Combining Functional and Topological Properties to Identify Novel Hubs in Protein-Protein Interaction Networks
Date Issued
2009
Date
2009
Author(s)
Lee, Sheng-An
Abstract
The scope of this doctoral dissertation deals mainly on the protein-protein interactions (PPIs) of various biological questions pertaining to the computational goals of database, mining, and prediction. Specifically, this dissertation assorts respective works towards the indicated PPI studies including (i) constructing spatial- and temporal-composite global network, (ii) mining spatial- and temporal-decomposed sub-network, and (iii) inferring putative biological cascade and unobserved interactome. Towards these goals, various computational PPI prediction methods, including the location feature among gene neighbors and gene clusters; the evolutionary feature of phylogenetic profile, Rosetta Stone method, and sequence-based co-evolution; and the temporal feature of co-expression and cell cycle specified expression, may be integrated as by the gene ontology in order to be exploited by synthetic lethality. n a spatial- and temporal-composite manner, (i) the implemented POINeT database with PPI network display bases on retrieving multiple PPI data sources and extends with putative interologs. The confidences of PPIs are evaluated by literature numbers, experimental techniques, interacted protein queries, gene ontology, and interologs. (ii) Novel hubs among PPI network nodes are likely mined while with the spatial- and temporal-relevant PPI sub-network such as exemplified by the POINeT sub-network fetched with up-regulated microarray genes. The PPI mining for important hubs within PPI sub-network is primarily based on biological features and network topological features in order for hubs prioritization based on the degree of a given sub-network node and degree statistics of given node from randomly sampled sub-networks with equivalent nodes size. The implemented mining algorithms include the centrality indices on all protein nodes in a PPI sub-network as well as the sub-network specificity score on spatial and temporal relevance. (iii) From the verified sub-network with specified relevance and hubs, the pursued PPI prediction is progressively explored with clique analysis on sub-network topology evaluation in the aspects of inferring putative human PPI complexes from known yeast sets within PPI sub-network and predicting inter-species bounding between PPI sub-networks of host and pathogen by interologs analysis with ortholog information. oreover, (i) the POINeT along with implemented algorithms are applied in PPI sub-networks of mining mitotic midbody sub-network along with predicting mitotic spindle sub-network and inter-species bounding between host and pathogen. The prioritization of previously unobserved 5 candidate proteins of (ii) mining output demonstrates satisfactory consistence with biologically verified 183 midbody proteins despite that one putative protein fused with antigen tag for analytic monoclonal antibody has never been shown to be spatially co-localized at midbody. In addition, (iii) the prediction pursue with highly iterative cliques analyzed on sub-networks not only has revealed the conserved spindle network from yeast to human in a pathway format evidenced by SEPT6 co-localization assay and also has unveiled previously unobserved inter-species interactome of host and pathogen. Further advances and applications with POINeT and assorted in-house algorithms are likely the future works towards expanded mining and accurate prediction on PPI sub-networks.
Subjects
Protein-Protein Interaction Database
Protein-Protein Interaction
Protein-Protein Interaction Network
Network Biology
Inter-Species Protein-Protein Interaction
Host-Pathogen Interaction
Midbody
Spindle
POINeT
Protein Complex
Type
thesis
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