Abstract
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/.
Highlights
The dynamical behavior of a biological network is highly related to its structural characteristics [1,2]
Parallel detection of feedback loops (FBLs) and feedforward loops (FFLs) In NetDS, a user could search FBLs and FFLs for a specified maximal length (L). This function was implemented using a depthfirst search, which is a kind of graph traversal method. It will take a long time for a large network to be traversed, though, so we introduced a parallel algorithm for FBL and FFL searches to reduce the computation time by using the OpenCL library
We show two case studies regarding the relationships between dynamics and structural properties in two signaling networks, human signaling network (HSN) and a canonical cell signaling network (CCSN) obtained from http://stke.sciencemag. org/, including 818 nodes and 1,801 links [6]
Summary
The dynamical behavior of a biological network is highly related to its structural characteristics [1,2]. Coherent coupling of FBLs is a design principle of a robust cell signaling network [6]. With respect to a feed-forward loop structure, its dynamical role was explained in various biological processes, for example, in guaranteeing robust carbohydrate uptake in Escherichia coli [4] or adapting to variations in the critical morphogen level in a switch of the cell fate [9]. The degree to which an FFL consisting of three positive transcriptional regulators was sensitive to primary level perturbation was related to the robustness [10], and the coherent FFLs can be considered as a design principle of human signaling networks that improve network robustness against update-rule perturbations [5]
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