This is the manual of AEON, a tool for long term analysis of Boolean networks. In particular, AEON allows you to define Boolean networks with partially unknown update functions. Then, for such a network, you can compute its asynchronous attractors and explore the state space of these attractors. Finally, you can investigate how the attractor structure changes depending on the unknown parts of the network (attractor bifurcation) as well as which variables are stable, unstable, or switched under different conditions (stability analysis). For all of this, AEON uses efficient symbolic algorithms based on BDDs, which make it possible to handle even networks with 1000 or more variables.

In this document, you can find information on how to use AEON to:

  • Create a Boolean network or import it from an existing .sbml or .aeon file.
  • Create a partially unknown Boolean network with logical parameters.
  • Check that the network is consistent with its regulatory graph.
  • Compute asynchronous attractors of a Boolean network.
  • Visualize the state space of the computed attractors.
  • Construct a bifurcation decision tree: A visual representation of the dependence between network parameters and attractors.
  • Perform stability analysis of the attractors for different conditions.

AEON is a constantly evolving academic project. If you have any problem or run into some unexpected behaviour, please contact us at We will be happy to help you and make AEON a more useful tool for you. Finally, if you found AEON useful in your research, please cite it using the following publication:

Beneš, N., Brim, L., Kadlecaj, J., Pastva, S., & Šafránek, D. (2020, July). 
AEON: Attractor Bifurcation Analysis of Parametrised Boolean Networks. 
In International Conference on Computer Aided Verification (pp. 569-581).