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Statistical analysis of protein interaction network topology

Author(s)
Dong, Yu-An, 1974-
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Massachusetts Institute of Technology. Dept. of Mathematics.
Advisor
Bonnie Berger.
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M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
Complex networks arise in diverse areas of natural and social sciences and network topology is a key determinant of such systems. In this work we investigate the protein-protein interaction network of the KSHV herpesvirus, which is the first viral system available, and compare it to a prototypical cellular system. On the local level, we investigated the relationship between interaction and sequence evolution, functional class, phylogenetic class, and expression profiles. On the global level, we focused on large-scale properties like small-world, scale-free, and attack tolerance. Major differences were discovered between viral and cellular systems, and we were able to pinpoint directions for further investigation, both theoretically and experimentally. New approaches to discover functional associations through interaction patterns were also presented and validated. To put the KSHV network in the context of host interactions, we were able to predict interactions between KSHV and human proteins and use them to connect the KSHV and human PPI networks. Though simulations, we show that the combined viral-host network is distinct from and superior to equivalent randomly combined networks. Our combined network provides the first-draft of a viral-host system, which is crucial to understanding viral pathogenicity. In a separate chapter, the results of a project combining experiments and bioinformatics are also presented. We were able to report [approximately]30 new yeast protein-protein interactions and pinpoint the biological significance of some of those interactions. The methodology of yeast two-hybrid itself is also tested and assessed.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, February 2005.
 
Includes bibliographical references (leaves 116-117).
 
Date issued
2005
URI
http://hdl.handle.net/1721.1/28925
Department
Massachusetts Institute of Technology. Department of Mathematics
Publisher
Massachusetts Institute of Technology
Keywords
Mathematics.

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