Identifying influential nodes in a wound healing-related network of biological processes using mean first-passage time

In this study we offer an approach to network physiology, which proceeds from transcriptomic data and uses gene ontology analysis to identify the biological processes most enriched in several critical time points of wound healing process (days 0, 3 and 7). The top-ranking differentially expressed genes for each process were used to build two networks: one with all proteins regulating the transcription of selected genes, and a second one involving the proteins from the signaling pathways that activate the transcription factors. The information from these networks is used to build a network of the most enriched processes with undirected links weighted proportionally to the count of shared genes between the pair of processes, and directed links weighted by the count of relationships connecting genes from one process to genes from the other. In analyzing the network thus built we used an approach based on random walks and accounting for the temporal aspects of the spread of a signal in the network (mean-first passage time, MFPT). The MFPT scores allowed identifying the top influential, as well as the top essential biological processes, which vary with the progress in the healing process. Thus, the most essential for day 0 was found to be the Wnt-receptor signaling pathway, well known for its crucial role in wound healing, while in day 3 this was the regulation of NF-kB cascade, essential for matrix remodeling in the wound healing process. The MFPT-based scores correctly reflected the pattern of the healing process dynamics to be highly concentrated around several processes between day 0 and day 3, and becoming more diffuse at day 7.