Computational Methods for Knowledge Integration in the Analysis of Large-scale Biological Networks

i ABSTRACT As we migrate into an era of personalized medicine, understanding how bio-molecules interact with one another to form cellular systems is one of the key focus areas of systems biology. Several challenges such as the dynamic nature of cellular systems, uncertainty due to environmental influences, and the heterogeneity between individual patients render this a difficult task. In the last decade, several algorithms have been proposed to elucidate cellular systems from data, resulting in numerous data-driven hypotheses. However, due to the large number of variables involved in the process, many of which are unknown or not measurable, such computational approaches often lead to a high proportion of false positives. This renders interpretation of the data-driven hypotheses extremely difficult. Consequently, a dismal proportion of these hypotheses are subject to further experimental validation, eventually limiting their potential to augment existing biological knowledge. This dissertation develops a framework of computational methods for the analysis of such data-driven hypotheses leveraging existing biological knowledge. Specifically, I show how biological knowledge can be mapped onto these hypotheses and subsequently augmented through novel hypotheses. Biological hypotheses are learnt in three levels of abstraction-individual interactions, functional modules and relationships between pathways, corresponding to three complementary aspects of biological systems. The computational methods developed in this dissertation are applied to high throughput cancer data, resulting in novel hypotheses with potentially significant biological impact. ii To Grandpa iii ACKNOWEDGEMENTS Several faculty, friends and family members have helped me complete this dissertation and I would like to express my gratitude to them for their assistance. I would like to begin by thanking my advisor, Seungchan Kim, for being a supportive advisor through out my graduate education. From coming up with an appropriate topic for my dissertation to helping me prepare for my defense, he has been instrumental in seeing me through completion of this program. I am grateful to him for his guidance, mentoring and most importantly, being available for discussions on an almost daily basis, in spite of his busy schedule. His emphasis on quality and excellence has made a significant impact on my work and I am indebted to him for this. Special thanks to Pat Langley, member of my dissertation committee. He has worked with me patiently, reviewing my writing and providing me with several painstaking comments. While working on solutions towards a specific application (such as a biological problem) one often forgets the bigger picture. However, …