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(This description, from the last instance of this course, may be
revised for fall 2003.) Computation is vital for modern molecular
biology, helping scientists to model, predict the behaviors of, and
control the molecular machinery of the cell. This course will study
algorithmic challenges in analyzing sequences (what genes encode an
organism, and how are genes related across organisms?), structures
(what do the proteins constructed for these genes look like, and what
does that imply about their functions?), interactions (how are
proteins helping and hindering each other in complex networks?), and
the underlying experimental data. The computational techniques
applied include all your favorites -- dynamic programming, graph
search, hidden Markov models, clustering, optimization,
simulation, .... This course is targeted at CS graduate and advanced
undergraduate students. A background in biology is not required, but
students should be interested in catching up quickly on relevant
biochemistry and biophysics. Non-CS students with an interest in
computational issues are invited as well; please speak with me about
your background first. In general, I expect some background in
design, analysis, and implementation of algorithms, since the class
will build on that without substantial review.
For more information, please see:
http://www.cs.purdue.edu/homes/cbk/590B/
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