An approach to gene regulatory network inference.

基因调控网络推断的方法。

• 批准号: 8320163
• 负责人: Wing H. WONG
• 金额: $ 38.01万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-17 至 2014-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8320163
• 关键词: Binding; Complex; Computer Analysis; Data; Data Set; Experimental Designs; Gene Expression; Gene Targeting; Genes; Genomics; Goals; Human; Location; Maintenance; Mammalian Cell; Mammals; Measurement; Methods; Modeling; Mus; Organism; Pattern; Regulator Genes; Structure; System; Technology; Time; Undifferentiated; Variant; base; cell type; design; embryonic stem cell; functional genomics; meetings; pluripotency; response; self-renewal; success; transcription factor

DESCRIPTION (provided by applicant): Recent advances in genomics technologies have provided the means to efficiently generate several types of global data that are informative on gene regulatory systems in human and mouse. In this project we will develop the methods in experimental design and computational analysis that are necessary for the successful use of these technologies in the study of complex gene regulatory networks in these species. Aim 1: Computational inference of gene regulatory network. We will combine the power of targeted gene perturbations, global functional genomic measurements, and computational inference and modeling, to develop an integrated, predictive approach for the study of gene regulatory networks in mammals. Although the combined analysis of gene expression data and transcription factor binding location data have been useful for the inference of gene regulatory network in simple organisms, such analyses have so far met with limited success in mammals. We will identify the key factors impeding progress and will design ways to ameliorate them. We will develop a "structure oriented" approach for the inference of a gene regulatory network from diverse data types, including data on the responses to gene perturbations. Our goal is to design and demonstrate an effective approach to infer complex gene regulatory networks responsible for maintaining stable cellular states in mammalian cells. Aim 2: Implementation our method to study gene regulatory systems in ESC The approach developed in aim 1 will be applied to study the gene regulatory network underlying pluripotency and self-renewal in mouse embryonic stem cells (ESC). We have previously shown that in this important cell type a major portion of the variation in global gene expression (65%) can be predicted by the binding patterns of a moderate number (12) of transcription factors. We will generate global gene expression data at selected time points after targeted gene perturbations on these and other regulators. Based on these data sets, together with the large amount of regulator binding location data already available for unperturbed ESC, we will use the methods developed in aim 1 to infer the gene regulatory network responsible for the maintenance of the undifferentiated state.

描述（由申请人提供）：基因组学技术的最新进展提供了有效生成人类和小鼠基因调控系统信息的几种类型的全球数据的方法。在这个项目中，我们将开发实验设计和计算分析的方法，这些方法对于成功使用这些技术研究这些物种的复杂基因调控网络是必要的。目的1：基因调控网络的计算推理。我们将结合目标基因扰动、全球功能基因组测量、计算推理和建模的力量，为哺乳动物基因调控网络的研究开发一种集成的、预测的方法。虽然基因表达数据和转录因子结合位置数据的联合分析对简单生物基因调控网络的推断是有用的，但迄今为止，这种分析在哺乳动物中取得的成功有限。我们将确定阻碍进展的关键因素，并设计改善这些因素的方法。我们将开发一种“面向结构”的方法，用于从不同数据类型推断基因调控网络，包括对基因扰动的响应数据。我们的目标是设计并证明一种有效的方法来推断负责维持哺乳动物细胞稳定状态的复杂基因调控网络。在目标1中开发的方法将应用于研究小鼠胚胎干细胞（ESC）多能性和自我更新的基因调控网络。我们之前已经证明，在这种重要的细胞类型中，全球基因表达变化的主要部分（65%）可以通过中等数量（12个）转录因子的结合模式来预测。我们将在这些和其他调节因子的目标基因扰动后，在选定的时间点生成全球基因表达数据。基于这些数据集，再加上大量已可用于未受干扰ESC的调节器结合位置数据，我们将使用目标1中开发的方法来推断负责维持未分化状态的基因调控网络。