The central topic of this course is probabilistic modeling with
emphasis on biological sequence comparison and applications in
functional analysis of DNA and protein sequences and their
evolution. Biological experiments will be introduced to motivate
new concepts and enhance understanding of the material covered.
Emphasis will be on comprehending the biological and mathematical
principals underlying the models introduced and applying this
understanding to evaluate and interpret the biological significance
of experimental results.
Basic concepts of probability will be presented, with a special
attention to conditional probability. Probabilistic models and
algorithms used in global and local pairwise sequence alignment
will be developed. The expectation, variance, and standard
deviation of discrete and continuous random variables, along with
a number of common distribution functions, will be explained.
Markov models and the application of discrete Markov chains
in biology and biological sequence analysis will also be
covered. These concepts will be used to extend the ideas from
the pairwise sequence alignments to the problems of multiple
sequence alignment, evolutionary distances, and phylogenetic
tree construction. Offered every other Spring semester.
Questions and comments concerning this page are to be
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rdaileda@trinity.edu .
