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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.
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