Skip to main content

Composite Waves for a Cell Population System Modeling Tumor Growth and Invasion

  • Chapter
Partial Differential Equations: Theory, Control and Approximation

Abstract

In the recent biomechanical theory of cancer growth, solid tumors are considered as liquid-like materials comprising elastic components. In this fluid mechanical view, the expansion ability of a solid tumor into a host tissue is mainly driven by either the cell diffusion constant or the cell division rate, with the latter depending on the local cell density (contact inhibition) or/and on the mechanical stress in the tumor.

For the two by two degenerate parabolic/elliptic reaction-diffusion system that results from this modeling, the authors prove that there are always traveling waves above a minimal speed, and analyse their shapes. They appear to be complex with composite shapes and discontinuities. Several small parameters allow for analytical solutions, and in particular, the incompressible cells limit is very singular and related to the Hele-Shaw equation. These singular traveling waves are recovered numerically.

Project supported by the ANR grant PhysiCancer and the BMBF grant LungSys.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Adam, J., Bellomo, N.: A Survey of Models for Tumor-Immune System Dynamics. BirkhƤuser, Boston (1997)

    BookĀ  MATHĀ  Google ScholarĀ 

  2. Ambrosi, D., Preziosi, L.: On the closure of mass balance models for tumor growth. Math. Models Methods Appl. Sci. 12(5), 737ā€“754 (2002)

    ArticleĀ  MathSciNetĀ  MATHĀ  Google ScholarĀ 

  3. Anderson, A., Chaplain, M.A.J., Rejniak, K.: Single-Cell-Based Models in Biology and Medicine. Birkhauser, Basel (2007)

    BookĀ  MATHĀ  Google ScholarĀ 

  4. Araujo, R., McElwain, D.: A history of the study of solid tumor growth: the contribution of mathematical models. Bull. Math. Biol. 66, 1039ā€“1091 (2004)

    ArticleĀ  MathSciNetĀ  Google ScholarĀ 

  5. Bellomo, N., Li, N.K., Maini, P.K.: On the foundations of cancer modelling: selected topics, speculations, and perspectives. Math. Models Methods Appl. Sci. 4, 593ā€“646 (2008)

    ArticleĀ  MathSciNetĀ  Google ScholarĀ 

  6. Bellomo, N., Preziosi, L.: Modelling and mathematical problems related to tumor evolution and its interaction with the immune system. Math. Comput. Model. 32, 413ā€“452 (2000)

    ArticleĀ  MathSciNetĀ  MATHĀ  Google ScholarĀ 

  7. Berestycki, H., Hamel, F.: Reaction-Diffusion Equations and Propagation Phenomena. Springer, New York (2012)

    Google ScholarĀ 

  8. Breward, C.J.W., Byrne, H.M., Lewis, C.E.: The role of cell-cell interactions in a two-phase model for avascular tumor growth. J. Math. Biol. 45(2), 125ā€“152 (2002)

    ArticleĀ  MathSciNetĀ  MATHĀ  Google ScholarĀ 

  9. Byrne, H., Drasdo, D.: Individual-based and continuum models of growing cell populations: a comparison. J. Math. Biol. 58, 657ā€“687 (2009)

    ArticleĀ  MathSciNetĀ  Google ScholarĀ 

  10. Byrne, H.M., King, J.R., McElwain, D.L.S., Preziosi, L.: A two-phase model of solid tumor growth. Appl. Math. Lett. 16, 567ā€“573 (2003)

    ArticleĀ  MathSciNetĀ  MATHĀ  Google ScholarĀ 

  11. Byrne, H., Preziosi, L.: Modelling solid tumor growth using the theory of mixtures. Math. Med. Biol. 20, 341ā€“366 (2003)

    ArticleĀ  MATHĀ  Google ScholarĀ 

  12. Chaplain, M.A.J., Graziano, L., Preziosi, L.: Mathematical modeling of the loss of tissue compression responsiveness and its role in solid tumor development. Math. Med. Biol. 23, 197ā€“229 (2006)

    ArticleĀ  MATHĀ  Google ScholarĀ 

  13. Chatelain, C., Balois, T., Ciarletta, P., Amar, M.: Emergence of microstructural patterns in skin cancer: a phase separation analysis in a binary mixture. New J. Phys. 13, 115013+21 (2011)

    ArticleĀ  Google ScholarĀ 

  14. Chedaddi, I., Vignon-Clementel, I.E., Hoehme, S., etĀ al.: On constructing discrete and continuous models for cell population growth with quantitatively equal dynamics. (2013, in preparation)

    Google ScholarĀ 

  15. Ciarletta, P., Foret, L., Amar, M.B.: The radial growth phase of malignant melanoma: multi-phase modelling, numerical simulations and linear stability analysis. J. R. Soc. Interface 8(56), 345ā€“368 (2011)

    ArticleĀ  Google ScholarĀ 

  16. Colin, T., Bresch, D., Grenier, E., et al.: Computational modeling of solid tumor growth: the avascular stage. SIAM J. Sci. Comput. 32(4), 2321ā€“2344 (2010)

    ArticleĀ  MathSciNetĀ  MATHĀ  Google ScholarĀ 

  17. Cristini, V., Lowengrub, J., Nie, Q.: Nonlinear simulations of tumor growth. J. Math. Biol. 46, 191ā€“224 (2003)

    ArticleĀ  MathSciNetĀ  MATHĀ  Google ScholarĀ 

  18. De Angelis, E., Preziosi, L.: Advection-diffusion models for solid tumor evolution in vivo and related free boundary problem. Math. Models Methods Appl. Sci. 10(3), 379ā€“407 (2000)

    ArticleĀ  MathSciNetĀ  MATHĀ  Google ScholarĀ 

  19. Drasdo, D.: In: Alt, W., Chaplain, M., Griebel, M. (eds.) On Selected Individual-Based Approaches to the Dynamics of Multicellular Systems, Multiscale Modeling. Birkhauser, Basel (2003)

    Google ScholarĀ 

  20. Evans, L.C.: Partial Differential Equations. Graduate Studies in Mathematics, vol. 19. AMS, Providence (1998)

    MATHĀ  Google ScholarĀ 

  21. Friedman, A.: A hierarchy of cancer models and their mathematical challenges. Discrete Contin. Dyn. Syst., Ser. B 4(1), 147ā€“159 (2004)

    ArticleĀ  MathSciNetĀ  MATHĀ  Google ScholarĀ 

  22. Funaki, M., Mimura, M., Tsujikawa, A.: Traveling front solutions in a chemotaxis-growth model. Interfaces Free Bound. 8, 223ā€“245 (2006)

    ArticleĀ  MathSciNetĀ  MATHĀ  Google ScholarĀ 

  23. Gardner, R.A.: Existence of travelling wave solution of predator-prey systems via the connection index. SIAM J. Appl. Math. 44, 56ā€“76 (1984)

    ArticleĀ  MathSciNetĀ  MATHĀ  Google ScholarĀ 

  24. Hoehme, S., Drasdo, D.: A cell-based simulation software for multi-cellular systems. Bioinformatics 26(20), 2641ā€“2642 (2010)

    ArticleĀ  Google ScholarĀ 

  25. Lowengrub, J.S., Frieboes, H.B., Jin, F., et al.: Nonlinear modelling of cancer: bridging the gap between cells and tumors. Nonlinearity 23, R1ā€“R91 (2010)

    ArticleĀ  MathSciNetĀ  MATHĀ  Google ScholarĀ 

  26. Murray, J.D.: Mathematical Biology. Springer, New York (1989)

    BookĀ  MATHĀ  Google ScholarĀ 

  27. Nadin, G., Perthame, B., Ryzhik, L.: Traveling waves for the Keller-Segel system with Fisher birth terms. Interfaces Free Bound. 10, 517ā€“538 (2008)

    ArticleĀ  MathSciNetĀ  MATHĀ  Google ScholarĀ 

  28. Perthame, B., QuirĆ³s, F., VĆ”zquez, J.L.: The Hele-Shaw asymptotics for mechanical models of tumor growth. (2013, in preparation)

    Google ScholarĀ 

  29. Preziosi, L., Tosin, A.: Multiphase modeling of tumor growth and extracellular matrix interaction: mathematical tools and applications. J. Math. Biol. 58, 625ā€“656 (2009)

    ArticleĀ  MathSciNetĀ  Google ScholarĀ 

  30. Radszuweit, M., Block, M., Hengstler, J.G., et al.: Comparing the growth kinetics of cell populations in two and three dimensions. Phys. Rev. E 79, 051907 (2009)

    ArticleĀ  MathSciNetĀ  Google ScholarĀ 

  31. Ranft, J., Basan, M., Elgeti, J., et al.: Fluidization of tissues by cell division and apoptosis. Proc. Natl. Acad. Sci. USA 107(49), 20863ā€“20868 (2010)

    ArticleĀ  Google ScholarĀ 

  32. Roose, T., Chapman, S., Maini, P.: Mathematical models of avascular tumor growth: a review. SIAM Rev. 49(2), 179ā€“208 (2007)

    ArticleĀ  MathSciNetĀ  MATHĀ  Google ScholarĀ 

  33. SĆ”nchez-GarduƱo, F., Maini, P.K.: Travelling wave phenomena in some degenerate reaction-diffusion equations. J. Differ. Equ. 117(2), 281ā€“319 (1995)

    ArticleĀ  MATHĀ  Google ScholarĀ 

  34. Weinberger, H.F., Lewis, M.A., Li, B.: Analysis of linear determinacy for spread in cooperative models. J. Math. Biol. 45, 183ā€“218 (2002)

    ArticleĀ  MathSciNetĀ  MATHĀ  Google ScholarĀ 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Min Tang .

Editor information

Editors and Affiliations

Appendix: Derivation of the Cuboid State Equation

Appendix: Derivation of the Cuboid State Equation

Cells are modelled as cuboidal elastic bodies of dimensions at rest L 0Ɨl 0Ɨh 0 in x,y,z directions aligned in a row in x direction. At rest, the lineic mass density of the row of cells, in contact but not deformed, is \(\rho_{0} = \frac{M_{\rm cell}}{L_{0}}\). We consider the case that the cells are confined in a tube of section l 0Ɨh 0, where the only possible deformation is along the x axis. This situation can be tested in a direct in-vitro experiment. Moreover, this limit would be expected in case a tumor composed of elastic cells is sufficiently large, such that for the ratio of the cell size L and the radius of curvature R, \(\frac{L}{R}\ll 1\) holds, and the cell division is mainly oriented in radial direction as well as the cell-cell tangential friction is sufficiently small, such that a fingering or buckling instability does not occur.

When cells are deformed, we assume that stress and deformation are uniformly distributed, and that the displacements are small. Let L be the size of the cells. The lineic mass density is \(\rho = \frac{\rho_{0}L_{0}}{L}\). For Ļ<Ļ 0, the cells are not in contact and Ī£(Ļ)=0; for Ļā‰„Ļ 0, a variation dL of the size L of the cell corresponds to an infinitesimal strain \(\mathrm{d}u = \frac{\mathrm{d}L}{L}\). Therefore, the strain for a cell of size L is \(u = \ln(\frac{L}{L_{0}})\). Assuming that a cell is a linear elastic body with Young modulus E and Poisson ratio Ī½, one finds that the component Ļƒ xx of the stress tensor can be written as

$$\sigma_{xx} = -\frac{1-\nu}{(1-2\nu)(1+\nu)}E\ln \biggl(\frac{\rho}{\rho_0} \biggr). $$

The state equation is given by

$$\varSigma(\rho) = \left\lbrace \begin{array}{l@{\quad}l} 0, & \text{if}\ \rho \leq \rho_0,\\ {\frac{1-\nu}{(1-2\nu)(1+\nu)}E\ln(\frac{\rho}{\rho_0})}, & \text{otherwise.} \end{array} \right. $$

Here, Ī£(Ļ)=āˆ’Ļƒ xx is the pressure. Let \(\overline{\rho} = \frac{\rho}{\rho_{0}}\), \(\overline{\varSigma} = \frac{\varSigma}{E_{0}}\) and \(\overline{E}=\frac{E}{E_{0}}\) be the dimensionless density, pressure and Young modulus respectively, with E 0 a reference Young modulus. Then the state equation can be written as

$$\overline{\varSigma}(\overline{\rho}) = \left\lbrace \begin{array}{l@{\quad}l} 0, &\text{if}\ \overline{\rho} \leq 1,\\ C_\nu\ln(\overline{\rho}),&\text{otherwise}, \end{array} \right. $$

where \(C_{\nu}= {\frac{\overline{E}(1-\nu)}{(1-2\nu)(1+\nu)}}\). In the article, equations are written in the dimensionless form, and the bars above dimensionless quantities are removed.

Rights and permissions

Reprints and permissions

Copyright information

Ā© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Tang, M., Vauchelet, N., Cheddadi, I., Vignon-Clementel, I., Drasdo, D., Perthame, B. (2014). Composite Waves for a Cell Population System Modeling Tumor Growth and Invasion. In: Ciarlet, P., Li, T., Maday, Y. (eds) Partial Differential Equations: Theory, Control and Approximation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41401-5_16

Download citation

Publish with us

Policies and ethics