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. 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? We "break down" existing state-of-the-art methods that use both different cost functions and different alignment strategies to evaluate 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. Then, since human aging is hard to study experimentally due to long lifespan, we use NA to transfer aging-related knowledge from well annotated model species to poorly annotated human between aligned network regions. By doing so, we produce novel agingrelated 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.