CS 594, Special Topics in Computer Science
Fall Term, 2003

Topic: Algorithmic Methods for Bioinformatics

Instructor: Michael A. Langston

Section Number: 31234

Time and Place:

Mondays and Wednesdays, from 3:40 to 4:55pm
Rm 206, Claxton Building


Introduction to Computational Molecular Biology
Joao Meidanis & Joao Carlos Setubal
PWS Publishing Company, Boston
ISBN 0-534-95262-3


This class is intended primarily for CS and GST students, and is focused on fundamental computational tools for bioinformatics. It may also be of interest to students from ECE and Mathematics. Three hours credit will be given. Requisite complexity-theoretic issues will be reviewed, including: asymptotics and order notation; P, NP and NP-completeness; fixed-parameter tractability and fast exact algorithms; and search, decision, optimization and approximation. Algorithmic paradigms of widespread biological relevance will be emphasized. These include: dynamic programming and space management; parallel and grid computing; and simulation and the use of fast heuristic algorithms. Selection of individual topics from biology will be based in part on student interest and background. Class format will be informal lecture and discussion. Outside reading and class participation will be expected. Guest lecturers from GST, ORNL and elsewhere are scheduled to assist. The textbook will be augmented with the liberal use of research papers and other publications.

Special Notes

(1) This will be the first time this course is offered. As such, a major aim is to develop a core list of subjects so that, if there is sufficient student interest, this may evolve into a regular, mainstream CS/GST offering.

(2) For students who need 600 rather than 500 level hours, this course may be taken as CS680, Section 31323.

(3) Students interested in this class are encouraged to participate in a journal club being organized by Dr. J. Snoddy. It is listed as BCMB 608, Section 21565, and will meet in Claxton 205 from 2:30-3:30 on Mondays. For more information visit the journal club page.

(4) For students unsure of the importance of foundational computational techniques in the setting of GST, a little testimonial is humbly provided (with permission).


Getting Started: Lecture 1

Some Computational Fundamentals
Mathematical Preliminaries: Lecture 2
A Little Complexity Theory: Lecture 3
A Little More Complexity Theory: Lecture 4
Will It Ever End?: Lecture 5

Some Biological Fundamentals
Biology at Last!: Lecture 6
More Biology: Lecture 7
A Light Day: Lecture 8
Back to Biology: Lecture 9

Course Content Update: Lecture 10

Back to the Grind
Recurrence Relations: Lecture 11
Last Take on Biological Fundamentals: Lecture 12

Dynamic Programming
Introduction: Lecture 13
Continued: Lecture 14
Applications, Treewidth: Lecture 15
Applications, Sequence Alignment: Lecture 16

Gene Regulatory Network Modeling
Digraphs and Bayesian Networks: Lecture 17
DEs and Stochastic Methods: Lecture 18

Homework Review and Team Presentations: Lecture 19

Statistics for Bioinformatics: Lecture 20

The Human Genome Project: Lecture 21

An Introduction to Proteomics: Lecture 22

Computational Gene Finding: Lecture 23

Analysis of Differential Gene Expression Data: Lecture 24

Grid Computing: Lecture 25

Evolutionary Biology: Lecture 26

Fitness Landscapes: Lecture 27