This work focuses on the use of network graph theory in biological networks. I explore how network graph theory informs our understanding of biological networks such as protein interaction networks. I show that the human protein interaction network forms dynamic, modular structures that organize cell signaling pathways into higher order units. The misregulation of the dynamic, modular structure of the protein interaction network in breast cancer tumours is associated with outcome of the disease, suggesting that the altered structure of the protein interaction network is directly related to the phenotype of the tumour. I also demonstrate that the human protein interaction network is fractal in nature and thus forms self-similar structures within the network. The fractal skeletons of the protein interaction network contain critical information and therefore can be used alone in determining the phenotype of breast cancer tumours by examing the disruption of dynamic network structures. The self-similar fractal backbones deconvolve the protein interaction network into layers of independent function, resulting in improved description of breast cancer outcome using the dynamic network modularity algorithm. Finally, I discuss how the discoveries and technologies described within can be improved and how these discoveries can lead to a network based modality of medicine.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/35072 |
| Date | 28 February 2013 |
| Creators | Taylor, Ian |
| Contributors | Wrana, Jeffrey |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis, Dataset |
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