MORO: a Cytoscape app for relationship analysis between modularity and robustness in large-scale biological networks. Issue 4 (December 2016)

• Record Type: Journal Article
• Authors: Truong, Cong-Doan; Tran, Tien-Dzung; Kwon, Yung-Keun
• Is Part Of: BMC systems biology. Volume 10:Issue 4(2016)
• Page Start: 521
• Page End: 530
• Publication Date: 2016-12
• Subjects: Cytoscape app -- Boolean network -- Network robustness -- Modularity -- Centrality -- Gene-ontology -- Parallel computation -- OpenCL; Biological systems -- Periodicals; Biology -- Research -- Periodicals; Cell physiology -- Periodicals; Genes -- Analysis -- Periodicals; 571
• DOI: 10.1186/s12918-016-0363-3
• Languages: English
• ISSNs: 1752-0509

Abstract

Abstract Background 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. Results Accordingly, we developed a novel Cytoscape app called MORO, which can conveniently analyze the relationship between network modularity and robustness. We 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, we 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. We tested the proposed app to analyze large signaling networks and showed an interesting relationship between network modularity and robustness. Conclusions Our app can be a promising tool which efficiently analyzes the relationship between modularity and robustness in large signaling networks.