Recon3D: A Resource Enabling A Three-Dimensional View of Gene Variation in Human Metabolism
By Elizabeth Brunk, Swagatika Sahoo, Daniel C. Zielinski, Ali Altunkaya, Andreas Dräger, Nathan Mih, F. Gatto, Avlant Nilsson, G. A. Preciat Gonzalez, Maike K. Aurich and 11 more. Nature Biotechnology, 2018
500 citations recorded by the source corpus.
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.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/0464fe20b651c44c65b936f78b772cb1137acaa6
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In Molecular Biology
- Ines Thiele
- Jens Nielsen
- Adil Mardinoglu
- Ronan M. T. Fleming
- Almut Heinken
- David S. Wishart
- Alberto Noronha
- David J. Beale
Citations in the corpus
- An atlas of human metabolism (2020, methodology): “This effort began in earnest with the development of Recon1 (10) and the Edinburgh Human Metabolic Network (EHMN) (11), which served as the starting point for two parallel model series: the Recon series (Recon1, 2, and 3D) (10, 12, 13) and the Human Metabolic Reaction series (HMR1 and 2) (14, 15).”
- A white-box machine learning approach for revealing antibiotic mechanisms of action (2019, unknown): “Similar to the present work on antibiotics, cancer drugs may be counter-screened against a library of metabolites in human cancer cells and coupled with network simulations using models of human metabolism (Brunk et al., 2018) to discover metabolic mechanisms of action for existing cancer drugs.”
- The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease (2018, methodology): “The content of Recon3D has been assembled through an extensive literature review over the past decade by the systems biology community5, 16–18.”
- Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community (2019, methodology): “These include the Human Metabolome Database or HMDB [102], Recon 2 [12], Recon3D [80] and the Virtual Metabolic Human database [82].”