Investigating nonlinear association of multiple genome-wide factors on cellular aging through network permutation
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Recent studies suggest that yeast replicative aging is stochastic process in the Waddington landscape. Here, we try to estimate this landscape using the protein interaction networks. We generated permuted null networks to evaluate the over- and under-representations of observed interactions. We then convert Z-score into probability and generated a probability landscape to describe the interaction patterns yeast replicative lifespan with factors, such as the growth fitness and differential effect of calorie restriction. Both pairwise and triplet associations are investigated. Our results show valleys and ridges in the probability landscape, and some interesting clustering of genes with known effect on lifespan.