Evidence map›Paper›PMID 42156656›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Constraint-Based Metabolic Modeling Approach for Microbial Communities.

Satyajit Beura, Sayan Saha Roy, Amit Kumar Das, Amit Ghosh

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Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Satyajit BeuraDepartment of Bioscience and Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India.
Sayan Saha RoySchool of Nano Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India.
Amit Kumar DasDepartment of Bioscience and Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India.
Amit GhoshDepartment of Bioscience and Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India. amitghosh@iitkgp.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microorganisms grow in complex communities by fostering symbiotic relationships to uphold the integrity and functionality of the consortium. Deciphering the metabolic interactions within microbial communities and their impact on host environments is essential due to their association with major domains, including human health, bioremediation, and bioenergy production. However, unraveling their metabolic activity in laboratory conditions is challenging, as many microbes resist cultivation, and recreating their complex natural ecosystem with all its biological parameters presents additional hurdles. Therefore, modeling the microbial communities has become crucial for comprehending the intricate interactions within diverse microbial populations. In this chapter, we elucidate an in silico methodology for reconstructing a genome-scale metabolic model of a microbial consortium. This community modeling approach encompasses the reconstruction of microbial models, the integration of individual models into a community, and the optimization of the community model under different environmental conditions. Furthermore, a wide range of flux analysis techniques, like Flux Balance Analysis (FBA), Flux Variability Analysis (FVA), and Flux Sampling (FS), were described to investigate both the community-wide flux profile and intermicrobial interactions.

Indexed as

Computational BiologyMicrobial ConsortiaMicrobiotaModels, BiologicalComputer SimulationMetabolic Flux AnalysisMetabolic Networks and PathwaysFlux balance analysisFlux samplingFlux variability analysisGenome-scale community metabolic modelMetabolic interactionMicrobial consortium

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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.