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Research Mentor and Department
Dr. Hesham Ali
Network theory has been used by researchers for multiple purposes in the past to model biological data as well as social, transportation, business, and many other types of data in various domains. When analyzing a network that represents protein-protein interactions (PPIs), identifying elements of significant importance and biological-relevance is critical. In this work, we hypothesize that there is a high correlation between structural properties associated with elements in a PPI network and their biological significance. We identify special elements, such as hubs and driver nodes, as well as special sub-networks, such as dense clusters in the obtained biological networks. We then investigate the hypothesis that relationships between topological and biological importance can be seen in/between hub nodes and driver nodes within a network and within clusters. Our proposed approach includes how to identify these types of nodes and examine their relationships within human, yeast, rat, and mouse PPI networks. In addition, we examined their relationships with other types of significant elements, with their neighbors, and with the rest of the network. We performed numerous tests to explore potential relationships between network properties and their associated biological significance. Obtained results showed that identifying and cross-referencing different types of topologically significant elements (nodes in the PPI networks) can exemplify properties such as transcription factor enrichment, lethality, clustering, and gene ontology enrichment. Overall, we verify our original hypothesis that structurally important nodes were found to have significant biological relevance.