Research Overview

Computational Analysis of Gene Regulation and Genome Organization

The advent of genome-scale biology has provided biologists with enormous amounts of data to analyze, understand, and incorporate into ever-improving models of how organisms function at a molecular level. A fundamental problem in these studies is the identification and characterization of an organism’s genes, especially those that code for proteins. While a great deal of attention understandably has been focused on delineation of the function of the resulting proteins, it is equally important to ascertain the context in which genes function.

The control of when and where genes become activated is broadly referred to as gene regulation. Gene regulation is controlled by a complex mixture of forces that can act at any of the stages of gene activation: transcription of DNA to a precursor RNA, post-transcriptional processing of the RNA, translation of the RNA to a protein, or post-translational modification of the final protein product. As we improve our understanding of the fundamental mechanisms of gene regulation, we correspondingly improve our ability to understand how gene regulation can be disrupted, which often results in either disease or developmental problems. We use computational methods, including Hidden Markov Models (HMMs) and Bayesian techniques, e.g. Gibbs Sampling, to study regulatory sequences associated with mRNA processing in a variety of organisms.

The physical and functional organizations of the mammalian genome are correlated outcomes of evolution. Inbred strains of mice provide a unique opportunity for exploring these relationships, representing as they do, diverse genomes originally separated by millions of generations that were then scrambled in the laboratory and subjected to intense selection during inbreeding to homozygosity. In collaborative work, we have shown that the resulting pattern of chromosome organization includes regional domains of functionally related elements that promote the co-inheritance and survival of compatible sets of alleles.