Proceeding talk – Theme: Systems.
Abstract
Based on a graphical model representation for biological networks, there were many studies regarding effects of node-based mutations on network dynamics, whereas little attention was paid to edgetic mutations. In this paper, we defined an edgetic sensitivity measure which quantifies how likely a converging attractor is changed by edge-removal mutations in a Boolean network. In this paper, we showed that the sensitive edges tend to connect knockout-susceptible nodes in random networks and drug-targets in human signaling networks. This led the edgetic sensitivity measure to better predict drug-targets than a node-based sensitivity. In addition, the sensitive edges showed distinguished structural characteristics in terms of connectivity, feedback loops and betweenness. Moreover, genes incident to the highly sensitive interactions are more central by forming a large connected component in human signaling networks. Finally, our approach was applied to case studies in identifying edgetic drug targets in p53 and T-cell signaling networks.
Authors
Hung-Cuong Trinh, University of Ulsan, Republic of Korea
Yung-Keun Kwon, University of Ulsan, Republic of Korea