Cadeias estocásticas parcimoniosas com aplicações à classificação e filogenia das seqüências de proteínas. / Parsimonious stochastic chains with applications to classification and phylogeny of protein sequences.

AUTOR(ES)

Florencia Graciela Leonardi

DATA DE PUBLICAÇÃO

2007

RESUMO

In this thesis we present some theoretical and practical results, concerning symbolic sequence modeling with parsimonious stochastic chains. Parsimonious stochastic chains, which include variable memory stochastic chains, constitute a generalization of fixed order Markov chains. The symbolic sequences modeled with parsimonious stochastic chains were the sequences of amino acids. First, we introduce a new algorithm, called SPST, to select the model of parsimonious stochastic chain that fits better to a sample of sequences. Then, we use the SPST algorithm to study two important problems of genomics. These problems are the classification of proteins into families and the study of the evolution of biological sequences. Finally, we find upper bounds for the rate of convergence of some algorithms related with the estimation of a subclass of parsimonious stochastic chains; namely, the variable memory stochastic chains. In consequence, we generalize a previous result about the exponential rate of convergence of the PST algorithm, in the case of unbounded variable memory stochastic chains. On the other hand, we prove a result about the rate of convergence of a generalized version of the Bayesian Information Criterion (BIC), also known as SchwarzCriterion.

ASSUNTO(S)

parsimonious stochastic chains rate of convergence of algorithms classificação de proteínas phylogenetic analysis of proteins análise filogenética de proteínas protein classification cadeias estocásticas parcimoniosas velocidade de convergência de algoritmos

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