Robust and structural ergodicity analysis of stochastic biomolecular networks involving synthetic antithetic integral controllers

被引:1
|
作者
Briat, Corentin [1 ]
Khammash, Mustafa [1 ]
机构
[1] Swiss Fed Inst Technol, Dept Biosyst Sci & Engn, Zurich, Switzerland
来源
IFAC PAPERSONLINE | 2017年 / 50卷 / 01期
关键词
Stochastic reaction networks; antithetic integral control; synthetic biology; robustness; polynomial methods; COPOSITIVE LYAPUNOV FUNCTIONS; SYSTEMS; POLYNOMIALS; MATRICES; NOISE;
D O I
10.1016/j.ifacol.2017.08.2457
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The concepts of ergodicity and output controllability have been shown to be fundamental for the analysis and synthetic design of closed-loop stochastic reaction networks, as exemplified by the use of antithetic integral feedback controllers. In [Gupta, Briat Khammash, PLoS Comput. Biol., 2014], some ergodicity and output controllability conditions for unimolecular and certain classes of bimolecular reaction networks were obtained and formulated through linear programs. To account for context dependence, these conditions were later extended in [Briat & Khammash, CDC, 2016] to reaction networks with uncertain rate parameters using simple and tractable, yet potentially conservative, methods. Here we develop some exact theoretical methods for verifying, in a robust setting, the original ergodicity and output controllability conditions based on algebraic and polynomial techniques. Some examples are given for illustration. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码:10918 / 10923
页数:6
相关论文
共 50 条
  • [21] Robust Passivity Analysis of Stochastic Genetic Regulatory Networks with Levy Noise
    Jothiappan, Palraj
    Kalidass, Mathiyalagan
    INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2022, 20 (10) : 3241 - 3251
  • [22] Robust Passivity Analysis of Stochastic Genetic Regulatory Networks with Levy Noise
    Palraj Jothiappan
    Mathiyalagan Kalidass
    International Journal of Control, Automation and Systems, 2022, 20 : 3241 - 3251
  • [23] Robust stabilization and H∞ controllers design for stochastic genetic regulatory networks with time-varying delays and structured uncertainties
    He, Yong
    Zeng, Jin
    Wu, Min
    Zhang, Chuan-Ke
    MATHEMATICAL BIOSCIENCES, 2012, 236 (01) : 53 - 63
  • [25] Adaptive Robust Stochastic Configuration Networks for Near-Infrared Multivariate Analysis
    Li, Yuqiang
    Du, Wenli
    Wang, Xinjie
    Yang, Minglei
    Zhao, Yunmeng
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2025,
  • [26] Robust stability analysis of neutral stochastic neural networks with delay: An LMI approach
    Cui, Baotong
    Lou, Xuyang
    2007 IEEE INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION, VOLS 1-7, 2007, : 2609 - 2614
  • [27] Robust Stability Analysis for Stochastic Neural Networks With Time-Varying Delay
    Chen, Wu-Hua
    Zheng, Wei Xing
    IEEE TRANSACTIONS ON NEURAL NETWORKS, 2010, 21 (03): : 508 - 514
  • [28] Robust passivity analysis of a class of discrete-time stochastic neural networks
    Guodong Shi
    Qian Ma
    Yi Qu
    Neural Computing and Applications, 2013, 22 : 1509 - 1517
  • [29] Stochastic robust stability analysis for Markovian jumping neural networks with time delays
    Xie, L
    2005 IEEE Networking, Sensing and Control Proceedings, 2005, : 923 - 928
  • [30] Stochastic robust stability analysis for Markovian jump neural networks with time delay
    Xie, L
    ADVANCES IN NATURAL COMPUTATION, PT 1, PROCEEDINGS, 2005, 3610 : 386 - 389