ReviewNature reviews. Genetics2025
Genome-scale models in human metabologenomics.
Review in Nature reviews. Genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Phospho-JNK agonists show promising effects for the treatment of hepatocellular carcinoma.iScience · 2026Article
- Enzyme-constrained genome-scale modeling resolves growth-production trade-offs in fermentative biohydrogen production.Environmental science and ecotechnology · 2026Article
- OncoRisk: a state-of-the-art web server for bridging the oncogenic databases and pan-cancer cohorts to the translational oncology.Communications biology · 2026Article
- Dual-encoder contrastive learning accelerates enzyme discovery.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Whole-cell modeling predicts alternative proteome allocation strategies in the archaeon Methanococcus maripaludis.Scientific reports · 2026Article
- Targeting PKLR and lipogenic enzymes through JNK inhibition to develop a therapeutic strategy for MASLD and MASH.Frontiers in pharmacology · 2026Article
- Article
- SIMBA-GNN: mechanistic graph learning for microbiome prediction.NPJ systems biology and applications · 2025Article
- MicroMap: a network visualisation resource for human microbiome metabolism.NPJ biofilms and microbiomes · 2025Article
- Unravelling the Complexity of Sarcopenia Through a Systems Biology Approach.International journal of molecular sciences · 2025Review
- Longitudinal big biological data in the AI era.Molecular systems biology · 2025Review
- Integrative systems biology approaches for analyzing microbiome dysbiosis and species interactions.Briefings in bioinformatics · 2025Review
- Systematically identification of survival-associated eQTLs in a Japanese kidney cancer cohort.PLoS genetics · 2025Article
- Systems Modeling Reveals Shared Metabolic Dysregulation and Potential Treatments in ME/CFS and Long COVID.International journal of molecular sciences · 2025Article
- Atlas-scale metabolic activities inferred from single-cell and spatial transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- Article
- Design, Synthesis, and Computational Evaluation of 3,4-Dihydroquinolin-2(Pharmaceuticals (Basel, Switzerland) · 2025Article
- In silico encounters: harnessing metabolic modelling to understand plant-microbe interactions.FEMS microbiology reviews · 2025Review
- Identifying Hub Genes and Metabolic Pathways in Collagen VI-Related Dystrophies: A Roadmap to Therapeutic Intervention.Biomolecules · 2024Article
- Unveiling the Molecular Mechanisms of Glioblastoma through an Integrated Network-Based Approach.Biomedicines · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs - from cells and tissues to microbiomes and the whole body - have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.
Indexed as
Identifiers
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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.