Pint implements static analyses for computing dynamical properties on very large-scale Automata Networks, ranging from hundreds to several thousands of concurrently interacting components. Provided analyses include notably the listing of fixed points, successive reachability properties, cut sets and mutations for reachability, and model reduction preserving transient dynamics. The translation to related formalisms, in particular Boolean and multi-valued networks, is also provided.

Automata Networks are defined by a set of finite-state machines whose local transitions can be conditionned by the state of other automata in the network. Applications are in particlar in systems biology with the modelling and analysis of signalling pathways and gene regulatory networks, gathering multiple interacting components with a few local states.

Pint comes with several command line tools to perform formal analyses, reductions, simulations, and translation to other formalisms. A Python interface, and seamless integration with Jupyter notebook is available. An OCaml library, with possible C bindings, can also be compiled in order to embed the static analyses in other frameworks.

Authors and contributors

Pint has been created and is maintained by Loïc Paulevé. It also contains contributions from Maxime Folschette (inference of interaction graph and Boolean networks).

Support and contact

If you have any question regarding Pint, please contact or open an issue on Pint GitHub.