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A Web Tool for Generating High Quality Machine-readable Biological Pathways
Published on: February 8, 2017
BoolNet--an R package for generation, reconstruction and analysis of Boolean networks
Christoph Müssel1, Martin Hopfensitz, Hans A Kestler
1Institute of Neural Information Processing, University of Ulm and Department of Internal Medicine I, University Hospital Ulm, 89081 Ulm, Germany.
Bioinformatics (Oxford, England)
|April 10, 2010
Summary
BoolNet is a new R package that integrates various Boolean network (BN) methods for analyzing gene expression dynamics. It offers tools for network reconstruction, simulation, and analysis, enhancing systems biology research.
Area of Science:
- Systems Biology
- Computational Biology
- Bioinformatics
Background:
- Mathematical models and simulations are crucial for understanding complex cellular regulatory networks.
- Boolean networks (BNs) are low-complexity models capturing essential gene-regulatory network properties.
- Existing BN implementations lack seamless integration into preprocessing pipelines.
Purpose of the Study:
- To develop an integrated R package for Boolean network analysis.
- To provide a comprehensive tool for studying gene expression dynamics.
Main Methods:
- The BoolNet package integrates synchronous, asynchronous, and probabilistic Boolean network models.
- Includes methods for network reconstruction from time series data.
- Offers robustness analysis, Markov chain simulations, and attractor identification/visualization.
Main Results:
- BoolNet provides efficient integration of diverse BN analysis methods.
- Enables comprehensive analysis of gene regulatory network dynamics.
- Facilitates network reconstruction, simulation, and stability analysis.
Conclusions:
- BoolNet enhances the analysis of gene regulatory networks by offering an integrated platform.
- The package supports various BN models and analysis techniques.
- It is freely available for use in systems biology research.
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