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Catalog Description: Introduction to the use of computational methods for understanding biological systems at the molecular level. Problem areas such as mapping and sequencing, sequence analysis, structure prediction, phylogenic inference, motif discovery, expression analysis, and regulatory analysis. Techniques such as dynamic programming, Markov models, MCMC, expectation-maximization, and local search.
Learning Objectives: The availability of the complete genome sequences of humans and other organisms is one of the landmark achievements of science. Understanding this enormous volume of data is a problem that will challenge scientists for decades to come, and the nature and scope of the problem means that computer scientists will play a vital role. The primary objective of the course is for students to understand the variety of computational problems and solutions that arise in this interdisciplinary field. Students will learn enough of the basic concepts of molecular biology to understand the context for the computational problems presented in the rest of the course. They will learn how some of the computational methods they have encountered in other courses can be applied to solve problems in modern molecular biology. An important component is to learn the nature and capabilities of some of the key public databases available for the solution of these problems, as well as publicly available computational analysis tools and the algorithmic principles underlying them.
Grading: Homework-based (no exams). Homework will include programming, paper & pencil exercises and some online discussion of readings.
Late Policy: In general, assignments are due at or before the start of class on the assigned date. The occasional assignment turned in a day or two late is not a problem, but I will start deducting points beyond that. Contact me if you get in a bind this way.
Extra Credit: Assignments may include "extra credit" sections. These will enrich your understanding of the material, but at a low points per hour ratio. Do them for the glory, not the points, and don't start extra credit until the basics are complete.
Textbook: Richard Durbin, Sean R. Eddy, Anders Krogh and Graeme Mitchison, Biological Sequence Analysis: Probabilistic models of proteins and nucleic acids, Cambridge, 1998. (Available from U Book Store, Amazon, etc.) Errata.
References: See Schedule & Reading.
Portions of the CSE P527 Web may be reprinted or adapted for academic nonprofit purposes, providing the source is accurately quoted and duly credited. The CSE P527 Web: © 1993-2018, the Authors and the Department of Computer Science and Engineering, University of Washington.
Computer Science & Engineering|
University of Washington
Seattle, WA 98195-2350
(206) 543-1695 voice, (206) 543-2969 FAX