CAREER: Novel Statistical Models and Computational Algorithms for Evolutionary Genomics

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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.