Gene Regulation in Phage Lambda: A Real-Time Study with Single-Event Resolution

描述（由申请人提供）：由大肠杆菌及其病毒噬菌体λ组成的系统，长期以来一直是基因调控驱动细胞状态选择的简单范例，细胞状态的可遗传记忆，以及从一种状态切换到另一种状态。λ系统已广泛表征使用遗传和生化方法。最近，它已经成为试图形成生命系统定量叙述的第一个试验台之一，以数学模型的形式将细胞中的微观物理化学反应与系统级特性联系起来。然而，由于在所需的时空分辨率下缺乏基于实验的基因调控描述，这些模型的预测能力仍然有限。我们的目标是通过在单个噬菌体和细胞，细胞中的单个基因拷贝，单个分子的分辨率上量化lambda系统中的基因调控，继续缩小这一知识差距

DESCRIPTION (provided by applicant): 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