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Welcome to BioNetGen! Flagman

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.

For more information, visit BioNetGen’s documentation.

Using BioNetGen with the VS Code Extension

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Editor window
Easily construct models with snippets and completion
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Simulation plotting
Run a simulation and plot the results with a single click
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Network visualization
Browse multiple diagrams of a model and rule structure with the yEd graph editor


Note: yEd graph editor must be installed separately.

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 bionetgen.help@gmail.com. All help requests, including models or model snippets, will be treated confidentially.

Download

To begin using BioNetGen, see the installation instructions. This will guide new users through installing VS Code and the BNG extension.

News

  • If you’ve spent some time browsing this site, please consider filling out this feedback form to help improve it!
  • 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 .bash-profile to .zprofile.

Citing BioNetGen

If you use BioNetGen for a project please cite

In addition, consider sending us an email or tweet @bionetgen. We’d love to hear about it!

Acknowledgements

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.

BioNetGen and this website are maintained by the Lab of James Faeder in the Department of Computational and Systems Biology and the University of Pittsburgh School of Medicine.