Quantitative Modeling of Transcriptional Information in the Drosophila Genome

概述：（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，从而在发育胚胎的调控网络背景下产生基因表达模式。该计划项目将集成计算建模和湿实验室方法来解决这一挑战，因为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。该建议建立在一组互补的定量数据集上，这些数据集是我们为果蝇早期胚胎调控网络建立的，以及针对DNA的因子靶向和随后产生的特定转录输出模式的初始计算模型。这些初步实验说明了这些因素

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.