Fig. 6: Prospective resilience and randomized gene expression.
From: A computational exploration of resilience and evolvability of protein–protein interaction networks
We examine if specific gene expression is driving the high prospective resilience of the expression-based attachment rule or if merely attaching nodes based on a shuffled gene expression distribution could bring about these results. Each new node joins with m = 5 for S. cerevisiae and E. coli, and m = 6 for H. sapiens. These values were selected so that the slope of the prospective resilience would be closest to 0.0 when the gene expression was not shuffled (0% shuffled). See Table 2 for how the correlation between a node’s degree and its gene expression changes as noise increases. a Prospective resilience of S. cerevisiae ribosomal network. b Prospective resilience of E. coli ribosomal network. c Prospective resilience of H. sapiens ribosomal network. Notably, we find that the prospective resilience of the networks increases simply by increasing the fraction of nodes with shuffled gene expressions.
