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

描述(由申请人提供)：作为正常生命周期的一部分，细胞一直在做出决定。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，例如细胞是否会进入不受控制的增殖状态或表达抗生素耐药性基因。这里提出的工作的首要目标是破译管理蜂窝决策的规则，并对监管过程的模型进行严格的实验测试，目的是发展对监管法规的预测性理解。虽然我们开发的模型可以应用于原核生物和真核生物的调控电路，但我们首先专注于细菌，以便建立一整套工具，从Basepair分辨率的调控结构发现一直到对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，我们几乎对其中一半是如何调控的一无所知。这里提出的工作是围绕三个主要目标建立的，这些目标加在一起将产生一幅转录调控的预测性图景。第一个目标中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星座 它们控制着特定的感兴趣基因。我们将使用这种方法作为设计合成网络的工具，并作为发现一些基本未知的重要细菌基因调控结构的基础。有了这些调控信息，在第二个目标中，我们将表征其架构已使用排序-序列方法表征的调控元件的输入-输出响应。具体地说，将进行系统的实验，以测试基因表达的可变性如何取决于关键参数，如转录因子的数量和转录因子结合位点的强度。最神秘的一类调控网络涉及转录因子与它们控制的基因之间的距离结合。第三个目标发展了对这些反作用因素如何控制利益推动者的机械性理解。这项分析将结合活体实验和单分子测量进行。这项工作的结果将是一个稳健的框架，使我们能够从碱基对解析的监管体系结构发现到系统地定量确定通用监管体系结构的输入输出功能。

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.