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: 8897395
• 负责人: Mark D BIGGIN
• 金额: $ 22.02万
• 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 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8897395
• 关键词: Address; Affect; Affinity; Algebra; Animals; Architecture; Arithmetic; Automobile Driving; Belief; Binding; Biological Assay; Biology; Cell Nucleus; Cells; Collaborations; Complex; Computer Simulation; DNA; DNA Binding; Data; Databases; Dependence; Development; Differential Equation; Dimerization; Drosophila genome; Elements; Embryo; Gene Expression Profile; Gene Targeting; Genes; Genetic Transcription; Genome; Genomic Segment; 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学习如何通过转录因子读取基因组序列信息，以在开发胚胎的调节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，以应对这一挑战，以此认为只有定量，预测性 经过实验验证的数学模型可以提供建模动物转录网络所需的严格理解。 项目4对整个程序的贡献将是严格，系统地评估有关转录因子的组合如何与C/S调节模块（CRMS）结合，产生复杂的空间和时间表达方式。我们将通过开发一系列模型来实现这一目标，这些模型通过单核分辨率准确预测蛋白质浓度数据的转录模式以及从芯片实验中获得的体内DNA结合的信息 由项目3产生。我们将探讨CRM转录输出对序列级体系结构的依赖性（即单个识别位点的配置和方向）；关于蛋白质浓度从细胞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。 项目4将大大扩展我们开发的两个初始模型。第一个使用常规微分方程（ODE），将转录因子蛋白和靶基因mRNA表达数据用于预测胚胎的哪个细胞在哪些因子中激活或抑制给定基因以及该调节的程度。第二种使用广义线性模型（GLM），该模型将体内DNA结合信息拟合到转基因胚胎中CRM驱动的转录，以了解转录因子如何在CRM中相互作用以驱动复杂的时空转录模式。该项目的AIM 1将通过还将每个CRM中每个转录因子的平均占用率的芯片数据纳入我们现有的ODE模型。这 将提供哪些因素调节哪些基因组区域的概率。 AIM 2将开发广义的线性混合模型（GLMM），以汇总一系列假定的因果相互作用，例如同源性合作，局部抑制，特定架构的效果，同时使用AIM 1的输出来限制我们需要探索的参数空间。我们的模型将与项目2和 使用转基因构建体的表达和数据库核心确定对善意CRM中转录因子识别位点的亲和力和位置的转录的影响，并发现哪些基因组区域受体内因子绑定的基因组区域是功能性CRMS，哪些代表低水平的非功能相互作用。 通过帮助建立如何阅读动物基因组中的转录信息，该项目将有助于为人类遗传疾病的治疗学发展和对动物发育的理解的发展。