Title page for ETD etd-06272008-131204

Type of Document Dissertation
Author Dyer, Matthew David
Author's Email Address dyermd@vt.edu
URN etd-06272008-131204
Title Pathosystems Biology: Computational Prediction and Analysis of Host-Pathogen Protein Interaction Networks
Degree PhD
Department Genetics, Bioinformatics, and Computational Biology
Advisory Committee
Advisor Name Title
Murali, T. M. Committee Co-Chair
Sobral, Bruno Committee Co-Chair
Setubal, Joao Carlos Committee Member
Tyler, Brett M. Committee Member
  • Pathosystems Biology
  • Protein Interaction Networks
  • Host-Pathogen Interactions
Date of Defense 2008-06-26
Availability unrestricted
An important aspect of systems biology is the elucidation of the protein-protein interactions (PPIs) that control important biological processes within a cell and between organisms. In particular, at the cellular and molecular level, interactions between a pathogen and its host play a vital role in initiating infection and a successful pathogenesis. Despite recent successes in the advancement of the systems biology of model organisms to understand complex diseases, the analysis of infectious diseases at the systems-level has not received as much attention. Since pathogen related disease is responsible for millions of deaths and billions of dollars in damage to crops and livestock, understanding the mechanisms employed by pathogens to infect their hosts is critical in the development of new and effective therapeutic strategies. The research presented here is one of the first computational approaches to studying host-pathogen PPI networks. This dissertation has two main aims. First, we discuss analytical tools for studying host-pathogen networks to identify common pathways perturbed and manipulated by pathogens. We present the first global comparison of the host-pathogen PPI networks of 190 different pathogens and their interactions with human proteins. We also present the construction and analysis of three highly infectious human-bacterial PPI networks: Bacillus anthracis, Francislla tularensis, and Yersinia pestis. The second aim of the research presented here is the development of predictive models for identifying PPIs between host and pathogen proteins. We present two methods: (i) a domain-based approach that uses frequency of domain-pairs in intra-species PPIs, and (ii) a supervised machine learning method that is trained on known inter-species PPIs. The techniques developed in this dissertation, along with the informative datasets presented, will serve as a foundation for the field of computational pathosystems biology.
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