Reduction of Boolean network models

Boolean networks have been successfully used in modelling gene regulatory networks. However, for large networks, analysis by simulation becomes unfeasible. In this paper we propose a reduction method for Boolean networks that decreases the size of the network, while preserving important dynamical properties and topological features. As a result, the reduced network can be used to infer properties about the original network and to gain a better understanding of the role of network topology on the dynamics. In particular, we use the reduction method to study steady states of Boolean networks and apply our results to models of Th-lymphocyte differentiation and the lac operon.

► A powerful reduction method for Boolean networks is proposed. ► It can help in understanding dynamical properties of large networks. ► It is proven that the reduction method preserves the steady states and topological features. ► Steady states of Boolean models can easily be identified.