Course Descriptions
PhD in Computational Biology & Bioinformatics (CBB)
- CBB 200 Independent Study
- CBB 209 Special Topics in Computational Biology
- CBB 210 Computational Biology Seminar*
- CBB 211 Journal Club/Research in Progress
- CBB 212 Responsible Genomics
- CBB 220 Genomic Tools and Technologies*
- CBB 221 Gene Expression Analysis
- CBB 223 Computational Immunology
- CBB 225 Conceptual Hierarchies Connecting Computation and Biology
- CBB 230 Algorithms in Computational Biology*
- CBB 231 Computational Biology of Gene Regulation
- CBB 232 Computational Functional Genomics
- CBB 233 Advanced Database Systems
- CBB 234 Computational Geometry
- CBB 240 Statistical Methods for Computational Biology*
- CBB 241 Statistical Genetics
- CBB 250 Computational Structural Biology
- CBB 252 Structural Biology of Macromolecules
- CBB 258 Structural Biochemistry I
- CBB 259 Structural Biochemistry II
- Additional Courses
*denotes required course. See course catalog for this semester's course offerings
Descriptions
CBB 200 Independent Study
Faculty-directed experimental or theoretical research.
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.
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.
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.
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.
CBB 220 Genomic Tools and Technologies
This course introduces the experimental biology, laboratory and computational methodologies for genetic and protein sequencing, mapping expression measurement.
CBB 221 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.
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.
CBB 225 Conceptual Hierarchies Connecting Computation and 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.
CBB 231 Computational Biology of Gene Regulation
Application of Machine Learning to problems in computational molecular biology. Focus on eukaryotic genes and gene regulation, and on probabilistic approaches, e.g. Hidden Markov models. Detection of coding and non-coding genes; RNA structure prediction; Models for regulatory elements and regions, including post-transcriptional gene regulation; Comparative Genomics; Regulatory Networks. Lectures and discussions of primary literature. Problem sets focusing on practical aspects and algorithm implementation. Prerequisites: basic probability theory and statistics (STA213 or equiv.), basic molecular biology (BIO118 or equiv.), basic knowledge of program design and implementation.
CBB 232 Computational Functional Genomics
Provides a perspective on current issues in the field of computational functional genomics, focusing on computational and statistical methods for elucidating the functional roles of genes. Topics include technologies for generating genomic data (sequence, expression, and binding location), clustering and classification of genomic data, functional annotation of genes and diagnosis of tissue types, modeling genetic regulatory networks, and applications of machine learning and graphical models to the automatic elucidation of these networks. No previous biological training assumed; all necessary biological background provided. Opportunity to read seminal papers in the field and engage a specific subtopic through an independent research project.
CBB 233 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.
CBB 234 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.
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 BGT program prerequisites.
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.
CBB 250 Computational Structural Biology
Introduction to theory and computation for studying 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).
CBB 252 Structural Biology of Macromolecules
Computer graphics intensive study of some of the biological macromolecules whose three-dimensional structures have been determined at high resolution. Emphasis on the patterns and determinants of protein structure. Two-hour discussion session each week along with computer-based lessons and projects.
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.
CBB 259 Structural Biochemistry II
Continuation of Biochemistry 258. Structure/function analysis of proteins as enzymes, multiple ligand binding, protein folding and stability, allostery, protein-protein interactions. Prerequisites: Biochemistry 258, organic chemistry, physical chemistry, and introductory biochemistry.
Additional Departmental Graduate Courses as Potential Electives
Biochemistry
- BCH 267 Molecular Genetics I: DNA and Genome Stability
- BCH 268 Molecular Genetics II: From RNA to Protein
- BCH 298 Physical Chemistry for Biologists
Biology
- BIO 270S Genomics & Evolution of Complex Traits
- BIO 280S Genetic Engineering and Biotechnology
Cell and Molecular Biology
- CMB 297 Modern Techniques in Molecular Biology
Chemistry
- CHM 306 Biomolecular Mass Spectrometry
- CHM 311 Biological Chemistry
- CHM 312 Chemistry and Biology of Nucleosides, Nucleotides and Nucleic Acids
- CHM 314 Chemical Genomics
- CHM 336 Biophysical Chemistry
Computer Science
- CPS 208 Programming Methodology
- CPS 212 Distributed Information Systems
- CPS 214 Computer Networks
- CPS 230 Analysis of Algorithms
- CPS 237 Randomized Algorithms
- CPS 240 Computational Complexity
- CPS 250 Numerical Analysis
- CPS 270 Artificial Intelligence
- CPS 296.1 Introduction to Computer Vision
- CPS 296.2 Computer Security
- CPS 296.3 Data Compression
- CPS 296.4 Bionanotechnology
Biomedical Engineering
- BME 220L Introduction to Biomolecular Engineering
- BME 228 Laboratory in Cellular and Biosurface Engineering
Statistics
- STA 213 Introduction to Statistical Methodology
- STA 214 Probability & Statistical Models
- STA 215 Statistical Inference
- STA 216 Generalized Linear Models
- STA 244 Linear Models
- STA 290 Statistical Laboratory
- STA 293/4 Special Topics in Statistics
- STA 376 Advanced Modeling and Scientific Computing
- STA 395 Readings in Statistical Science
Mathematics
- MATH 216 Applied Stochastic Processes
- MATH 229 Mathematical Modeling
- MATH 231 Ordinary Differential Equations