An efficient method for approximating attractors in large-scale asynchronous Boolean models

Boolean networks (BNs) play a crucial role in modeling and analyzing biological systems especially gene regulatory networks. One of the central issues in the analysis of BNs is attractor detection, i.e., detecting all possible attractors of a BN. This problem becomes more challenging for large asynchronous random Boolean networks (ARBNs) because of the asynchronous and non-deterministic updating scheme. In this paper, we state and prove several relations between dynamics of ARBNs and generalized asynchronous random Boolean networks (GARBNs). Based on these relations, we propose an efficient method called ApproARBN for approximating attractors of ARBNs. The experimental results on real biological networks justify the accuracy of ApproARBN and show the efficiency of ApproARBN since ApproARBN outperforms two state-of-the-art methods and can handle large networks whose sizes are up to 101 nodes.