Examining Topology and Dynamics-Related Modular Robustness in a Large-Scale Signaling Network

Abstract

AbstractThe discovery of the role of structural properties and in-/out-module robustness for predicting important modules of biological networks is a challenge. Many existing studies have been proposed to detect modules in biological network significantly. However, the topology and robustness of modules have not been well discovered yet. Therefore, there is a pressing need to conduct that. In particular, we detected all modules by using an existing algorithm in a large-scale signaling network. Next, the in-/out-module robustness computation was implemented using the OpenCL library for parallel computation to reduce computation time. Then, for the purpose of evaluating the position of modules in the network, we use five central metrics. Moreover, all modules were divided into two groups (Group 1, Group 2) based on a mean value of module size in which Group1 contained larger modules and Group 2 consisted of smaller modules. We compared gene ontology (GO) analysis of Group 1 with Group 2. We also examined the proportion of crucial genes such as disease, drug-target, and essential genes in the former and that in the latter. Consequently, module sizes were positively correlated with centrality values. It meant that the group of larger modules was located in center of network. Regarding to in-/out-module robustness, the average value of in-module robustness of Group 1 was slightly higher than that of Group 2. On the other hand, the average value of out-module of Group 1 was smaller than that of Group 2. Additionally, the GO of Group 1 was enriched more significant than that of Group 2. Finally, the number of important genes in group 2 was less than in group 1. Taken together, the group of larger modules is more important than that of smaller modules in large-scale signaling network.KeywordsBoolean dynamicsModule detectionIn/out module robustnessNetwork structureEssential genesGene ontologyBiological signal network