CAREER: Dissecting the Mechanisms of Genetic Control of Biological Systems via High-Dimensional Sparse Graphical Models

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Since the completion of genome sequencing projects for various organisms including human and other model organisms, the fundamental goal of research in computational genomics, systems biology, and genetics has been to gain a complete understanding of how the instruction sets encoded in genomes get executed within a cell system and organism. The recent advances in the high-throughput technology and next-generation sequencing technology have allowed the researchers to collect a large amount of data for the genomes and various other aspects of a cell system. Such datasets hold the key to understanding the detailed mechanisms of the genetic control of a biological system and further deepening our knowledge of cell biology with the potential for broad application. This project will develop statistical machine learning methods based on high-dimensional sparse graphical models for integrative analysis of genomic datasets. As graphical models provide a powerful tool for representing the complex structure of the unknown biological processes that underlie the observed genomic data, the computational methods to be developed in this project will be able to extract rich information on the genetic control of gene regulation systems from genome-scale datasets. This project will also include training the next-generation computational biologists by supervising graduate students and incorporating the research results into the curriculum. The project will involve collaboration between computational scientists and biologists to participate in outreach programs for high-school students to present them an alternative career path that combines biological and computer sciences. In addition, the project will contribute to increasing womens participation in science and engineering.