Early and late stage profiles for a chemotaxis model with density-dependent jump probability

Tianyuan Xu; Shanming Ji; Chunhua Jin; Ming Mei; Jingxue Yin

doi:10.3934/mbe.2018062

Article Contents

2018, Volume 15, Issue 6: 1345-1385. Doi: 10.3934/mbe.2018062

This issue Previous Article Modeling the control of infectious diseases: Effects of TV and social media advertisements Next Article Ebola: Impact of hospital's admission policy in an overwhelmed scenario

Early and late stage profiles for a chemotaxis model with density-dependent jump probability

1.
School of Mathematical Sciences, South China Normal University, Guangzhou, Guangdong 510631, China
2.
School of Mathematics, South China University of Technology, Guangzhou, Guangdong 510641, China
3.
Department of Mathematics, Champlain College Saint-Lambert, Quebec, J4P 3P2, Canada
4.
Department of Mathematics and Statistics, McGill University, Montreal, Quebec, H3A 2K6, Canada

* Corresponding author: jism@scut.edu.cn
* Corresponding author: jism@scut.edu.cn

Received: October 18, 2017

Accepted: July 23, 2018

Published: November 2018

Abstract Full Text(HTML) Figure(2) Related Papers Cited by

Abstract

In this paper, we derive a chemotaxis model with degenerate diffusion and density-dependent chemotactic sensitivity, and we provide a more realistic description of cell migration process for its early and late stages. Different from the existing studies focusing on the case of non-degenerate diffusion, this model with degenerate diffusion causes us some essential difficulty on the boundedness estimates and the propagation behavior of its compact support. In the presence of logistic damping, for the early stage before tumour cells spread to the whole domain, we first estimate the expanding speed of tumour region as $O(t^{β})$ for $ 0 < β < \frac{1}{2}$ . Then, for the late stage of cell migration, we further prove that the asymptotic profile of the original system is just its corresponding steady state. The global convergence of the original weak solution to the steady state with exponential rate $O(e^{-ct})$ for some $c>0$ is also obtained.

Keywords:

Mathematics Subject Classification: Primary: 35K51, 35K65; Secondary: 92C17.

Citation:

FullText(HTML)

Figure 1. (a) Comparison of experimental and simulated cell distribution for MG63 cells. The measured cell density (gray histogram) are fitted using the solution of degenerate nonlinear diffusion model (gray lines). (b) Comparison of experimental and simulated cell distribution for HBMSC cells. The measured cell density (gray histogram) are matched with the solution of linear diffusion model (gray lines). This diagram was redrawn from the one in Ref. [30]

Download: Full-size image PowerPoint slide

Figure 2. The growth curve of HEPA-1 spheroids. The solid line represents the position of the outer tumour boundary. Dimensional diameters are shown in $\mu m$. This diagram was redrawn from the one in Ref. [27]

Download: Full-size image PowerPoint slide

Related Papers

Cited by

References

[1]	N. Bellomo, A. Bellouquid, Y. Tao and M. Winkler, Toward a mathematical theory of Keller-Segel models of pattern formation in biological tissues, Math. Models Methods Appl. Sci., 25 (2015), 1663-1763. doi: 10.1142/S021820251550044X.
[2]	A. A. Blumberg, Logistic growth rate functions, J. Theor. Biol., 21 (1968), 42-44.
[3]	H. M. Byrne, Dissecting cancer through mathematics: from the cell to the animal model, Nature Reviews Cancer, 10 (2010), 221-230.
[4]	M. A. Chaplain and A. R. Anderson, Mathematical modelling of tissue invasion, in Cancer Model. Simul., Chapman and Hall/CRC, London, (2003), 269-297.
[5]	Y. H. Du, F. Quirós and M. L. Zhou, Logarithmic corrections in Fisher-KPP type Porous Medium Equations, preprint, arXiv: 1806.02022.
[6]	P. Friedl and K. Wolf, Tumour-cell invasion and migration: Diversity and escape mechanisms, Nature Reviews Cancer, 3 (2003), 362-374.
[7]	K. Fujie, A. Ito, M. Winkler and T. Yokota, Stabilization in a chemotaxis model for tumor invasion, Disc. Cont. Dyn. Syst., 36 (2016), 151-169. doi: 10.3934/dcds.2016.36.151.
[8]	R. A. Gatenby and E. T. Gawlinski, A reaction-diffusion model of cancer invasion, Cancer Res., 56 (1996), 5745-5753.
[9]	W. S. C. Gurney and R. M. Nisbet, The regulation of inhomogeneous population, J. Theors. Biol., 52 (1975), 441-457.
[10]	M. E. Gurtin and R. C. MacCamy, On the diffusion of biological populations, Math. Biosci., 33 (1977), 35-49. doi: 10.1016/0025-5564(77)90062-1.
[11]	T. Hillen and K. Painter, Global existence for a parabolic chemotaxis model with prevention of overcrowding, Adv. Appl. Math., 26 (2001), 280-301. doi: 10.1006/aama.2001.0721.
[12]	T. Hillen and K. J. Painter, A user's guide to pde models in a chemotaxis, J. Math. Biology, 58 (2009), 183-217. doi: 10.1007/s00285-008-0201-3.
[13]	C. H. Jin, Boundedness and global solvability to a chemotaxis model with nonlinear diffusion, J. Differential Equations, 263 (2017), 5759-5772. doi: 10.1016/j.jde.2017.06.034.
[14]	C. H. Jin, Boundedness and global solvability to a chemotaxis-haptotaixs model with slow and fast diffusion, Disc. Cont. Dyn. Syst., 23 (2018), 1675-1688. doi: 10.3934/dcdsb.2018069.
[15]	E. F. Keller and L. A. Segel, Model for chemotaxis, J. Theor. Biology, 30 (1971), 225-234.
[16]	R. D. Leek, The role of tumour associated macrophages in breast cancer angiogenesis, Ph.D thesis, Oxford Brookes University, Oxford, 1999.
[17]	D. Li, C. Mu and P. Zheng, Boundedness and large time behavior in a quasilinear chemotaxis model for tumor invasion, Math. Models Methods Appl. Sci., 28 (2018), 1413-1451. doi: 10.1142/S0218202518500380.
[18]	J. Liu and Y. F. Wang, A quasilinear chemotaxis-haptotaxis model: The roles of nonlinear diffusion and logistic source, Math. Methods Appl. Sci., 40 (2017), 2107-2121. doi: 10.1002/mma.4126.
[19]	Y. Lou and M. Winkler, Global existence and uniform boundedness of smooth solutions to a cross-diffusion system with equal diffusion rates, Comm. Part. Diff. Equ., 40 (2015), 1905-1941. doi: 10.1080/03605302.2015.1052882.
[20]	P. Lu, V. Weaver and Z. Werb, The extracellular matrix: a dynamic niche in cancer progression, J. Cell Biol., 196 (2012), 395-406.
[21]	H. McAneney and S. F. C. O'Rourke, Investigation of various growth mechanisms of solid tumour growth within the linear quadratic model for radiotherapy, Phys. Med. Biol., 52 (2007), 1039-1054.
[22]	M. Mei, H. Y. Peng and Z. A. Wang, Asymptotic profile of a parabolic-hyperbolic system with boundary effect arising from tumor angiogenesis, J. Differential Equations, 259 (2015), 5168-5191. doi: 10.1016/j.jde.2015.06.022.
[23]	Y. Mimura, The variational formulation of the fully parabolic Keller-Segel system with degenerate diffusion, J. Differential Equations, 263 (2017), 1477-1521. doi: 10.1016/j.jde.2017.03.020.
[24]	J. D. Murry, Mathematical Biology I: An Introduction, Springer, New York, USA, 2002.
[25]	A. Okubo and S. A. Levin, Diffusion and Ecological Problems: Modern Perspectives, Springer Science Business Media, 2013. doi: 10.1007/978-1-4757-4978-6.
[26]	M. E. Orme and M. A. J. Chaplain, A mathematical model of vascular tumour growth and invasion, Math. Comput. Modelling, 23 (1996), 43-60.
[27]	M. R. Owen, H. M. Byrne and C. E. Lewis, Mathematical modelling of the use of macrophages as vehicles for drug delivery to hypoxic tumour sites, J. Theor. Biol., 226 (2004), 377-391. doi: 10.1016/j.jtbi.2003.09.004.
[28]	K. J. Painter and T. Hillen, Volume-filling and quorum-sensing in models for chemosensitive movement, Canadian Appl. Math. Quart., 10 (2002), 501-543.
[29]	K. J. Painter and J. A. Sherratt, Modelling the movement of interacting cell populations, J. Theor. Biol., 225 (2003), 327-339. doi: 10.1016/S0022-5193(03)00258-3.
[30]	B. G. Sengers, C. P. Please and R. O. C. Oreffo, Experimental characterization and computational modelling of two-dimensional cell spreading for skeletal regeneration, J. R. Soc. Interface, 4 (2007), 1107-1117.
[31]	J. A. Sherratt, On the form of smooth-front travelling waves in a reaction-diffusion equation with degenerate nonlinear diffusion, Math. Model. Nat. Phenom, 5 (2010), 64-79. doi: 10.1051/mmnp/20105505.
[32]	J. A. Sherratt and M. A. J. Chaplain, A new mathematical model for avascular tumour growth, J. Math. Biol., 43 (2001), 291-312. doi: 10.1007/s002850100088.
[33]	J. A. Sherratt and J. D. Murray, Models of epidermal wound healing, Proc. R. Soc. London B, 241 (1990), 29-35.
[34]	M. J. Simpson, R. E. Baker and S. W. McCue, Models of collective cell spreading with variable cell aspect ratio: A motivation for degenerate diffusion models, Phys. Rev. E, 83 (2011), 021901.
[35]	M. J. Simpson, D. C. Zhang, M. Mariani, K. A. Landman and D. F. Newgreen, Cell proliferation drives neural crest cell invasion of the intestine, Dev. Biol., 302 (2007), 553-568.
[36]	A. Stevens and H. G. Othmer, Aggregation, blowup, and collapse: The abc's of taxis in reinforced random walks, SIAM J. Appl. Math., 57 (2001), 1044-1081. doi: 10.1137/S0036139995288976.
[37]	Z. Szymańska, C. M. Rodrigo, M. Lachowicz and M. A. Chaplain, Mathematical modelling of cancer invasion of tissue: The role and effect of nonlocal interactions, Math. Models Methods Appl. Sci., 19 (2009), 257-281. doi: 10.1142/S0218202509003425.
[38]	Y. Tao and M. Winkler, A chemotaxis-haptotaxis model: The roles of nonlinear diffusion and logistic source, SIAM J. Math. Anal., 43 (2011), 685-705. doi: 10.1137/100802943.
[39]	Y. Tao and M. Winkler, Global existence and boundedness in a Keller-Segel-Stokes model with arbitrary porous medium diffusion, Discrete Contin. Dyn. Syst., 32 (2012), 1901-1914. doi: 10.3934/dcds.2012.32.1901.
[40]	Y. Tao and M. Winkler, Dominance of chemotaxis in a chemotaxis-haptotaxis model, Nonlinearity, 27 (2014), 1225-1239. doi: 10.1088/0951-7715/27/6/1225.
[41]	M. E. Turner, B. A. Blumenstein and J. L. Sebaugh, A generalization of the logistic law of growth, Biometrics, 25 (1969), 577-580.
[42]	H. A. S. Van den Brenk, Studies in restorative growth processes in mammalian wound healing, Br. J. Surg., 43 (1956), 525-550.
[43]	J. L. Vàzquez, The Porous Medium Equation: Mathematical Theory, Oxford Univ. Press, Oxford, 2007.
[44]	L. Von Bertalanffy, Quantitative laws in metabolism and growth, Q. Rev. Biol., 32 (1957), 217-231.
[45]	Y. F. Wang, Boundedness in a multi-dimensional chemotaxis-haptotaxis model with nonlinear diffusion, Appl. Math. Lett., 59 (2016), 122-126. doi: 10.1016/j.aml.2016.03.019.
[46]	Y. F. Wang, Boundedness in the higher-dimensional chemotaxis-haptotaxis model with nonlinear diffusion, J. Differential Equations, 260 (2016), 1975-1989. doi: 10.1016/j.jde.2015.09.051.
[47]	Y. F. Wang and Y. Y. Ke, Large time behavior of solution to a fully parabolic chemotaxis-haptotaxis model in higher demensions, J. Differential Equations, 260 (2016), 6960-6988. doi: 10.1016/j.jde.2016.01.017.
[48]	Z. A. Wang, M. Winkler and D. Wrzosek, Singularity formation in chemotaxis systems with volume-filling effect, Nonlinearity, 24 (2011), 3279-3297. doi: 10.1088/0951-7715/24/12/001.
[49]	Z. A. Wang, Z. Y. Xiang and P. Yu, Asymptotic dynamics on a singular chemotaxis system modeling onset of tumor angiogenesis, J. Differential Equations, 260 (2016), 2225-2258. doi: 10.1016/j.jde.2015.09.063.
[50]	Z. A. Wang, M. Winkler and D. Wrzosek, Global regularity vs. infinite-time singularity formation in chemotaxis model with volume-filling effect and degenerate diffusion, SIAM J. Math. Anal., 44 (2012), 3502-3525. doi: 10.1137/110853972.
[51]	M. Winkler and K. C. Djie, Boundedness and finite-time collapse in a chemotaxis system with volume-filling effect, Nonlinear Analysis, 72 (2010), 1044-1064. doi: 10.1016/j.na.2009.07.045.
[52]	M. Winkler, Global existence and stabilization in a degenerate chemotaxis-Stokes system with mildly strong diffusion enhancement, J. Differential Equations, 264 (2018), 6109-6151, arXiv: 1704.05648. doi: 10.1016/j.jde.2018.01.027.
[53]	M. Winkler, Boundedness and large time behavior in a three-dimensional chemotaxis-Stokes system with nonlinear diffusion and general sensitivity, Calc. Var. Partial Differential Equations, 54 (2015), 3789-3828. doi: 10.1007/s00526-015-0922-2.
[54]	M. Winkler, Finite-time blow-up in the higher-dimensional parabolic-parabolic Keller-Segel system, J. Math. Pures Appl., 100 (2013), 748-767. doi: 10.1016/j.matpur.2013.01.020.
[55]	T. P. Witelski, Segregation and mixing in degenerate diffusion in population dynamics, J. Math. Biol., 35 (1997), 695-712. doi: 10.1007/s002850050072.
[56]	Z. Wu, J. Zhao, J. Yin and H. Li, Nonlinear Diffusion Equations, World Scientific Publishing Co. Pvt. Ltd., 2001. doi: 10.1142/9789812799791.
[57]	T. Y. Xu, S. M. Ji, M. Mei and J. X. Yin, Global existence of solutions to a chemotaxis-haptotaxis model with density-dependent jump probability and quorum-sensing mechanisms, Math. Meth. Appl. Sci., 41 (2018), 4208-4226.
[58]	T. Y. Xu, S. M. Ji, M. Mei and J. X. Yin, Traveling waves for time-delayed reaction diffusion equations with degenerate diffusion, J. Differential Equations, 265 (2018), 4442-4485. doi: 10.1016/j.jde.2018.06.008.
[59]	A. Zhigun, C. Surulescu and A. Uatay, Global existence for a degenerate haptotaxis model of cancer invasion, Z. Angew. Math. Phys., 67 (2016), 1-29. doi: 10.1007/s00033-016-0741-0.