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Revision with unchanged content. A new approach in the analysis of viral evolution is presented in this book, an approach that merges Phylogenetic analysis and Bioinformatic techniques. Patterns of viral evolution are inferred from serially-sampled sequence data, i.e., sequence data obtained from strains isolated at consecutive time points from a single patient or host. Traditional phylogenetic methods assume a tree-like evolutionary model, while many RNA viruses have the capacity to exchange genetic material with one another using a process called re combination. A genealogy involving recombination is best described by a net work structure. A more general approach was implemented in a new compu tational tool, Sliding MinPD, one that is mindful of the sampling times of the input sequences and that reconstructs the viral evolutionary relationships in the form of a network structure with implicit representations of recombi nation events. The underlying network organization reveals unique patterns of viral evolution and could help explain the emergence of disease-associated mutants and drug-resistant strains, with implications for patient prognosis and treatment strategies.

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Ph.D.Research scientist with the Center for Computational Science at the Univ. of Miami (UM) and visiting assistant professor at the UM Biology department. Her research is in the areas of Bioinformatics, Algorithms, Phylogenetics and Genomics. More information about Patricia Buendia can be found in her web site: http://www.bio.miami.edu/pbuendia/.

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