Single-Molecule and Single-Cell Analysis of Transcription Factor-DNA Complexes

描述（由申请人提供）：作为细胞正常生命周期的一部分，细胞每时每刻都在做决定。这些决定的范围从代谢偏好的表达到对人类健康至关重要的选择，如细胞是否会进入不受控制的增殖状态或表达抗生素抗性基因。这里提出的工作的总体目标是破译管理细胞决策的规则，并对监管过程的模型进行严格的实验测试，目的是开发对监管代码的预测性理解。虽然我们开发的各种模型可以应用于原核生物和真核生物的调节回路，但我们首先关注的是细菌，以便建立一套完整的工具，使我们从碱基对分辨率的调节结构发现一直到这些电路的输入输出功能的系统定量确定。大肠杆菌是人们最了解的生物体之一。然而，在它的4000多个基因中，我们几乎不知道其中一半是如何被调节的。这里提出的工作是围绕三个主要目标建立的，它们共同将导致转录调控的预测图景。第一个目标的工作使用了一种称为Sort-Seq的方法，该方法可以识别转录因子的结合位点群

DESCRIPTION (provided by applicant): Cells make decisions all the time as part of their normal life cycles. These decisions range from the expression of metabolic preferences to choices critical to human health such as whether cells will enter a state of unchecked proliferation or express antibiotic resistance genes. The overarching goal of the work proposed here is to decipher the rules that manage cellular decisions and to subject models of the regulatory process to stringent experimental tests with the aim of developing a predictive understanding of the regulatory code. Though the kinds of models we develop can be applied to both prokaryotic and eukaryotic regulatory circuits, we are focusing first on bacteria in order to build up an entire suite of tools taking us from regulatory architecture discovery at basepair resolution all the way to the systematic quantitative determination of the input-output functions for these circuits. E. coli is one of the best understood of organisms. And yet, out of its more than four thousand genes, we know almost nothing about how half of them are regulated. The work proposed here is built around three main aims that together will result in a predictive picture of transcriptional regulation. The work in the first aim uses a method known as Sort-Seq that makes it possible to identify the constellation of binding sites for the transcription factors that control a given gene of interest. We will use this method as a tool both to design synthetic networks and as the basis of discovering the regulatory architectures for a number of important bacterial genes for which essentially nothing is known. With this regulatory information in hand, in the second aim, we will characterize the input-output response of regulatory elements whose architectures have been characterized using the Sort-Seq approach. Specifically, systematic experiments will be performed to test how variability in gene expression depends upon key parameters, such as the number of transcription factors and the strength of transcription factor binding sites. One of the most mysterious classes of regulatory network involves the binding of transcription factors at a distance from the genes they control. The third aim develops a mechanistic understanding of how these trans-acting factors control the promoter of interest. This analysis will be made using a combination of in vivo experiments and single-molecule measurements. The outcome of this work will be a robust framework permitting us to go all the way from regulatory architecture discovery with base pair resolution to the systematic quantitative determination of the input- output functions of generic regulatory architectures.