INVESTIGATION ON MODULARITY AND DYNAMICS IN SIGNALING NETWORKS

Abstract

Although there have been many studies revealing that dynamic robustness of a biological network is related to its modularity characteristics, no proper tool exists to investigate the relation between network dynamics and modularity. Accordingly, I developed a novel Cytoscape app called MORO, which can conveniently analyze the relationship between network modularity and robustness. I employed an existing algorithm to analyze the modularity of directed graphs and a Boolean network model for robustness calculation. In particular, to ensure the robustness algorithm’s applicability to large-scale networks, I implemented it as a parallel algorithm by using the OpenCL library. A batch -mode simulation function was also developed to verify whether an observed relationship between modularity and robustness is conserved in a large set of randomly structured networks. The app provides various visualization modes to better elucidate topological relations between modules, and tabular results of centrality and gene ontology enrichment analyses of modules. I tested the proposed app to analyze large signaling networks and showed an interesting relationship between network modularity and robustness. My app can be a promising tool which efficiently analyzes the relationship between modularity and robustness in large signaling networks. Secondly, biological networks consisting of molecular components and interactions are represented by a graph model. There have been some studies based on that model to analyze a relationship between structural characteristics and dynamical behaviors in signaling network. However, little attention has been paid to changes of modularity and robustness in mutant networks. Therefore, I investigated the changes of modularity and robustness by edge-removal mutations in three signaling networks. I first observed that both the modularity and robustness increased on average in the mutant network by the edge-removal mutations. However, the modularity change was negatively correlated with the robustness change. This implies that it is unlikely that both the modularity and the robustness values simultaneously increase by the edgeremoval mutations. Another interesting finding is that the modularity change was positively correlated with the degree, the number of feedback loops, and the edge betweenness of the removed edges whereas the robustness change was negatively correlated with them. I note that these results were consistently observed in randomly structure networks. Additionally, I identified two groups of genes which are inciden t to the highly -modularity-increasing and the highly -robustness-decreasing edges with respect to the edge-removal mutations, respectively, and observed that they are likely to be central by forming a connected component of a considerably large size. The gene-ontology enrichment of each of these gene groups was significantly different from the rest of genes. Finally, I showed that the highly -robustness-decreasing edges can be promising edgetic drug-targets, which validates the usefulness of my analysis. Taken together, the analysis of changes of robustness and modularity against edgeremoval mutations can be useful to unravel novel dynamical characteristics underlying in signaling networks.