturbance is removed. We hope that, in the future, the GutLogo model is utilized and customized by interested parties, as GutLogo can serve as a basic modeling framework for simulating a variety of physiological scenarios and can be extended to capture additional complexities of interest.

Indexed as

Gastrointestinal MicrobiomeModels, BiologicalSpatio-Temporal AnalysisHumansProbiotics

Identifiers

PMID30412640
PMCPMC6226173

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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.