Shen Liangzhong, Zan Xiangzhen, Liu Wenbin
Department of Information Engineering, Wenzhou Business College, Wenzhou, Zhejiang, People's Republic of China.
Institute of Computational Science and Technology, Guangzhou University, Guangzhou, Guangdong, People's Republic of China.
IET Syst Biol. 2018 Aug;12(4):148-153. doi: 10.1049/iet-syb.2017.0091.
Boolean networks are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long-term behavior of systems. Here, the authors investigate the 1 bit perturbation, which falls under the category of structural intervention. The authors' idea is that, if and only if a perturbed state evolves from a desirable attractor to an undesirable attractor or from an undesirable attractor to a desirable attractor, then the size of basin of attractor of a desirable attractor may decrease or increase. In this case, if the authors obtain the net BOS of the perturbed states, they can quickly obtain the optimal 1 bit perturbation by finding the maximum value of perturbation gain. Results from both synthetic and real biological networks show that the proposed algorithm is not only simpler and but also performs better than the previous basin-of-states (BOS)-based algorithm by Hu et al..
布尔网络被广泛用于对基因调控网络进行建模，并设计治疗干预策略以影响系统的长期行为。在此，作者研究了属于结构干预范畴的1位扰动。作者的想法是，当且仅当一个受扰动状态从一个期望的吸引子演变为一个不期望的吸引子，或者从不期望的吸引子演变为期望的吸引子时，那么一个期望吸引子的吸引域大小可能会减小或增加。在这种情况下，如果作者获得了受扰动状态的净吸引域大小（BOS），他们可以通过找到扰动增益的最大值来快速获得最优的1位扰动。来自合成生物网络和真实生物网络的结果表明，所提出的算法不仅更简单，而且比Hu等人先前基于状态吸引域（BOS）的算法表现更好。
