Dannie Durand Associate Professor Ph.D. Computer Science, Columbia University Postdoctoral Appointments University of Pennsylvania Computational Biology Group Sloan Computational Biology Fellowship, Molecular Biology, Princeton University durand@cmu.edu Laboratory Website 412-268-6036 (Phone) 412-268-7129 (Fax) 646 Mellon Institute Department of Biological Sciences Carnegie Mellon University 4400 Fifth Avenue Pittsburgh, PA 15213

Computational analysis of molecular sequence data is a key component in solving three critical biological problems of the 21st century: how genes interact to produce living cells, how gene malfunction causes disease and how complex, multicellular organisms evolved from simple, unicellular organisms. In my research, I use computational approaches to studying the role of gene duplication in the acquisition of new gene function and the evolution of vertebrate genomes. New genes arise through gene duplications, errors during cell division that result in extra copies of genes. These extra copies subsequently mutate to take on new functional roles in the cell. The duplication of large regions, ranging from chromosomal segments to the entire genome, is believed to have played a crucial role in early vertebrate evolution. According to the hypothesis, the new genes that resulted from these massive duplications are responsible for the evolution of innovations, such as skeletal structure, limbs, and a complex central nervous system, that distinguish vertebrates from other life forms. If we can understand how these genes acquired new function following duplication, we will have a better understanding of how we evolved and the role those genes play in vertebrates living today.

Selected Publications

Raghupathy N and Durand D. Gene cluster statistics with gene families. Molecular Biology and Evolution, in press.

Vernot B, Stolzer M, Goldman A and Durand D. Reconciliation with non-binary species trees. Journal of Computational Biology, 15(8): 981-1006, 2008.

Song N, Joseph JM, Davis GB and Durand D. Sequence similarity network reveals common ancestry of multidomain proteins. PLOS Computational Biology, 4(5): e1000063, 2008.

Raghupathy N, Hoberman R and Durand D. Two plus two does not equal three: Statistical tests for multiple genome comparison. Journal of Bioinformatics and Computational Biology, 6(1): 1-22, 2008.

Song N, Sedgewick RD and Durand D. Domain architecture comparison for multidomain homology identification. Journal of Computational Biology, 14(4): 496-516, 2007.

Durand D, Halldorsson BV and Vernot B. Hybrid micro-macroevolutionary approach to gene tree reconstruction. Journal of Computational Biology, 13 (2): 320-335, 2006.

Przytycka T, Davis GB, Song N and Durand D. Graph theoretical insights into evolution of multidomain proteins. Journal of Computational Biology, 13 (2): 351-363, 2006.

Hoberman R, Sankoff D and Durand D. The statistical analysis of spatially clustered genes under the maximum gap criterion. Journal of Computational Biology, 12(8): 1081-1100, 2005.

Durand D and Sankoff D. Tests for gene clustering. Journal of Computational Biology, 10 (3/4): 453–482, 2003.

Chen K, Durand D and Farach-Colton M. Notung: A program for dating gene duplications and optimizing gene family trees. Journal of Computational Biology, 7 (3/4):429–447, 2000.