论文标题

大规模布尔网络的分布式固定集稳定

Distributed Pinning Set Stabilization of Large-Scale Boolean Networks

论文作者

Zhu, Shiyong, Lu, Jianquan, Sun, Liangjie, Cao, Jinde

论文摘要

在本文中,我们将分布式固定控制器设计为全球稳定布尔网络(BN),特别是一个稀疏连接的大规模连接,通过节点到节点消息交换,将状态空间的固定子集固定在固定的状态空间。鉴于指定的状态集,系统节点分为两个不相交的部分,它们分别收集了与给定状态集的状态固定或任意的节点。使用这样的节点分裂,选择了三个部分的节点,并且对状态反馈控制器进行了相应的设计,使得BN满足三个条件:其他节点的状态不能影响固定状态节点的淋巴结动力学,而固定态节点的固定状态是固定状态的固定状态,以及由固定状态的稳定状态,以及由固定状态的稳定状态,由固定状态定为固定状态。如果对照后的BN是无环的,则稳定时间不超过当前网络结构加一个最长路径的长度。这使我们能够在稳定时间的限制下进一步设计固定控制器。指出,相对于固定节点的动力学数量最多的功能变量,总体过程在指数级增加的时间内运行,可以在合理的时间内很好地解决固定节点的动力学。最后,我们在T-LGL存活信号网络中演示了我们的理论结果的应用,该网络具有$ 29 $节点和T细胞受体信号网络,并带有90美元的节点。

In this article, we design the distributed pinning controllers to globally stabilize a Boolean network (BN), specially a sparsely connected large-scale one, towards a preassigned subset of state space through the node-to-node message exchange. Given an appointed state set, system nodes are partitioned into two disjoint parts, which respectively gather the nodes whose states are fixed or arbitrary with respect to the given state set. With such node division, three parts of pinned nodes are selected and the state feedback controllers are accordingly designed such that the resulting BN satisfies three conditions: the states of the other nodes cannot affect the nodal dynamics of fixed-state nodes, the subgraph of network structure induced by the fixed-state nodes is acyclic, and the steady state of the subnetwork induced by the fixed-state nodes lies in the state set given beforehand. If the BN after control is acyclic, the stabilizing time is revealed to be no more than the length of the longest path in the current network structure plus one. This enables us to further design the pinning controllers with the constraint of stabilizing time. Noting that the overall procedure runs in an exponentially increasing time with respect to the largest number of functional variables in the dynamics of pinned nodes, the sparsely-connected large-scale BNs can be well addressed in a reasonable amount of time. Finally, we demonstrate the applications of our theoretical results in a T-LGL survival signal network with $29$ nodes and T-cell receptor signaling network with $90$ nodes.

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