Boolean networks (BNs) provide an effective modelling tool for various phenomena from science and engineering. Any long-term behaviour of a BN eventually converges to either a single state, or a cycle of states, called attractor. Depending on various logical parameters, the structure and quality of attractors can undergo a significant change, known as bifurcation. AEON enables analysing bifurcations in asynchronous parametrised Boolean networks. To fight the state-space and parameter-space explosion problem the tool uses a symbolic algorithm.

AEON.py is a Python library for the analysis of the long-term behaviour in very large asynchronous Boolean networks. It provides significant computational improvements over the state of the art methods for attractor detection. Furthermore, it admits the analysis of partially specified Boolean networks with uncertain update functions. It also includes techniques for identifying viable source-target control strategies and the assessment of their robustness with respect to parameter perturbations.

A tool paper about AEON has been published at CAV 2020.

A paper about AEON.py has been published in Bioinformatics.