Duke University 2009-2010 - Office of the Registrar - Duke University

211. Journal Club/Research in Progress. A weekly series of discussions led by students that focus on current topics in
computational biology. Topics of discussion may come form recent or seminal publications in computational biology
or from research interests currently being pursued by students. First and second year CBB doctoral and certificate
students are strongly encouraged to attend as well as any student interested in learning more about the new field of
computational biology. Instructor: Furey. 1 unit.
212. Responsible Genomics. Selected advanced topics. Instructor: Staff. 3 units.
213. Topics in Genome Sciences and Policy. Exploration of current approaches to the study of the genome sciences and
their application to research, medicine and society from multi-and interdisciplinary perspectives. Topics will be
introduced through the Genomes@4 seminar and followed by in-depth discussion relying on the primary literature.
Weekly attendance is required. Prerequisite: advanced coursework in genetics and/or genomics such as Biology 118,
Biology 119 or Biology 271; or permission of the instructor. Instructor: Staff. 1 unit.
220. Genome Tools and Technologies. This course introduces the laboratory and computational methodologies for
genetic and protein sequencing, mapping and expression measurement. Instructor: Dietrich. 3 units.
221. Computational 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. Prerequisites:
Statistics 213, or 214 or 216. Instructor: Staff. 1 unit. C-L: Statistics and Decision Sciences 278, Molec Genetics &
Microbiology 221
222. Genome Science & Technology Lab (GE, MC). Variable credit. C-L: see Biomedical Engineering 258L
223. Computational Immunology. Course will integrate empirical and computational perspectives on immunology and
host defense. Students are expected to have significant preparation in either biomedicine or a quantitative science.
Topics covered are intended to provide an entree into the use of computational methods for research and practice in
immunology and infectious disease, from basic science to medical applications. Consent of instructor required.
Instructors: Kepler and Cowell. 3 units. C-L: Immunology 213S
225. Core Concepts Bridging Genomic and Computational Biology. Advances in the biological sciences are often the
result of multi-disciplinary teams of investigators. Successful collaboration requires effective communication, which
in turn is facilitated by the construction of a hierarchical "concept map" that spans both disciplines and can be used as
the basis of new shared insights and analysis. This course will use important publications that resulted from the
successful alignment of biological and computational investigations to help students develop such concept maps and
use them to enhance their cross-disciplinary communication. At each session, two faculty representing the appropriate
disciplines will be present. Instructor: Staff. 2 units.
230S. Modeling of Biological Systems. 1 unit. C-L: see Mathematics 214S; also C-L: Modeling Biological Systems 214S
233. Data-Intensive Computing Systems. 3 units. C-L: see Computer Science 216; also C-L: Computational Biology
and Bioinformatics 265
234. Computational Geometry. 3 units. C-L: see Computer Science 234; also C-L: Computational Biology and
Bioinformatics 264
240. Statistical Methods for Computational Biology. Methods of statistical inference and stochastic modeling with
application to functional genomics and computational molecular biology. Topics include: statistical theory underlying
sequence analysis and database searching; Markov models; elements of Bayesian and likelihood inference; multivariate
high-dimensional regression models, applied linear regress analysis; discrete data models; multivariate data decomposition
methods (PCA, clustering, multi-dimensional scaling); software tools for statistical computing. Prerequisites:
multivariate calculus, linear algebra and Statistics 213. Instructor: Mukherjee. 3 units. C-L: Statistics and Decision
Sciences 270
241. 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
250. Computational Structural Biology. 3 units. C-L: see Computer Science 263B; also C-L: Statistics and Decision
Sciences 277
252. Structure of Biological Macromolecules. 3 units. C-L: see Biochemistry 222; also C-L: Structural Biology and
Biophysics 222
258. Structural Biochemistry I. 2 units. C-L: see Biochemistry 258; also C-L: Cell and Molecular Biology 258, Cell
Biology 258, University Program in Genetics 258, Immunology 258, Structural Biology and Biophysics 258
259. Structural Biochemistry II. 2 units. C-L: see Biochemistry 259; also C-L: Cell Biology 259, Immunology 259,
Structural Biology and Biophysics 259, University Program in Genetics 259
261. Computational Sequence Biology. 3 units. C-L: see Computer Science 261
262. Computational Systems Biology. 3 units. C-L: see Computer Science 262
Departments, Programs, and Course Offerings 81