CAREER: Novel Statistical Models and Computational Algorithms for Evolutionary Genomics

卡内基-梅隆大学获得了美国国家科学基金会教师早期职业发展（Career）计划的资助，寻找一名有前途的年轻研究人员，以解决有关后生动物物种调节进化的几个具有挑战性的计算和理论问题，并结合卡内基梅隆大学和匹兹堡大学将进化基因组学整合到计算生物学（CompBio）课程中的教育议程。该研究部分将开发新的计算方法和理论模型，以研究形成后生动物物种转录调控网络的进化机制和过程。必须解决许多技术挑战，包括绘制监管元素，特别是结构复杂的顺式监管模块（CRM），以开发适当的模型来捕捉这些元素的结构和功能演变。为解决这些挑战，本研究将集中于以下具体目标：目标1：开发新的方法来破译后生动物物种的顺式调控代码和转录调控网络；并应用它们来绘制果蝇体内潜在的所有调控元件。目标2：基于调节元素的结构和功能转换，开发新的理论和算法来模拟调节进化；并将它们与实验手段结合起来，研究果蝇的环境依赖顺式调控进化。目标3：基于结构/功能系统发育的新形式（即来自目标2），开发比较基因组和网络推断的算法。主要的方法创新有：(1)基于结构和语法的CRM搜索算法；(2)动态贝叶斯网络，用于推断监管网络；(3)基于上下文的高阶结构/功能演化随机模型；(4)系统基因组CRM查找器和网络结构预测器。教育部分的重点是为CMU/Pitt联合博士项目开发一个新的，更好的平衡和深化的计算生物学课程，提供更广泛的基础数学和计算机科学原理，以及生物和生物医学领域的工作知识。其他教育计划包括指导学生，协调和参与课程建设。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外，计算和统计建模方面的方法进步可以很容易地转化为强大而通用的数据挖掘工具，适用于生物学以外的复杂数据。

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.