IEEE/ACM Trans Comput Biol Bioinform. 2022 Mar-Apr;19(2):806-818. doi: 10.1109/TCBB.2020.3028862. Epub 2022 Apr 1.
Boolean networks (BNs)play a crucial role in modeling and analyzing biological systems. One of the central issues in the analysis of BNs is attractor detection, i.e., identification of all possible attractors. 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 present and formally prove several relations between feedback vertex sets (FVSs)and dynamics of BNs. From these relations, we propose an FVS-based method for detecting attractors in ARBNs. Our approach relies on the principle of removing arcs in the state transition graph to get a candidate set and the reachability property to filter the candidate set. We formally prove the correctness of our method and show its efficiency by conducting experiments on real biological networks and randomly generated N- K networks. The obtained results are very promising since our method can handle large networks whose sizes are up to 101 without using any network reduction technique.
布尔网络（BNs）在建模和分析生物系统中起着至关重要的作用。BNs 分析中的一个核心问题是吸引子检测，即识别所有可能的吸引子。由于异步和非确定性更新方案，这个问题对于大型异步随机布尔网络（ARBNs）来说更加具有挑战性。在本文中，我们提出并正式证明了几个关于反馈顶点集（FVS）和 BN 动力学之间的关系。从这些关系中，我们提出了一种基于 FVS 的方法来检测 ARBNs 中的吸引子。我们的方法依赖于在状态转移图中删除弧以得到候选集的原理，以及使用可达性属性来过滤候选集。我们通过在真实生物网络和随机生成的 N-K 网络上进行实验，正式证明了我们方法的正确性，并展示了其效率。我们的方法可以处理大小高达 10^1 的大型网络，而无需使用任何网络简化技术，这一结果非常有前景。
