Abstract
Aging is one of the challenging problem in biology and medicine. Exploring the underlying behind the topological network of aging process will be advantageous for human health. This study analyses the behavior or characteristics of aging through network approach. As the network in this study is dynamic, we are able to observe topological changes of network over time of the aging key players. Integration of static protein-protein interaction (PPI) network data with human brain gene expressions is an approach we used to construct dynamical network. Through the network measurements; Degree Centrality, Closeness Centrality, Betweenness Centrality, Local Clustering Coefficient and Local Assortativity, the behavior of age-related protein are observed topologically. In this paper, we focused on network topology in elder and senior age category as these categories reflect the important topological changes.
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