CBB Course Descriptions

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.

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 required to attend as well as any student interested in learning more about the new field of computational biology

This course introduces the experimental biology, laboratory and computational methodologies for genetic and protein sequencing, mapping expression measurement.
Instructor: Dietrich

Data science is "the science of planning for, acquisition, management, analysis of, and inference from data". This course systematically covers the concepts, ideas, tools, and example applications of data science in an end-to-end manner. We emphasize data-driven thinking, data processing and analytics, and extracting actionable values from data. We focus on the interactions between data and applications, data modeling, and data processing, data analytics, and the essential algorithms and tools. Prerequisites: A statistics course (Statistics 111 or higher), data structures and algorithms (Computer Science 201), and relational databases (Computer Science 216 or 316).

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.

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 330) or equivalent, probability and statistics (STA 611) or equivalent), molecular biology (BIO 201L) or equivalent.

Cryo-electron microscopy (EM) is a Nobel Prize winning technique to determine the structure of proteins and protein complexes at molecular resolution. Computational imaging aspects of cryo-EM, including image enhancement, reconstruction, classification and burst movie processing used to determine the high-resolution structure of proteins in 3D. Overview of the structure determination pipeline, focusing primarily on the data analysis aspects of the technique including the application of machine learning and deep learning strategies to extract atomic resolution information from millions of noisy images of proteins. Recommended prerequisite: Programming experience.

This course discusses modeling and engineering gene circuits, such as prokaryotic gene expression, cell signaling dynamics, cell-cell communication, pattern formation, stochastic dynamics in cellular networks and its control by feedback or feedforward regulation, and cellular information processing. The theme is the application of modeling to explore "design principles" of cellular networks, and strategies to engineer such networks. Students need to define an appropriate modeling project. At the end of the course, they are required to write up their results and interpretation in a research-paper style report and give an oral presentation. Prerequisites: Biomedical Engineering 260L or consent of instructor.
Instructor: You

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.

Faculty-directed experimental or theoretical research.

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.

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.
Instructor: Beese

Continuation of CBB 658. Structure/function analysis of proteins as enzymes, multiple ligand binding, protein folding and stability, allostery, protein-protein interactions. Prerequisites: CBB 658, organic chemistry, physical chemistry, and introductory biochemistry.
Instructor: Zhou

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 330) or equivalent, probability and statistics (STA 611) or equivalent, molecular biology (BIO 201L) or equivalent, computer programming. Alternatively, consent of instructor.
Instructor: Donald

Introduction to probabilistic graphical models and structured prediction, with applications in genetics and genomics.  Random fields, stochastic grammars, Markov models, Bayesian hierarchical models, neural networks, and approaches to integrative modeling.  Algorithms for exact and approximate inference.  Applications in DNA/RNA analysis, phylogenetics, sequence alignment, gene expression, genome editing and CRISPR screens, allelic phasing and imputation, genome/epigenome annotation, and gene regulation.  Prerequisites: Introductory probability and statistics (STA 611 / BIOSTAT 701 or equivalent), and some programming experience with python, R, or similar language.