Global reconstruction of the human metabolic network based on genomic and bibliomic data.

TitleGlobal reconstruction of the human metabolic network based on genomic and bibliomic data.
Publication TypeJournal Article
Year of Publication2007
AuthorsDuarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, Srivas R, Palsson BØ
JournalProc Natl Acad Sci U S A
Volume104
Issue6
Pagination1777-82
Date Published2007 Feb 6
ISSN0027-8424
KeywordsComputational Biology, Computer Simulation, Gastric Bypass, Gene Expression Profiling, Genome, Human, Humans, Metabolism, Muscle, Skeletal, Systems Biology
Abstract

Metabolism is a vital cellular process, and its malfunction is a major contributor to human disease. Metabolic networks are complex and highly interconnected, and thus systems-level computational approaches are required to elucidate and understand metabolic genotype-phenotype relationships. We have manually reconstructed the global human metabolic network based on Build 35 of the genome annotation and a comprehensive evaluation of >50 years of legacy data (i.e., bibliomic data). Herein we describe the reconstruction process and demonstrate how the resulting genome-scale (or global) network can be used (i) for the discovery of missing information, (ii) for the formulation of an in silico model, and (iii) as a structured context for analyzing high-throughput biological data sets. Our comprehensive evaluation of the literature revealed many gaps in the current understanding of human metabolism that require future experimental investigation. Mathematical analysis of network structure elucidated the implications of intracellular compartmentalization and the potential use of correlated reaction sets for alternative drug target identification. Integrated analysis of high-throughput data sets within the context of the reconstruction enabled a global assessment of functional metabolic states. These results highlight some of the applications enabled by the reconstructed human metabolic network. The establishment of this network represents an important step toward genome-scale human systems biology.

DOI10.1073/pnas.0610772104
PubMed URLhttp://www.ncbi.nlm.nih.gov/pubmed/17267599?dopt=Abstract
PMCPMC1794290
Alternate JournalProc. Natl. Acad. Sci. U.S.A.
PubMed ID17267599