title: Network-based prediction of human tissue-specific metabolism.
abstract: Direct in vivo investigation of mammalian metabolism is complicated by the distinct metabolic functions of different tissues. We present a computational method that successfully describes the tissue specificity of human metabolism on a large scale. By integrating tissue-specific gene- and protein-expression data with an existing comprehensive reconstruction of the global human metabolic network, we predict tissue-specific metabolic activity in ten human tissues. This reveals a central role for post-transcriptional regulation in shaping tissue-specific metabolic activity profiles. The predicted tissue specificity of genes responsible for metabolic diseases and tissue-specific differences in metabolite exchange with biofluids extend markedly beyond tissue-specific differences manifest in enzyme-expression data, and are validated by large-scale mining of tissue-specificity data. Our results establish a computational basis for the genome-wide study of normal and abnormal human metabolism in a tissue-specific manner.
authors: School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel. shlomito@post.tau.ac.il
authors: Tomer
authors: T
authors: Shlomi
authors: Moran N
authors: MN
authors: Cabili
authors: Markus J
authors: MJ
authors: Herrgård
authors: Bernhard Ø
authors: BØ
authors: Palsson
authors: Eytan
authors: E
authors: Ruppin
mesh_terms: D002468:Cell Physiological Phenomena; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D015894:Genome, Human; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics* / Q000502:physiology; D008957:Models, Genetic; D015233:Models, Statistical; D015203:Reproducibility of Results; D012984:Software