A way to overcome these limitations is through computational modeling and data integration. Notably, omics-based indirect characterizations can be integrated with cell physiological information such as proliferation rate, cell size, cytokine excretion rate, and crucially, the consumption and release rates (CORE) of several nutrients and waste products, which can be accurately measured in cell culture using mass spectrometry . This integration can be achieved using the flux balance analysis (FBA) framework (Figure ). While omics data do not uniquely determine the fluxes using FBA, they do constrain the plausible fluxes to a feasible space (Figure ). To further identify unique fluxes, FBA approaches typically invoke optimality of an underlying objective function, for example, fast growth or maximum yield, to obtain a unique flux solution . While these optimality-based approaches have been quite successful in modeling the metabolism of single cell organisms 50 , mammalian cells have not necessarily evolved for fast growth, and the specific metabolic objective (e.g., lipid production, cytokine secretion, cell proliferation) may depend on cell type and extracellular environment, and may not even be metabolic in nature. The fluxes are reported as an average of measurements on T cells harvested from n = 4 mice for each condition.