Evidence map›Paper›PMID 36655763›Full record

ArticleBioinformatics (Oxford, England)2023

Hierarchical modelling of microbial communities.

Manuel Glöckler, Andreas Dräger, Reihaneh Mostolizadeh

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Genome-scale metabolic models consistently predictFrontiers in bioinformatics · 2023
    Article
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

3 authors.

Manuel GlöcklerDepartment of Computer Science, Eberhard Karl University Tübingen, Sand 14, Tübingen 72076, Germany.ORCID 0000-0002-3707-9395
Andreas DrägerDepartment of Computer Science, Eberhard Karl University Tübingen, Sand 14, Tübingen 72076, Germany.ORCID 0000-0002-1240-5553
Reihaneh MostolizadehDepartment of Computer Science, Eberhard Karl University Tübingen, Sand 14, Tübingen 72076, Germany.ORCID 0000-0003-2479-6851

Funding

Baden-Württemberg Ministry of ScienceBMBFCluster of Excellence EXC 2124 Controlling Microbes to Fight InfectionsDFGExcellence Strategy of the German Federal and State GovernmentsGerman Center for Infection Research 8020708703
6 · The paper itself

Abstract

summaryThe human body harbours a plethora of microbes that play a fundamental role in the well-being of the host. Still, the contribution of many microorganisms to human health remains undiscovered. To understand the composition of their communities, the accurate genome-scale metabolic network models of participating microorganisms are integrated to construct a community that mimics the normal bacterial flora of humans. So far, tools for modelling the communities have transformed the community into various optimization problems and model compositions. Therefore, any knockout or modification of each submodel (each species) necessitates the up-to-date creation of the community to incorporate rebuildings. To solve this complexity, we refer to the context of SBML in a hierarchical model composition, wherein each species's genome-scale metabolic model is imported as a submodel in another model. Hence, the community is a model composed of submodels defined in separate files. We combine all these files upon parsing to a so-called 'flattened' model, i.e., a comprehensive and valid SBML file of the entire community that COBRApy can parse for further processing. The hierarchical model facilitates the analysis of the whole community irrespective of any changes in the individual submodels. AVAILABILITY AND IMPLEMENTATION: The module is freely available at https://github.com/manuelgloeckler/ncmw.

Indexed as

MicrobiotaSoftwareBacteriaGenomeHumansMetabolic Networks and Pathways

Identifiers

PMID36655763
PMCPMC9887087

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.