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, System Biology

C. Bolling

Charité University Medicine Berlin

Sergio Velasco

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

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, System Biology

N. Le Novere

European Bioinformatics Institute

The 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

Free University of 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, System Biology

H.V. Westerhoff

University of Manchester

Free University of 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)

Vol. 31 5 419-+

Areas of Advance

Information and Communication Technology

Life Science Engineering (2010-2018)

Subject Categories

Microbiology

DOI

10.1038/nbt.2488

More information

Latest update

9/6/2018 1