Quantitative Modeling of Sequence-to-Expression Relationship

序列与表达关系的定量建模

• 批准号: 8864340
• 负责人: Saurabh Sinha
• 金额: $ 25.57万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-15 至 2018-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8864340
• 关键词: Algorithms; Anterior; Binding; Binding Sites; Biochemical; Cataloging; Catalogs; Cells; Collaborations; Communities; Computer software; DNA; DNA Binding; DNA Sequence; DNA Sequence Alteration; Data; Data Analyses; Development; Developmental Biology; Disease; Dorsal; Dreams; Drosophila genus; Embryo; Enhancers; Evaluation; Gene Expression; Gene Expression Regulation; Genes; Genetic Variation; Genome; Genomic Segment; Genomics; Goals; Health; Human; Imagery; Individual; Insecta; Knowledge; Language; Larva; Learning; Ligands; Limb Development; Mammals; Maps; Modeling; Motivation; Nature; Nucleic Acid Regulatory Sequences; Nucleotides; Pattern; Predisposition; Reading; Regulator Genes; Reporting; Scientist; Signaling Molecule; Statistical Methods; Structure; System; Testing; Thermodynamics; Training; Uncertainty; Visual; Wing; abstracting; base; cell type; cofactor; combinatorial; computer framework; computerized tools; design; dosage; experience; genetic regulatory protein; improved; innovation; novel; promoter; public health relevance; reconstruction; research study; response; software systems; tool; transcription factor

 DESCRIPTION (provided by applicant): Understanding how genes are turned on and off, and how their precise levels of expression are regulated, is critical to describing the connection between genetic variations and human health. On-going community-wide efforts promise to catalog vast amounts of information (data) about genomic states in a variety of conditions, including specific disease states. Such catalogs are expected to help identify key regulators of condition-specific gene expression. However, the ultimate dream of `reading' the DNA sequence and accurately predicting expression levels in any given cell is likely to remain elusive. We propose to develop advanced computational tools that will help biologists and genome scientists realize this final goal of predicting gene expression levels from sequence. The first and main goal of this proposal is to build a software system that will help a biologist model how gene expression relates to regulatory sequences. Here, `model' refers to describing the relationship between sequence and expression in a quantitative language, with a very high level of accuracy. The proposed software system, to be called `GEM' (Gene Expression Modeling), will consolidate our efforts in this direction for the last five years, and also incorpoate novel biochemical aspects to the model. In a departure from the norm in this field, the proposed software will present to the biologist all models consistent with the collected data, and not just the single most agreeable model. In other words, the scientist will get to see all possible interpretations of their data in terms of gene regulatory interactions in the cell. The second aim is devoted to presenting the model to the scientist in easily interpretable formats, including a variety of visual representations. The goal here is to connect the typically quantitative and abstract form of the above-mentioned models to the more tangible notions the biologist has about gene regulation mechanisms. The third aim of this proposal is to help the biologist improve the models created in Aim 1, either by hypothesizing the existence of hitherto unknown regulators of the gene, or by generating additional data. The software system will use rigorous statistical methods and objective criteria to help the biologist decide which experiments should be most productive in advancing their understanding of the gene regulatory system. All specific aims will be evaluated on four important regulatory systems from insects and mammals.

 描述（由申请人提供）：了解基因如何打开和关闭，以及如何调节其精确的表达水平，对于描述遗传变异与人类健康之间的联系至关重要。正在进行的全社区努力有望对各种条件下（包括特定疾病状态）的基因组状态的大量信息（数据）进行分类。此类目录有望帮助识别特定条件基因表达的关键调节因子。然而，“读取”DNA 序列并准确预测任何给定细胞中表达水平的最终梦想可能仍然难以实现。我们建议开发先进的计算工具，帮助生物学家和基因组科学家实现从序列预测基因表达水平的最终目标。该提案的第一个也是主要目标是构建一个软件系统，帮助生物学家模拟基因表达与调控序列的关系。这里的“模型”是指用定量语言描述序列和表达之间的关系，具有非常高的准确性。拟议的软件系统称为“GEM”（基因表达建模），将巩固我们过去五年在这个方向上的努力，并将新颖的生化方面纳入模型中。与该领域的规范不同的是，所提出的软件将向生物学家呈现与收集的数据一致的所有模型，而不仅仅是单个最令人满意的模型。换句话说，科学家将能够根据细胞中的基因调控相互作用来了解其数据的所有可能解释。第二个目标致力于以易于解释的格式（包括各种视觉表示）向科学家展示模型。这里的目标是将上述模型的典型定量和抽象形式与生物学家关于基因调控机制的更具体的概念联系起来。该提案的第三个目标是帮助生物学家改进目标 1 中创建的模型，或者通过假设迄今为止未知的基因调节因子的存在，或者通过生成额外的数据。该软件系统将使用严格的统计方法和客观标准来帮助生物学家决定哪些实验应该最有成效地增进他们对基因调控系统的理解。所有具体目标都将根据昆虫和哺乳动物的四个重要调节系统进行评估。