Course Announcement:
CSE 527: Computational Biology

An introduction to the use of computational methods for the understanding of biological systems at the molecular level. Intended for graduate students in biological sciences interested in learning about algorithms and computational methods, and for graduate students in computer science, mathematics or statistics interested in applications of those fields to molecular biology.

Time:

MW 12:00-1:20

Place:

MGH 284

Instructor:

Larry Ruzzo (554 Allen Center, ruzzo@cs.washington.edu)

Course web pages:

http://www.cs.washington.edu/527

Course mailing list:

Catalog Description (somewhat out of date):

CSE 527 Computational Biology (3) Introduces computational methods for understanding biological systems at the molecular level. Problem areas such as mapping and sequencing, sequence analysis, structure prediction, phylogenic inference, regulatory analysis. Techniques such as dynamic programming, Markov models, expectation-maximization, local search. Prerequisite: graduate standing in biological, computer, mathematical or statistical science, or permission of instructor.

Workload:

Notes, problem sets and projects. We will encourage projects in which a biologist and a mathematical scientist work together to model and solve a biological problem.

Desired Prerequisites:

Ideally, students will have a considerable knowledge of one of computer science, biology, or probability/statistics, together with introductory knowledge of the other two feilds. We'll try to supplement as needed (via lecture, outside reading, project teams, etc.) so that everyone has enough background in the immediately relevant areas to fruitfully proceed.

Rough Course Outline

Essential Background from Molecular Biology

Microarray Analysis

Clustering, classification, feature selection for analysis of large scale gene expression data sets generated by microarrays and similar technologies.

Sequence Analysis

Statistical modeling of families of DNA or protein sequences: profiles, motif discovery, hidden Markov Models, Expectation - Maximization algorithm. Gene finding.

Molecular Structure Prediction (time permitting)

RNA secondary structure prediction; the protein folding problem; protein threading.