Markov chain Monte Carlo and its applications to phylogenetic tree construction
Name
720640723-MIT.pdf
Description
Full printable version
Size
5.24 MB
Format
Adobe PDF
Checksum (MD5)
f277132f4aeea1d396ae06ef09db5e9d
Author(s)
Luczynska, Marta Magdalena
Advisor(s)
Kph-Ping Dunn and Manolis Kellis.
Date Issued
2007
Publisher
Massachusetts Institute of Technology
Abstract
This thesis addresses the application of Bayesian methods to problems in phylogenetics. Specifically, we focus on using genetic data to estimate phylogenetic trees representing the evolutionary history of genes and species. Knowledge of this common ancestry has implications for the identification of functions and properties of genes, the effect of mutations and their roles in particular diseases, and other diverse aspects of the biology of cells. Improved algorithms for phylogenetic inference should increase our potential for understanding biological organisms while remaining computationally efficient. To this end, we formulate a novel Bayesian model for phylogenetic tree construction based on recent studies that incorporates known information about the evolutionary history of the species, referred to as the species phylogeny, in a statistically rigorous way. In addition, we develop an inference algorithm for this model based on a Markov chain Monte Carlo method in order to overcome the computational complexity inherent in the problem. Initial results show potential advantages over methods for phylogenetic tree estimation that do not make use of the species phylogeny.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 93-96).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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