Evidence map›Paper›PMID 40705360›Full record

ReviewFEMS microbiology reviews2025

In silico encounters: harnessing metabolic modelling to understand plant-microbe interactions.

Martina Feierabend, Nadine Töpfer

Abstract readReview
In one paragraph

Review in FEMS microbiology reviews, 2025. 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. 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

2 authors.

Martina FeierabendInstitute for Plant Sciences, Cluster of Excellence on Plant Sciences (CEPLAS), University of Cologne, Zülpicher Straße 47b, 50674 Cologne, Germany.ORCID 0000-0002-2537-5832
Nadine TöpferInstitute for Plant Sciences, Cluster of Excellence on Plant Sciences (CEPLAS), University of Cologne, Zülpicher Straße 47b, 50674 Cologne, Germany.ORCID 0000-0002-3027-5799

Funding

Deutsche Forschungsgemeinschaft 390686111Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen PB22-025A
6 · The paper itself

Abstract

Understanding plant-microbe interactions is vital for developing sustainable agricultural practices and mitigating the consequences of climate change on food security. Plant-microbe interactions can improve nutrient acquisition, reduce dependency on chemical fertilizers, affect plant health, growth, and yield, and impact plants' resistance to biotic and abiotic stresses. These interactions are largely driven by metabolic exchanges and can thus be understood through metabolic network modelling. Recent developments in genomics, metagenomics, phenotyping, and synthetic biology now enable researchers to harness the potential of metabolic modelling at the genome scale. Here, we review studies that utilize genome-scale metabolic modelling to study plant-microbe interactions in symbiotic, pathogenic, and microbial community systems. This review catalogues how metabolic modelling has advanced our understanding of the plant host and its associated microorganisms as a holobiont. We showcase how these models can contextualize heterogeneous datasets and serve as valuable tools to dissect and quantify underlying mechanisms. Finally, we consider studies that employ metabolic models as a testbed for in silico design of synthetic microbial communities with predefined traits. We conclude by discussing broader implications of the presented studies, future perspectives, and outstanding challenges.

Indexed as

Models, BiologicalPlantsBacteriaComputer SimulationMetabolic Networks and PathwaysSymbiosiscomputational modellinggenome-scale metabolic modellingmetabolic interactionsplant immunometabolismplant–microbe interactionsplant microbiota

Identifiers

PMID40705360
PMCPMC12398279

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.