Research article

Analysis of finite-time stability in genetic regulatory networks with interval time-varying delays and leakage delay effects

  • Received: 01 July 2024 Revised: 05 August 2024 Accepted: 16 August 2024 Published: 27 August 2024
  • MSC : 34K20, 93D05, 93D40, 34D20

  • We primarily examined the effect of leakage delays on finite-time stability problems for genetic regulatory networks with interval time-varying delays. Since leakage delays can occur within the negative feedback components of networks and significantly impact their dynamics, they may potentially cause instability or suboptimal performance. The derived criteria encompass both leakage delays and discrete interval time-varying delays through the construction of a Lyapunov-Krasovskii function. We employed the estimation of various integral inequalities and a reciprocally convex technique. Additionally, these models consider lower bounds on delays, which may be either positive or zero, and allow for the derivatives of delays to be either positive or negative. Consequently, new criteria for genetic regulatory networks with interval time-varying delays under the effect of leakage delays are expressed in the form of linear matrix inequalities. Ultimately, a numerical example is presented to show the effect of leakage delays and to emphasize the significance of our theoretical findings.

    Citation: Nayika Samorn, Kanit Mukdasai, Issaraporn Khonchaiyaphum. Analysis of finite-time stability in genetic regulatory networks with interval time-varying delays and leakage delay effects[J]. AIMS Mathematics, 2024, 9(9): 25028-25048. doi: 10.3934/math.20241220

    Related Papers:

  • We primarily examined the effect of leakage delays on finite-time stability problems for genetic regulatory networks with interval time-varying delays. Since leakage delays can occur within the negative feedback components of networks and significantly impact their dynamics, they may potentially cause instability or suboptimal performance. The derived criteria encompass both leakage delays and discrete interval time-varying delays through the construction of a Lyapunov-Krasovskii function. We employed the estimation of various integral inequalities and a reciprocally convex technique. Additionally, these models consider lower bounds on delays, which may be either positive or zero, and allow for the derivatives of delays to be either positive or negative. Consequently, new criteria for genetic regulatory networks with interval time-varying delays under the effect of leakage delays are expressed in the form of linear matrix inequalities. Ultimately, a numerical example is presented to show the effect of leakage delays and to emphasize the significance of our theoretical findings.



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    [1] T. Akutsu, S. Miyano, S. Kuhara, Identification of genetic networks from a small number of gene expression patterns under the Boolean network model, Pac. Symp. Biocomputing, 4 (1999), 17–28. https://doi.org/10.1142/9789814447300_0003 doi: 10.1142/9789814447300_0003
    [2] M. J. Beal, F. Falciani, Z. Ghahramani, C. Rangel, D. L. Wild, A Bayesian approach to reconstructing genetic regulatory networks with hidden factors, Bioinform., 21 (2005), 349–356. http://doi.org/10.1093/bioinformatics/bti014 doi: 10.1093/bioinformatics/bti014
    [3] J. Cao, F. Ren, Exponential stability of discrete-time genetic regulatory networks with delays, IEEE Trans. Neural Netw., 19 (2008), 520–523. http://doi.org/10.1109/TNN.2007.911748 doi: 10.1109/TNN.2007.911748
    [4] T. Chen, H. L. He, G. M. Church, Modeling gene expression with differential equations, Pac. Symp. Biocomput., 4 (1999), 29–40. http://doi.org/10.1142/9789814447300_0004 doi: 10.1142/9789814447300_0004
    [5] L. Chen, K. Aihara, Stability of genetic regulatory networks with time delay, IEEE Trans. Circuits Syst. I, Fundam. Theory Appl., 49 (2002), 602–608. http://doi.org/10.1109/TCSI.2002.1001949 doi: 10.1109/TCSI.2002.1001949
    [6] C. Darabos, F. Di Cunto, M. Tomassini, J. H. Moore, P. Provero, M. Giacobini, Additive functions in Boolean models of gene regulatory network modules, PloS One., 6 (2011), e25110. http://doi.org/10.1371/journal.pone.0025110 doi: 10.1371/journal.pone.0025110
    [7] H. De Jong, Modeling and simulation of genetic regulatory systems, J. Comput. Biol., 9 (2002), 67–103. http://doi.org/10.1089/10665270252833208 doi: 10.1089/10665270252833208
    [8] P. Dorato, An Overview of finite-time stability, Current trends in nonlinear dystems and contro, Birkh$\ddot{a}$user Boston: New York, 2006,185–194. http://dx.doi.org/10.1007/0-8176-4470-9_10
    [9] K. Gu, Stability of time-delay systems, Berlin: Birkhäuser, 2003. http://doi.org/10.1007/978-1-4612-0039-0
    [10] J. Hu, J. Liang, J. Cao, Stabilization of genetic regulatory networks with mixed time-delay: An adaptive control approach, IMA J. Math. Control Inform., 32 (2015), 343–358. http://doi.org/10.1093/imamci/dnt048 doi: 10.1093/imamci/dnt048
    [11] S. Lakshmanan, J. H. Park, R. Rakkiyappan, H. Y. Jung, State estimator for neural networks with sampled data using discontinuous Lyapunov functional approach, Nonlinear Dyn., 73 (2013), 509–520. http://doi.org/10.1007/s11071-013-0805-z doi: 10.1007/s11071-013-0805-z
    [12] C. Li, L. Chen, K. Aihara, Stability of genetic networks with SUM regulatory logic: Lur'e system and LMI approach, IEEE Trans. Circuits Syst. I, Reg. Papers, 53 (2006), 2451–2458. http://doi.org/10.1109/TCSI.2006.883882 doi: 10.1109/TCSI.2006.883882
    [13] T. Li, S. M. Fei, Q. Zhu, Design of exponential state estimator for neural networks with distributed delays, Nonlinear Anal. Real World Appl., 10 (2009), 1229–1242. http://doi.org/10.1016/j.nonrwa.2007.10.017 doi: 10.1016/j.nonrwa.2007.10.017
    [14] L. Li, Y. Yang, C. Bai, Effect of leakage delay on stability of neutral-type genetic Regulatory Networks, Abstr. Appl. Anal., 2015 (2015), 8 pages. http://doi.org/10.1155/2015/826020 doi: 10.1155/2015/826020
    [15] A. Liu, L. Yu, W. A. Zhang, B. Chen, Finite-time $H \infty$ state estimation for discrete Time-Delayed genetic regulatory networks under stochastic communication protocols, IEEE Trans. Circuits Syst. I, Reg. Papers, 65 (2018), 3481–3491. http://doi.org/10.1109/TCSI.2018.2815269 doi: 10.1109/TCSI.2018.2815269
    [16] T. Liu, X. Zhang, X. Gao, Stability analysis for continuous-time and discrete-time genetic regulatory networks with delays, Appl. Math. Comput., 274 (2016), 628–643. http://doi.org/10.1016/j.amc.2015.11.040 doi: 10.1016/j.amc.2015.11.040
    [17] Y. Lv, J. Zhang, A genetic regulatory network based method for multi-objective sequencing problem in mixed-model assembly lines, Math. Biosci. Eng., 16 (2019), 1228–1243. http://doi.org/10.3934/mbe.2019059 doi: 10.3934/mbe.2019059
    [18] S. Pandiselvi, R. Raja, J. Cao, G. Rajchakit, Stabilization of switched stochastic genetic regulatory networks with leakage and impulsive effects, Neural Process Lett., 49 (2019), 593–610. http://doi.org/10.1007/s11063-018-9843-3
    [19] P. Park, J. W. Ko, C. Jeong, Reciprocally convex approach to stability of systems with time-varying delays, Automatica, 47 (2011), 235–238. http://doi.org/10.1016/j.automatica.2010.10.014 doi: 10.1016/j.automatica.2010.10.014
    [20] P. Park, W. I. Lee, S. Y. Lee, Auxiliary function-based integral inequal ities for quadratic functions and their applications to time-delay systems, J. Frankl. Inst., 352 (2015), 1378–1396. http://doi.org/10.1016/j.jfranklin.2015.01.004 doi: 10.1016/j.jfranklin.2015.01.004
    [21] J. Qiu, K. Sun, C. Yang, X. Chen, X. Chen, A. Zhang, Finite-time stability of genetic regulatory networks with impulsive effects, Neurocomputing., 219 (2017), 9–14. http://doi.org/10.1016/j.neucom.2016.09.017 doi: 10.1016/j.neucom.2016.09.017
    [22] A. Saadatpour, R. Albert, Boolean modeling of biological regulatory networks: A methodology tutorial, Methods, 62 (2013), 3–12. http://doi.org/10.1016/j.ymeth.2012.10.012 doi: 10.1016/j.ymeth.2012.10.012
    [23] S. Saravanan, M. Syed Ali, Improved results on finite-time stability analysis of neural networks with time-varying delays, J. Dyn. Sys., Meas., Control., 140 (2018), 1–10. http://doi.org/10.1115/1.4039667 doi: 10.1115/1.4039667
    [24] S. Saravanan, M. Syed Ali, G. Rajchakit, B. Hammachukiattikul, B. Priya, G. K. Thakur, Finite-time stability analysis of switched genetic regulatory networks with time-varying delays via wirtinger's Integral Inequality, Complexity, 2021 (2021), 1–21. http://doi.org/10.1155/2021/9540548 doi: 10.1155/2021/9540548
    [25] S. Shanmugam, M. Syed Ali, K. S. Hong, Q. Zhu, Robust resilient $H_{\infty}$ performance for finite-time boundedness of neutral-type neural networks with time-varying delays, Asian J. Control, 23 (2021), 2474–2483. http://dx.doi.org/10.1002/asjc.2361 doi: 10.1002/asjc.2361
    [26] X. She, L. Wang, Y. Zhang, Finite-time stability of genetic regulatory networks with non-differential delays, IEEE Trans. Circuits Syst. II Express Briefs, 70 (2023), 2107–2111. http://doi.org/10.1109/TCSII.2022.3233797 doi: 10.1109/TCSII.2022.3233797
    [27] P. Singkibud, K. Mukdasai, Robust passivity analysis of uncertain neutral-type neural networks with distributed interval time-varying delay under the effects of leakage delay, J. Math. Comput. Sci., 26 (2022), 269–290. http://doi.org/10.3934/math.2021170 doi: 10.3934/math.2021170
    [28] P. Smolen, D. A. Baxter, J. H. Byrne, Mathematical modeling of gene networks review, Neuron, 26 (2000), 567–580. http://doi.org/10.1016/S0896-6273(00)81194-0 doi: 10.1016/S0896-6273(00)81194-0
    [29] R. Somogyi, C. A. Sniegoski, Modeling the complexity of genetic networks: Understanding multigenic and pleiotropic regulation, Complexity, 1 (1996), 45–63. http://doi.org/10.1002/cplx.6130010612 doi: 10.1002/cplx.6130010612
    [30] J. Sun, G. P. Liu, J. Chen, D. Rees, Improved delay-range-dependent stability criteria for linear systems with time-varying delays, Automatica, 46 (2010), 466–470. http://doi.org/10.1016/j.automatica.2009.11.002 doi: 10.1016/j.automatica.2009.11.002
    [31] W. Wang, Y. Wang, S. K. Nguang, S. Zhong, F. Liu, Delay partition method for the robust stability of uncertain genetic regulatory networks with time-varying delays, Neurocomputing, 173 (2016), 899–911. http://doi.org/10.1016/j.neucom.2015.08.045 doi: 10.1016/j.neucom.2015.08.045
    [32] D. C. Weaver, C. T. Workman, G. D. Stormo, Modeling regulatory networks with weight matrices, Proc Pac Symp Biocomput., 4 (1999), 112–123. http://doi.org/10.1142/9789814447300_0011 doi: 10.1142/9789814447300_0011
    [33] H. Wu, X. Liao, S. Guo, W. Feng, Z. Wang, Stochastic stability for uncertain genetic regulatory networks with interval time-varying delays, Neurocomputing, 72 (2009), 3263–3276. http://doi.org/10.1016/j.neucom.2009.02.003 doi: 10.1016/j.neucom.2009.02.003
    [34] L. Yin, Y. Liu, Exponential stability analysis for genetic regulatory networks with both time-varying and continuous distributed delays, Abstr. Appl. Anal., 2014 (2014). http://doi.org/10.1155/2014/897280
    [35] T. Zhang, Y. Li, Exponential Euler scheme of multi-delay Caputo-Fabrizio fractional-order differential equations, Appl. Math. Lett., 124 (2022), 107709. http://doi.org/10.1016/j.aml.2021.107709 doi: 10.1016/j.aml.2021.107709
    [36] T. Zhang, Y. Li, Global exponential stability of discrete-time almost automorphic Caputo-Fabrizio BAM fuzzy neural networks via exponential Euler technique, Knowl. Based Syst., 246 (2022), 108675. http://doi.org/10.1016/j.knosys.2022.108675 doi: 10.1016/j.knosys.2022.108675
    [37] T. Zhang, H. Qu, J. Zhou, Asymptotically almost periodic synchronization in fuzzy competitive neural networks with Caputo-Fabrizio operator, Fuzzy Sets Syst., 471 (2023), 108676. http://doi.org/10.1016/j.fss.2023.108676 doi: 10.1016/j.fss.2023.108676
    [38] X. Zhang, Y. Xue, A novel $H_\infty$ state observer design method for genetic regulatory networks with time-varying delays, AIMS Math., 9 (2023), 3763–3787. http://doi.org/10.3934/math.2024185 doi: 10.3934/math.2024185
    [39] J. Zhou, S. Xu, H. Shen, Finite-time robust stochastic stability of uncertain stochastic delayed reaction-diffusion genetic regulatory networks, Neurocomputing, 74 (2011), 2790–2796. http://doi.org/10.1016/j.neucom.2011.03.041 doi: 10.1016/j.neucom.2011.03.041
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