Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma
Fig 5
Models for the identification of significant signaling-metabolic (SM) pathway protein pairs and interconnecting paths.
Models were implemented to identify significant SM pairs and paths from the weighted network. Weights of the nodes were defined based on whether the proteins were differentially expressed (dEXP), signal crosstalk (SC) proteins, rate-limiting enzymes (RLE) or none of these. The edge weights were defined as interaction probabilities derived from the gene expression values of two interacting proteins in GBM patients. (A) Model 1 was used to identify significant SM pairs from the GBM-specific weighted network. Four types of interconnection with none, one, two and three PPIs between S-M pairs were considered as separate models (i to iv). Two hypothetical proteins were considered on each side (SH and MH) to implement positional probabilities based on the node weights of all proteins including S and M. Using these models and the Hidden Markov Model (HMM) logic, the path scores were calculated from the node and edge weights. The path scores were converted to Z-scores, and scores above the cut-off value of Z≥1 or Z≥3 were considered as significant and highly significant, respectively, and the respective interconnected SM pairs as significant pairs. (B) Model 2 was used to identify important linking-paths among the total possible paths formed between significant SM pairs from Model 1. Path scores were calculated from the positional probabilities as above. Higher path scores indicate higher significance.