boolforge 1.0.1

Methods to generate and analyze random Boolean functions and Boolean networks, with a focus on canalization.

BoolForge

BoolForge is a Python toolbox for generating, sampling, and analyzing Boolean functions and Boolean networks, with a particular emphasis on canalization and the uniform random generation of functions with prescribed structure.

While many existing tools focus on simulation and dynamical analysis, BoolForge emphasizes controlled generation and analysis of Boolean functions and networks, enabling systematic studies of canalization, robustness, and ensemble properties.

The package provides tools for:

• random sampling of Boolean functions with prescribed canalizing structure,
• generation of Boolean networks with controlled update rules and wiring diagrams,
• analysis of canalization, activity, sensitivity, and related structural measures,
• interoperability with other Boolean network software and model formats.

BoolForge is designed for researchers working with regulatory networks, discrete dynamical systems, and random Boolean network ensembles in systems biology and network science.

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Installation

Stable release (recommended)

Install the latest stable version from PyPI:

pip install boolforge

BoolForge requires Python 3.10 or later.

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Development version

To install the latest development version directly from GitHub:

pip install git+https://github.com/ckadelka/BoolForge

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Optional dependencies (extended functionality)

BoolForge is fully usable with its core dependencies, but some features rely on optional packages that can be installed via extras.

Performance acceleration

Some internal routines are automatically accelerated if numba is available. Exact asynchronous attractor computation requires numba.

To enable numba acceleration:

pip install boolforge[speed]

When numba is not installed, BoolForge transparently falls back to pure-Python implementations.

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Plotting and visualization

Plotting of wiring diagrams and network structure requires matplotlib.

To enable plotting:

pip install boolforge[plot]

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CANA integration

Some methods interface with the CANA package for advanced canalization measures.

To enable CANA-based functionality:

pip install boolforge[cana]

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Symbolic logic and expression minimization

Symbolic representations and logical expression minimization rely on PyEDA.

To enable symbolic functionality:

pip install boolforge[symbolic]

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Biological model retrieval

The retrival and loading of hundreds of published biological Boolean network models relies on the requests package for web access.

To enable biological model retrieval:

pip install boolforge[bio]

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All optional features

To install BoolForge with all optional dependencies:

pip install boolforge[all]

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Compatibility and interoperability

BoolForge supports import and export of Boolean network representations used by other software packages.

In particular, BoolForge supports the .bnet format commonly used by pyboolnet, without requiring pyboolnet itself to be installed.

BoolForge also supports conversion to and from the format used by CANA.

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Documentation

Full documentation, including tutorials and API reference, is available at:

https://kadelkalab.github.io/BoolForge/

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Citation

If you use BoolForge in your research, please cite the accompanying application note:

Kadelka, C., & Coberly, B. (2025).
BoolForge: Controlled Generation and Analysis of Boolean Functions and Networks.
arXiv:2509.02496.
https://arxiv.org/abs/2509.02496

A machine-readable citation file (CITATION.cff) is included in the repository and can be used directly by GitHub, Zenodo, and reference managers.

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License

BoolForge is released under the MIT License.