Optimizing Network Structure in a Network Model of Human Aging

Our network model of aging represents aspects of human health as nodes in a complex network, these nodes damage stochastically over time based on the health of their neighbours. This represents the accumulation of damage leading to poor health and eventual mortality. Previous work has shown that by using a scale free network this model captures the phenomenology of health and mortality in human populations. Why do scale free networks best capture this phenomenology? Do these scale free networks represent an organism’s robustness to damage in a meaningful way? We address this question from the bottom up using a network optimization approach. Beginning at a random network structure, we optimize the network structure with respect to various health outcomes, such as longevity and health-related quality of life. We investigate which network motifs emerge depending on the optimization health outcome, and how these aspects of the network structure affect damage propagation. Knowledge of how damage propagates between different types of nodes will aid our understanding of how different aspects of human health interact, and how humans accumulate health deficits over time.