pybnf

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PyBioNetFit (PyBNF) is a general-purpose program for parameterizing biological models specified using the BioNetGen rule-based modeling language (BNGL) or the Systems Biology Markup Language (SBML). PyBioNetFit offers a suite of parallelized metaheuristic algorithms (differential evolution, particle swarm optimization, scatter search) for parameter optimization. In addition to model parameterization, PyBNF supports uncertainty quantification by bootstrapping or Bayesian approaches, and model checking. PyBNF includes an adaptive Markov chain Monte Carlo (MCMC) sampling algorithm, which supports Bayesian inference. PyBNF includes the Biological Property Specification Language (BPSL) for defining qualitative data for use in parameterization or checking. It runs on most Linux and macOS workstations as well on computing clusters.

For documentation, refer to the online documentation at https://lanl.github.io/PyBNF/ or Documentation_PyBioNetFit.pdf.

Installation

PyBNF requires Python 3.11 or higher.

python3 -m pip install pybnf

With uv, PyBNF can also be installed as a command-line tool:

uv tool install pybnf

PyBNF installs its Python dependencies, including BNGsim and libRoadRunner, through the package metadata. BNGL workflows can still require a BioNetGen installation for BNG2.pl; see the installation documentation for simulator setup details.

Development

After cloning, run once:

make bootstrap

This installs the pre-push git hook (via pre-commit) so the bngsim test subset runs locally before any git push.

pytest runs the fast suite by default. Two opt-in tiers are deselected unless requested: pytest -m slow (statistical recovery against analytical targets) and pytest -m recovery (parameter recovery through the real bngsim backend; needs bngsim + BNG2.pl). Run pytest --markers for the full list, or see tests/README_integration.md.

PyBioNetFit is released under the BSD-3 license. For more information, refer to the LICENSE. LANL code designation: C18062

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