A BDD-Based Approach to Model Reduction of Boolean Networks

A Boolean network (BN) is well known as a discrete model for analysis and control of complex networks such as gene regulatory networks. Since complex networks are large-scale in general, it is important to consider the model reduction. In this paper, a model reduction method of BNs using a BDD (binary decision diagram) is proposed. Logical operations of Boolean functions using a BDD are more efficient than using the semi-tensor product (STP) method, which is widely used in BNs. First, the outline of BNs is explained. Next, the existing model reduction method used in this paper is introduced. In the model reduction studied here, the information on singleton attractors (fixed points) is preserved. Under these preparations, an implementation method using a BDD is proposed. Finally, the effectiveness of the proposed method is presented by large-scale examples that the STP method cannot be applied.