Attractor detection and enumeration algorithms for Boolean networks

Abstract

The Boolean network (BN) is a mathematical model used to represent various biological processes such as gene regulatory networks. The state of a BN is determined from the previous state and eventually reaches a stable state called an attractor. Due to its significance for elucidating the whole system, extensive studies have been conducted on analysis of attractors. However, the problem of detecting an attractor from a given BN has been shown to be NP-hard, and for general BNs, the time complexity of most existing algorithms is not guaranteed to be less than O(2[n]). Therefore, the computational difficulty of attractor detection has been a big obstacle for analysis of BNs. This review highlights singleton/periodic attractor detection algorithms that have guaranteed computational complexities less than O(2[n]) time for particular classes of BNs under synchronous update in which the maximum indegree is limited to a constant, each Boolean function is AND or OR of literals, or each Boolean function is given as a nested canalyzing function. We also briefly review practically efficient algorithms for the problem.