Evidence map›Paper›PMID 42156657›Full record

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

Constraint-Based Modeling of Microbial Communities for Metabolite Production.

Maziya Ibrahim, Karthik Raman

Abstract read
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In one paragraph

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
–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

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

2 authors.

Maziya IbrahimDepartment of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India.
Karthik RamanCentre for Integrative Biology and Systems Medicine, Wadhwani School of Data Science and AI, Indian Institute of Technology Madras, Chennai, India. kraman@iitm.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this chapter, we describe an in silico approach called CAMP (Co-Culture/Community Analyses for Metabolite Production) to predict two-species microbial communities best suited for producing a desired metabolite. Here, we use genome-scale metabolic models (GEMs) to build microbial communities and constraint-based modeling methods such as flux balance analysis (FBA) to assess and identify suitable communities. Flux variability analysis (FVA) detects the maximum product flux in the communities. The interaction behavior between community members, i.e., mutualism, commensalism, parasitism, and competition, can be deduced based on the variations in the predicted growth rates of the species as monocultures and in co-cultures. In silico community optimization strategies to predict reaction knockouts that improve product flux have also been implemented. CAMP source codes are available from https://github.com/RamanLab/CAMP/tree/master/Protocol .

Indexed as

Computational BiologyMicrobiotaModels, BiologicalCoculture TechniquesComputer SimulationMetabolic Flux AnalysisMetabolic Networks and PathwaysBiobased economyBiological productionCross-feedingGenome-scale metabolic modelsReaction knockoutsSustainable bioprocesses

Identifiers

PMID42156657

What OpenQuestion holds

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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.