bngsim

bngsim

Embeddable simulation engine for biochemical reaction networks.

bngsim is a high-performance C++ simulation kernel with Python bindings that replaces BioNetGen's subprocess-based run_network driver. The library also includes two network-free simulators. It loads BioNetGen .net and .xml files, runs deterministic and stochastic simulations in-process, and returns results as NumPy arrays — no file I/O, no subprocess spawning, no Perl dependency.

Highlights

  • Fast — in-process execution with the GIL released during simulation; thread-parallel batch sweeps
  • No toolchain at runtime — import bngsim; no compilers, no BNGPATH, no Perl
  • Modern SUNDIALS — v7.x CVODE/CVODES with re-entrant SUNContext
  • Multi-format — loads BioNetGen .net and .xml, Antimony (.ant), and SBML (.xml) models SBML Test Suite results
  • Rich results — NumPy arrays, named observable access, pandas/xarray, HDF5 save/load
  • Standards interchange — .net/cBNGL ⇄ SBML and SED-ML + OMEX packaging, every conversion verified faithful
  • Gradient-ready — CVODES forward sensitivities, Fisher information
  • Validated — matches run_network to ~10⁻¹² (ODE) and cross-checked against RoadRunner and the SBML semantic test suite

Installation

pip install bngsim

Prebuilt wheels ship for common platforms; large models may benefit from the optional sparse (KLU) solver. See the installation guide for source builds, the KLU dependency, and optional extras.

Quickstart

import bngsim

# Load a model (BioNetGen .net/.xml, Antimony, or SBML) and run an ODE simulation
model = bngsim.Model.from_net_file("model.net")
sim = bngsim.Simulator(model, method="ode")
result = sim.run(t_end=100.0, n_steps=1000)

result.times          # (1001,) NumPy array of time points
result["A"]           # trajectory of observable "A"
result.to_dataframe() # pandas DataFrame of all observables

Stochastic (method="ssa" / "psa"), network-free (NFsim / RuleMonkey), sensitivity analysis, steady-state, and events are all covered in the quickstart and user guide.

Documentation

Full documentation is hosted at bngsim.readthedocs.io and lives in docs/:

Benchmarks & validation

bngsim beats run_network on every SSA/PSA model measured (geometric-mean speedup ~8.7× SSA), agrees with libRoadRunner across the full BioModels ODE corpus, and scores 1577 Match on the SBML semantic test suite with zero wrong-but-plausible answers. The numbers, methodology, and reproducible harnesses are in benchmarks & validation, with the committed cross-engine snapshots under parity_checks/. To re-run the suites and reproduce the numbers — the pinned-fetch model, corpus provenance, tool versions, and a no-download smoke path — see REPRODUCING.md. The supported-construct matrix is in SUPPORT_MATRIX.md.

Contributing

Build-from-source, test, and CI instructions are in CONTRIBUTING.md and the development docs. Release history is in CHANGELOG.md.

License

MIT. See LICENSE for the full text and the Triad/LANL copyright notice.

Third-party code and model/test-data corpora that BNGsim redistributes are listed, with their licenses, in NOTICE.

© 2026. Triad National Security, LLC. All rights reserved. This is a Los Alamos National Laboratory open-source release; LANL software release reference O5098.

Citation

If you use bngsim in your research, please cite:

Preprint coming soon!

Acknowledgments

BNGsim was developed and validated against RoadRunner, AMICI, COPASI, BioNetGen, and the SBML/DSMTS test suites. See ACKNOWLEDGMENTS.md for citations.

Project links

Data verified by PyPI on Aug 4, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.15.1)

1 maintainer

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Avatar for bhlavacek from gravatar.com bhlavacek

Credits

Author: William S. Hlavacek

GitHub Statistics

Data verified by PyPI on Aug 4, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.10

Provides Extra

antimony pandas hdf5 test jax vivarium benchmarks roadrunner dev

Classifiers

Report project as malware

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