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On controllability and stabilizability of probabilistic Boolean control networks

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Abstract

The controllability of probabilistic Boolean control networks (PBCNs) is first considered. Using the input-state incidence matrices of all models, we propose a reachability matrix to characterize the joint reachability. Then we prove that the joint reachability and the controllability of PBCNs are equivalent, which leads to a necessary and sufficient condition of the controllability. Then, the result of controllability is used to investigate the stability of probabilistic Boolean networks (PBNs) and the stabilization of PBCNs. A necessary and sufficient condition for the stability of PBNs is obtained first. By introducing the control-fixed point of Boolean control networks (BCNs), the stability condition has finally been developed into a necessary and sufficient condition of the stabilization of PBCNs. Both necessary and sufficient conditions for controllability and stabilizability are based on reachability matrix, which are easily computable. Hence the two necessary and sufficient conditions are straightforward verifiable. Numerical examples are provided from case to case to demonstrate the corresponding theoretical results.

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References

  1. Kitano H. Foundations of Systems Biology. Cambrige: MIT Press, 2001

    Google Scholar 

  2. Kauffman S A. Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol, 1969, 22: 437–467

    Article  MathSciNet  Google Scholar 

  3. Huang S. Gene expression profiling, genetic networks, and cellular states: an integrating concept for tumorigenesis and drug discovery. J Mol Med, 1999, 77: 469–480

    Article  Google Scholar 

  4. Shmulevich I, Dougherty E R, Kim S, et al. Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics, 2002, 2: 261–274

    Article  Google Scholar 

  5. Xu H L, Wang S T. Research on the decomposition method of probabilistic Boolean gene regulatory networks (in Chinese). China J Bioinform, 2005, 4: 159–162

    Google Scholar 

  6. Brun M, Dougherty E R, Shmulevich I. Steady-state probabilities for attractors in probabilistic Boolean networks. Signal Process, 2005, 85: 1993–2013

    Article  MATH  Google Scholar 

  7. Shmulevich I, Hashimoto R F, Dougherty E R, et al. Steaty-state analysis of genetic regulatory networks modeled by probabilistic Boolean networks. Comp Funct Genomics, 2003, 4: 601–608

    Article  Google Scholar 

  8. Ideker T, Galitski T, Hood L. A new approach to decoding life: systems biology. Annu Rev Genomics Hum Genet, 2001, 2: 343–372

    Article  Google Scholar 

  9. Akutsu T, Hayashida M, Ching W K, et al. Control of Boolean networks: hardness results and algorithms for tree structured networks. J Theor Biol, 2007, 244: 670–679

    Article  MathSciNet  Google Scholar 

  10. Pal R, Datta A, Dougherty E R. Optimal infinite-horizon control for probabilistic Boolean networks. IEEE Trans Signal Process, 2006, 54: 2375–2387

    Article  Google Scholar 

  11. Faryabi B, Datta A, Dougherty E R. On approximate stochastic control in genetic regulatory networks. IET Syst Biol, 2007, 1: 361–368

    Article  Google Scholar 

  12. Ching W, Zhang S, Jiao Y, et al. Optimal control policy for probabilistic Boolean networks with hard constraints. IET Syst Biol, 2008, 3: 90–99

    Article  Google Scholar 

  13. Cheng D, Qi H. A linear representation of dynamics of Boolean networks. IEEE Trans Automat Contr, 2010, 55: 2251–2258

    Article  MathSciNet  Google Scholar 

  14. Cheng D. Semi-tensor product of matrices and its applications-a survey. In: Proceedings of ICCM, Hangzhou, 2007. 641–668

    Google Scholar 

  15. Cheng D, Qi H. State-space analysis of Boolean networks. IEEE Trans Neural Networks, 2010, 21: 584–594

    Article  MathSciNet  Google Scholar 

  16. Cheng D, Qi H, Li Z. Analysis and Control of Boolean Networks-A Semi-tensor Product Approach. London: Springer, 2011

    Book  MATH  Google Scholar 

  17. Cheng D, Qi H, Zhao Y. Analysis and control of Boolean networks: a semi-tensor product approach (in Chinese). ACTA Automat Sin, 2011, 37: 529–540

    MATH  MathSciNet  Google Scholar 

  18. Cheng D, Zhao Y. Semi-tensor product of matrices: a convenient new tool (in Chinese). Chin Sci Bull, 2011, 56: 2664–2674

    Article  MathSciNet  Google Scholar 

  19. Laschov D, Margaliot M. A maximum principle for single-input Boolean control networks. IEEE Trans Automat Control, 2011, 56: 913–917

    Article  MathSciNet  Google Scholar 

  20. Li F, Sun J. Controllability of Boolean control networks with time delays in states. Automatica, 2011, 47: 603–607

    Article  MATH  Google Scholar 

  21. Li H, Wang Y, Liu Z. Simultaneous stabilization of Boolean control networks via semi-tensor product method. In: Proceedings of 30th Chinese Control Conference, Yantai, 2011. 6386–6391

    Google Scholar 

  22. Silverman L M, Anderson B D O. Controllability, observability and stability of linear systems. SIAM J Contr Optimizat, 1968, 6: 121–130

    Article  MATH  MathSciNet  Google Scholar 

  23. Sun Y, Guo L. On global asymptotic controllability of planar affine nonlinear systems. Sci China Ser F-Inf Sci, 2005, 48: 703–712

    Article  MATH  MathSciNet  Google Scholar 

  24. Wang L, Jiang F C, Xie G M, et al. Controllability of multi-agent systems based on agreement protocols. Sci China Ser F-Inf Sci, 2009, 52: 2074–2088

    Article  MATH  MathSciNet  Google Scholar 

  25. Wang J H, Cheng D Z. Stability of switched nonlinear systems via extensions of LaSalle’s invariance principle. Sci China Ser F-Inf Sci, 2009, 52: 84–90

    Article  MATH  Google Scholar 

  26. Cheng D, Qi H. Controllability and observability of Boolean control networks. Automatica, 2009, 45: 1659–1667

    Article  MATH  MathSciNet  Google Scholar 

  27. Zhao Y, Qi H, Cheng D. Input-state incidence matrix of Boolean control networks and its applications. Syst Control Lett, 2010, 46: 767–774

    Article  MathSciNet  Google Scholar 

  28. Cheng D, Qi H, Li Z Q, et al. Stability and stabilization of Boolean networks. Int J Robust Nonlinear Contr, 2011, 21: 134–156

    Article  MATH  MathSciNet  Google Scholar 

  29. Li F, Sun J. Controllability of probabilistic Boolean control networks. Automatica, 2011, 47: 2765–2771

    Article  MATH  Google Scholar 

  30. Qi H, Cheng D, Hu X. Stabilization of random Boolean networks. In: Proceedings of 8th World Congress on Intelligent Control and Automation, Jinan, 2010. 1969–1973

    Google Scholar 

  31. Farrow C, Heidel J, Maloney H, et al. Scalar equations for synchronous Boolean networks with biological applications. IEEE Trans Neural Networks, 2004, 15: 348–354

    Article  Google Scholar 

  32. Brzezniak Z, Zastawniak T. Basic Stochastic Processes. London: Springer, 1999

    Book  MATH  Google Scholar 

  33. Ross K A, Wright C R B. Discrete Mathematics. 5th ed. New York: Prentice Hall, 2003

    Google Scholar 

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Correspondence to DaiZhan Cheng.

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Zhao, Y., Cheng, D. On controllability and stabilizability of probabilistic Boolean control networks. Sci. China Inf. Sci. 57, 1–14 (2014). https://doi.org/10.1007/s11432-013-4851-4

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