BoolForge: Random Generation and Analysis of Boolean Functions and Networks in Python
Abstract
Boolean networks are a powerful and popular modeling framework in systems biology, enabling the study of complex processes underlying gene regulation, signal transduction, and cellular decision-making. Most biological networks exhibit a high degree of canalization, a property of the Boolean update rules that stabilizes network dynamics. Despite its importance, existing software packages provide hardly any support for generating Boolean networks with defined canalization properties. We present BoolForge, a Python toolbox for the analysis and random generation of Boolean functions and networks, with a particular focus on canalization. BoolForge allows users to (i) generate random Boolean functions with specified canalizing depth, layer structure, or other structural constraints; (ii) construct random Boolean networks with tunable topological and functional properties; and (iii) compute structural and dynamical features including network attractors, robustness, and modularity. BoolForge enables researchers to rapidly prototype biological Boolean network models, explore the relationship between structure and dynamics, and generate ensembles of networks for statistical analysis. It is lightweight, adaptable, and fully compatible with existing Boolean network analysis tools. BoolForge is implemented in Python (version3.8+), with no platform-specific dependencies. The software is distributed under the MIT License and will be maintained for at least two years following publication. Source code, documentation, and tutorial notebooks are freely available at: https://github.com/ckadelka/BoolForge. BoolForge can be installed via pip install git+https://github.com/ckadelka/BoolForge.