ArticleNature biotechnology2018
Recon3D enables a three-dimensional view of gene variation in human metabolism.
Article in Nature biotechnology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 426 papers.
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Who cites it
426 citing papers in PubMed.
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- hypeR-GEM: connecting metabolite signatures to enzyme-coding genes via genome-scale metabolic models.Bioinformatics (Oxford, England) · 2026Article
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- A historical journey of metabolite-protein interaction discovery: from data harmonization to AI-driven prediction.Briefings in bioinformatics · 2026Review
- EpiATLAS - a reference for human epigenomic research.bioRxiv : the preprint server for biology · 2026Article
- A community reconstruction of Chinese hamster metabolism and structural systems biology elucidate metabolic rewiring in lactate-free CHO cells.Cell systems · 2026Article
- mFLIP: metabolic flux interval prediction.BMC bioinformatics · 2026Article
- Integrating Genome-Scale Metabolic Modeling with Machine Learning Improves Gene Essentiality Prediction in Triple-Negative Breast Cancer.International journal of molecular sciences · 2026Article
- Metab8D: a metabolic regulome network from multiomics and machine learning.Communications biology · 2026Article
- Spontaneous Non-Catalyzed Molecular Reactions and Interactions in the Human Body: Biomedical Implications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Polyploid cancer cells surviving cisplatin reallocate central carbon sources to fuel antioxidant metabolism for survival.Molecular metabolism · 2026Article
- XomicsToModel: omics data integration and generation of thermodynamically consistent metabolic models.Nature protocols · 2026Article
366 more citing papers are in PubMed but not listed here.
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21 authors.
Funding
Abstract
Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.
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