Fluxes and metabolites are the functional manifestations of a living cell. Metabolic flux analysis evolved as a powerful means for systems biology to quantitatively analyze intracellular flux distributions. With the integration of data from tracer experiments, the formerly descriptive methodology has turned into a versatile tool to validate assumptions on genome-derived flux networks. Powerful modeling frameworks balancing ‘isotopomers’, ‘cumomers’, or ‘elementary modeling units’ have reduced computational effort and introduced rigorous statistical quality measures. The advent of metabolomics, stimulus response experiments, and highly sensitive mass spectrometry techniques for mass isotopomer analysis has extended the reach of metabolic flux analysis from steady-state to highly dynamic conditions. With the integration of regulatory circuits and more ‘omics’ data into mechanistic flux models, the simulation-based prediction of cellular responses to environmental and network perturbations becomes possible — an in silico cell.