In this paper, the stability and bifurcation of a two–dimensional p53 gene regulatory network without and with time delay are taken into account by rigorous theoretical analyses and numerical simulations. In the absence of time delay, the existence and local stability of the positive equilibrium are considered through the Descartes' rule of signs, the determinant and trace of the Jacobian matrix, respectively. Then, the conditions for the occurrence of codimension–1 saddle–node and Hopf bifurcation are obtained with the help of Sotomayor's theorem and the Hopf bifurcation theorem, respectively, and the stability of the limit cycle induced by hopf bifurcation is analyzed through the calculation of the first Lyapunov number. Furthermore, codimension-2 Bogdanov–Takens bifurcation is investigated by calculating a universal unfolding near the cusp. In the presence of time delay, we prove that time delay can destabilize a stable equilibrium. All theoretical analyses are supported by numerical simulations. These results will expand our understanding of the complex dynamics of p53 and provide several potential biological applications.
Citation: Xin Du, Quansheng Liu, Yuanhong Bi. Bifurcation analysis of a two–dimensional p53 gene regulatory network without and with time delay[J]. Electronic Research Archive, 2024, 32(1): 293-316. doi: 10.3934/era.2024014
In this paper, the stability and bifurcation of a two–dimensional p53 gene regulatory network without and with time delay are taken into account by rigorous theoretical analyses and numerical simulations. In the absence of time delay, the existence and local stability of the positive equilibrium are considered through the Descartes' rule of signs, the determinant and trace of the Jacobian matrix, respectively. Then, the conditions for the occurrence of codimension–1 saddle–node and Hopf bifurcation are obtained with the help of Sotomayor's theorem and the Hopf bifurcation theorem, respectively, and the stability of the limit cycle induced by hopf bifurcation is analyzed through the calculation of the first Lyapunov number. Furthermore, codimension-2 Bogdanov–Takens bifurcation is investigated by calculating a universal unfolding near the cusp. In the presence of time delay, we prove that time delay can destabilize a stable equilibrium. All theoretical analyses are supported by numerical simulations. These results will expand our understanding of the complex dynamics of p53 and provide several potential biological applications.
[1] | Q. Zhu, J. Shen, F. Han, W. Lu, Bifurcation analysis and probabilistic energy landscapes of two-component genetic network, IEEE Access, 8 (2020), 150696–150708. https://doi.org/10.1109/ACCESS.2020.3013615 doi: 10.1109/ACCESS.2020.3013615 |
[2] | L. Fang, Y. Li, L. Ma, Q. Xu, F. Tan, G. Chen, GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions, Nucleic Acids Res., 49 (2021), D97–D103. https://doi.org/10.1093/nar/gkaa995 doi: 10.1093/nar/gkaa995 |
[3] | X. Zhang, X. Zhao, K. He, L. Lu, Y. Cao, J. Liu, et al., Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information, Bioinformatics, 28 (2012), 98–104. https://doi.org/10.1093/bioinformatics/btr626 doi: 10.1093/bioinformatics/btr626 |
[4] | T. Yu, X. Zhang, G. Zhang, B. Niu, Hopf bifurcation analysis for genetic regulatory networks with two delays, Neurocomputing, 164 (2015), 190–200. https://doi.org/10.1016/j.neucom.2015.02.070 doi: 10.1016/j.neucom.2015.02.070 |
[5] | B. S. Stikker, R. W. Hendriks, R. Stadhouders, Decoding the genetic and epigenetic basis of asthma, Allergy, 78 (2023), 940–956. https://doi.org/10.1111/all.15666 doi: 10.1111/all.15666 |
[6] | B. Huang, M. Lu, M. Galbraith, H. Levine, J. N. Onuchic, D. Jia, Decoding the mechanisms underlying cell-fate decision-making during stem cell differentiation by random circuit perturbation, J. R. Soc. Interface, 17 (2020), 20200500. https://doi.org/10.1098/rsif.2020.0500 doi: 10.1098/rsif.2020.0500 |
[7] | A. Ghaffarizadeh, G. J. Podgorski, N. S. Flann, Applying attractor dynamics to infer gene regulatory interactions involved in cellular differentiation, Biosystems, 155 (2017), 29–41. https://doi.org/10.1016/j.biosystems.2016.12.004 doi: 10.1016/j.biosystems.2016.12.004 |
[8] | S. Vyas, A. J. Rodrigues, J. M. Silva, F. Tronche, O. F. Almeida, N. Sousa, et al., Chronic stress and glucocorticoids: from neuronal plasticity to neurodegeneration, Neural Plast., 2016 (2016), 6391686. https://doi.org/10.1155/2016/6391686 doi: 10.1155/2016/6391686 |
[9] | J. Chrol-Cannon, Y. Jin, Computational modeling of neural plasticity for self-organization of neural networks, Biosystems, 125 (2014), 43–54. https://doi.org/10.1016/j.biosystems.2014.04.003 doi: 10.1016/j.biosystems.2014.04.003 |
[10] | Y. Meng, Y. Jin, J. Yin, Modeling activity-dependent plasticity in BCM spiking neural networks with application to human behavior recognition, IEEE Trans. Neural Networks, 22 (2011), 1952–1966. https://doi.org/10.1109/TNN.2011.2171044 doi: 10.1109/TNN.2011.2171044 |
[11] | X. Shi, M. Sun, H. Liu, Y. Yao, R. Kong, F. Chen, et al., A critical role for the long non-coding RNA GAS5 in proliferation and apoptosis in non-small-cell lung cancer, Mol. Carcinog., 54 (2015), E1–E12. https://doi.org/10.1002/mc.22120 doi: 10.1002/mc.22120 |
[12] | D. Chudasama, V. Bo, M. Hall, V. Anikin, J. Jeyaneethi, J. Gregory, et al., Identification of cancer biomarkers of prognostic value using specific gene regulatory networks (GRN): a novel role of RAD51AP1 for ovarian and lung cancers, Carcinogenesis, 39 (2018), 407–417. https://doi.org/10.1093/carcin/bgx122 doi: 10.1093/carcin/bgx122 |
[13] | H. C. Lo, J. H. Hsu, L. C. Lai, M. H. Tsai, E. Y. Chuang, MicroRNA-107 enhances radiosensitivity by suppressing granulin in PC-3 prostate cancer cells, Sci. Rep., 10 (2020), 14584. https://doi.org/10.1038/s41598-020-71128-1 doi: 10.1038/s41598-020-71128-1 |
[14] | J. Eliaš, C. K. Macnamara, Mathematical modelling of p53 signalling during DNA damage response: a survey, Int. J. Mol. Sci., 22 (2021), 10590. https://doi.org/10.3390/ijms221910590 doi: 10.3390/ijms221910590 |
[15] | Q. Zheng, J. Shen, Z. Wang, Pattern formation and oscillations in Reaction-Diffusion model with p53-Mdm2 feedback Loop, Int. J. Bifurcation Chaos, 29 (2019), 1930040. https://doi.org/10.1142/S0218127419300404 doi: 10.1142/S0218127419300404 |
[16] | Y. Bi, Q. Liu, L. Wang, W. Yang, X. Wu, Bifurcation and potential landscape of p53 dynamics depending on pdcd5 level and atm degradation rate, Int. J. Bifurcation Chaos, 30 (2020), 2050134. https://doi.org/10.1142/S0218127420501345 doi: 10.1142/S0218127420501345 |
[17] | Y. Bi, Z. Yang, C. Zhuge, J. Lei, Bifurcation analysis and potential landscapes of the p53-mdm2 module regulated by the co-activator programmed cell death 5, Chaos, 25 (2015), 113103. https://doi.org/10.1063/1.4934967 doi: 10.1063/1.4934967 |
[18] | J. Hou, Q. Liu, H. Yang, L. Wang, Y. Bi, Stability and bifurcation analyses of p53 gene regulatory network with time delay, Electron. Res. Arch., 30 (2022), 850–873. https://doi.org/10.3934/era.2022045 doi: 10.3934/era.2022045 |
[19] | C. Gao, F. Chen, Dynamics of p53 regulatory network in DNA damage response, Appl. Math. Modell., 88 (2020), 701–714. https://doi.org/10.1016/j.apm.2020.06.057 doi: 10.1016/j.apm.2020.06.057 |
[20] | Y. Song, X. Cao, T. Zhang, Bistability and delay-induced stability switches in a cancer network with the regulation of microRNA, Commun. Nonlinear Sci. Numer. Simul., 54 (2018), 302–319. https://doi.org/10.1016/j.cnsns.2017.06.008 doi: 10.1016/j.cnsns.2017.06.008 |
[21] | T. Sun, R. Yuan, W. Xu, F. Zhu, P. Shen, Exploring a minimal two-component p53 model, Phys. Biol., 7 (2010), 036008. https://doi.org/10.1088/1478-3975/7/3/036008 doi: 10.1088/1478-3975/7/3/036008 |
[22] | L. Perko, Differential Equations and Dynamical Systems, 1991. https://doi.org/10.1007/978-1-4684-0392-3 |
[23] | B. Hat, M. Kochanczyk, M. N. Bogdal, T. Lipniacki, Feedbacks, bifurcations, and cell fate decision-making in the p53 system, PLoS Comput. Biol., 12 (2016), e1004787. https://doi.org/10.1371/journal.pcbi.1004787 doi: 10.1371/journal.pcbi.1004787 |
[24] | Y. A. Kuznetsov, Elements of Applied Bifurcation Theory, 1998. https://doi.org/10.1007/b98848 |
[25] | D. Mu, C. Xu, Z. Liu, Y. Pang, Further insight into bifurcation and hybrid control tactics of a chlorine dioxide-iodine-malonic acid chemical reaction model incorporating delays, Match-Commun. Math. Comput. Chem., 89 (2023), 529–566. https://doi.org/10.46793/match.89-3.529M doi: 10.46793/match.89-3.529M |
[26] | Y. Xiang, Y. Jiao, X. Wang, R. Yang, Dynamics of a delayed diffusive predator-prey model with Allee effect and nonlocal competition in prey and hunting cooperation in predator, Electron. Res. Arch., 31 (2023), 2120–2138. https://doi.org/10.3934/era.2023109 doi: 10.3934/era.2023109 |
[27] | M. Sui, Y. Du, Bifurcations, stability switches and chaos in a diffusive predator-prey model with fear response delay, Electron. Res. Arch., 31 (2023), 5124–5150. https://doi.org/10.3934/era.2023262 doi: 10.3934/era.2023262 |
[28] | C. Wang, F. Yan, H. Liu, Y. Zhang, Theoretical study on the oscillation mechanism of p53-Mdm2 network, Int. J. Biomath., 11 (2018), 1850112. https://doi.org/10.1142/S1793524518501127 doi: 10.1142/S1793524518501127 |
[29] | Y. Bi, Y. Li, J. Hou, Q. Liu, Multiple time delays induced dynamics of p53 gene regulatory network, Int. J. Bifurcation Chaos, 31 (2021), 2150234. https://doi.org/10.1142/S0218127421502345 doi: 10.1142/S0218127421502345 |
[30] | J. Xia, X. Li, Bifurcation analysis in a discrete predator-prey model with herd behaviour and group defense, Electron. Res. Arch., 31 (2023), 4484–4506. https://doi.org/10.3934/era.2023229 doi: 10.3934/era.2023229 |
[31] | D. Hu, H. Cao, Stability and bifurcation analysis in a predator-prey system with Michaelis-Menten type predator harvesting, Nonlinear Anal. Real World Appl., 33 (2017), 58–82. https://doi.org/10.1016/j.nonrwa.2016.05.010 doi: 10.1016/j.nonrwa.2016.05.010 |
[32] | M. Liu, F. Meng, D. Hu, Bogdanov-Takens and Hopf bifurcations analysis of a genetic regulatory network, Qual. Theory Dyn. Syst., 21 (2022), 45. https://doi.org/10.1007/s12346-022-00575-0 doi: 10.1007/s12346-022-00575-0 |
[33] | H. Zhou, B. Tang, H. Zhu, S. Tang, Bifurcation and dynamic analyses of non-monotonic predator-prey system with constant releasing rate of predators, Qual. Theory Dyn. Syst., 21 (2022), 10. https://doi.org/10.1007/s12346-021-00539-w doi: 10.1007/s12346-021-00539-w |
[34] | C. Shan, Y. Yi, H. Zhu, Nilpotent singularities and dynamics in an SIR type of compartmental model with hospital resources, J. Differ. Equations, 260 (2016), 4339–4365. https://doi.org/10.1016/j.jde.2015.11.009 doi: 10.1016/j.jde.2015.11.009 |
[35] | C. Xu, Q. Cui, Z. Liu, Y. Pan, X. Cui, W. Ou, et al., Extended hybrid controller design of bifurcation in a delayed chemostat model, Match-Commun. Math. Comput. Chem., 90 (2023), 609–648. https://doi.org/10.46793/match.90-3.609X doi: 10.46793/match.90-3.609X |
[36] | J. Sotomayor, Generic bifurcations of dynamical systems, Dyn. Syst., (1973), 561–582. https://doi.org/10.1016/B978-0-12-550350-1.50047-3 |
[37] | B. D. Hassard, N. D. Kazarinoff, Y. H. Wan, Theory and Applications of Hopf Bifurcation, 1981. |
[38] | P. Wan, Dynamic behavior of stochastic predator-prey system, Electron. Res. Arch., 31 (2023), 2925–2939. https://doi.org/10.3934/era.2023147 doi: 10.3934/era.2023147 |
[39] | Y. Hou, C. Wei, Y. Ding, Dynamic analysis of reaction-diffusion dual carbon model considering economic development in China, Electron. Res. Arch., 31 (2023), 2438–2500. https://doi.org/10.3934/era.2023126 doi: 10.3934/era.2023126 |
[40] | W. Li, H. Wang, Dynamics of a three-molecule autocatalytic Schnakenberg model with cross-diffusion: turing patterns of spatially homogeneous Hopf bifurcating periodic solutions, Electron. Res. Arch., 31 (2023), 4139–4154. https://doi.org/10.3934/era.2023211 doi: 10.3934/era.2023211 |
[41] | C. Xu, Z. Liu, P. Li, J. Yan, L. Yao, Bifurcation mechanism for fractional-order three-triangle multi-delayed neural networks, Neural Process. Lett., 55 (2023), 6125–6151. https://doi.org/10.1007/s11063-022-11130-y doi: 10.1007/s11063-022-11130-y |
[42] | C. Xu, D. Mu, Y. Pan, C. Aouiti, L. Yao, Exploring bifurcation in a fractional-order predator-prey system with mixed delays, J. Appl. Anal. Comput., 13 (2023), 1119–1136. https://doi.org/10.11948/20210313 doi: 10.11948/20210313 |
[43] | P. Li, Y. Lu, C. Xu, J. Ren, Insight into Hopf bifurcation and control methods in fractional order BAM neural networks incorporating symmetric structure and delay, Cognit. Comput., 15 (2023), 1825–1867. https://doi.org/10.1007/s12559-023-10155-2 doi: 10.1007/s12559-023-10155-2 |
[44] | P. Li, X. Peng, C. Xu, L. Han, S. Shi, Novel extended mixed controller design for bifurcation control of fractional-order Myc/E2F/miR-17-92 network model concerning delay, Math. Methods Appl. Sci., 46 (2023), 18878–18898. https://doi.org/10.1002/mma.9597 doi: 10.1002/mma.9597 |