Collaborartive Research: Monte Carlo Study of Pseudoknotted RNA Molecules: Motifs, Structure and Folding

合作研究：假结 RNA 分子的蒙特卡罗研究：基序、结构和折叠

• 批准号: 0800183
• 负责人: Rong Chen
• 金额: $ 68.96万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-15 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0800183&HistoricalAwards=false
• 关键词: Collaborartive; Research; Monte; Carlo; Study

RNA molecules are an important component of the cellular machinery. They are now known to be essential for numerous biological processes, including protein synthesis, transcription regulation, chromosome replication, viral infection, and RNA interference. However, our knowledge of RNA molecules is still limited. This research project fills important gaps in current RNA studies by introducing novel molecular models and efficient computational tools. Specifically, the research team aims to solve the following problems under a coherent theme of studying pseudoknotted RNA structure and understanding their properties: (1) Estimation of entropy of key secondary elements of RNA molecules; (2) Identification of stable pseudoknot motifs from RNA sequences and developing libraries of pseudoknot motifs for RNA families; (3) Prediction of three dimensional ensemble of pseudoknotted RNA molecules and characterize their folding mechanism. All these problems involve exploration of probability distributions on very large state spaces where novel mathematical and statistical tools must be developed. Specifically, the research team studies and develops several techniques including efficient constrained Sequential Monte Carlo (SMC) methods, efficient Markov Chain Monte Carlo (MCMC) methods and mixing rate acceleration schemes and their combinations. The methodological development provides a solid foundation for solving the underlying biological problems. In return, those problems serve as the testing ground and inspiration of new statistical ideas and procedures. The cross-fertilization is ideal for significant advances in both biological and statistical sciences. It provides a perfect environment of education and training of the next generation of scientists and researchers in the interdisciplinary field of mathematics/ statistics and biology. Integrated education and research activities at post-doc, graduate and undergraduate levels are conducted. A set of free software are produced for implementing the developed algorithms.This project intends to improve our understanding of RNA, an important class of biomolecules and an important component of the cellular machinery. They are now known to be essential for numerous biological processes. A deeper understanding of RNA, its dynamics and functionality, will increase our ability to develop new medicines and diagnostic procedure and propel further technological advancement, hence beneficial to the human society. Innovative statistical tools are developed to solve the underlying problems. Such tools can also be used in many other applications. The project is a cross-fertilization between statistical science and bioinformatics, computational biology, and biophysics. It provides a perfect environment of education and training of the next generation of scientists and researchers in the interdisciplinary field of mathematics/statistics and biology. Integrated education and research activities at post-doc, graduate and undergraduate levels are conducted and special attentions are paid to attract women and minority students into the wonderful research career in the field of math-biology. A set of public and free software are developed for implementing the developed algorithms. It is able to empower biologists and bioinformatics researchers with new algorithms and software in their own research and discovery.

RNA分子是细胞机械的重要组成部分。现在众所周知，它们对于许多生物学过程至关重要，包括蛋白质合成，转录调节，染色体复制，病毒感染和RNA干扰。但是，我们对RNA分子的了解仍然有限。该研究项目通过引入新的分子模型和有效的计算工具来填补当前RNA研究中的重要空白。具体而言，研究小组的目的是在研究伪KNOTED RNA结构并理解其特性的一致主题下解决以下问题：（1）RNA分子的关键次要元素的熵估计； （2）从RNA序列和开发RNA家族的伪基主题的库中鉴定稳定的伪单基序； （3）伪RNA分子的三维集合的预测并表征其折叠机制。 所有这些问题都涉及在必须开发新的数学和统计工具的非常大的状态空间上探索概率分布。具体而言，研究小组研究并开发了几种技术，包括有效约束的蒙特卡洛（SMC）方法，有效的马尔可夫链蒙特卡洛（MCMC）方法和混合速率加速度方案及其组合。方法论发展为解决潜在的生物学问题提供了坚实的基础。作为回报，这些问题是新的统计思想和程序的测试基础和灵感。交叉利用是生物学和统计科学的重大进展的理想选择。它为下一代科学家和研究人员提供了一个完美的教育环境，并提供了数学/统计与生物学跨学科领域的研究。在大多数情况下，研究生和本科阶段进行了综合教育和研究活动。生产了一组免费软件，用于实施开发的算法。该项目旨在提高我们对RNA的理解，重要的生物分子和蜂窝机械的重要组成部分。现在众所周知，它们对于众多生物过程至关重要。对RNA的深入了解，其动态和功能，将提高我们开发新药物和诊断程序的能力，并推动进一步的技术进步，从而对人类社会有益。开发了创新的统计工具来解决潜在的问题。此类工具也可以在许多其他应用程序中使用。该项目是统计科学与生物信息学，计算生物学和生物物理学之间的交叉利用。它为下一代科学家和研究人员提供了一个完美的教育环境，并提供了数学/统计与生物学跨学科领域的研究。 在大多数情况下，研究生和本科阶段进行了综合的教育和研究活动，并获得了特别注意，以吸引妇女和少数民族学生进入数学生物学领域的出色研究职业。开发了一组公共和免费软件，用于实施开发的算法。它能够在自己的研究和发现中使用新的算法和软件来使生物学家和生物信息学研究人员能够使用新算法和软件。