Complicated dynamics of tumor-immune system interaction model with distributed time delay

Min Yu; Gang Huang; Yueping Dong; Yasuhiro Takeuchi

doi:10.3934/dcdsb.2020015

Article Contents

2020, Volume 25, Issue 7: 2391-2406. Doi: 10.3934/dcdsb.2020015

This issue Previous Article Asymptotic properties of a stochastic chemostat model with two distributed delays and nonlinear perturbation Next Article Stability of stochastic heroin model with two distributed delays

Complicated dynamics of tumor-immune system interaction model with distributed time delay

1.
College of Science and Engineering, Aoyama Gakuin University, Sagamihara 252-5258, Japan
2.
School of Mathematics and Physics, China University of Geoscience, Wuhan 430074, China
3.
School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China
4.
School of Mathematics and Physics, Aoyama Gakuin University, Sagamihara 252-5258, Japan

*Corresponding author: ypdong@mail.ccnu.edu.cn
*Corresponding author: ypdong@mail.ccnu.edu.cn

Received: March 31, 2019

Revised: July 31, 2019

Early access: April 2020

Published: July 2020

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Abstract

In this paper, we propose a distributed delay model to investigate the dynamics of the interactions between tumor and immune system. And we choose a special form of delay kernel which combines two delay kernels: a monotonic delay kernel representing a fading memory and a nonmonotonic delay kernel describing a peaking memory. Then, we discuss the effect of such delay kernel on system dynamics. The results show that the introduction of nonmonotonic delay kernel does not change the stability of tumor-free equilibrium, but it can induce stability switches of tumor-presence equilibrium and cause a rich pattern of dynamical behaviors including stabilization. Moreover, our numerical simulation results reveal that the nonmonotonic delay kernel has more complicated effects on the stability compared with the monotonic delay kernel.

Keywords:

Mathematics Subject Classification: Primary: 92B05, 92D25; Secondary: 37N25, 34C23.

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Figure 1. The black curves represent relatively small $ b $ and imply strong delay. The red curves represent relatively large $ b $ and imply weak delay. (a) The dashed curves correspond to the monotonic function $ F_1(t) $ with respect to $ t $. (b) The solid curves correspond to the nonmonotonic function $ F_2(t) $ with respect to $ t $

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Figure 2. (a) The monotonic decreasing function $ D_4 $ with respect to $ c_2 $. (b) The quadratic function $ H(D_4) $ with respect to $ D_4 $

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Figure 3. Log plots for the stability regions of tumor-presence equilibrium $ E^+ $ and stability boundary curves representing by black curves in $ c_2-b $ parameter plane. (a) $ E^+ $ is stable in the area outside these two stability boundary curves, while $ E^+ $ is unstable in the area between these two stability boundary curves. (b) $ E^+ $ is stable in the area below the stability boundary curve, while $ E^+ $ is unstable in the area above the stability boundary curve

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Figure 4. Stability boundary curves of $ E^+ $ are shown in $ \rho-\omega $ parameter plane for different $ b $. $ E^+ $ is stable below each stability boundary curve, while $ E^+ $ is unstable above each stability boundary curve. (a) We fix $ c_2 = 0.2(<0.5) $. (b) We fix $ c_2 = 0.8(>0.5) $

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Figure 5. Stability boundary curves of $ E^+ $ are shown in $ \rho-\omega $ parameter plane for different $ c_2 $. $ E^+ $ is stable below each stability boundary curve, while $ E^+ $ is unstable above each stability boundary curve. (a) We fix $ b = 0.001 $. (b) We fix $ b = 0.1 $

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Figure 7. Schematic diagram of $ F(t) $ with respect to $ t $ for different $ c_2 $ and fixed $ b $

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Figure 6. The function $ F(t) $ with respect to $ t $. The black curves are given for relatively small $ b $ and imply strong delay. The red curves are given for relatively large $ b $ and imply weak delay. (a) We fix $ c_2 = 0.2(<0.5) $. The dashed curves correspond to relatively small $ c_2 $. (b) We fix $ c_2 = 0.8(>0.5) $. The solid curves correspond to relatively large $ c_2 $

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