Statistical Physics Approaches to RNA Editing

RNA 编辑的统计物理方法

• 批准号: 0706002
• 负责人: Ralf Bundschuh
• 金额: $ 24万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0706002&HistoricalAwards=false
• 关键词: Statistical; Physics; Approaches; RNA; Editing

TECHNICAL SUMMARY:This award supports theoretical research and education at the interface of biology with statistical physics. The PI seeks to apply the methods of statistical physics to the problem of RNA editing which bears on how biological systems synthesize proteins from the instructions encoded in DNA. RNA is a biomolecule that is deeply involved in all aspects of molecular biology, such as protein production, gene regulation, and viral replication. In spite of RNA's importance for all living organisms, there are many significant aspects of RNA biology that we know surprisingly little about. One such significant biological mystery surrounding RNA is the mechanism of RNA editing. RNA editing is the process in which individual or short stretches of nucleotides in a messenger or functional RNA are inserted, deleted, or substituted. Different forms of RNA editing occur in many different organisms.In this project, the combined information in the mitochondrial genomes of two related organisms with insertional RNA editing, Physarum polycephalum and Didymium iridis, and in the millions of protein sequences from other organisms is exploited to computationally identify genes in the two organisms and to provide experimentally verifiable predictions about the location of editing sites within these genes. A complete annotation of the mitochondrial genome and characterization of the editing sites in these genes addresses two questions about the so far elusive RNA editing mechanism: (i) Sequence features identified from the editing sites in the new genes indicate how the RNA editing machinery knows which sites to edit. (ii) The new genes themselves reveal what some of the constituents of the RNA editing machinery itself are. The new approaches developed to meet this end are based on interpreting prediction quality as a complex energy landscape over the space of all possible predictions. This allows computational methods from statistical physics to be used to find optimal predictions.This project will yield new algorithms for the identification of genes and editing sites in the presence of RNA editing. These algorithms as well as the editing site data will be freely accessible to non commercial users.All projects will involve undergraduate and graduate students who will receive an interdisciplinary education and training in computational techniques. They will help address the high demand in the workforce for individuals with a strong quantitative and biological background.NON-TECHNICAL SUMMARY:This award supports theoretical research and education at the interface of biology and statistical physics. This area has been experiencing explosive growth owing to new experimental techniques for biology and high intellectual opportunity. The PI will apply methods of statistical physics to understand aspects of how the instructions contained in DNA are used to create proteins and their role in other cellular functions. The information contained in DNA is used to create RNA which is instrumental in how cells make proteins. In this way, information from DNA is reflected in the created proteins. In some organisms the RNA is modified after it is created by a process known as RNA editing, leading to changes in the resulting proteins. The PI will use the methods of statistical physics to better understand the process of RNA editing. The theoretical and computational techniques that are developed will in turn contribute to the methods of statistical physics and be used to attack other problems. The synergy helps to advance both fields. The concepts of statistical mechanics are applicable to a wide range of systems and phenomena in our world and have the potential to illuminate our understanding to advance fundamental knowledge as well as for practical benefit.This project will yield new computational methods for the identification of genes and editing sites in the presence of RNA editing. These methods will be freely accessible to non commercial users.This project will involve undergraduate and graduate students who will receive an interdisciplinary education and training in computational techniques. They will help address the high demand in the workforce for individuals with a strong quantitative and biological background.

技术摘要：该奖项支持生物学与统计物理学交叉领域的理论研究和教育。 PI 寻求将统计物理学方法应用于 RNA 编辑问题，该问题关系到生物系统如何根据 DNA 编码的指令合成蛋白质。 RNA是一种生物分子，深入参与分子生物学的各个方面，例如蛋白质生产、基因调控和病毒复制。尽管 RNA 对所有生物体都很重要，但我们对 RNA 生物学的许多重要方面却知之甚少。围绕 RNA 的一个重要生物学谜团是 RNA 编辑机制。 RNA 编辑是插入、删除或替换信使或功能性 RNA 中的单个或短核苷酸片段的过程。不同形式的 RNA 编辑发生在许多不同的生物体中。在该项目中，利用插入 RNA 编辑的两种相关生物体（多头绒泡菌和虹膜镨）的线粒体基因组中的组合信息，以及来自其他生物体的数百万个蛋白质序列中的信息，以计算方式识别这两种生物体中的基因，并提供有关这些生物体中编辑位点位置的可实验验证的预测。 基因。线粒体基因组的完整注释和这些基因中编辑位点的表征解决了有关迄今为止难以捉摸的RNA编辑机制的两个问题：（i）从新基因中的编辑位点识别出的序列特征表明RNA编辑机制如何知道要编辑哪些位点。 (ii) 新基因本身揭示了 RNA 编辑机制本身的一些组成部分。为实现这一目标而开发的新方法基于将预测质量解释为所有可能预测空间上的复杂能源景观。这允许使用统计物理学的计算方法来找到最佳预测。该项目将产生新的算法，用于在存在 RNA 编辑的情况下识别基因和编辑位点。这些算法以及编辑站点数据将可供非商业用户免费访问。所有项目都将涉及本科生和研究生，他们将接受计算技术的跨学科教育和培训。他们将有助于满足劳动力对具有强大的定量和生物学背景的个人的高需求。非技术摘要：该奖项支持生物学和统计物理学界面的理论研究和教育。由于新的生物学实验技术和高智力机会，该领域正在经历爆炸性增长。 PI 将应用统计物理学方法来了解 DNA 中包含的指令如何用于创建蛋白质及其在其他细胞功能中的作用。 DNA 中包含的信息用于生成 RNA，而 RNA 在细胞制造蛋白质方面发挥着重要作用。通过这种方式，DNA 的信息就会反映在所产生的蛋白质中。在某些生物体中，RNA 在通过称为 RNA 编辑的过程产生后会被修改，从而导致所得蛋白质发生变化。 PI将使用统计物理学的方法来更好地理解RNA编辑的过程。所开发的理论和计算技术反过来将有助于统计物理学方法并用于解决其他问题。这种协同作用有助于推动这两个领域的发展。统计力学的概念适用于我们世界上广泛的系统和现象，并有可能阐明我们的理解，以推进基础知识并带来实际利益。该项目将产生新的计算方法，用于在 RNA 编辑存在的情况下识别基因和编辑位点。这些方法将免费提供给非商业用户。该项目将涉及本科生和研究生，他们将接受计算技术的跨学科教育和培训。它们将有助于满足劳动力对具有强大的定量和生物学背景的个人的高需求。