Developing Attractor Analysis Techniques for a Compositional Boolean Network Framework

Abstract

Boolean networks are an important qualitative modelling technique that provide techniques for analysing the attractors (important cyclic behaviour) in a model. However, their practical application is limited by the state space explosion problem and this had led to researchers considering compositional techniques. In this paper we take a recently developed compositional framework for Boolean networks based on using logical connectives to merge entities and extend it with compositional techniques for attractor analysis. Our approach is based on using strongly connected components to identify potential cyclic behaviour taking into account the interference arising from a composition. We develop tool support for our approach and illustrate its practical application by a case study.