Department of Physics, Washington College, Chestertown, MD 21620, USA.
Department of Mechanical Engineering, University of Texas at Dallas, Richardson, TX 75080, USA.
Sci Rep. 2017 Apr 12;7:46251. doi: 10.1038/srep46251.
Many dynamic systems display complex emergent phenomena. By directly controlling a subset of system components (nodes) via external intervention it is possible to indirectly control every other component in the system. When the system is linear or can be approximated sufficiently well by a linear model, methods exist to identify the number and connectivity of a minimum set of external inputs (constituting a so-called minimal control topology, or MCT). In general, many MCTs exist for a given network; here we characterize a broad ensemble of empirical networks in terms of the fraction of nodes and edges that are always, sometimes, or never a part of an MCT. We study the relationships between the measures, and apply the methodology to the T-LGL leukemia signaling network as a case study. We show that the properties introduced in this report can be used to predict key components of biological networks, with potentially broad applications to network medicine.
许多动态系统表现出复杂的涌现现象。通过直接控制系统组件的一个子集（节点），可以通过外部干预间接控制系统中的每个其他组件。当系统是线性的或可以通过线性模型很好地近似时，就存在确定最小数量外部输入的方法（构成所谓的最小控制拓扑结构，或 MCT）。一般来说，对于给定的网络，存在许多 MCT；在这里，我们根据总是、有时或从不作为 MCT 一部分的节点和边的分数来描述广泛的经验网络集合。我们研究了这些度量之间的关系，并将该方法应用于 T-LGL 白血病信号网络作为案例研究。我们表明，本报告中引入的特性可用于预测生物网络的关键组成部分，这可能在网络医学中有广泛的应用。
