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.

描述总体上：（申请人提供）：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。这个提出的计划项目将集成计算建模和湿实验室方法，以应对这一挑战，即只有经过实验验证的定量，预测的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节网络中建立的一系列互补的定量数据集，以及初始计算模型，用于将因子靶向DNA以及随后生成的转录输出特定模式。这些初步实验说明了因素在体内与高度重叠的基因组区域的结合和功能表现出令人震惊的广泛，定量的连续性，并提出了主要负责体内驱动DNA结合的分子机制。我们的建议组织为四个相互依存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胚胎中特定识别位点的结合；通过广泛测量因子识别位点中点突变对体内因子占用以及空间和时间转录输出的影响，测试我们计算模型的预测；建立图像分析方法，通过细胞测量核转录细胞的相对速率；并开发有序的一系列计算模型，该模型将输入和输出数据集链接起来，以在转录网络中建立关键的分子相互作用，并在功能因素识别站点组织中建立语法规则。我们的项目将为研究转录的发展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。公共卫生相关性：许多遗传疾病，包括癌症，是由于基因组序列的突变变化引起的，这些变化导致转录失误调节。生理和发育的最正常变化涉及通过序列特定转录因子活性的变化对转录的协调调节。通过建立如何阅读动物基因组中的转录信息，我们将大力帮助遗传疾病的治疗剂发展和对动物发育的理解。