Stochastic fluctuations can 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, makes predictive understanding and control all but impossible. However, if we examine cellular fluctuations more closely and match them to discrete stochastic analyses, we discover virtually untapped, yet powerful sources of information and opportunities. 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. I will finish with the discussion of new opportunities in which noise analysis not only helps us to better understand gene regulation phenomena, but where it actually introduces new opportunities to more precisely control these phenomena.