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From BioNetWiki
This wiki serves the BioNetGen user community by providing information about BioNetGen and tools for the development, annotation, and discussion of BioNetGen models.
BioNetWiki is read-only unless you obtain a username and password by sending email to bionetgen@lanl.gov. In addition, pdf files and downloads are only accessible when logged in.
Contents |
News
- A full-length paper describing RuleBender, our graphical front-end for rule-based modeling, was just published in a BMC Bioinformatics Supplement Highlights of the 1st IEEE Symposium on Biological Data Visualization (BioVis 2011).
- Join the new BioNetGen users' google group to get all of the latest updates. It's also a great forum for asking questions and starting discussions.
- BioNetGen (2.2.0-stable) is now available for download, featuring:
- Full support for ratelaws defined by functions of model observables.
- Partitioned Leaping Algorithm (PLA) for accelerated stochastics.
- Improved support for the Windows platform.
- The BioNetGen codebase is now maintained at GoogleCode.
- For help, email us at bionetgen.help@gmail.com.
- Check out the new BioNetGen FaceBook page and follow us on Twitter at @bionetgen!
- We've added a Quick Reference Guide to our Facebook page summarizing the most commonly-used BNG actions and associated arguments.
- Please take the BioNetGen/NFsim/RuleBender User Survey.
- RuleBender paper wins Best Paper award at BioVis 2011. See the Final Program (Warning: big file) for cover art and listing of the award on p. 12.
- A short commentary on rule-based modeling for intracellular and cellular dynamics just appeared in BMC Biology.
- Materials for the tutorial at the 2011 q-bio conference are here.
- In a HOT article, Chylek et al. have recently proposed conventions for visualizing and annotating rule-based models. See the wiki page about Extended Contact Maps or the Molecular BioSystems Blog.
- A new (for 2012) review about rule-based modeling is available here.
Access older news items here.
Main Menu
- Getting Started - Start here if you are new to BioNetGen.
- Source Code - Go here to download the latest BioNetGen source code distribution.
- Documentation
- Tutorials
- Model Examples
- Extended Contact Maps
- Rule-Based Modeling Community
- Feature Requests NEW
- ModelFest
Tips
- Trajectory Continuation
- Mex code generator for simulating ODE network models from the MATLAB platform.
- Hybrid particle-population model generator for memory efficient network-free simulations.
- Introducing delay in a BNG model
- Partitioned leaping: A tau-leaping variant
- Loading BioNetGen trajectories into MATLAB
About BioNetGen
BioNetGen is software for the specification and simulation of rule-based models of biochemical systems, including signal transduction, metabolic, and genetic regulatory networks. A comprehensive introduction to modeling with BioNetGen is available and is augmented by the models found in the BioNetGen distribution and in the Model Examples. The BioNetGen language has recently been extended to include explicit representation of compartments. A recent review of methods for rule-based modeling is available in Science Signaling (formerly known as Science's STKE).
The BioNetGen software package was initially developed by the Cell Signaling Team at Los Alamos National Laboratory. The current development team includes researchers in the Theoretical Division and Center for Nonlinear Studies at Los Alamos National Laboratory, the Departments of Biology and Computer Science at the University of New Mexico, the Department of Computational and Systems Biology at the University of Pittsburgh School of Medicine, the Center for Cell Analysis and Modeling at the University of Connecticut Health Center, and the Department of Biological Chemistry at the Johns Hopkins University School of Medicine.
BioNetGen is supported by NIH grant GM076570 and work has been performed under DOE contract DE-AC52-06NA25396. Additional support for BioNetGen has been provided by NIH grants GM085273, AI35997, and CA109552, NSF grant 0829788, the Arizona Biomedical Research Commission, and the Department of Computational Biology at the University of Pittsburgh School of Medicine.
