Next, we integrated the measured the uptake and excretion rates with measured gene expression of metabolic enzymes using a model of the human metabolic network and the FBA framework. By sampling the feasible space of intracellular fluxes constructed using the FBA framework (see methods), we obtained posterior distributions of intracellular fluxes. Fluxes from these distributions satisfied mass balance and laws of thermodynamics as imposed by the metabolic network. The fluxes were constrained to reproduce the measured growth rate and nutrient exchange rates. The fluxes were also biased to align with gene expression profiles obtained by RNA sequencing. Figure and show that according to the Bayesian model, there are network-wide differences in the metabolism of T cells. Overall, TC T cells had a more active metabolism, ∼ 76% of the reactions in the model had a higher flux in the lupus mice. These preliminary analyses show that simple biophysical and metabolic experiments, combined with transcriptomics and genome-wide metabolic models can allow us to estimate a detailed picture of intracellular fluxes.