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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

About

An application for parameterization of biological models available in SBML and BNGL formats. Features include parallelization, metaheuristic optimization algorithms, and an adaptive Markov chain Monte Carlo (MCMC) sampling algorithm.

Resources

Readme
License

Contributing

Contributing
Activity
Custom properties

Stars

25 stars

Watchers

3 watching

Forks

23 forks
Report repository

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Used by

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GitHub - lanl/PyBNF: An application for parameterization of biological models available in SBML and BNGL formats. Features include parallelization, metaheuristic optimization algorithms, and an adaptive Markov chain Monte Carlo (MCMC) sampling algorithm. · GitHub
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alt text
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
About
An application for parameterization of biological models available in SBML and BNGL formats. Features include parallelization, metaheuristic optimization algorithms, and an adaptive Markov chain Monte Carlo (MCMC) sampling algorithm.
Resources
Readme
License
Contributing
Contributing
Activity
Custom properties
Stars
25 stars
Watchers
3 watching
Forks
23 forks
Report repository
Releases
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Used by
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GitHub - lanl/PyBNF: An application for parameterization of biological models available in SBML and BNGL formats. Features include parallelization, metaheuristic optimization algorithms, and an adaptive Markov chain Monte Carlo (MCMC) sampling algorithm. · GitHub
Skip to content
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alt text
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
About
An application for parameterization of biological models available in SBML and BNGL formats. Features include parallelization, metaheuristic optimization algorithms, and an adaptive Markov chain Monte Carlo (MCMC) sampling algorithm.
Resources
Readme
License
Contributing
Contributing
Activity
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Watchers
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23 forks
Report repository
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GitHub - lanl/PyBNF: An application for parameterization of biological models available in SBML and BNGL formats. Features include parallelization, metaheuristic optimization algorithms, and an adaptive Markov chain Monte Carlo (MCMC) sampling algorithm. · GitHub
Skip to content
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alt text
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
About
An application for parameterization of biological models available in SBML and BNGL formats. Features include parallelization, metaheuristic optimization algorithms, and an adaptive Markov chain Monte Carlo (MCMC) sampling algorithm.
Resources
Readme
License
Contributing
Contributing
Activity
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25 stars
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Forks
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Report repository
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Skip to content
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alt text
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
About
An application for parameterization of biological models available in SBML and BNGL formats. Features include parallelization, metaheuristic optimization algorithms, and an adaptive Markov chain Monte Carlo (MCMC) sampling algorithm.
Resources
Readme
License
Contributing
Contributing
Activity
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25 stars
Watchers
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Forks
23 forks
Report repository
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Skip to content
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alt text
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
About
An application for parameterization of biological models available in SBML and BNGL formats. Features include parallelization, metaheuristic optimization algorithms, and an adaptive Markov chain Monte Carlo (MCMC) sampling algorithm.
Resources
Readme
License
Contributing
Contributing
Activity
Custom properties
Stars
25 stars
Watchers
3 watching
Forks
23 forks
Report repository
Releases
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Used by
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GitHub - lanl/PyBNF: An application for parameterization of biological models available in SBML and BNGL formats. Features include parallelization, metaheuristic optimization algorithms, and an adaptive Markov chain Monte Carlo (MCMC) sampling algorithm. · GitHub
Skip to content
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alt text
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
About
An application for parameterization of biological models available in SBML and BNGL formats. Features include parallelization, metaheuristic optimization algorithms, and an adaptive Markov chain Monte Carlo (MCMC) sampling algorithm.
Resources
Readme
License
Contributing
Contributing
Activity
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GitHub - lanl/PyBNF: An application for parameterization of biological models available in SBML and BNGL formats. Features include parallelization, metaheuristic optimization algorithms, and an adaptive Markov chain Monte Carlo (MCMC) sampling algorithm. · GitHub
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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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An application for parameterization of biological models available in SBML and BNGL formats. Features include parallelization, metaheuristic optimization algorithms, and an adaptive Markov chain Monte Carlo (MCMC) sampling algorithm.
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