Evidence map›Paper›PMID 30846976›Full record

ArticleFrontiers in microbiology2019

Web-gLV: A Web Based Platform for Lotka-Volterra Based Modeling and Simulation of Microbial Populations.

Bhusan K Kuntal, Chetan Gadgil, Sharmila S Mande

Erratum issuedAbstract read
In one paragraph

Article in Frontiers in microbiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Microbiome modeling: a beginner's guide.Frontiers in microbiology · 2024
    Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Review
  12. Network analysis methods for studying microbial communities: A mini review.Computational and structural biotechnology journal · 2021
    Review
  13. Experimental and computational approaches to unravel microbial community assembly.Computational and structural biotechnology journal · 2020
    Review
  14. Article
  15. Article
  16. Visual exploration of microbiome data.Journal of biosciences · 2019
    Review
  17. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Bhusan K KuntalBio-Sciences R&D Division, TCS Research, Tata Consultancy Services Ltd., Pune, India.
Chetan GadgilCSIR-National Chemical Laboratory, Chemical Engineering and Process Development Division, Pune, India.
Sharmila S MandeBio-Sciences R&D Division, TCS Research, Tata Consultancy Services Ltd., Pune, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The affordability of high throughput DNA sequencing has allowed us to explore the dynamics of microbial populations in various ecosystems. Mathematical modeling and simulation of such microbiome time series data can help in getting better understanding of bacterial communities. In this paper, we present Web-gLV-a GUI based interactive platform for generalized Lotka-Volterra (gLV) based modeling and simulation of microbial populations. The tool can be used to generate the mathematical models with automatic estimation of parameters and use them to predict future trajectories using numerical simulations. We also demonstrate the utility of our tool on few publicly available datasets. The case studies demonstrate the ease with which the current tool can be used by biologists to model bacterial populations and simulate their dynamics to get biological insights. We expect Web-gLV to be a valuable contribution in the field of ecological modeling and metagenomic systems biology.

Indexed as

lotka-volterramicrobial populationmicrobiomemodelingnumerical-simulationtime-seriesvisualizationweb-server

Identifiers

PMID30846976
PMCPMC6394339

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.