How do we obtain a network-wide characterization of T cell metabolic activity? Advances in genomics, proteomics, and metabolomics allow us to obtain a high dimensional and high-resolution characterization of cellular metabolism. However, these measurements only provide indirect information about the metabolic state of cells -network-scale enzyme activity or reaction rates. This is because reaction fluxes are a complex function of enzyme kinetics 45 , thermodynamics , metabolomics , and gene expression , and therefore are not uniquely determined by -omics characterizations. For example, metabolite levels may be high either because of a high rate of production or a low rate of clearance. Similarly, high gene expression levels may imply higher reaction rates or a compensatory mechanism to maintain constant reaction rates. Therefore, typical omics measurements cannot be directly used to infer differences in metabolic states of cells. Moreover, while labeled carbon experiments allow estimation of intracellular fluxes in bacteria , a direct measurement of most intracellular fluxes is not possible in mammalian cells, owing to compartmentalization . Consequently, metabolic states cannot be characterized using direct measurements either.