Individual cells utilize series of biochemical reactions, called signaling pathways, to translate environmental conditions to physiological responses. Consequently, the emergent properties of these signaling pathways are constrained to the physico-chemical laws of their biochemical constituents - they are strongly dependent on the number of molecular components per cell, intrinsically stochastic (noisy), and are inherently nonlinear. While these properties provide the plasticity required for a functioning living system, they present challenges for our understanding and control of cellular behavior. In this thesis I present single cell measurements (i.e. flow cytometry data) and physical models that we developed to track fluctuations in protein and phospho-protein abundance throughout biochemical reaction networks, and demonstrate how the nonlinear properties of biochemical reactions produce unique network responses to the targeted chemical inhibition of enzymes. We track the logarithmic fluctuations of biochemical components using a system of chemical Langevin equations and the corresponding Lyapunov equation. Used together, these equations uncover the connection between the organization of signaling pathway constituents and the covariance matrix estimated from the experimental data. With this formalism we theoretically explore the unique covariance representations of various signaling pathways, and experimentally validate our method in two established systems: a synthetic E. coli gene regulatory network and the Mitogen Activated Protein Kinase (MAPK) cascade in primary mouse T lymphocytes. In addition, we use single cell measurements to mechanistically uncover the unique responses of signaling pathways, analog or digital, to targeted chemical inhibition. We extend these short time-scale properties of signaling pathways to a functional response, proliferation. Lastly, we show how the endogenous diversity of protein abundance among single cell clones provides a mechanism of resilience to chemical inhibition. Together, our combined experimental and theoretical approach provides novel insights to cellular systems, a method for directional inference, and optimal drug selection.