A community-driven global reconstruction of human metabolism
Artikel i vetenskaplig tidskrift, 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/.
Författare
I. Thiele
Háskóli Íslands
N. Swainston
University of Manchester
R. M. T. Fleming
Háskóli Íslands
A. Hoppe
Charité Universitätsmedizin Berlin
S. Sahoo
Háskóli Íslands
M. K. Aurich
Háskóli Íslands
H. Haraldsdottir
Háskóli Íslands
M. L. Mo
University of California
O. Rolfsson
Háskóli Íslands
M. D. Stobbe
Netherlands Bioinformatics Centre - NBIC
Universiteit Van Amsterdam
S. G. Thorleifsson
Háskóli Íslands
Rasmus Ågren
Chalmers, Kemi- och bioteknik, Livsvetenskaper
C. Bolling
Charité Universitätsmedizin Berlin
Sergio Velasco
Chalmers, Kemi- och bioteknik, Livsvetenskaper
A. K. Chavali
University of Virginia
P. Dobson
University of Sheffield
W. B. Dunn
University of Manchester
NHS Foundation Trust
L. Endler
Universität Wien
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
Háskóli Íslands
N. Juty
European Bioinformatics Institute
S. Keating
European Bioinformatics Institute
Intawat Nookaew
Chalmers, Kemi- och bioteknik, Livsvetenskaper
N. Le Novere
European Bioinformatics Institute
Babraham Institute
N. Malys
The University of Warwick
University of Manchester
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
Háskóli Íslands
E. Simeonidis
Université du Luxembourg
Institute for Systems Biology
N. Sonnenschein
Jacobs University Bremen
K. Smallbone
University of Manchester
A. Sorokin
University of Edinburgh
Russian Academy of Sciences
Jhgm van Beek
Vrije Universiteit Amsterdam
Netherlands Consortium for Systems Biology
VU University Medical Center
D. Weichart
University of Manchester
I. Goryanin
Okinawa Institute of Science and Technology Graduate University
University of Edinburgh
Jens B Nielsen
Chalmers, Kemi- och bioteknik, Livsvetenskaper
H.V. Westerhoff
Swammerdam Institute for Life Sciences
Vrije Universiteit Amsterdam
University of Manchester
D. B. Kell
University of Manchester
P. Mendes
University of Manchester
Virginia Polytechnic Institute and State University
B. O. Palsson
Háskóli Íslands
University of California
Nature Biotechnology
1087-0156 (ISSN) 15461696 (eISSN)
Vol. 31 5 419-+Styrkeområden
Informations- och kommunikationsteknik
Livsvetenskaper och teknik (2010-2018)
Ämneskategorier (SSIF 2011)
Mikrobiologi
DOI
10.1038/nbt.2488