Evidence map›Paper›PMID 41910316›Full record

ArticlemSystems2026

High-throughput generation and comparison of genome-scale metabolic models reveal strain-specific metabolic diversity in 439

Jildau Emma Bras, Benjamín J Sánchez, Nikolaus Sonnenschein, Ahmad A Zeidan

Abstract readComparative Study
In one paragraph

Article in mSystems, 2026. 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. Menaquinone biosynthesis inEngineering microbiology · 2026
    Review
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.

Jildau Emma BrasBioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen, Hørsholm, Denmark.ORCID 0009-0004-7533-3034
Benjamín J SánchezBioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen, Hørsholm, Denmark.ORCID 0000-0001-6093-4110
Nikolaus SonnenscheinDTU Bioengineering, Technical University of Denmark, Kgs. Lyngby, Denmark.
Ahmad A ZeidanBioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen, Hørsholm, Denmark.ORCID 0000-0001-9984-5625

Funding

Innovationsfonden 9065-00172B
6 · The paper itself

Abstract

The emergence of automated methods for the generation of genome-scale metabolic models (GEMs) has enabled the use of these models to study metabolic differences between large sets of different microorganisms. Current methods are often optimized for either handling large sets of strains or highly accurately reflecting the metabolism of selected strains; however, both aspects are necessary for analyzing the metabolic differences among large numbers of strains of the same or closely related species. In this study, we present a workflow for the high-throughput generation of high-quality GEMs of closely related strains, which has been applied for the generation of 439 GEMs of

Indexed as

Genome, BacterialLactococcusModels, BiologicalFermentationLactococcus lactisMetabolic Networks and PathwaysSpecies Specificitybiotechnologycomparative studiescomputational biologydairygenome-scale metabolic modelinggenomicsLactococcusmetabolic modeling

Identifiers

PMID41910316
PMCPMC13098203

What OpenQuestion holds

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LicenceCC BY
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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.