Type 2 Diabetes (T2D) is a result of systemic disturbances in metabolism characterized mainly by impaired insulin action in peripheral tissues such as liver, muscle, and adipose tissue. In light of the highly interconnected and coordinated nature of substrate metabolism in health, and its failure in T2D, current research necessitates an integrated in-vivo systems biology strategy to investigate in-vivo multi-tissue metabolic alterations in the etiology of insulin resistance in obesity and diabetes. Pre-clinical PET is unique in that multiple tissues are in the field-of-view (FOV). This realization affords the opportunity to perform non-invasive multi-tissue quantitative imaging and metabolic phenotyping through multi-tracer experiments coupled with mathematical models of tracer kinetics. With that in mind and given our experience in quantifying myocardial substrate metabolism in rodents, we hypothesize that employing a similar approach will provide quantitative measures of substrate metabolism in liver, muscle, and adipose tissue. The metabolic tracers considered in this proposal include [11C]Palmitate for FA oxidation and triglycerides synthesis (storage), 18FTHA for fatty acid oxidation; [11C]Glucose for glucose oxidation and glycogen synthesis, 18FDG for glucose utilization, and [11C]Lactate as a potential imaging marker for gluconeogenesis. Given the dual-input function to the liver, in Specific Aim 1 we will validate and optimize an algorithm to reconstruct the liver dual-input function in multi- tracer imaging of hepatic substrate metabolism. With the liver dual input function at hand, in Specific Aim 2 we construct and validate compartmental models of 18FDG, [11C]Glucose, [11C]Lactate, [11C]Palmitate, [11C]Acetate, and 18FTHA metabolism through interventions that enhance the dynamic range of metabolic response. Having constructed and validated compartmental models for the tracers in this proposal, we assess and characterize metabolic disturbances in the pathogenesis of T2D by performing multi-tissue multi-tracer time-course metabolic phenotyping. In-vivo metabolic phenotyping will be coupled with expression array analysis to correlate genomics alterations to metabolic disturbances. We anticipate that successful completion of the proposed work will provide an integrated in-vivo metabolic phenotyping platform linking genomic alterations in transgenetic/knockout animal models of disease to multi-tissue metabolic disturbances in the study of T2D. In addition, the proposed work will facilitate characterization of the interplay between T2D and cardiovascular disease, among others highlighted in the proposal. Equally important, we anticipate that strategy and insights derived from this work will be translated to clinical applications.