Learning how different configurations of bound transc factors affect transc rates

了解绑定交易因子的不同配置如何影响交易率

• 批准号: 8545190
• 负责人: Mark D BIGGIN
• 金额: $ 21.79万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8545190
• 关键词: Address; Affect; Affinity; Animals; Architecture; Automobile Driving; Belief; Binding; Biological Assay; Biology; Cell Nucleus; Cells; Collaborations; Complex; Computer Simulation; DNA; DNA Binding; Data; Databases; Dependence; Development; Dimerization; Drosophila genome; Elements; Embryo; Equation; Gene Expression Profile; Gene Targeting; Genes; Genetic Transcription; Genome; Genomics; Goals; Hereditary Disease; Homo; Human Genetics; Individual; Laboratories; Learning; Letters; Linear Models; Literature; Location; Messenger RNA; Methods; Modeling; Molecular; Nature; Nucleosomes; One-Step dentin bonding system; Output; Pattern; Probability; Production; Proteins; Reading; Regulation; Repression; Resolution; Series; Site; Structure; System; Techniques; Testing; Time; Transgenic Organisms; Translating; Work; abstracting; base; design; embryo cell; feeding; in vivo; mRNA Expression; mathematical model; model design; programs; protein expression; protein protein interaction; research study; therapeutic development; tool; transcription factor; trend

Project Summary/Abstract One of the greatest challenges in animal biology is to learn how genomic sequence information is read by transcription factors to produce patterns of gene expression within the context of regulatory networks in developing embryos. Project 4 is part of a broader Program Project that will integrate computational modeling and wet laboratory methods to address this challenge in the belief that only quantitative, predictive mathematical models that have been validated experimentally can provide the rigorous understanding required for modeling transcriptional networks of animals. Project 4's contribution to the overall program will be to rigorously and systematically evaluate different hypotheses about how combinations of transcription factors, bound to c/s-regulatory modules (CRMs), generate complex spatial and temporal patterns of expression. We will do this by developing a series of models that accurately predict, with single nucleus resolution, patterns of transcription given protein concentration data and information of DNA binding in vivo taken from ChIP experiments or from models produced by Project 3. We will explore the dependence of CRM transcriptional output on sequence-level architecture (i.e. the configuration and orientation of individual recognition sites); on changes in protein concentration from nucleus to nucleus or time-point to time-point; and on the structure and state of nucleosomes. Project 4 will greatly extend two initial models that we have developed. The first uses Ordinary Differential Equations (ODEs) that take transcription factor protein and target gene mRNA expression data to predict in which cells of the embryo each factor either activates or represses a given gene as well as the degree of that regulation. The second uses a generalized linear model (GLM) that fits in vivo DNA binding information to the transcription driven by CRMs in transgenic embryos to learn how transcription factors interact within CRMs to drive complex spatio temporal transcription patterns. Aim 1 of this Project will extend our existing ODE model by also incorporating ChIP data on the average occupancy of each transcription factor across each CRM. This will provide probabilities for which factors regulate which genomic regions. Aim 2 will develop generalized linear mixed models (GLMMs) to aggregate a range of postulated causal interactions, e.g. homomeric cooperativity, local repression, architecture specific effects, while using the output of Aim 1 to restrict the space of parameters that we need to explore. Our models will be validated in collaboration with Project 2 and the Expression and Database Core using transgenic constructs to determine the effect on transcription of modifying the affinity and locations of transcription factor recognition sites within bona fide CRMs and also to discover which genomic regions bound by factors in vivo are functional CRMs and which represent low level non functional interactions. By helping to establishing how to read transcriptional information in animal genomes, this Project will aid both the development of therapeutics for human genetic diseases and the understanding of animal development.

项目摘要/摘要 动物生物学中最大的挑战之一是了解基因组序列信息是如何被转录因子读取的，以在发育中的胚胎的调控网络的背景下产生基因表达的模式。项目4是一个更广泛的计划项目的一部分，该项目将整合计算建模和湿实验室方法来应对这一挑战，相信只有定量的、可预测的 经过实验验证的数学模型可以提供对动物转录网络建模所需的严格理解。 项目4‘S对整个计划的贡献将是严格和系统地评估关于c/S调节模块(CRM)结合的转录因子组合如何产生复杂的时空表达模式的不同假设。为了做到这一点，我们将开发一系列模型，以单核分辨率准确地预测转录模式，给定蛋白质浓度数据和来自芯片实验或模型的体内dna结合信息。 我们将探讨CRM转录输出对序列水平结构(即单个识别位点的结构和方向)、蛋白质浓度从核到核或时间点到时间点的变化以及核小体的结构和状态的依赖。 项目4将极大地扩展我们开发的两个初始模型。第一种方法使用常微分方程式(ODES)，利用转录因子蛋白和靶基因的mRNA表达数据来预测每个因子在胚胎的哪些细胞中激活或抑制给定的基因，以及这种调控的程度。第二种方法使用广义线性模型(GLM)，该模型将体内DNA结合信息与转基因胚胎中CRM驱动的转录进行拟合，以了解转录因子如何在CRM内相互作用来驱动复杂的时空转录模式。该项目的目标1将扩展我们现有的ODE模型，包括关于每个转录因子在每个CRM中的平均占有率的芯片数据。这 将提供哪些因素调节哪些基因组区域的概率。Aim 2将开发广义线性混合模型(GLMM)，以聚合一系列假设的因果交互作用，例如同构体协作性、局部抑制、体系结构特定效应，同时使用Aim 1的输出来限制我们需要探索的参数空间。我们的模型将与Project 2和 表达和数据库核心使用转基因构建物，以确定改变转录因子识别位点在真实CRM中的亲和力和位置对转录的影响，并发现哪些基因组区域与体内的因子结合是功能性CRM，哪些代表低水平的非功能相互作用。 通过帮助建立如何读取动物基因组中的转录信息，该项目将有助于人类遗传病治疗方法的发展和对动物发育的理解。