Asymptotic Synchronization Analysis in Drive-Response Markovian Jump Boolean Networks

This paper explores the asymptotic synchronization of drive-response Markovian jump Boolean networks (MJBNs) using an algebraic state space representation. For two switching signals, an augmented variable is introduced, recasting the synchronization problem into an asymptotic set stability challenge. Initially, a criterion is developed to identify all nonnegative solutions of a related equation via stochastic processes. Then, an alternative criterion based on invariant subsets and the largest singular value is proposed, where the equivalence of the proposed two criteria is demonstrated. Contrasting with deterministic BNs, where synchronization methods depend on finite power of state transition matrices, the criteria for MJBNs involve a more intricate approach. Numerical examples are included to validate the effectiveness of the proposed theoretical concepts.

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Publication Details

Journal
IEEE Transactions on Computational Biology and Bioinformatics
Published
2025-03-06
DOI
https://doi.org/10.1109/tcbbio.2025.3548559
Citations
2
Primary Topic
Gene Regulatory Network Analysis
Type
article
Field-Weighted Citation Impact
1.61

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article

Asymptotic Synchronization Analysis in Drive-Response Markovian Jump Boolean Networks

Yong Wang, Jie Zhong, Chi Huang, Yaqi Wang et al.
2 citations
IEEE Transactions on Computational Biology and Bioinformatics
Gene Regulatory Network Analysis
1.61
article

Asymptotic Synchronization Analysis in Drive-Response Markovian Jump Boolean Networks

Yong Wang, Jie Zhong, Chi Huang, Yaqi Wang, Qinyao Pan, Ning Li
article en
2 citations

Abstract

This paper explores the asymptotic synchronization of drive-response Markovian jump Boolean networks (MJBNs) using an algebraic state space representation. For two switching signals, an augmented variable is introduced, recasting the synchronization problem into an asymptotic set stability challenge. Initially, a criterion is developed to identify all nonnegative solutions of a related equation via stochastic processes. Then, an alternative criterion based on invariant subsets and the largest singular value is proposed, where the equivalence of the proposed two criteria is demonstrated. Contrasting with deterministic BNs, where synchronization methods depend on finite power of state transition matrices, the criteria for MJBNs involve a more intricate approach. Numerical examples are included to validate the effectiveness of the proposed theoretical concepts.

IEEE Transactions on Computational Biology and BioinformaticsVol. 22(3)
Zhejiang Normal University (CN), Qufu Normal University (CN), Southwestern University of Finance and Economics (CN), Henan University of Economic and Law (CN)
National Natural Science Foundation of China, Natural Science Foundation of Shandong Province, Science and Technology Innovation Talents in Universities of Henan Province
Openalex Percentile: Top 19%
Gene Regulatory Network Analysis
1.61
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Asymptotic Synchronization Analysis in Drive-Response Markovian Jump Boolean Networks — Yong Wang, Jie Zhong, et al. · IEEE Transactions on Computational Biology and Bioinformatics (2025) | TGRS Research Map | TGRS