Distributed nonsynchronous event-triggered state estimation of genetic regulatory networks with hidden Markovian jumping parameters

Chao Ma; Yanfeng Lu; Chao Ma; Yanfeng Lu

doi:10.3934/mbe.2022647

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 12: 13878-13910. doi: 10.3934/mbe.2022647

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Research article

Distributed nonsynchronous event-triggered state estimation of genetic regulatory networks with hidden Markovian jumping parameters

Chao Ma ¹,
Yanfeng Lu ^{2,3
,
,}

1.
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Science, Beijing 100190, China
3.
State Key Lab of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Academic editor: Danilo Pelusi

Received: 03 July 2022 Revised: 24 August 2022 Accepted: 01 September 2022 Published: 20 September 2022

In this paper, the distributed state estimation problem of genetic regulatory networks (GRNs) with hidden Markovian jumping parameters (HMJPs) is explored. Furthermore, in order to improve the communication efficiency among state estimation sensors, the event-triggered strategy is employed in the distributed framework for sensor networks. Particularly, by considering the fact that the true modes are always unaccessible, a novel nonsynchronous state estimation (NSE) strategy is utilized based on observed hidden mode information. By means of Lyapunov-Krasovski method, sufficient stochastic state estimation analysis and synthesis results are established, such that the concentrations of mRNA and protein in GRNs can be both well estimated by convex optimization. Finally, an illustrative example with relevant simulations results is provided to validate the applicability and effectiveness of the developed state estimation approach.
Citation: Chao Ma, Yanfeng Lu. Distributed nonsynchronous event-triggered state estimation of genetic regulatory networks with hidden Markovian jumping parameters[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 13878-13910. doi: 10.3934/mbe.2022647

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Abstract

In this paper, the distributed state estimation problem of genetic regulatory networks (GRNs) with hidden Markovian jumping parameters (HMJPs) is explored. Furthermore, in order to improve the communication efficiency among state estimation sensors, the event-triggered strategy is employed in the distributed framework for sensor networks. Particularly, by considering the fact that the true modes are always unaccessible, a novel nonsynchronous state estimation (NSE) strategy is utilized based on observed hidden mode information. By means of Lyapunov-Krasovski method, sufficient stochastic state estimation analysis and synthesis results are established, such that the concentrations of mRNA and protein in GRNs can be both well estimated by convex optimization. Finally, an illustrative example with relevant simulations results is provided to validate the applicability and effectiveness of the developed state estimation approach.

References

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