IGSP Home » CBB » Courses

Course Descriptions

PhD in Computational Biology & Bioinformatics (CBB)

Courses

*denotes required course. See course catalog for this semester's course offerings

Descriptions

CBB 200 Independent Study
Faculty-directed experimental or theoretical research.

Return to top

CBB 209 Special Topics in Computational Biology
Allows the doctoral student the opportunity to study special topics in computational biology and bioinformatics on an occasional basis depending on the availability and interests of students and faculty.

Return to top

CBB 210 Computational Biology Seminar
A weekly series of seminars on topics in 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 the semester of graduation.

Return to top

CBB 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 from 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. Furey

Return to top

CBB 212 Responsible Genomics
This course will introduce students to issues that arise in doing, interpreting, or applying genomics research. It includes (1) introduction to ethical reasoning and examination of selected issues calling for such analysis, including potential for conflicts among roles that an individual is expected to fulfill; (2) skills needed in any subsequent career path that involves doing or interpreting bioinformatics or genomics research, including research or professional school; doing presentations, writing a policy memo, and working in a group; (3) understanding why there are special procedures for research involving human participants, and how to respect privacy and confidentiality of genetic information; (4) historical and political background on sources of health research funding, and (5) issues involving public–private research interactions such as intellectual property and conflict of interest. Cook-Deegan

Return to top

CBB 220 Genomic Tools and Technologies
This course introduces the experimental biology, laboratory and computational methodologies for genetic and protein sequencing, mapping expression measurement. Dietrich

Return to top

CBB 221 Computational Gene Expression Analysis
This course covers topics spanning the biological, technological and computational areas of modern gene expression analysis, developing computational methods in important and current problems of clinical and physiological phenotyping. Emphasis is on the use and development of modern methods of computational statistics, and the integration of biological theory and concepts with empirical studies. The course is taught using a range significant real genomic case studies and students will be involved in in-depth study of one or more of these problem areas as well as computer algorithm and statistical analysis development. Coverage also includes in-depth study of DNA microarray technologies and the use and integration of biological data base resources. Chi and Lucas

Return to top

CBB 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. Kepler, Cowell and Chan

Return to top

CBB 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. Febbo

Return to top

CBB 230S Modeling of Biological Systems
Research seminar on mathematical methods for modeling biological systems. Exact content based on research interests of the students. Review of methods of differential equations and probability. Discussions on use of mathematical techniques in the development of models in biology. Student presentations and class discussions on individual research projects. Students will complete and present a substantial individual modeling project to be agreed upon with the professor during the first weeks of the course. This course can serve as the capstone course for the MBS certificate. Not open to students who have had MBS 200S. Prerequisites: Mathematics 107 or 131 or consent of instructor. Harer

Return to top

CBB 240 Statistical Methods for Computational Biology
This course covers methods of statistical inference and stochastic modeling with applications to functional genomics and computational molecular biology. Students will be immersed in computational work using and hands-on data analysis for biological datasets. Topics include: statistical theory underlying sequence analysis and database searching; Markov chains and hidden Markov models; elements of Bayesian and likelihood inference; discrete data models; applied linear regression analysis; multivariate data decomposition methods (PCA, clustering); software tools for statistical computing. This course presupposes previous exposure to mathematics and statistics at the level of the CBB program prerequisites. Mukherjee and Schmidler

Return to top

CBB 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. Hauser

Return to top

CBB 258 Structural Biochemistry I
Principles of modern structural biology. Protein-nucleic acid recognition, enzymatic reactions, viruses, immunoglobulins, signal transduction, and structure-based drug design described in terms of the atomic properties of biological macromolecules. Discussion of methods of structure determination with particular emphasis on macromolecular X-ray crystallography NMR methods, homology modeling, and bioinformatics. Students use molecular graphics tutorials and Internet databases to view and analyze structures. Beese

Return to top

CBB 259 Structural Biochemistry II
Continuation of CBB 258. Structure/function analysis of proteins as enzymes, multiple ligand binding, protein folding and stability, allostery, protein-protein interactions. Prerequisites: CBB 258, organic chemistry, physical chemistry, and introductory biochemistry. Hellinga

Return to top

CBB 261 Computational Sequence Biology
Introduction to algorithmic and computational issues in analysis of biological sequences: DNA, RNA, and protein. Emphasizes probabilistic approaches and machine learning methods, e.g. Hidden Markov models. Explores applications in genome sequence assembly, protein and DNA homology detection, gene and promoter finding, motif identification, models of regulatory regions, comparative genomics and phylogenetics, RNA structure prediction, post-transcriptional regulation. Prerequisites: basic knowledge algorithmic design (COMPSCI 230 or equivalent), probability and statistics (STA 213 or equivalent), molecular biology (BIO 118 or equivalent). Alternatively, consent of instructor. Ohler or Hartemink

Return to top

CBB 262 Computational Systems Biology

Provides a systematic introduction to algorithmic and computational issues present in the analysis of biological systems. Emphasizes probabilistic approaches and machine learning methods. Explores modeling basic biological processes (e.g., transcription, splicing, localization and transport, translation, replication, cell cycle, protein complexes, evolution) from a systems biology perspective. Lectures and discussions of primary literature. Prerequisites: basic knowledge of algorithm design (COMPSCI 230 or equiv.), probability and statistics (STA 213 or equiv.), molecular biology (BIO 118 or equiv.), and computer programming. Alternatively, consent of instructor. Ohler or Hartemink

Return to top

CBB 263A Algorithms in Structural Biology & Biophysics

Introduction to algorithmic and computational issues in structural molecular biology and molecular biophysics. Emphasizes geometric algorithms, approximation algorithms, computational biophysics, molecular interactions, computational structural biology, proteomics, rational drug design, and protein design. Explores computational methods for discovering new pharmaceuticals, NMR and x-ray data, and protein-ligand docking. Prerequisites: basic knowledge algorithms design (COMPSCI 230 or equivalent), probability and statistics (STA 213 or equivalent), molecular biology (BIO 118 or equivalent), computer programming. Alternatively, consent of instructor. Donald

Return to top

CBB 263B Computational Structural Biology

Introduction to theory and computation of macromolecular structure. Principles of biopolymer structure: computer representations and database search; molecular dynamics and Monte Carlo simulation; statistical mechanics of protein folding; RNA and protein structure prediction (secondary structure, threading, homology modeling); computer-aided drug design; proteomics; statistical tools (neural networks, HMMs, SVMs). Prerequisites: basic knowledge algorithmic design (COMPSCI 230 or equivalent), probability and statistics (STA 213 and 244 or equivalent), molecular biology (BIO 118 or equivalent), and computer programming. Alternatively, consent of instructor. Schmidler

Return to top

CBB 264 Advanced Database Systems
Advanced database management system design principles and techniques. Materials drawn from both classic and recent research literature. Possible topics include access methods, query processing and optimization, transaction processing distributed databases, object-oriented and object relational databases, data warehousing, data mining, web and semistructured data, search engines. Programming projects required.

Return to top

CBB 265 Computational Geometry
Models of computation and lower-bound techniques; storing and manipulating orthogonal objects; orthogonal and simplex range searching, convex hulls, planar point location, proximity problems, arrangements, linear programming and parametric search technique, probabilistic and incremental algorithms.

Return to top

Additional departmental graduate courses may be taken as electives. Please see the Graduate School Bulletin or each department's website for additional information.