BioNetGen is software designed for modular, structure-based modeling of biochemical reaction networks. It can be applied to many other types of modeling as well. It provides a simple, graph-based syntax that lets users build reaction models out of structured objects that can bind and undergo modification.
Windows users must have Perl installed to run BioNetGen at all. We recommend Strawberry Perl.
To begin using BioNetGen, see the installation instructions in the documentation. This will guide new users through installing VS Code, the BNG extension, and PyBioNetGen.
The best way to get help, report a bug, or request a feature is to post an issue on the appropriate project’s GitHub issues page. Otherwise, you may send an email to firstname.lastname@example.org. All help requests, including models or model snippets, will be treated confidentially.
- A command line tool for weighted ensemble sampling of BNGL models, WEBNG, has been released. See the GitHub repository and the documentation.
- WARNING for MacOS users: New versions of OS X (11.5 or newer) might force you to switch your default shell to zsh (see here). This will break the extension if you are using Anaconda Python, since it will no longer be your default Python in zsh. Try renaming your
- RuleBender provides an Eclispe-based interface for BioNetGen that includes interactive model visualization capabilities. NOTICE: RuleBender is no longer being developed or supported. Please use PyBioNetGen and the VS Code extension instead.
- BioNetGen command line. The core BioNetGen code includes a command-line interface that may be useful to developers and advanced users. The BioNetGen code is bundled with both PyBioNetGen and RuleBender.
If you use BioNetGen for a project please cite
- Harris, L. A. et al. BioNetGen 2.2: advances in rule-based modeling. Bioinformatics 32, 3366–3368 (2016).
Current development of BioNetGen is supported in part by the NIGMS-funded (P41GM103712) National Center for Multiscale Modeling of Biological Systems (MMBioS). Past support has been provided by NIH grants (GM076570, GM103712, GM085273, AI35997, CA109552), NSF grant 0829788, the Arizona Biomedical Research Commission, and the Department of Computational Biology at the University of Pittsburgh School of Medicine.