Classifying Aging- and Non-Aging-Related Genes in a Dynamic Protein-Protein Interaction (PPI) Network



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Network science studies relationships between objects, representing each object as a node and the relationship between two nodes as an edge. In a protein-protein interaction (PPI) network, nodes represent proteins (gene products) and edges represent bindings between proteins. Specifically, we are interested in studying the human PPI network and changes in its structure and thus cellular functioning with age. This is important, because incidence of serious diseases increases with age. Due to biotechnological limitations, the current PPI network of human is static, as it spans many different biological contexts. However, we can extract a part of the static network that is specific to a given age, which over all ages results in a dynamic aging-related PPI network. We do this, by integrating the static PPI network with dynamic, aging-related gene expression data. We calculate network positions (centralities) of every node at every age and evaluate whether aging-related or non-aging-related genes show higher or lower fluctuation of a node's centrality over time. Initial results reveal that aging-related genes fluctuate less than non-aging-related genes. Additionally, aging-related genes have higher centrality values (averaged over all ages) than non-aging-related genes, matching findings obtained from existing static network analyses of aging. This project was completed with contributions from Tijana Milenkovic from the University of Notre Dame Computer Science and Engineering Department.