Evidence map›Paper›PMID 42156660›Full record

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

Describing and Designing Microbial Community Metabolic Models In Silico: A Comprehensive Protocol Utilizing FLYCOP.

Ana Del Ramo, David San León Granado, Juan Nogales

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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
–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

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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

3 authors.

Ana Del Ramo *Department of Systems Biology, Centro Nacional de Biotecnología CSIC, Madrid, Spain.
David San León Granado *Department of Systems Biology, Centro Nacional de Biotecnología CSIC, Madrid, Spain.
Juan NogalesDepartment of Systems Biology, Centro Nacional de Biotecnología CSIC, Madrid, Spain. jnogales@cnb.csic.es.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microbial communities play crucial roles in multiple natural and engineered environments, contributing to biogeochemical cycling, waste treatment, and biotechnological processes. Much like for single organisms, genome-scale metabolic models (GEMs) have become essential in contextualizing, designing and optimizing microbiomes. Computational modeling of microbial community metabolism provides valuable insights into community dynamics, interactions, and metabolic capabilities. Here, we present a comprehensive protocol for describing and engineering microbial community metabolic models in silico, leveraging FLYCOP (FLexible sYnthetic Consortium Optimization). Starting with available individual GEMs, this protocol covers the construction of condition-specific GEMs for community members, the generation of community-based metabolic models, and the analysis of community-wide metabolic capabilities and interactions. Furthermore, we showcase the utility of FLYCOP by illustrating its application in: (i) describing a community-driven complex biological process (e.g., denitrification) and (ii) designing a synthetic community for biotechnological purposes (e.g., production of violacein).

Indexed as

Computational BiologyComputer SimulationMicrobial ConsortiaMicrobiotaModels, BiologicalMetabolic EngineeringMetabolic Networks and PathwaysSoftwareCOMETSFlux Balance AnalysisFLYCOPGenome-scale metabolic modelSynthetic Microbial Consortia

Identifiers

PMID42156660

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.