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.