Even genetically identical cells in the same environment can exhibit wildly different behaviors due to spatial, temporal and stochastic fluctuations. Often labeled “noise,” these fluctuations were previously considered a nuisance that compromised cellular responses, complicated modeling, and made predictive understanding all but impossible. However, if we examine these fluctuations more closely through the lens of new experimental and computational techniques, we actually discover a powerful information resource. Different cellular mechanisms affect these cellular fluctuations in different ways, and the resulting “fluctuation fingerprints” can help us to identify new properties of hidden cellular mechanisms. Understanding these fluctuations (or “Listening to the noise”) requires a strong integration of single-cell measurements with stochastic analyses. I will discuss a few key examples where we have used such integrated tools to gain predictive understanding of natural and synthetic transcriptional regulation pathways in bacteria and yeast. In these case studies, I will show how we generate large numbers of different model structures and then use parameter estimation and uncertainty quantification analyses to determine the best model with the greatest predictive capability. I will discuss how these models yield new insight into biological dynamics, illustrate how these models can be used to predict transcriptional responses in new conditions, and comment on the use of such models to help optimize or control gene regulatory behaviors.
