ArticlePloS one2014
PANET: a GPU-based tool for fast parallel analysis of robustness dynamics and feed-forward/feedback loop structures in large-scale biological networks.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 36 citations in OpenAlex.
- In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model.Biomolecules · 2022Article
- Identification, visualization, statistical analysis and mathematical modeling of high-feedback loops in gene regulatory networks.BMC bioinformatics · 2021Article
- A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data.Bioinformatics (Oxford, England) · 2021Article
- Effects of ordered mutations on dynamics in signaling networks.BMC medical genomics · 2020Article
- RMut: R package for a Boolean sensitivity analysis against various types of mutations.PloS one · 2019Article
- CANA: A Python Package for Quantifying Control and Canalization in Boolean Networks.Frontiers in physiology · 2018Article
- Construction and analysis of gene-gene dynamics influence networks based on a Boolean model.BMC systems biology · 2017Article
- MORO: a Cytoscape app for relationship analysis between modularity and robustness in large-scale biological networks.BMC systems biology · 2016Article
- Properties of Boolean dynamics by node classification using feedback loops in a network.BMC systems biology · 2016Article
- A HIF-1α-driven feed-forward loop augments HIF signalling in Hep3B cells by upregulation of ARNT.Cell death & disease · 2016Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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/.
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