Hundreds of molecular-level changes within central metabolism allow a cell to adapt to the changing environment. A primary challenge in cell physiology is to identify which of these molecular-level changes are active regulatory events. Here, we introduce pseudo-transition analysis, an approach that uses multiple steady-state observations of 13C-resolved fluxes, metabolites, and transcripts to infer which regulatory events drive metabolic adaptations following environmental transitions. Pseudo-transition analysis recapitulates known biology and identifies an unexpectedly sparse, transition-dependent regulatory landscape: typically a handful of regulatory events drive adaptation between carbon sources, with transcription mainly regulating TCA cycle flux and reactants regulating EMP pathway flux. We verify these observations using time-resolved measurements of the diauxic shift, demonstrating that some dynamic transitions can be approximated as monotonic shifts between steady-state extremes. Overall, we show that pseudo-transition analysis can explore the vast regulatory landscape of dynamic transitions using relatively few steady-state data, thereby guiding time-consuming, hypothesis-driven molecular validations. Display omitted •Steady-state comparisons reveal governing regulators of E. coli carbon metabolism•Active regulation of fluxes is sparse, transition dependent, and pathway specific•Transcription mainly regulates TCA cycle fluxes, and metabolites EMP pathway fluxes•Dynamic regulators are identified assuming monotonic shifts between steady states Gerosa et al. show that the regulators governing metabolic adaptations can be identified by comparing molecular-level changes of their steady-state extremes. This principle, applied to study E. coli adaptations between eight different carbon sources, reveals sparse, transition-dependent regulation of fluxes by transcription in the TCA cycle and by metabolites in the EMP pathway. This approach, termed pseudo-transition analysis, thus allows exploration of large numbers of dynamic adaptations using comparatively few stationary observations, thereby guiding the efficient exploration of regulatory landscapes.