PANET: a GPU-based tool for fast parallel analysis of robustness dynamics and feed-forward/feedback loop structures in large-scale biological networks

• Published in: PloS one Vol. 9; no. 7; p. e103010
• Main Authors: Trinh, Hung-Cuong, Le, Duc-Hau, Kwon, Yung-Keun
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.07.2014; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology and Life Sciences; Case studies; Central processing units; Computational Biology - methods; Computer applications; Computer engineering; Computer Graphics; Computer programs; Computer simulation; Cost analysis; CPUs; CPUs (Central processing units); Dynamic structural analysis; E coli; Electrical engineering; Escherichia coli; Feedback; Feedback loops; Graphics processing units; Hand tools; Humans; Microprocessors; Molecular biology; Networks; Physiology; Proteins; Research and Analysis Methods; Robustness; Signal Processing, Computer-Assisted; Signal Transduction; Signaling; Software; Software development tools; Systems Biology - methods; Variables; South Korea
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0103010

It has been a challenge in systems biology to unravel relationships between structural properties and dynamic behaviors of biological networks. A Cytoscape plugin named NetDS was recently proposed to analyze the robustness-related dynamics and feed-forward/feedback loop structures of biological networks. Despite such a useful function, limitations on the network size that can be analyzed exist due to high computational costs. In addition, the plugin cannot verify an intrinsic property which can be induced by an observed result because it has no function to simulate the observation on a large number of random networks. To overcome these limitations, we have developed a novel software tool, PANET. First, the time-consuming parts of NetDS were redesigned to be processed in parallel using the OpenCL library. This approach utilizes the full computing power of multi-core central processing units and graphics processing units. Eventually, this made it possible to investigate a large-scale network such as a human signaling network with 1,609 nodes and 5,063 links. We also developed a new function to perform a batch-mode simulation where it generates a lot of random networks and conducts robustness calculations and feed-forward/feedback loop examinations of them. This helps us to determine if the findings in real biological networks are valid in arbitrary random networks or not. We tested our plugin in two case studies based on two large-scale signaling networks and found interesting results regarding relationships between coherently coupled feed-forward/feedback loops and robustness. In addition, we verified whether or not those findings are consistently conserved in random networks through batch-mode simulations. Taken together, our plugin is expected to effectively investigate various relationships between dynamics and structural properties in large-scale networks. Our software tool, user manual and example datasets are freely available at http://panet-csc.sourceforge.net/.