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分子是细胞机器的重要组成部分。现在已知它们对于许多生物过程至关重要，包括蛋白质合成、转录调节、染色体复制、病毒感染和 RNA 干扰。然而，我们对 RNA 分子的了解仍然有限。该研究项目通过引入新颖的分子模型和高效的计算工具，填补了当前 RNA 研究的重要空白。具体而言，研究团队旨在以研究假结RNA结构并了解其性质为主题，解决以下问题：（1）RNA分子关键二级元件的熵估计； (2) 从RNA序列中鉴定稳定的假结基序并开发RNA家族的假结基序文库； (3) 预测假结RNA分子的三维整体并表征其折叠机制。 所有这些问题都涉及探索非常大的状态空间上的概率分布，必须开发新的数学和统计工具。具体来说，研究团队研究和开发了多种技术，包括高效约束序列蒙特卡罗（SMC）方法、高效马尔可夫链蒙特卡罗（MCMC）方法和混合速率加速方案及其组合。方法论的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，这些问题成为新的统计思想和程序的试验场和灵感。异花受精对于生物和统计科学的重大进步来说是理想的选择。它为数学/统计学和生物学跨学科领域的下一代科学家和研究人员提供了完美的教育和培训环境。开展博士后、研究生和本科生的综合教育和研究活动。开发了一套免费软件来实现所开发的算法。该项目旨在提高我们对 RNA（一类重要的生物分子和细胞机器的重要组成部分）的理解。现在已知它们对于许多生物过程至关重要。对RNA及其动力学和功能的更深入了解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，从而造福人类社会。开发创新的统计工具是为了解决根本问题。此类工具还可以用于许多其他应用程序。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融合。它为数学/统计学和生物学跨学科领域的下一代科学家和研究人员提供了完美的教育和培训环境。 开展博士后、研究生和本科生的综合教育和研究活动，并特别注重吸引女性和少数民族学生进入数学生物学领域的精彩研究生涯。开发了一套公共和免费软件来实现所开发的算法。它能够为生物学家和生物信息学研究人员在他们自己的研究和发现中提供新的算法和软件。