Nonlinear Control And Drug Target Identification Of Complex Disease Networks

Because of the diversity of pathogenic causes and the complexity of treatment,complex diseases have become the leading killer of human health and one of the major challenges in biomedical research in the 21 st century.At present,the network model has been successfully applied to the study of complex diseases.Using disease data to establish single-layer or multilayer biological network model,from the perspective of network dynamics to study the pathogenic genes of complex diseases,determine the drug targets of complex diseases,and so on.The solution of these problems plays an extremely important role in the cure of complex diseases.Because the ultimate goal of studying biological network is to guide the state of biological system to the ideal state by applying signals,that is to realize the control of the network.Therefore,the study of network control is particularly important for the study of complex diseases.At present,although great progress has been made in the study of linear and nonlinear control of single-layer networks,it has been observed that many complex biological systems actually have multilayer network structures and extremely complex non-linear processes.In this paper,a general framework is proposed to transform the non-linear control problem of multilayer networks into the minimum union optimization problem.In particular,we propose a new algorithm that can determine the minimum set of driver nodes for a multilayer biological network to control the state of a multilayer non-linear dynamic system.Three actual disease-related biological multilayer networks were used to justify the method.We found that in the multilayer network minimum driver nodes set identified by the algorithm proposed in this paper,some nodes have been proved to be drug targets in biological experiments.By consulting the literature,other nodes that have not yet been considered as drug targets are still involved in important biological processes.