Cellular processes are complex and involve many components whose interactions are stochastic and only partially characterized. Instead of guessing unknown details, our work focuses on analyzing classes of biochemical reaction networks that share some features but are left to vary arbitrarily in all unknown features. This allows us to derive general impossibility constraints to guide the design of synthetic cellular circuits and understand the operating principles of naturally occurring processes. For example, I will show that feedback control in cells must involve at least one “sacrificial” component with increased fluctuations to suppress fluctuations in other components. Furthermore, I will demonstrate how to exploit fluctuations constraints to detect causal effects in complex gene regulatory networks.