Quantitative Modeling of Transcriptional Information in the Drosophila Genome

果蝇基因组转录信息的定量建模

基本信息

批准号：
8214811
负责人：
Mark D BIGGIN
金额：
$ 196.8万
依托单位：
UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-09-13 至 2017-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8214811
关键词：
Address Animals Atlases Automobile Driving Base Pairing Belief Binding Binding Sites Biological Biological Assay Biology Blastoderm Cataloging Catalogs Cells Chromatin Chromatin Structure Code Complex Computer Simulation DNA DNA Binding DNA Sequence Data Data Set Development Drosophila genome Drosophila genus Embryo Equation Gene Expression Profile Gene Targeting Generations Genes Genetic Transcription Genome Genomics Goals Hereditary Disease Image Analysis In Vitro Laboratories Learning Linear Models Link Location Malignant Neoplasms Maps Measures Methods Modeling Molecular Nature Nuclear Nucleic Acid Regulatory Sequences Output Pattern Physiology Platelet Factor 4 Point Mutation Property Proteins Reading Regulation Regulator Genes Relative (related person)Research Personnel Research Project Grants Resolution Resource Sharing Resources Series Site Specificity System Testing Training Transcriptional Regulation Transgenic Organisms Validation Work cell type embryo cell genome sequencing genome-wide in vivo insight mRNA Expression markov model mathematical model predictive modeling programs research study success therapeutic development transcription factor

项目摘要

DESCRIPTION OVERALL: (provided by applicant): 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. This proposed Program Project 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. The proposal builds on a set of complementary, quantitative datasets that we have established for the Drosophila early embryo regulatory network, together with initial computational models for the targeting of factors to DNA and for the subsequent generation of specific patterns of transcriptional output. These preliminary experiments illustrate that factors show a shockingly broad, quantitative continuum of binding and function to highly overlapping genomic regions in vivo and suggest the molecular mechanism chiefly responsible for driving DNA binding in vivo. Our proposal is organized into four interdependent Research Projects and one Shared Resource Core. These will map at a new, much higher resolution the binding of transcription factors to their specific recognition sites in embryos; test the predictions of our computational models by extensively measuring the effect of point mutations in factor recognition sites on both in vivo factor occupancy and spatial and temporal transcriptional outputs; establish image analysis methods to measure relative rates of nuclear transcription cell by cell; and develop an ordered series of computational models that link input and output datasets to establish the key molecular interactions within a transcription network and grammar rules for the organization of functional factor recognition sites. Our project will provide uniquely detailed datasets and modeling strategies for studying the developmental control of transcription, including extensive experimental testing and validation of the models predictions. PUBLIC HEALTH RELEVANCE: Many genetic diseases, including cancer, result from mutational changes in genome sequence that cause transcriptional miss regulation. Most normal changes in physiology and development involve the coordinated modulation of transcription via changes in the activity of sequence specific transcription factors. By establishing how to read transcriptional information in animal genomes, we will greatly aid both the development of therapeutics for genetic diseases and the understanding of animal development.

总体描述：（由申请人提供）：动物生物学中最大的挑战之一是了解转录因子如何读取基因组序列信息，以在发育胚胎的调控网络背景下产生基因表达模式。该计划项目将整合计算模型和湿实验室方法来应对这一挑战，因为我们相信只有经过实验验证的定量、预测数学模型才能提供所需的严格理解。该提案建立在我们为果蝇早期胚胎调控网络建立的一组互补的定量数据集的基础上，以及用于将因子靶向 DNA 以及随后生成特定转录输出模式的初始计算模型。这些初步实验表明，因素显示出与体内高度重叠的基因组区域的结合和功能的惊人广泛的、定量的连续体，并提出了主要负责驱动体内DNA结合的分子机制。我们的提案分为四个相互依赖的研究项目和一个共享资源核心。这些将以新的、更高的分辨率绘制转录因子与其在胚胎中的特定识别位点的结合；通过广泛测量因子识别位点点突变对体内因子占用以及空间和时间转录输出的影响来测试我们的计算模型的预测；建立图像分析方法来测量细胞核转录的相对速率；并开发一系列有序的计算模型，连接输入和输出数据集，以建立转录网络内的关键分子相互作用和用于组织功能因子识别位点的语法规则。我们的项目将为研究转录的发育控制提供独特的详细数据集和建模策略，包括广泛的实验测试和模型预测的验证。公共健康相关性：包括癌症在内的许多遗传疾病都是由基因组序列突变导致转录失调所致。大多数正常的生理和发育变化都涉及通过序列特异性转录因子活性的变化来协调转录调节。通过建立如何读取动物基因组中的转录信息，我们将极大地帮助遗传疾病疗法的开发和对动物发育的理解。