Project Summary 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 fates, the inheritable memory of cell identity, and the switching from one cell state to another. Phage lambda has been extensively characterized using genetic and biochemical approaches. It was one of the first testbeds for the attempt to create a quantitative narrative for a living system, in the form 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, and the resulting cell fate, at the resolution of individual phages and cells, 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 events. By using simple, coarse-grained theoretical models, we distill our experimental findings into general principles, which advance our system-level understanding of lambda. The same experimental and theoretical tools are then applied, mutatis mutandis, to higher systems. As outcomes of the proposed work, (1) we will advance the formation of a quantitative narrative for gene regulation at the cellular, “mesoscopic” scale, providing the missing link between the microscopic details of molecular interactions obtained in vitro and the system-level phenotype. (2) We will reveal to what degree the observed heterogeneity (“noise”) in gene regulation and cell-fate choice are a manifestation of true biochemical stochasticity, or instead, represent our inability to measure critical “hidden variables”, which have a deterministic effect on cell behavior. (3) The experimental, computational and theoretical tools developed for the project will continue to be directly applied for the interrogation of analogous questions in higher systems, and will ultimately further our understanding of how gene regulation drives cell-fate choices in the context of human health and disease.