Type of Document Dissertation Author Westbrooks, Kelly Anthony URN etd-04232009-173258 Title Biological Inference using Flow Networks Degree Ph.D. Department Computer Science Advisory Committee
Advisor Name Title Alexander Zelikovsky Committee Chair Bhaskar DasGupta Committee Member Rajshekhar Sunderraman Committee Member Robert Harrison Committee Member Yury Khudyakov Committee Member Keywords
- Quasispecies
- Flow networks
- HCV
Date of Defense 2009-04-15 Availability restricted Abstract Many bioinformatics problems are inference problems: Given partial or incomplete information about something, use that information to infer the missing or unknown data. This work addresses two inference problems in bioinformatics. The rst problem is inferring viral quasispecies sequences and their frequencies from 454 pyrosequencing reads. The second problem is inferring the structure of signal transduction networks from observations of interactions between cellular components. At first glance, these problems appear to be unrelated to each other. However, this work successfully penetrates both problems using the machinery ofow networks and transitive reduction, tools from classical computer science that prove useful in a wide array of application domains.
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