Duke University 2003-2004 - Office of the Registrar - Duke University

presupposes previous exposure to mathematics and statistics at the level of the BGT
program prerequisites. Instructor: Staff. 3 units. C-L: Statistics and Decision Sciences 270
201. Statistical Genetics. Mechanisms, probability models and statistical analysis in
examples of classical and population genetics, aimed at covering the basic quantitative
concepts and tools for biological scientists. This module will serve as a primer in basic
statistics for genomics, also involving computing and computation using standard
languages. Instructor: Staff. 3 units. C-L: Statistics and Decision Sciences 271
202. Comparative & Evolutionary Genomics. This course will deal with structure of
genes and genomes, and with genomic diversity and genomic evolution. It will also
address methods of analyzing the evolutionary relationships among genes. All genes
have evolved from pre-existing genes and their structure preserves information about
their evolutionary history as well as their function. This module will focus on the
rationale and methods for reconstructing the relationships among gene families and on
the techniques for reconstructing the evolutionary relationships among them. It will
give the students an understanding of the causes of genomic structure and mechanisms
of genomic diversification. Instructor: Staff. 1.5 units.
203. Genome Informatics and Sequence Analysis. This course covers the key concepts,
methods and computational aspects of modern functional sequence analysis in
genomics. Instructor: Staff. 3 units. C-L: Statistics and Decision Sciences 273
204. Algorithms in Computational Biology. 3 units. C-L: see Computer Science 260
206. Genome Technologies. This course introduces the laboratory and computational
methodologies for genetic and protein sequencing, mapping and expression
measurement. Instructor: Staff. 3 units.
207. Computational Methods for Macromolecular Structure. This course covers
concepts of modeling and computation in approaches to structure prediction in modern
proteomics. It introduces advanced probabilistic models and associated inference tools
and will involve custom computer programming in standard languages. Instructor:
Staff. 3 units. C-L: Statistics and Decision Sciences 277
208. Gene Expression Analysis. This course covers topics spanning the technological
and computational areas of modern gene expression analysis, developing computational
methods in important and current problems of clinical and physiological
phenotyping, including custom computation and algorithmic development. Instructor:
Staff. 3 units. C-L: Statistics and Decision Sciences 278
209. Computational Genetics of Complex Traits. This module is intended to give
students an understanding of the genetic architecture of complex traits. Complex traits
are traits whose structure and function depend on the interaction of many genes. Since
these interactions are typically non-linear, this leads to a variable, context-dependent,
association of genetic variation and trait variation. Here we address methods for
studying the casual association between genetic/genomic variation and trait variation
by developing dynamic models of the ontogeny and function of complex traits.
Instructor: Staff. 3 units.
211. Computational Functional Genomics. 1 unit. C-L: see Computer Science 262
225. Special Topics: Comparative Genomics. This module will deal with the structure
of genes and genomes, and with genomic diversity and genomic evolution. It will give
students an understanding of the origins of genomic structure and the mechanisms of
genomic diversification. Instructor: Staff. 1.5 units.
Biological Anthropology and Anatomy (BAA)
Professor Kay, Chair (267 Sands); Professor Van Schaik, Director of Graduate Studies
(01AA Bio Sci); Professors Bassett, Cartmill, Glander, Hylander, Simons, Smith, and
Terborgh; Associate Professors Churchill and Roth; Associate Research Professor Pope;
Assistant Professors Alberts, Drea, Platt, Myers, and Schmitt
Biological Anthropology and Anatomy (BAA) 77