ISTINGUISHED
ECTURERS
J EFF T HORNE
Statistical Genetics and Bioinformatics
North Carolina State University
November 12, 2004
K IM B OYER
Department of Electrical Engineering
The Ohio State University
November 19, 2004
D AN R EED
Institute for Renaissance Computing
UNC-Chapel Hill, Duke & NC State
December 3, 2004
J ACK D ONGARRA
Department of Computer Science
The University of Tennessee - Knoxville
February 4, 2005
K ANG G.
S HIN
Electrical Engineering and Computer Science
The University of Michigan
February 18, 2005
For details see http://www.cse.sc.edu/~mgv/colloquia/index.html
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H O R N E
Jeff Thorne was raised in Kohler, Wisconsin. In
1986, he earned B.S. degrees in mathematics and molecular biology from the University of
Wisconsin – Madison. In 1991, he received a
Ph.D. in Genetics from the University of
Washington. His thesis advisor was Dr. Joseph
Felsenstein and his dissertation topic was statistical aspects of DNA and protein sequence alignment.
Jeff was a postdoctoral associate with Dr. Gary
Churchill in the Biometrics Unit at Cornell
University. He joined the North Carolina State
University faculty in 1993. He holds a joint appointment in the Genetics and Statistics
Departments and is actively involved in the
Bioinformatics Graduate Program.
Thorne’s research focus is the development of statistical techniques for studying molecular evolution. He currently has two main lines of research. One concerns the relationship between protein structure and protein evolution. The other involves estimating chronological rates of molecular evolution as well as speciation times by combining fossil information with DNA or protein sequence data.
Jeff and his wife Leigh are the proud parents of a baby girl named Evelyn and an Australian
Shepherd named Sterling.
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A B S T R A C T
The relationship between phenotype and survival of the genotype is central to both genetics and evolution. Protein-coding DNA sequences are genotype whereas protein tertiary structures are a fundamental unit of phenotype.
We are studying the impact of protein structure on the evolution of protein-coding DNA sequences. In contrast to widely used procedures that largely model change of genotype as being independent of phenotype, our approach incorporates phenotype via adoption of sequence-structure compatibility measures that computational biologists originally designed for the purpose of protein fold recognition. A consequence of our genotype-phenotype models is the necessity to abandon computational strategies that are conventionally employed to study molecular evolution. In addition to protein structure, there are diverse other aspects of phenotype that are being predicted in silico by computational biologists. We emphasize that these other in silico systems can also be exploited to better characterize the evolutionary relationship between genotype and phenotype.