Recon3D enables a three-dimensional view of gene variation in human metabolism

Elizabeth Brunk, Swagatika Sahoo, Daniel C. Zielinski, Ali Altunkaya, Andreas Dräger, Nathan Mih, Francesco Gatto, Avlant Nilsson, German Andres Preciat Gonzalez, Maike Kathrin Aurich, Andreas Prlic, Anand Sastry, Anna D. Danielsdottir, Almut Heinken, Alberto Noronha, Peter W. Rose, Stephen K. Burley, Ronan M.T. Fleming, Jens Nielsen, Ines ThieleBernhard O. Palsson

Research output: Contribution to journalArticlepeer-review

177 Scopus citations


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

Original languageEnglish (US)
Pages (from-to)272-281
Number of pages10
JournalNature biotechnology
Issue number3
StatePublished - Mar 1 2018

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology
  • Molecular Medicine
  • Biomedical Engineering


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