Learning how different configurations of bound transc factors affect transc rates

项目总结/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如何被转录因子读取，以在发育胚胎的调控网络中产生基因表达模式。项目4是一个更广泛的计划项目的一部分，该计划项目将整合计算建模和湿实验室方法，以应对这一挑战，相信只有定量，预测 已经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对整个项目的贡献将是严格和系统地评估不同的假设，关于转录因子的组合如何与c/s-调控模块（CRM）结合，产生复杂的空间和时间表达模式。我们将通过开发一系列模型来做到这一点，这些模型可以准确地预测单个核分辨率，给定来自ChIP实验或模型的体内蛋白质浓度数据和DNA结合信息的转录模式 由Project 3制作。我们将探讨CRM转录输出对序列水平结构（即单个识别位点的配置和方向）的依赖性;从细胞核到细胞核或时间点到时间点的蛋白质浓度变化;以及核小体的结构和状态。 项目4将大大扩展我们已经开发的两个初始模型。第一种方法使用常微分方程（ODE），该方程获取转录因子蛋白和靶基因mRNA表达数据，以预测每个因子激活或抑制给定基因的胚胎细胞以及该调节的程度。第二种方法使用广义线性模型（GLM），该模型将体内DNA结合信息拟合到转基因胚胎中由CRM驱动的转录，以了解转录因子如何在CRM内相互作用以驱动复杂的时空转录模式。本项目的目标1将通过纳入每个CRM中每个转录因子平均占用率的ChIP数据来扩展我们现有的ODE模型。这 将提供哪些因子调节哪些基因组区域的概率。目标2将开发广义线性混合模型（GLSTs）来聚合一系列假设的因果相互作用，例如同聚协同性，局部抑制，结构特异性效应，同时使用目标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 表达和数据库核心使用转基因构建体来确定改变真正CRM内转录因子识别位点的亲和力和位置对转录的影响，并且还发现哪些基因组区域被体内因子结合是功能性CRM，哪些代表低水平的非功能性相互作用。 通过帮助建立如何读取动物基因组中的转录信息，该项目将有助于开发人类遗传疾病的治疗方法和了解动物发育。

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.