Open AccessComputational reconstruction of tissue‐specific metabolic models: application to human liver metabolism
Author(s) -
Jerby Livnat,
Shlomi Tomer,
Ruppin Eytan
Publication year - 2010
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.1038/msb.2010.56
Subject(s) - computational biology , biology , human liver , metabolomics , flux (metallurgy) , variety (cybernetics) , computer science , bioinformatics , biochemistry , artificial intelligence , enzyme , chemistry , organic chemistry
The computational study of human metabolism has been advanced with the advent of the first generic (non‐tissue specific) stoichiometric model of human metabolism. In this study, we present a new algorithm for rapid reconstruction of tissue‐specific genome‐scale models of human metabolism. The algorithm generates a tissue‐specific model from the generic human model by integrating a variety of tissue‐specific molecular data sources, including literature‐based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying the algorithm, we constructed the first genome‐scale stoichiometric model of hepatic metabolism. The model is verified using standard cross‐validation procedures, and through its ability to carry out hepatic metabolic functions. The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46). Finally, the model better predicts biomarker changes in genetic metabolic disorders than the generic human model (accuracy of 0.67 versus 0.59). The approach presented can be used to construct other human tissue‐specific models, and be applied to other organisms.