Skip navigation
Please use this identifier to cite or link to this item:
Title: Organization and Function Acquisition in Protein-Protein Interaction Networks
Authors: Farnham, Jesse
Advisors: Singh, Mona
Contributors: Computer Science Department
Keywords: Protein-protein interaction networks
Subjects: Computer science
Issue Date: 2013
Publisher: Princeton, NJ : Princeton University
Abstract: Protein-protein interaction (PPI) networks enable the transmission of biological information throughout cells, allowing cells to respond to environmental stimuli. PPI networks can be represented as graphs, and graph analysis techniques have been applied in order to determine the topological roles played by individual proteins in PPI network structure. However, more complex analysis is needed to study the functional organization of PPI networks. In addition, the proteins that make up PPI networks change and evolve new functions over time. In the first part of this thesis, we introduce a metric, functional insularity, to measure the degree to which proteins physically interact with functionally related proteins. Proteins in PPI networks exhibit significant variation in insularity values, suggesting the presence of a tradeoff between network modularity and connectivity. Low-insularity proteins|those that interact with many functionally unrelated proteins|are more crucial than high-insularity proteins to maintaining network connectivity, are less likely to be essential, and have more regulators. Furthermore, we show that between-species homologs tend to have similar levels of functional insularity. Low-insularity proteins are found between topological network modules as well as within them. We find that functional and topological network modules contain proteins with a range of insularity values, including low-insularity proteins that might may function as "interfaces" to other modules. Finally, we show how functional insularity analysis can be applied to improve network clustering analyses. In the second part of this thesis, we study the acquisition of new functions by proteins and their integration into the PPI network. We first use a maximum parsimony-based approach to infer the ages of human proteins. We then determine various function-related traits for each age group, such as protein-protein interaction count, expression ubiquity, and number of unique domains. We find that young proteins in human have fewer protein-protein interactions, have fewer unique domains, are expressed in fewer tissues, and are less likely to be essential than older proteins. In addition, we find that proteins tend to physically interact mainly with other proteins of similar age. Finally, we find that younger pairs of paralogs are more coexpressed and share more common regulators than older pairs. In sum, this thesis advances our understanding of PPI networks by showing that the dual requirements of modularity and connectivity are balanced using "connector" proteins and "module" proteins, which have distinct biological traits, and by uncovering differences between young and old proteins that suggest that proteins gain functions and integrate into networks over time.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Computer Science

Files in This Item:
File Description SizeFormat 
Farnham_princeton_0181D_10759.pdf3.41 MBAdobe PDFView/Download

Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.