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Markov切换时滞基因调控网络的均方同步和随机无源同步

曹娟 任凤丽

曹娟,任凤丽. Markov切换时滞基因调控网络的均方同步和随机无源同步 [J]. 应用数学和力学,2022,43(2):198-206 doi: 10.21656/1000-0887.420256
引用本文: 曹娟,任凤丽. Markov切换时滞基因调控网络的均方同步和随机无源同步 [J]. 应用数学和力学,2022,43(2):198-206 doi: 10.21656/1000-0887.420256
CAO Juan, REN Fengli. Mean-Square Synchronization and Stochastically Passive Synchronization of Delayed Gene Regulatory Networks With Markovian Switching[J]. Applied Mathematics and Mechanics, 2022, 43(2): 198-206. doi: 10.21656/1000-0887.420256
Citation: CAO Juan, REN Fengli. Mean-Square Synchronization and Stochastically Passive Synchronization of Delayed Gene Regulatory Networks With Markovian Switching[J]. Applied Mathematics and Mechanics, 2022, 43(2): 198-206. doi: 10.21656/1000-0887.420256

Markov切换时滞基因调控网络的均方同步和随机无源同步

doi: 10.21656/1000-0887.420256
详细信息
    作者简介:

    曹娟(1997—),女,硕士(E-mail:caojuan@nuaa.edu.cn)

    任凤丽(1978—),女,副教授(通讯作者. E-mail:flren@nuaa.edu.cn)

  • 中图分类号: O193

Mean-Square Synchronization and Stochastically Passive Synchronization of Delayed Gene Regulatory Networks With Markovian Switching

  • 摘要:

    基因调控网络(GRNs)及其动力学模型的研究在后基因组时代是一个重要的研究领域。定性分析基因调控网络及其动力学对系统地认识生物体具有重要意义。该文提出了一类具有时变时滞和Markov切换的随机基因调控网络模型,研究了其均方同步和随机无源同步问题。通过设计合适的Lyapunov-Krasovskii泛函(LKF),并利用Lyapunov稳定性理论、线性矩阵不等式方法和随机分析技巧,得到了均方同步和随机无源同步的充分条件。此外,通过与其他文献进行比较,显示了该文结果的理论价值。数值模拟验证了所得充分条件的有效性。

  • 图  1  第一个基因mRNA浓度误差

    Figure  1.  The error state of mRNA concentration of the 1st gene

    图  2  第二个基因mRNA浓度误差

    Figure  2.  The error state of mRNA concentration of the 2nd gene

    图  3  第一个基因蛋白质浓度误差

    Figure  3.  The error state of protein concentration of the 1st gene

    图  4  第二个基因mRNA浓度误差

    Figure  4.  The error state of protein concentration of the 2nd gene

    图  5  Markov链的切换模式

    Figure  5.  The evolutionary process of Markovian switching

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  • 被引次数: 0
出版历程
  • 收稿日期:  2021-08-31
  • 录用日期:  2021-08-31
  • 修回日期:  2021-12-15
  • 网络出版日期:  2022-01-06
  • 刊出日期:  2022-02-01

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