Quantitative Modeling of Transcriptional Information in the Drosophila Genome

果蝇基因组转录信息的定量建模

• 批准号: 8703720
• 负责人: Mark D BIGGIN
• 金额: $ 194.01万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 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/8703720
• 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; Differential Equation; Drosophila genome; Drosophila genus; Embryo; 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

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.

总体描述：(申请人提供)：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，以在发育中的胚胎调控网络的背景下产生基因表达模式。这一拟议的计划项目将整合计算建模和湿实验室方法来应对这一挑战，相信只有经过实验验证的定量、预测性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补充的、定量的数据集的基础上，以及将因子靶向DNA和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， 显示了一个令人震惊的广泛的，数量的连续结合和功能高度重叠的基因组区域在体内，并提出了分子机制，主要负责驱动DNA结合在体内。 我们的提案被组织成四个相互依赖的研究项目和一个共享资源核心。这些将以一个新的、更高的分辨率映射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上的点突变对体内因子占有率以及空间和时间转录输出的影响来测试我们计算模型的预测；建立图像分析方法来测量逐个细胞的核转录相对速率；以及开发一系列有序的计算模型，将输入和输出数据集连接起来，以建立转录网络内的关键分子相互作用和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详细的数据集和建模策略，包括广泛的实验测试和模型预测的验证。