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The Diffusion Limit of Transport Equations in Biology

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Mathematical Models and Methods for Living Systems

Part of the book series: Lecture Notes in Mathematics ((LNMCIME,volume 2167))

Abstract

Transport equations add a whole new level of modelling to our menu of mathematical models for spatial spread of populations. They are situated between individual based models, which act on the microscopic scale and reaction diffusion equations, which rank on the macro-scale. Transport equations are thus often associated with a meso scale. These equations use movement characteristics of individual particles (velocity, turning rate etc.), but they describe a population by a continuous density.In this chapter we introduce transport equations as a modelling tool for biological populations, where we outline the relations to biological measurements. The link to individual based random walk models and the relation to diffusion equations are discussed. In particular, the diffusion limit (or parabolic limit) forms the main part of this chapter. We present the detailed mathematical framework and we discuss isotropic versus non-isotropic diffusion. Throughout the manuscript we investigate a large variety of applications including bacterial movement, amoeboid movement, movement of myxobacteria, and pattern formation through chemotaxis, swarming or alignment. We hope to convince the reader that transport equations form a useful alternative to other models in certain situations. Their full strength arises in situations where directionality of movement plays an important role.

“A smart model is a good model.” -Tyra Banks

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References

  1. H.C. Berg, D.A. Brown, Chemotaxis in Escherichia coli. Analysis by three-dimensional tracking. Nature 239, 500–504 (1972)

    Google Scholar 

  2. A. Bressan, Hyperbolic Systems of Conservation Laws (Oxford University Press, Oxford, 2000)

    MATH  Google Scholar 

  3. C. Cercignani, R. Illner, M. Pulvirenti, The Mathematical Theory of Diluted Gases (Springer, New York, 1994)

    Book  MATH  Google Scholar 

  4. F.A.C.C. Chalub, P.A. Markovich, B. Perthame, C. Schmeiser, Kinetic models for chemotaxis and their drift-diffusion limits. Monatsh. Math. 142, 123–141 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  5. J.B. Conway, A Course in Functional Analysis (Springer, New York, 1985)

    Book  MATH  Google Scholar 

  6. J.C. Dallon, H.G. Othmer, A discrete cell model with adaptive signalling for aggregation of Dictyostelium discoideum. Philos. Trans. R. Soc. Lond. B 352, 391–417 (1997)

    Article  Google Scholar 

  7. R. Dautray, J.-L. Lions, Mathematical Analysis and Numerical Methods for Science and Technology (Springer, Heidelberg, 2000)

    Book  MATH  Google Scholar 

  8. Y. Dolak, C. Schmeiser, Kinetic models for chemotaxis: hydrodynamic limits and spatio-temporal mechanics. J. Math. Biol. 51, 595–615 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  9. G.A. Dunn, J.P. Heath, A new hypothesis of contact guidance in tissue cells. Exp. Cell Res. 101 (1), 1–14 (1976)

    Article  Google Scholar 

  10. R. Eftimie, Hyperbolic and kinetic models for self-organized biological aggregations and movement: a brief review. J. Math. Biol. 65 (1), 35–75 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  11. R. Eftimie, G. de Vries, M.A. Lewis, Complex spatial group patterns result from different animal communication mechanisms. Proc. Natl. Acad. Sci. 104, 6974–6979 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  12. K.J. Engel, R. Nagel, One-Parameter Semigroups for Linear Evolution Equations (Springer, New York, 2000)

    MATH  Google Scholar 

  13. C. Engwer, T. Hillen, M. Knappitsch, C. Surulescu, A DTI-based multiscale model for glioma growth including cell-ECM interactions. J. Math. Biol. 71 (3), 551–582 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  14. R. Erban, H. Othmer, From signal transduction to spatial pattern formation in E. coli: a paradigm for multiscale modeling in biology. Multiscale Model. Simul. 3 (2), 362–394 (2005)

    Google Scholar 

  15. R. Fürth, Die Brownsche bewegung bei berücksichtigung einer Persistenz der Bewegungsrichtung. Z. Phys. 2, 244–256 (1920)

    Article  Google Scholar 

  16. A. Giese, R. Bjerkvig, M.E. Berens, M. Westphal, Cost of migration: invasion of malignant gliomas and implications for treatment. J. Clin. Oncol. 21 (8), 1624–1636 (2003)

    Article  Google Scholar 

  17. A. Giese, L. Kluwe, B. Laube, H. Meissner, M.E. Berens, M. Westphal, Migration of human glioma cells on myelin. Neurosurgery 38 (4), 755–764 (1996)

    Article  Google Scholar 

  18. S. Goldstein, On diffusion by discontinuous movements and the telegraph equation. Q. J. Mech. Appl. Math. 4, 129–156 (1951)

    Article  MathSciNet  MATH  Google Scholar 

  19. K.P. Hadeler, Travelling fronts for correlated random walks. Can. Appl. Math. Q. 2, 27–43 (1994)

    MathSciNet  MATH  Google Scholar 

  20. K.P. Hadeler, Travelling fronts in random walk systems. FORMA Jpn. 10, 223–233 (1995)

    MathSciNet  MATH  Google Scholar 

  21. K.P. Hadeler, Reaction telegraph equations and random walk systems, in Stochastic and Spatial Structures of Dynamical Systems, ed. by S.J. van Strien, S.M. Verduyn Lunel (Royal Academy of the Netherlands, Amsterdam, 1996), pp. 133–161

    Google Scholar 

  22. K.P. Hadeler, Reaction transport systems, in Mathematics Inspired by Biology, ed. by V. Capasso, O. Diekmann. Lecture Notes in Mathematics, vol. 1714, CIME Letures 1997, Florence (Springer, New York, 1999), pp. 95–150

    Google Scholar 

  23. P.R. Halmos, V.S. Sunder, Bounded Integral Operators on L2 Spaces (Springer, Heidelberg, 1978)

    Book  MATH  Google Scholar 

  24. T. Hillen, A Turing model with correlated random walk. J. Math. Biol. 35, 49–72 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  25. T. Hillen, Qualitative analysis of semilinear Cattaneo systems. Math. Models Methods Appl. Sci. 8 (3), 507–519 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  26. T. Hillen, On the L 2-closure of transport equations: the general case. Discrete Contin. Dyn. Syst. Ser. B 5 (2), 299–318 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  27. T. Hillen, M 5 mesoscopic and macroscopic models for mesenchymal motion. J. Math. Biol. 53 (4), 585–616 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  28. T. Hillen, Existence theory for correlated random walks on bounded domains. Canad. Appl. Math. Q. 18 (1), 1–40 (2010)

    MathSciNet  MATH  Google Scholar 

  29. T. Hillen, H.G. Othmer, The diffusion limit of transport equations derived from velocity jump processes. SIAM J. Appl. Math. 61 (3), 751–775 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  30. T. Hillen, K.J. Painter, A user’s guide to PDE models for chemotaxis. J. Math. Biol. 58, 183–217 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  31. T. Hillen, K.J. Painter, Transport and anisotropic diffusion models for movement in oriented habitats. in Dispersal, Individual Movement and Spatial Ecology: A Mathematical Perspective, ed. by M. Lewis, P. Maini, S. Petrovskii (Springer, Heidelberg, 2013), p. 46

    Google Scholar 

  32. T. Hillen, A. Stevens, Hyperbolic models for chemotaxis in 1-d. Nonlinear Anal. Real World Appl. 1 (1), 409–433 (2001)

    MathSciNet  MATH  Google Scholar 

  33. T. Hillen, C. Rohde, F. Lutscher, Existence of weak solutions for a hyperbolic model for chemosensitive movement. J. Math. Anal. Appl. 260, 173–199 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  34. T. Hillen, P. Hinow, Z.A. Wang, Mathematical analysis of a kinetic model for cell movement in network tissues. Discrete Contin. Dyn. Syst. B 14 (3), 1055–1080 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  35. T. Hillen, K. Painter, A. Swan, Modelling with Transport Equations in Biology Springer (in preparation)

    Google Scholar 

  36. E.E. Holmes, Are diffusion models too simple? a comparison with telegraph models of invasion. Am. Nat. 142, 779–795 (1993)

    Article  Google Scholar 

  37. D. Horstmann, From 1970 until present: the Keller-Segel model in chemotaxis and its consequences I. Jahresberichte der DMV 105 (3), 103–165 (2003)

    MathSciNet  MATH  Google Scholar 

  38. D.D. Joseph, L. Preziosi, Heat waves. Rev. Mod. Phys. 61, 41–73 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  39. M. Kac, A stochastic model related to the telegrapher‘s equation. Rocky Mt. J. Math. 4, 497–509 (1956)

    Article  MathSciNet  MATH  Google Scholar 

  40. E.F. Keller, L.A. Segel, Initiation of slime mold aggregation viewed as an instability. J. Theor. Biol. 26, 399–415 (1970)

    Article  MATH  Google Scholar 

  41. M.A. Krasnoselskii, Positive Solutions of Operator Equations (P. Noordhoff, Groningen, 1964). English translation of Russian original

    Google Scholar 

  42. F. Lutscher, A. Stevens, Emerging patterns in a hyperbolic model for locally interacting cell systems. J. Nonlinear Sci. 12, 619–640 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  43. H.W. McKenzie, Linear features impact predator-prey encounters: analysis and first passage time, 2006. M.Sc. thesis, University of Alberta

    Google Scholar 

  44. H.W. McKenzie, E.H. Merrill, R.J. Spiteri, M.A. Lewis. How linear features alter predator movement and the functional response. R. Soc. Interface Focus 2 (2), 205–216 (2012)

    Article  Google Scholar 

  45. A. Okubo, S.A. Levin, Diffusion and Ecological Problems: Modern Perspectives (Springer, New York, 2002)

    MATH  Google Scholar 

  46. H.G. Othmer, S.R. Dunbar, W. Alt, Models of dispersal in biological systems. J. Math. Biol. 26, 263–298 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  47. H.G. Othmer, T. Hillen, The diffusion limit of transport equations II: chemotaxis equations. SIAM J. Appl. Math. 62 (4), 1122–1250 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  48. H.G. Othmer, C. Xue, Multiscale models of taxis-driven patterning in bacterial populations. SIAM Appl. Math. 70 (1), 133–167 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  49. K.J. Painter, Modelling migration strategies in the extracellular matrix. J. Math. Biol. 58, 511–543 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  50. K.J. Painter, T. Hillen, Mathematical modelling of glioma growth: the use of diffusion tensor imaging DTI data to predict the anisotropic pathways of cancer invasion. J. Theor. Biol. 323, 25–39 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  51. A. Pazy, Semigroups of Linear Operators and Applications to Partial Differential Equations (Springer, New York, 1983)

    Book  MATH  Google Scholar 

  52. B. Perthame, Transport Equations in Biology (Birkhäuser, Boston, 2007)

    MATH  Google Scholar 

  53. B. Pfistner, A one dimensional model for the swarming behavior of myxobacteria, in Biological Motion, ed. by W. Alt, G. Hoffmann. Lecture Notes on Biomathematics, vol. 89 (Springer, New York, 1990)

    Google Scholar 

  54. H. Poincaré, Sur la propagation de l’électricité. Compt. Rend. Ac. Sci. 117, 1027–1032 (1893)

    MATH  Google Scholar 

  55. D.W. Stroock, Some stochastic processes which arise from a model of the motion of a bacterium. Probab. Theory Relat. Fields 28, 305–315 (1974)

    MATH  Google Scholar 

  56. G.I. Taylor, Diffusion by discontinuous movements. Proc. Lond. Math. Soc. 20 (1), 196–212 (1920)

    MATH  Google Scholar 

  57. M.E. Taylor, Partial Differential Equations III (Springer, New York, 1996)

    Google Scholar 

  58. Z.A. Wang, T. Hillen, M. Li, Mesenchymal motion models in one dimension. SIAM J. Appl. Math. 69 (2), 375–397 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  59. M.L. Ware, M.S. Berger, D.K. Binder, Molecular biology of glioma tumorgenesis. Histol. Histopathol. 18, 207–216 (2003)

    Google Scholar 

  60. E. Zauderer, Partial Differential Equations, 3rd edn. (Wiley, Hoboken, 2006)

    MATH  Google Scholar 

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Acknowledgements

We are grateful for CIME to support this interesting summer school and who invited us to contribute this book chapter. We also thank Dr. K. Painter for continued collaboration and his support of these lecture notes. The work of TH and AS is supported by NSERC grants.

K.P. Hadeler [22] has published a book chapter entitled Reaction Random Walk Systems where the results of our Sect. 2.2.6 are discussed in great detail. Hadeler’s book chapter arose as one of the CIME lecture notes from a workshop in 1997. We are honoured to be able to continue Hadeler’s work through these CIME notes on transport equations.

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Correspondence to Thomas Hillen .

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Hillen, T., Swan, A. (2016). The Diffusion Limit of Transport Equations in Biology. In: Preziosi, L., Chaplain, M., Pugliese, A. (eds) Mathematical Models and Methods for Living Systems. Lecture Notes in Mathematics(), vol 2167. Springer, Cham. https://doi.org/10.1007/978-3-319-42679-2_2

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