Shulman Jason, Malatino Franck, Mo Alexander, Ryan Killian, Gunaratne Gemunu H
Department of Physics, Richard Stockton College of New Jersey, Galloway, NJ 08205.
Bellaire High School, Bellaire, TX 77041.
Sci Rep. 2014 Dec 19;4:7574. doi: 10.1038/srep07574.
Control of complex processes is a major goal of network analyses. Most approaches to control nonlinearly coupled systems require the network topology and/or network dynamics. Unfortunately, neither the full set of participating nodes nor the network topology is known for many important systems. On the other hand, system responses to perturbations are often easily measured. We show how the collection of such responses -a response surface- can be used for network control. Analyses of model systems show that response surfaces are smooth and hence can be approximated using low order polynomials. Importantly, these approximations are largely insensitive to stochastic fluctuations in data or measurement errors. They can be used to compute how a small set of nodes need to be altered in order to direct the network close to a pre-specified target state. These ideas, illustrated on a nonlinear electrical circuit, can prove useful in many contexts including in reprogramming cellular states.
复杂过程的控制是网络分析的一个主要目标。大多数控制非线性耦合系统的方法都需要网络拓扑结构和/或网络动态特性。不幸的是，对于许多重要系统而言，既不知道完整的参与节点集，也不清楚网络拓扑结构。另一方面，系统对扰动的响应通常很容易测量。我们展示了如何将这种响应的集合——一个响应面——用于网络控制。对模型系统的分析表明，响应面是平滑的，因此可以用低阶多项式进行近似。重要的是，这些近似对数据中的随机波动或测量误差很大程度上不敏感。它们可用于计算需要如何改变一小部分节点，以便将网络引导至接近预先指定的目标状态。这些在非线性电路中阐述的想法，在包括细胞状态重编程在内的许多情况下可能会很有用。
