- James Watson Professor, Dept. of Ecology & Evolution, University of Chicago
Department of Ecology & Evolution
The University of Chicago
1101 E. 57th St., Z202A
Chicago, IL 60637
Phone: 773 702 3104
Fax: 773 702 3104
My major interest is in the processes and mechanisms of molecular and genomic evolution, using both experimental and theoretical approaches. Current projects include:
I. Evolution of gene regulation.
The importance of regulatory evolution has been proposed long ago (for instance, in the conspicuous morphological differences between human and chimp), but it has not been well studied due to experimental limitations. Making use of recent advances we are pursuing the following studies:
(1) Evolution of gene regulation in yeast strains and species. Our major question is whether evolution of gene regulation is mainly due to changes in cis elements or in trans factors. We are using microarrays and real time PCR to study expression differences, computational analysis of genomic data to identify sites of interest, and site-directed mutagenesis and fitness assays to test effects of regulatory changes.
(2) Evolution of gene expression patterns in mammals. Using data in the public domain, and in collaboration with other labs, we are investigating changes in tissue expression patterns between species or duplicate genes.
(3) Evolution of cis-regulatory modules and gene networks. Using statistical and experimental approaches we are identifying cis elements and gene networks, and studying how they have evolved.
II. Evolution of duplicate genes.
Gene duplication is a major source of raw material during genome evolution, and the analysis of duplicate genes provides insight into many evolutionary processes. We study patterns of duplicate gene survival across diverse genomes and what factors, such as gene structure, expression, or protein interaction, influence these patterns. We also study rates and mechanisms of structural and functional divergence in duplicate genes.
III. Development of statistical methods and computational analysis of genomic data.
The huge amount of genomic data currently available is a tremendous resource for understanding the organization and evolution of genomes. We are currently developing tools for analysis of segmental duplications, protein interaction data, and genomic