The general aim of metabolomics is to identify, measure and interpret the complex time-related concentration, activity and flux of endogenous metabolites in cells, tissues, and other biosamples. We propose a three-pronged approach to metabolomics: (i) upgrading metabolite measurements by focusing on instrumentation to increase sensitivity, (ii) employing C-labeled substrates and 2H2O to generate data enabling estimates of fluxes in metabolic networks via mass isotopomer analysis, and (iii) utilizing state of the art models and multivariate statistics to test the hypothesis that specific tracers add information to metabolomics. The three aims of the study are: 1. To expand analytical procedures for the concentration and mass isotopomer distribution of metabolites (acyl-CoAs, acylcarnitines, aminoacids, carboxylic acids, as well as intermediates of glycolysis, the pentosephosphate pathway, and the citric acid cycle) extracted from perfused rat livers, as well as from the plasma and urine of control, and insulin resistant rats. A matrix isotopomer balance method will be used to fit isotopomer labeling patterns to fluxes. 2. To study the temporal patterns of concentration and mass isotopomer distribution of both known and unknown metabolites extracted from perfused liver, plasma, urine and organs of mice. Changes in the profiles following an intervention will identify discriminating intermediates and isotopomers, which may play a role in metabolic regulation. We will apply multivariate statistical methods to reduce the dimensions of the data set of unknown peaks, detecting those that discriminate between treatments. 3. To investigate the temporal pattern of concentration and mass isotopomer distribution of metabolites labeled from 2H-enriched water in perfused organs and in vivo. 2H2O, which is non-toxic at low enrichment, distributes evenly in all body compartments, and is an ideal as a metabolomics probe for human studies. Multivariate statistical tests will determine if 2H2O enhances concentration-based metabolomics. The significance of our study is two-fold. First, we provide a bridge in developing metabolomics, moving from a solid foundation in hypothesis-based research to a new data-driven "omics" approach. Second, we provide a rigorous test of the hypothesis that the mathematical analysis of mass isotopomer data enhances concentration-based metabolomics to provide new avenues for understanding metabolic diseases.