@article{Brunk2018,
  author = {Brunk, Elizabeth and Sahoo, Swagatika and Zielinski, Daniel C. and
        Dr\"ager, Andreas and Mih, Nathan and Gatto, Francesco and Nielsson, Avlant
        and Pericat Gonzalez, German Andres and Aurich, Maike Kathrin and Prli\'{c},
        Andreas and Sastry, Anand and Danielsdottir, Anna D. and Heinken, Almut and
        Noronha, Alberto and Rose, Peter W. and Burley, Stephen K. and Fleming,
        Ronan M. T. and Nielsen, Jens and Thiele, Ines and Palsson, Bernhard O.},
  title = {{Recon3D enables a three-dimensional view of gene variation in human
        metabolism}},
  journal = {Nature Biotechnology},
  year = {2018},
  volume = {36},
  pages = {272--281},
  month = feb,
  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 \url{http://vmh.life}.},
  doi = {10.1038/nbt.4072},
  pdf = {https://www.nature.com/articles/nbt.4072.epdf},
  url = {https://www.nature.com/articles/nbt.4072}
}