A community-driven global reconstruction of human metabolism
Journal article, 2013
Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including similar to 2x more reactions and similar to 1.7x more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.
Author
I. Thiele
University of Iceland
N. Swainston
University of Manchester
R. M. T. Fleming
University of Iceland
A. Hoppe
Charité University Medicine Berlin
S. Sahoo
University of Iceland
M. K. Aurich
University of Iceland
H. Haraldsdottir
University of Iceland
M. L. Mo
University of California
O. Rolfsson
University of Iceland
M. D. Stobbe
Netherlands Bioinformatics Centre - NBIC
University of Amsterdam
S. G. Thorleifsson
University of Iceland
Rasmus Ågren
Chalmers, Chemical and Biological Engineering, Life Sciences
C. Bolling
Charité University Medicine Berlin
Sergio Velasco
Chalmers, Chemical and Biological Engineering, Life Sciences
A. K. Chavali
University of Virginia
P. Dobson
University of Sheffield
W. B. Dunn
Christie Hospital NHS Foundation Trust
University of Manchester
L. Endler
University of Vienna
D. Hala
University of North Texas
M. Hucka
California Institute of Technology (Caltech)
D. Hull
University of Manchester
D. Jameson
University of Manchester
N. Jamshidi
University of California
J. J. Jonsson
University of Iceland
N. Juty
European Bioinformatics Institute
S. Keating
European Bioinformatics Institute
Intawat Nookaew
Chalmers, Chemical and Biological Engineering, Life Sciences
N. Le Novere
European Bioinformatics Institute
Babraham Institute
N. Malys
University of Manchester
The University of Warwick
A. Mazein
University of Edinburgh
J. A. Papin
University of Virginia
N. D. Price
Institute for Systems Biology
E. Selkov
Genome Designs, Inc.
M. I. Sigurdsson
University of Iceland
E. Simeonidis
University of Luxembourg
Institute for Systems Biology
N. Sonnenschein
Jacobs University Bremen
K. Smallbone
University of Manchester
A. Sorokin
Russian Academy of Sciences
University of Edinburgh
Jhgm van Beek
VU University Medical Center
Vrije Universiteit Amsterdam
Netherlands Consortium for Systems Biology
D. Weichart
University of Manchester
I. Goryanin
Okinawa Institute of Science and Technology Graduate University
University of Edinburgh
Jens B Nielsen
Chalmers, Chemical and Biological Engineering, Life Sciences
H.V. Westerhoff
University of Manchester
Vrije Universiteit Amsterdam
Swammerdam Institute for Life Sciences
D. B. Kell
University of Manchester
P. Mendes
University of Manchester
Virginia Polytechnic Institute and State University
B. O. Palsson
University of Iceland
University of California
Nature Biotechnology
1087-0156 (ISSN) 15461696 (eISSN)
Vol. 31 5 419-+Areas of Advance
Information and Communication Technology
Life Science Engineering (2010-2018)
Subject Categories
Microbiology
DOI
10.1038/nbt.2488