CAREER: Novel Statistical Models and Computational Algorithms for Evolutionary Genomics

职业：进化基因组学的新颖统计模型和计算算法

• 批准号: 0546594
• 负责人: Eric Xing
• 金额: $ 131.23万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2014-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0546594&HistoricalAwards=false
• 关键词: CAREER; Novel; Statistical; Models; Computational

Carnegie-Mellon University is awarded a grant by the NSF Faculty Early Career Development (CAREER) Program for a promising young researcher to address several challenging computational and theoretical problems regarding regulatory evolution in metazoan species combined with an education agenda concerning both integrating evolutionary genomics into the computational biology (CompBio) curriculum at Carnegie Mellon and the University of Pittsburgh. The research component will develop novel computational methods and theoretical models to study the evolutionary mechanisms and processes that shape transcription regulatory network in metazoan species. A number of technical challenges, ranging from mapping the regulatory elements, especially the structurally complex cis-regulatory modules (CRM), will have to be tackled to develop appropriate models to capture the structural and functional evolution of these elements. The proposed research will focus on the following specific aims to address these challenges: Aim 1: Develop new methods for deciphering the cis-regulatory codes and the transcriptional regulatory networks in metazoan species; and apply them to map potentially all regulatory elements in the fruit fly. Aim 2: Develop new theories and algorithms for modeling regulatory evolution based on structural and functional transformations of regulatory elements; and use them, together with experimental means, to investigate the context-dependent cis-regulatory evolution in the fly. Aim 3: Develop algorithms for comparative genomic and network inference based on the new formalism of structural/functional phylogeny (i.e., from Aim 2). The main methodological novelties are: (1) a structure- and syntax-based CRM search algorithm; (2) a dynamic Bayesian network for inferring regulatory network; (3) context-dependent stochastic models for higher-order structural/functional evolution; and (4) phylo-genomic CRM finder and network structure predictor. The educational component focuses on developing a new, better balanced and deepened computational biology curriculum for the Joint CMU/Pitt Ph.D. program that provides both a wider coverage of fundamental mathematics and computer science principles, and working knowledge of a substantially expanded span of biological and biomedical fields. Other education plans include mentoring students, coordinating and contributing to curriculum building efforts. Understanding the genetic variation and its evolution helps to address many human health issues, such as the detection of deleterious genetic predispositions and prediction of the behavior of fast-evolving biological systems such as HIV virus and immune systems. In addition to their relevance to biology and medicine, the methodological advances in computing and statistical modeling can be easily translated into powerful and generic data-mining tools applicable to complex data beyond biology.

美国国家科学基金会（NSF）的教师早期职业发展（CAREER）计划为一位有前途的年轻研究人员提供了一笔赠款，以解决有关后生动物物种监管进化的几个具有挑战性的计算和理论问题，并结合有关将进化基因组学整合到卡内基梅隆大学和匹兹堡大学的计算生物学（CompBio）课程中的教育议程。研究部分将开发新的计算方法和理论模型，以研究后生动物物种中形成转录调控网络的进化机制和过程。一些技术挑战，从映射的监管元件，特别是结构复杂的顺式调控模块（CRM），将不得不解决开发适当的模型，以捕捉这些元素的结构和功能的演变。拟议的研究将集中在以下具体目标，以解决这些挑战：目标1：开发新的方法破译的顺式调控代码和转录调控网络的后生动物物种，并将其应用到地图可能所有的果蝇调控元件。目标二：发展新的理论和算法，用于模拟调控元件的结构和功能转换的基础上的调控进化;并使用它们，与实验手段，调查的上下文依赖的顺式调控进化的苍蝇。目标3：基于结构/功能遗传学的新形式主义（即，目标2）。主要的方法学创新是：（1）基于结构和语法的CRM搜索算法;（2）用于推断调控网络的动态贝叶斯网络;（3）用于高阶结构/功能进化的上下文相关随机模型;以及（4）单基因组CRM发现器和网络结构预测器。教育部分的重点是开发一个新的，更好的平衡和深化的计算生物学课程的联合CMU/皮特博士。该计划提供了基础数学和计算机科学原理的更广泛的覆盖面，以及生物和生物医学领域的大幅扩展的工作知识。其他教育计划包括指导学生，协调和促进课程建设工作。了解遗传变异及其进化有助于解决许多人类健康问题，例如检测有害的遗传倾向和预测快速进化的生物系统（如HIV病毒和免疫系统）的行为。除了与生物学和医学相关之外，计算和统计建模方法的进步可以很容易地转化为适用于生物学以外的复杂数据的强大而通用的数据挖掘工具。