Supporting Information Randomness and preserved patterns in biological networks Aparna Rai, Vipin Menon and Sarika Jalan ∗ Breast cancer being the highest among all the cancers is a potential field of research. We apply RMT to understand the biological insights of the disease through network modeling. We construct both the networks of which the unconnected cluster can be seen in the figure (Fig. S1). Figure S1: Normal (left panel) and disease (right panel) breast cancer network. 1 Short range correlations in Eigenvalues The nearest and next-nearest spacing distribution (NNSD and nNNSD) account for the short range correlations in the eigenvalues. We calculate the NNSD of normal and breast cancer network and find that it follows random matrix predictions. As mentioned in the main article, we denote the eigenvalues of a network by λ i ,i = 1,...,N , where N is size of the network and λ 1 <λ 2 <λ 3 < ··· <λ N . The density distribution ρ(λ) follows semi-circular distribution for GOE statistics. For spacing distribution of eigenvalues, one needs to unfold the eigenvalues by a transformation λ i = N (λ i ), where 1