ReviewComputational and structural biotechnology journal2025
Metabolic modeling of host-microbe interactions.
Review in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Modeling Microbiome Modulation of Tumor Metabolic Networks to Predict Synergistic Therapies.bioRxiv : the preprint server for biology · 2026Article
- ModellingCurrent research in microbial sciences · 2026Article
- Microbiome-mycotoxin interactions and probiotic strategies: implications for gut health and cancer.Frontiers in nutrition · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Host-microbe interactions play an integral role in the function and survival of eukaryotes, influencing various processes ranging from metabolism to immune regulation. As our understanding of these interactions deepens, there is a growing shift toward integrative approaches that consider both host and microbial genotypic potential. However, capturing the complexity and dynamic nature of these relationships remains a significant challenge. Genome-scale metabolic models (GEMs) offer a powerful framework to investigate host-microbe interactions at a systems level. By simulating metabolic fluxes and cross-feeding relationships, GEMs enable the exploration of metabolic interdependencies and emergent community functions. These models can be applied independently or in conjunction with experimental data, supporting hypothesis generation and systems-level insights into host-microbe dynamics. In this review, we examine recent applications of GEMs to host-microbe studies, with a focus on how they reveal reciprocal metabolic influences. We also discuss the current technical challenges, highlight available tools and methodological strategies, such as model reconstruction, data integration, and simulation and analysis steps, and outline future directions for advancing host-microbe interaction study using GEMs.
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Registered trials
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.