The system comprising the bacterium Escherichia coli and its virus, bacteriophage lambda, has long served as a simple paradigm for the way gene regulation drives the choice between alternative cellular states, the inheritable memory of cell state, and the switching from one state to another. The lambda system has been extensively characterized using genetic and biochemical approaches. More recently, it has served as one of the first test beds for the attempt to form a quantitative narrative for a living system, in the shape of mathematical models connecting the microscopic physical-chemical reactions in the cell to the system-level properties. However, these models still have limited predictive power, due to the absence of an experimentally- based description of gene regulation at the required spatiotemporal resolution. Our goal in this competitive renewal is to continue closing this knowledge gap by quantifying gene regulation in the lambda system at the resolution of individual phages and cells, individual gene copies in the cell, individual molecules and discrete events in space and time. To achieve this goal, we will use single-cell and single-molecule fluorescence microscopy, which, combined with advanced image and data analysis algorithms, allow us to detect individual phage particles and individual molecules of DNA and RNA, count absolute protein numbers in individual cells and measure the discrete time-series of transcription. By using simple, coarse-grained theoretical models we are able to distill our experimental findings into general principles, which provide an improved system-level understanding of lambda, and can be directly applied to findings in higher systems. The outcome of the proposed work will be a quantitative description of gene regulation at the cellular, mesoscopic scale, providing a bridge between the two currently-existing levels of description: the microscopic details of molecular interactions governing gene regulation, obtained using traditional biochemical and biophysical tools in vitro, and large scale (macroscopic) topologies of gene networks, mapped using genetic and genomic methods. Specifically, the work will allow us to address the following questions: (1) To what degree is the observed heterogeneity (noise) in gene regulation a manifestation of actual biochemical stochasticity, or instead represents our inability to measure cellular hidden variables, which have a deterministic effect on cell behavior? (2) What role do spatial effects, beyond simple diffusion in a homogenous cytoplasm, play in gene regulation? Ultimately, the conceptual and experimental tools developed in this work will further our understanding of how gene regulation drives cell-fate choices in higher, multicellular systems, and in the context of human health and disease