On the stability of multilayer Boolean networks under targeted immunization

被引:10
作者
Wang, Jiannan [1 ,2 ]
Zhang, Renquan [3 ]
Wei, Wei [1 ,2 ]
Pei, Sen [4 ]
Zheng, Zhiming [1 ,2 ]
机构
[1] Beihang Univ, Sch Math & Syst Sci, Beijing 100191, Peoples R China
[2] Minist Educ, Key Lab Math Informat Behav Semant LMIB, Beijing 100191, Peoples R China
[3] Dalian Univ Technol, Sch Math Sci, Dalian 116024, Peoples R China
[4] Columbia Univ, Dept Environm Hlth Sci, Mailman Sch Publ Hlth, New York, NY 10032 USA
基金
中国国家自然科学基金;
关键词
COLLECTIVE INFLUENCE; INFLUENCE MAXIMIZATION; MODELS;
D O I
10.1063/1.5053820
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In this paper, we study targeted immunization in a multilayer Boolean network model for genetic regulatory networks. Given a specific set of nodes immune to perturbations, we find that the stability of a multilayer Boolean network is determined by the largest eigenvalue of the weighted non-backtracking matrix of corresponding aggregated network. Aimed to minimize this largest eigenvalue, we developed the metric of multilayer collective influence (MCI) to quantify the impact of immunizing individual nodes on the stability of the system. Compared with other competing heuristics, immunizing nodes with high MCI scores can stabilize an unstable multilayer network with higher efficiency on both synthetic and real-world networks. Moreover, despite that coupling nodes can exert direct influence across multiple layers, they are found to exhibit less importance as measured by the MCI score. Our work reveals the mechanism of maintaining the stability of multilayer Boolean networks and provides an efficient targeted immunization strategy, which can be potentially applied to the location of pathogenesis of diseases and the development of targeted therapy. Published under license by AIP Publishing.
引用
收藏
页数:10
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