a robust control centrality applicable to genetic regulatory networks with structured uncertainties

Background and objectives: in genetic network control, rc-centrality is introduced as a new control centrality measure to address the control of linear time-invariant networks. the objective of this study is to propose an optimal control centrality metric that quantifies the centrality of individual nodes or groups of nodes within a network. specifically, rc-centrality identifies key nodes or node groups that can act as controllers, such as genes regulating the gene expression process. to assess the effectiveness of this method, rc-centrality is compared with standard centralities in a real genetic network. additionally, the research delves into the role of uncertainty structure in altering the priority order of rc-centrality.methods: the rc-centrality measure is introduced based on an optimal control problem to address weighted, directed, and signed networks. robust controllers are designed to ensure lyapunov stability under uncertainty. a cost function is introduced to measure the performance metric represented by input energy in the presence of uncertainty.results: the study presents rc-centrality as an effective measure for identifying key nodes in genetic networks suitable for control. in-silico simulations are conducted to evaluate its performance in comparison to standard centralities. the research highlights the impact of uncertainty structure on the priority of rc-centrality.conclusion: rc-centrality offers a promising approach to identify essential nodes in genetic networks for control purposes. its performance is demonstrated through simulations, and the study emphasizes the influence of uncertainty structure on the centrality measure’s prioritization. this research has implications for understanding and controlling genetic networks, particularly in the presence of uncertainty.