Computing attractors of large-scale asynchronous boolean networks using minimal trap spaces

Abstract

Boolean Networks (BNs) play a crucial role in modeling, analyzing, and controlling biological systems. One of the most important problems on BNs is to compute all the possible attractors of a BN. There are two popular types of BNs, Synchronous BNs (SBNs) and Asynchronous BNs (ABNs). Although ABNs are considered more suitable than SBNs in modeling real-world biological systems, their attractor computation is more challenging than that of SBNs. Several methods have been proposed for computing attractors of ABNs. However, none of them can robustly handle large and complex models. In this paper, we propose a novel method called mtsNFVS for exactly computing all the attractors of an ABN based on its minimal trap spaces, where a trap space is a subspace of state space that no path can leave. The main advantage of mtsNFVS lies in opening the chance to reach easy cases for the attractor computation. We then evaluate mtsNFVS on a set of large and complex real-world models with crucial biologically motivations as well as a set of randomly generated models. The experimental results show that mtsNFVS can easily handle large-scale models and it completely outperforms the state-of-the-art method CABEAN as well as other recently notable methods.