Spatial, temporal and stochastic fluctuations cause genetically identical cells to exhibit wildly different behaviors. Often labeled "noise," these fluctuations are frequently considered a nuisance that compromises cellular responses, complicates modeling, and makes predictive understanding all but impossible. However, if we examine cellular fluctuations more closely and match them to discrete stochastic analyses, we discover an untapped, yet powerful information resource. In this talk, I will present our collaborative endeavors to integrate single-cell experiments with precise stochastic analyses to gain new insight and quantitatively predictive understanding for Mitogen Activated Protein Kinase (MAPK) signal-activated gene regulation. I will explain how we experimentally quantify transcription dynamics at high temporal (1-minute) and spatial (1-molecule) resolutions; how we use precise computational analyses to model this data and efficiently infer biological mechanisms and parameters; how we predict and evaluate the extent to which model constraints (i.e., data) and uncertainty (i.e., model complexity) contribute to our understanding, and how we design novel experiments to rapidly and systematically improve this understanding. I will illustrate the effectiveness of our integrated approach with the identification of predictive models for MAPK induction of transcription in yeast and mammalian systems.