A Boolean encoding of the Most Permissive semantics for Boolean networks
This work addresses the limitation of classical semantics in Boolean network modeling for biological research, offering a tool-compatible encoding that is incremental in nature.
The authors tackled the problem of accurately capturing transient or asymptotic behaviors in Boolean networks for modeling biological regulatory networks by providing a Boolean encoding of the Most Permissive semantics, proving that the asynchronous dynamics of the encoded network exactly reproduces the attainability properties of the original network under this semantics.
Boolean networks are widely used to model biological regulatory networks and study their dynamics. Classical semantics, such as the asynchronous semantics, do not always accurately capture transient or asymptotic behaviors observed in quantitative models. To address this limitation, the Most Permissive semantics was introduced by Paulevé et al., extending Boolean dynamics with intermediate activity levels that allow components to transiently activate or inhibit their targets during transitions. In this work, we provide a Boolean encoding of the Most Permissive semantics: each component of the original network is represented by a triplet of Boolean variables, and we derive the extended logical function governing the resulting network. We prove that the asynchronous dynamics of the encoded network exactly reproduces the attainability properties of the original network under Most Permissive semantics. This encoding is implemented as a modifier within the bioLQM framework, making it directly compatible with existing tools such as GINsim. To address scalability limitations, we further extend the tool to support partial unfolding, restricted to a user-defined subset of components.