Computational Biology and Bioinformatics (CBB)

Huntington F. Willard, Director (Institute for Genome Sciences and Policy); Terrence G. Oas, Director of Graduate Studies (Department of Biochemistry); 48 participating faculty

The Duke University PhD Program in Computational Biology and Bioinformatics (CBB) is an innovative degree program designed to provide rigorous training at the interface of the quantitative and biological sciences. CBB students receive their training both in the classroom and while engaged in original research projects under the supervision of Program faculty, who represent over fifteen departments spanning the biological and computational disciplines in both the medical and non-medical sides of campus.

The CBB program is explicitly designed to be responsive to the breadth and rapidly evolving nature of the CBB arena. To this end, the curriculum is flexible and tailored to the needs and interests of each student through regular meetings with the Student Advisory Committee, consisting of faculty experts in all areas of computational biology on campus.

The CBB core curriculum emphasizes the integration of biology and computation. This integration is reflected in the syllabus of each core course, including lectures on biological applications of the quantitative principles being discussed. The core courses, which are taken by most CBB students, include Computational Biology and Bioinformatics 220, Computational Biology and Bioinformatics 240, and one of Computational Biology and Bioinformatics 261-263. In addition to the core courses, all CBB graduate students are expected to take several elective courses, both within CBB and outside the program in their chosen areas of biological and quantitative expertise. In addition, all students must register for Computational Biology Seminar (Computational Biology and Bioinformatics 210) every semester until the semester of graduation.

Along with this didactic training, faculty supervised research is an integral component of the training program. This begins in the first year when students join faculty-lead research groups for a period of one semester. These research rotations introduce the student to new research problems and methods in an immersive environment where they can obtain original research results and meet other members of the group. Trainees conduct three or four research rotations in their first year of study and join a group by the end of the fall semester of their second year.

The Certificate Program in Computational Biology and Bioinformatics is intended for Duke students enrolled in departmental PhD programs who wish to expand their current studies to apply to or include the fields of computational biology and bioinformatics. A student may qualify for the Certificate program after completing the following course of study: two out of the three core courses (Computational Biology and Bioinformatics 220, 240, or 261-263); one additional Computational Biology and Bioinformatics course and registration for Computational Biology and Bioinformatics 210 every semester except the semester of graduation.

210. Computational Biology Seminar. A weekly series of seminars on topics in computational biology presented by invited speakers, Duke faculty and CBB doctoral and certificate graduate students. All registrants are expected to complete and submit evaluation forms after each seminar. This course is required for all CBB doctoral and certificate students every semester except semester of graduation. Instructor: Staff. 1 unit.

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.

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

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.

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

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

300. Internship. Student gains practical experience by taking an intership in industry, and writes a report about this experience. Requires prior consent from the studetnt's advisor and from the Director of Graduate Studies. May be repeated with consent of the advisor and the Director of Graduate Studies. Credit/no credit grading only. Instructor: Staff. 1 unit.

2008-09 Bulletin of the Duke University Graduate School

2008-09 Bulletin of the
Duke University Graduate School