Abstract
The network science-based determination of driver nodes and sensor placement has become increasingly popular in the field of dynamical systems over the last decade. In this paper, the applicability of the methodology in the field of life sciences is introduced through the analysis of the neural network of Caenorhabditis elegans. Simultaneously, an Octave and MATLAB-compatible NOCAD toolbox is proposed that provides a set of methods to automatically generate the relevant structural controllability and observability associated measures for linear or linearised systems and compare the different sensor placement methods.
Highlights
An alternative first paragraph: "In the life sciences, the determination of nodes that play a significant role in networks is an intensively researched field^1
The toolbox offers two methods to design a structurally controllable and observable system based on the adjacency matrix (AT)
The toolbox serves five methods to improve the number of drivers cost relative degree mean of rel. deg. input robustness input robustness (%) number of sensors cost relative degree mean of rel. deg. output robustness output robustness (%)
Summary
An alternative first paragraph: "In the life sciences, the determination of nodes that play a significant role in networks (e.g., in the emergence or the treatment of diseases) is an intensively researched field^1. You should start by briefly introducing the general ideas of dynamical analysis of networks, precise in what sense you use the term "driver node", which is sometimes used in a biological context etc. In the section entitled Methods, we introduced the theoretical background to network-based structural controllability and observability analysis. A reader coming from a control theory background might expect the toolbox to provide results based on Kalman’s definition of controllability and observability. We think that the definitions of controllability and observability should be mentioned on the main text, the authors provide some background on the implemented maximum matching algorithms in the toolbox.
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