Using global network alignment in the context of aging

Analogous to sequence alignment, network alignment (NA) can be used to transfer biological knowledge across species between conserved network regions. This is important when studying human aging: since human aging is hard to study experimentally due to long lifespan, the knowledge about aging needs to be transferred from model species. NA faces two algorithmic challenges: 1) Which cost function to use to capture "similarities" between nodes in different networks? 2) Which alignment strategy to use to rapidly identify "high-scoring" alignments from all possible alignments? Since existing NA methods typically use both different cost functions and different alignment strategies, we "break down" existing state-of-the-art methods to evalu- Ate each combination of their cost functions and alignment strategies. We find that a combination of the cost function of one method and the alignment strategy of another method beats the existing methods. Hence, we propose this combination as a novel superior NA method. Since susceptibility to diseases increases with age, studying aging is important. Thus, we use the existing and new NA methods to transfer aging-related knowledge from well annotated species to poorly annotated ones between aligned network regions. By doing so, we produce novel aging- related information, which complements currently available information about aging that has been obtained mainly by sequence alignment, especially in human. To our knowledge, we are the first to use NA to learn more about aging.