Journal
MOLECULAR SYSTEMS BIOLOGY
Volume 5, Issue -, Pages -Publisher
WILEY
DOI: 10.1038/msb.2009.22
Keywords
constraint-based modeling; disease biomarkers; human metabolism; inborn error of metabolism
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Funding
- Eshkol Fellowship from the Israeli Ministry of Science
- Israeli Science Foundation (ISF)
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Early diagnosis of inborn errors of metabolism is commonly performed through biofluid metabolomics, which detects specific metabolic biomarkers whose concentration is altered due to genomic mutations. The identification of new biomarkers is of major importance to biomedical research and is usually performed through data mining of metabolomic data. After the recent publication of the genome-scale network model of human metabolism, we present a novel computational approach for systematically predicting metabolic biomarkers in stochiometric metabolic models. Applying the method to predict biomarkers for disruptions of red-blood cell metabolism demonstrates a marked correlation with altered metabolic concentrations inferred through kinetic model simulations. Applying the method to the genome-scale human model reveals a set of 233 metabolites whose concentration is predicted to be either elevated or reduced as a result of 176 possible dysfunctional enzymes. The method's predictions are shown to significantly correlate with known disease biomarkers and to predict many novel potential biomarkers. Using this method to prioritize metabolite measurement experiments to identify new biomarkers can provide an order of a 10-fold increase in biomarker detection performance. Molecular Systems Biology 28 April 2009; doi:10.1038/msb.2009.22
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