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Algorithm to identify the optimal perturbation based on the net basin-of-state of perturbed states in Boolean network

机译:布尔网络中基于扰动状态净状态池的最优扰动识别算法

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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 Hunet al.n.
机译: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。在这里,作者研究了1位摄动,它属于结构干预类别。作者的想法是,当且仅当扰动状态从期望的吸引子演变为不期望的吸引子,或从不期望的吸引子发展为期望的吸引子时,期望的吸引子的吸引池的大小可以减小或增大。在这种情况下,如果作者获得扰动状态的净BOS,则他们可以通过找到扰动增益的最大值来快速获得最佳的1位扰动。来自合成和真实生物网络的结果均表明,与Hun n。

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