Structure-based approach to identifying small sets of driver nodes in biological networks
Structure-based approach to identifying small sets of driver nodes in biological networks
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DOI：
10.1063/5.0080843
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发表时间：
2022-06-01
期刊：
影响因子：
2.9
通讯作者：
Albert, Reka
中科院分区：
文献类型：
--
作者：
Newby, Eli;Zanudo, Jorge Gomez Tejeda;Albert, Reka
在网络控制理论中，通过节点状态覆盖驱动反馈顶点集（FVS）中的所有节点迫使网络进入其吸引子之一（长期动态行为）。 FVS 通常由比系统中实际可操作的节点更多的节点组成；例如，在蜂窝内网络中最多只能控制三个节点，而它们的FVS可能包含超过10个节点。因此，我们开发了一种方法，使用拓扑、动力学独立的测量方法对细胞内网络布尔模型上的 FVS 子集进行排序。我们研究了七种拓扑预测措施的使用，分为三类：中心性措施、传播措施和基于循环的措施。使用每种测量方法，对每个子集进行排名，然后根据两个基于动态的指标进行评估，这些指标衡量干预措施驱动系统接近或远离其吸引子的能力：控制和远离控制。在检查了一系列生物网络后，我们发现根据传播指标排名靠前的 FVS 子集可以最有效地控制网络。这一结果在第二组不同的生物网络布尔模型上得到了独立证实。因此，不需要覆盖整个 FVS 来将生物网络驱动到其吸引子之一，并且该方法提供了一种在不了解网络动态的情况下可靠地识别有效 FVS 子集的方法。由 AIP Publishing 独家许可出版。
In network control theory, driving all the nodes in the Feedback Vertex Set (FVS) by node-state override forces the network into one of its attractors (long-term dynamic behaviors). The FVS is often composed of more nodes than can be realistically manipulated in a system; for example, only up to three nodes can be controlled in intracellular networks, while their FVS may contain more than 10 nodes. Thus, we developed an approach to rank subsets of the FVS on Boolean models of intracellular networks using topological, dynamics-independent measures. We investigated the use of seven topological prediction measures sorted into three categories-centrality measures, propagation measures, and cycle-based measures. Using each measure, every subset was ranked and then evaluated against two dynamics-based metrics that measure the ability of interventions to drive the system toward or away from its attractors: To Control and Away Control. After examining an array of biological networks, we found that the FVS subsets that ranked in the top according to the propagation metrics can most effectively control the network. This result was independently corroborated on a second array of different Boolean models of biological networks. Consequently, overriding the entire FVS is not required to drive a biological network to one of its attractors, and this method provides a way to reliably identify effective FVS subsets without the knowledge of the network dynamics. Published under an exclusive license by AIP Publishing.