BoolDog

A Python package for integrated Boolean and semi-quantitative network modelling.

Homepage: https://github.com/NIB-SI/BoolDog
Documentation: https://nib-si.github.io/BoolDog
PyPI: https://pypi.org/project/BoolDog
License: GPL-3.0

Overview

BoolDog provides an integrated, Python-native platform for logic-based modelling, requiring minimal prior knowledge of kinetic or mechanistic parameters. Starting from a regulatory or Boolean network, BoolDog supports the full modelling workflow: from network import and visualisation, through Boolean simulation and attractor analysis, to transformation into continuous ordinary differential equation (ODE) systems and semi-quantitative simulation.

Key features include:

  • Model access: direct download of model and model metadata from BioModels repository

  • Model import and export: supports BoolNet, SBML-qual, TabularQual, GraphML, SIF, and native Python formats

  • Network-to-Boolean conversion: automatic transformation of regulatory networks into Boolean models

  • Visualisation: built in model visualisation in Cytoscape, further visualisation supported through interoperability with NetworkX and igraph

  • Model modification: add, remove, or update nodes and rules inplace

  • Boolean analysis synchronous simulation, state transition graphs, attractor and steady-state analysis via PyBoolNet

  • Boolean dynamics animation: generate animated GIFs of Boolean simulations using Cytoscape as a layout engine, allowing custom network layouts and visual styles

  • Semi-quantitative ODE transformation: conversion of Boolean logic into continuous ODE systems using the SQUAD and normalised Hill cube (ODEfy) schemes

  • Event-based continuous simulation: simulation of perturbations such as node knockouts or forced activations at defined time points, with visualisation via matplotlib

Manual and guides

API

Citation

If you use BoolDog in your research, please cite:

Bleker et al. (2026). BoolDog: integrated Boolean and semi-quantitative network modelling in Python. bioRix. https://doi.org/TTTTTT

BoolDog implements ODE transformation schemes from:

  • SQUAD: Di Cara, A., Garg, A., De Micheli, G., Xenarios, I., & Mendoza, L. (2007). Dynamic simulation of regulatory networks using SQUAD. BMC Bioinformatics, 8(1), 462. https://doi.org/10.1186/1471-2105-8-462

  • ODEfy: Krumsiek, J., Pölsterl, S., Wittmann, D. M., & Theis, F. J. (2010) Odefy – From discrete to continuous models. BMC Bioinformatics, 11(1), 233. https://doi.org/10.1186/1471-2105-11-233

BoolDog relies on PyBoolNet for Boolean analyses:

Indices