Minimum observability of probabilistic Boolean networks (2401.12468v1)
Abstract: This paper studies the minimum observability of probabilistic Boolean networks (PBNs), the main objective of which is to add the fewest measurements to make an unobservable PBN become observable. First of all, the algebraic form of a PBN is established with the help of semi-tensor product (STP) of matrices. By combining the algebraic forms of two identical PBNs into a parallel system, a method to search the states that need to be H-distinguishable is proposed based on the robust set reachability technique. Secondly, a necessary and sufficient condition is given to find the minimum measurements such that a given set can be H-distinguishable. Moreover, by comparing the numbers of measurements for all the feasible H-distinguishable state sets, the least measurements that make the system observable are gained. Finally, an example is given to verify the validity of the obtained results.
- K. Kauffman, “Metabolic stability and epigenesis in randomly constructed genetic nets,” Journal of Theoretical Biology, 1969, 22(3): 437-467.
- T. Akutsu, M. Hayashida, W. Ching, “Control of Boolean networks: hardness results and algorithms for tree structured networks,” Journal of Theoretical Biology, 2007, 244(4): 670-679.
- H. Lähdesmäki, I. Shmulevich, and O. Yli-Harja, “On learning gene regulatory networks under the Boolean network model,” Machine Learning, 2003, 52: 147-167.
- A. Nazi, M. Raj, M. Francesco, P. Ghoshet and S. Das, “Deployment of robust wireless sensor networks using gene regulatory networks: an isomorphism-based approach,” Pervasive And Mobile Computing, 2014, 13: 246-257.
- D. Green, T. Leishman, S. Sadedin, “The emergence of social consensus in Boolean networks,” IEEE Symposium on Artificial Life, 2007, 402-408.
- J. Lu, B. Li, J. Zhong, “A novel synthesis method for reliable feedback shift registers via Boolean networks,” Science China Information Sciences, 2021, 64: 1-14.
- H. Zheng, D. Shi, “A multi-agent system for environmental monitoring using boolean networks and reinforcement learning,” Journal of Cybersecurity, 2020, 2(2): 85.
- I. Shmulevich, E. Dougherty, S. Kim, W. Zhang, “Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks,” Bioinformatics, 2002, 18(2): 261-274.
- R. Pal, A. Datta, M. Bittner, “Intervention in context-sensitive probabilistic Boolean networks,” Bioinformatics, 2005, 21(7): 1211-1218.
- Q. Liu, “An optimal control approach to probabilistic Boolean networks,” Physica A-Statistical Mechanics And Its Applications, 2012, 39(24): 6682-6689.
- K. Kobayashi, K. Hiraishi, “Optimization-based approaches to control of probabilistic Boolean networks,” Algorithms, 2017, 10(1): 31.
- I. Shmulevich, I. Gluhovsky, R. Hashimoto, “Steady-state analysis of genetic regulatory networks modelled by probabilistic Boolean networks,” Comparative and functional genomics, 2003, 4(6): 601-608.
- K. Kobayashi, K. Hiraishi, “Reachability analysis of probabilistic Boolean networks using model checking,” Proceedings of SICE Annual Conference, IEEE, 2010, 829-832.
- D. Cheng and H. Qi, “A linear representation of dynamics of Boolean networks,” IEEE Transactions On Automatic Control, 2010, 55(10): 2251-2258.
- D. Cheng and H. Qi, “Controllability and observability of Boolean control networks,” Automatica, 2009, 45(7): 1659-1667.
- Y. Liu, H. Chen, J. Lu and B. Wu, “Controllability of probabilistic Boolean control networks based on transition probability matrices”, Automatica, 2015, 52: 340-345.
- H. Li, S. Wang, X. Li and G. Zhao, “Perturbation analysis for controllability of logical control networks,” Siam Journal On Control And Optimization, 2020, 58(6): 3632-3657.
- S. Zhu, J. Lu, S. Azuma, and W. Zheng, “Strong Structural Controllability of Boolean Networks: Polynomial-Time Criteria, Minimal Node Control, and Distributed Pinning Strategies,” IEEE Transactions on Automatic Control, 2022, 3226701.
- Y. Guo, “Observability of Boolean control networks using parallel extension and set reachability,” IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(12): 6402-6408.
- E. Fornasini and M. Valcher, “Observability and reconstructibility of probabilistic Boolean networks,” IEEE Control Systems Letters, 2020, 4(2): 319-324.
- S. Zhu, J. Lu, J. Zhong, Y. Liu, and J. Cao, “On the Sensors Construction of Large Boolean Networks via Pinning Observability,” IEEE Transactions on Automatic Control, 2022, 67(8): 4162-4169.
- B. Wang and J. Feng, “On detectability of probabilistic Boolean networks,” Information Sciences, 2019, 483: 383-395.
- B. Wang and J. Feng, “Detectability of Boolean networks with disturbance inputs,” Systems And Control Letters, 2020, 145: 104783.
- Y. Guo, R. Zhou, Y. Wu, W. Gui and C. Yang, “Stability and set stability in distribution of probabilistic Boolean networks,” IEEE Transactions on Automatic Control, 2018, 64(2): 736-742.
- H. Li, X. Yang, S. Wang, “Robustness for stability and stabilization of Boolean networks with stochastic function perturbations,” IEEE Transactions on Automatic Control, 2021, 66(3): 1231-1237.
- R. Li, M. Yang, and T. G. Chu, “State feedback stabilization for probabilistic Boolean networks,” Automatica, 2014, 50(4): 1272-1278.
- J. Feng, Y. Li, S. Fu and H. Lyu, “New method for disturbance decoupling of Boolean networks,” IEEE Transactions on Automatic Control, 2022, 3161609.
- Y. Wu and T. Shen, “Policy iteration algorithm for optimal control of stochastic logical dynamical systems,” IEEE Transactions On Neural Networks And Learning Systems, 2018, 29(5): 2031-2036.
- H. Li, X. Yang, “Robust optimal control of logical control networks with function perturbation,” Automatica, 2023, 152: 110970.
- S. Wang, H. Li, “Aggregation method to reachability and optimal control of large-size Boolean control networks,” Science China Information Sciences., 2023, 66(7): 179202.
- G. Zhao, Y. Wang, H. Li, “A matrix approach to the modeling and analysis of networked evolutionary games with time delays,” IEEE/CAA Journal of Automatica Sinica, 2016, 5(4): 818-826.
- Y. Wu, S. Le, K. Zhang, X. Sun, “Ex-ante agent transformation of Bayesian games,” IEEE Transactions on Automatic Control, 2022, 67(11): 5793-5808.
- Y. Yan, D. Cheng, J. Feng, H. Li, and J. Yue, “Survey on applications of algebraic state space theory of logical systems to finite state machines,” Science China Information Sciences, 2023, 66(1): 111201.
- D. Laschov, M. Margaliot, G. Even, “Observability of Boolean networks: A graph-theoretic approach,” Automatica 2013, 49(8), 2351-2362.
- Y. Wu, J. Xu, X. Sun, W. Wang, “Observability of Boolean multiplex control networks,” Scientific Reports, 2017, 7(46495): 1-15.
- D. Cheng, C. Li, F. He, “Observability of Boolean networks via set controllability approach,” Systems And Control Letters, 2018, 115: 22-25.
- K. Zhang, L. Zhang, “Observability of Boolean control networks: A unified approach based on finite automata,” IEEE Transactions on Automatic Control 2016, 61(9): 2733-2738.
- K. Zhang, K. Johansson, “Efficient verification of observability and reconstructibility for largeBoolean control networks with special structures,” IEEE Transactions on Automatic Control, 2020, 65(12): 5144-5158.
- J. Zhao, Z. Liu, “Observability of probabilistic Boolean networks,” Chinese Control Conference, 2015, 183-186.
- R. Zhou, Y. Guo, and W. Gui, “Set reachability and observability of probabilistic Boolean networks,” Automatica, 2019, 106: 230-241.
- E. Fornasini, M. Valcher, “Observability and reconstructibility of probabilistic Boolean networks,” IEEE Control Systems Letters, 2020, 4(2): 319-324.
- E. Weiss, M. Margaliot, “A polynomial-time algorithm for solving the minimal observability problem in conjunctive Boolean networks,” Transactions On Automatic Control, 2019, 64: 2727-2736.
- Y. Liu, L. Wang, Y. Yang and Z. Wu, “Minimal observability of Boolean control networks”, Systems And Control Letters, 2022, 163: 105204.
- Y. Liu, J. Zhong, “Minimal observability of Boolean networks”, Science China Information Sciences 2022, 65(5): 152203.
- J. Feng, J. Yao, P. Cui, “Singular Boolean networks: semi-tensor product approach,” Science China Information Sciences, 2013, 56: 1-14.
- X. Yang, H. Li. “Function perturbation impact on asymptotical stability of probabilistic Boolean networks: Changing to finite-time stability,” Journal of the Franklin Institute, 2020, 357(15): 10810-10827.
- A. Julius, A. Halasz, M. Sakar, H. Rubin, V. Kumar and G. Pappas, “Stochastic modeling and control of biological systems: The lactose regulation system of Escherichia coli,” IEEE Transactions On Automatic Control, 2008, 53: 51-65.
- Y. Guo, R. Zhou, Y. Wu, W. Gui and C. Yang. “Stability and set stability in distribution of probabilistic Boolean networks,” IEEE Transactions on Automatic Control, 2019, 64(2): 736-742