×

Robust \(\mu\)-stability analysis of Markovian switching uncertain stochastic genetic regulatory networks with unbounded time-varying delays. (English) Zbl 1251.92014

Summary: This paper investigates the global robust stability problem of Markovian switching uncertain stochastic genetic regulatory networks with unbounded time-varying delays and norm bounded parameter uncertainties. The structure variations at discrete time instances during the process of gene regulations known as hybrid genetic regulatory networks based on Markov process are proposed. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. The concept of global robust \(\mu \)-stability in the mean square for genetic regulatory networks is given. Based on Lyapunov functions, stochastic theory and Itô’s differential formula, the stability criteria are presented in the form of linear matrix inequalities (LMIs). Numerical examples are presented to demonstrate the effectiveness of the main result.

MSC:

92C42 Systems biology, networks
60J75 Jump processes (MSC2010)
15A45 Miscellaneous inequalities involving matrices
Full Text: DOI

References:

[1] Becskei, A.; Serrano, L., Engineering stability in gene networks by autoregulation, Nature, 405, 590-593 (2000)
[2] Chen, L.; Aihara, K., Stability of genetic regulatory networks with time delay, IEEE Trans Circuits Syst - Part I, 49, 602-608 (2002) · Zbl 1368.92117
[3] Gardner, T.; Cantor, C.; Collins, J., Construction of a genetic toggle switch in Escherichia coli, Nature, 403, 339-342 (2000)
[4] Casey, R.; Jong, H. D.; Gouzé, J.-L., Piecewise-linear models of genetic regulatory networks: equilibria and their stability, J Math Biol, 52, 27-56 (2006) · Zbl 1091.92030
[5] Wang, Y.; Ma, Z.; Shen, J.; Liu, Z.; Chen, L., Periodic oscillation in delayed gene networks with SUM regulatory logic and small perturbations, Math Biosci, 22034-22044 (2009)
[6] Wei G, Wang Z, Lam J, Fraser K, Prasada Rao G, Liu X. Robust filtering for stochastic genetic regulatory networks with time-varying delay, Math Biosci, in press. doi:10.1016/j.mbs.2009.04.002; Wei G, Wang Z, Lam J, Fraser K, Prasada Rao G, Liu X. Robust filtering for stochastic genetic regulatory networks with time-varying delay, Math Biosci, in press. doi:10.1016/j.mbs.2009.04.002 · Zbl 1168.92020
[7] Li, C.; Chen, L.; Aihara, K., Synchronization of coupled nonidentical genetic oscillators, Phys Biol, 3, 37-44 (2006)
[8] Chesi, G.; Chen, L.; Aihara, K., On the robust stability of time-varying uncertain genetic regulatory networks, Int J Robust Nonlinear Control, 21, 1778-1790 (2011) · Zbl 1226.92031
[9] Chesi, G., Robustness analysis of genetic regulatory networks affected by model uncertainty, Automatica, 47, 1131-1138 (2011) · Zbl 1235.93022
[10] Gibson, M.; Bruck, J., Efficient exact stochastic simulation of chemical systems with many species and many channels, J Phys Chem A, 104, 1876-1889 (2000)
[11] Phillips, R.; Kondev, J.; Theriot, J., Physical biology of the cell, Garland Science (2008)
[12] Ugander J. Delay-dependent stability of genetic regulatory networks. Master Thesis. July 2008.; Ugander J. Delay-dependent stability of genetic regulatory networks. Master Thesis. July 2008.
[13] Kauffman, S. A., Metabolic stability and epigenesis in randomly constructed genetic nets, J Theor Biol, 22, 437-467 (1969)
[14] Thomas, R., Boolean formalization of genetic control circuits, J Theor Biol, 42, 3, 563-585 (1973)
[15] Chen T, He H, Church G. Modelling gene expression with differential equations. In: Proceedings of the Pacific symposium on biocomputing, vol. 4; 1999. p. 29-40.; Chen T, He H, Church G. Modelling gene expression with differential equations. In: Proceedings of the Pacific symposium on biocomputing, vol. 4; 1999. p. 29-40.
[16] D’haeseleer P, Wen X, Fuhrman S, Somogyi R. Linear modelling of mRNA expression levels during CNS development and injury, In: Proceedings of the Pacific symposium on biocomputing, vol. 4, 1999. p. 41-52.; D’haeseleer P, Wen X, Fuhrman S, Somogyi R. Linear modelling of mRNA expression levels during CNS development and injury, In: Proceedings of the Pacific symposium on biocomputing, vol. 4, 1999. p. 41-52.
[17] de Hoon M, Imoto S, Kobayashi K, Ogasawara N, Miyano S. Infering gene regulatory networks from time-ordered gene expression data of Bacillus subtilis; de Hoon M, Imoto S, Kobayashi K, Ogasawara N, Miyano S. Infering gene regulatory networks from time-ordered gene expression data of Bacillus subtilis · Zbl 1219.92032
[18] Monk, N. A.M., Oscillatory expression of Hes1, p53, and NF-\( \kappa B\) driven by transcriptional time delays, Current Biol, 13, 1409-1413 (2003)
[19] Smolen, P.; Baxter, D.; Byrne, J., Mathematical modelling of gene networks, Neuron, 26, 567-580 (2000)
[20] Smolen, P.; Baxter, D.; Byrne, J., Modelling circadian oscillators with interlocking positive and negative feedback loops, J Neurosci, 21, 6644-6656 (2001)
[21] Tian, T.; Burragea, K.; Burragea, P. M.; Carlettib, M., Stochastic delay differential equations for genetic regulatory networks, J Comput Appl Math, 205, 696-707 (2007) · Zbl 1112.92029
[22] Hasty, J.; Pradlines, J.; Dolnik, M.; Collins, J. J., Noise-based switches and amplifiers for gene expression, Proc Matl Acad Sci, 97, 2075-2080 (2000)
[23] Kim, S., Can Markov chain models mimic biological regulation?, J Biol Syst, 10, 337-357 (2002) · Zbl 1113.92309
[24] Li, C.; Chen, L.; Aihara, K., Stability of genetic networks with sum regulatory logic: Lur’s system and LMI approach, IEEE Trans Circuits Syst - Part I, 53, 2451-2458 (2006) · Zbl 1374.92045
[25] Ren, F.; Cao, J., Asymptotic and robust stability of genetic regulatory networks with time-varying delays, Neurocomputing, 71, 834-842 (2008)
[26] Li, C.; Chen, L.; Aihara, K., Stochastic stability of genetic networks with disturbance attenuation, IEEE Trans Circuits Syst, I 54, 892-896 (2007)
[27] Wang, Z.; Gao, H.; Cao, J.; Liu, X., On delayed genetic regulatory networks with polytopic uncertainties: Robust stability analysis, IEEE Trans Nanobiosci, 7, 154-163 (2008)
[28] Chesi, G.; Hung, Y. S., Stability analysis of uncertain genetic SUM regulatory networks, Automatica, 44, 2298-2305 (2008) · Zbl 1153.93016
[29] Chesi, G., Computing equilibrium points of genetic regulatory networks, Trans Comput Syst Biol XI, LNBI, 5750, 268-282 (2009) · Zbl 1260.92026
[30] Wu H, Liao X, Guo S, Feng W, Wang Z. Stochastic stability for uncertain genetic regulatory networks with interval time-varying delays. Neurocomputing. doi:10.1016/j.neucom.2009.02.003; Wu H, Liao X, Guo S, Feng W, Wang Z. Stochastic stability for uncertain genetic regulatory networks with interval time-varying delays. Neurocomputing. doi:10.1016/j.neucom.2009.02.003 · Zbl 1194.92032
[31] Wang, G.; Cao, J., Robust exponential stability analysis for stochastic genetic networks with uncertain parameters, Commun Nonlinear Sci Numer Simul, 14, 3369-3378 (2009) · Zbl 1221.93217
[32] Sun, Y.; Feng, G.; Cao, J., Stochastic stability of Markovian switching genetic regulatory networks, Phys Lett A, 373, 1646-1652 (2009) · Zbl 1229.92041
[33] Chen, B.-S.; Wu, W.-S., Robust filtering circuit design for stochastic gene networks under intrinsic and extrinsic molecular noises, Math Biosci, 211, 342-355 (2008) · Zbl 1130.92023
[34] Liu, X.; Chen, T., Robust \(μ\) stability analysis for uncertain stochastic neural networks with unbounded time-varying delays, Physica A, 387, 2952-2962 (2008)
[35] Chen, T.; Wang, L., Global \(μ\) stability of delayed neural networks with unbounded time-varying delays, IEEE Trans Neural Networks, 18, 705-709 (2007)
[36] Elowitz, M. B.; Leibler, S., A synthetic oscillatory network of transcriptional regulators, Nature, 403, 335 (2000)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.