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Coupling and Quiescence

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Topics in Mathematical Biology

Abstract

Suppose we have two vector fields \(f,g: \mathbb{R}^{n} \rightarrow \mathbb{R}^{n}\) and the differential equations

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Notes

  1. 1.

    This situation corresponds to an ultimate discretization of the diffusion equation, with only two grid points.

  2. 2.

    We replace p, q by pε, qε and let ε → 0.

  3. 3.

    There are other notations for coupled systems. Hale [126] starts from systems \(\dot{z}_{j} = f_{j}(z_{j})\), \(j \in \mathbb{Z}\), in \(\mathbb{R}^{n}\), collects the z j in a “row” vector z, and the f j into f j (z) = ( f 1(z 1), , f m (z m ), and then, with a symmetric tridiagonal coupling matrix A, writes the system as \(\dot{z} = Az + f(z)\).

  4. 4.

    Notice that in this “additive” equation and in the “multiplicative” equation \(\dot{V } = AV P\) the operators VAV and V + VP commute. Thus, if μ i , λ j are the eigenvalues of A, P, respectively, then μ i + λ j and μ i λ j are the eigenvalues of the operators VAV + VP and AV P, respectively.

  5. 5.

    It suffices that each matrix has a unique positive Perron vector.

  6. 6.

    An example may be the European starling which was introduced in North America without effect until flocks of sixty to one hundred birds were settled in New York Central Park around 1890. Now there are an estimated 200 million starlings in North America.

  7. 7.

    In the thermodynamic interpretation of the heat equation the diffusion coefficient is k∕() where k is heat conductivity, c is the specific heat capacity, and ρ the specific mass.

  8. 8.

    This assumption is called the first Fickian law, the diffusion equation itself the second Fickian law. In an experimental setting, the first Fickian law does not hold for very small gradients. This assumption leads to the unwanted effect of infinitely fast propagation in the heat equation (its parabolic character as opposed to hyperbolic).

  9. 9.

    The transition from (1.40) to (1.42) is called Kac’ trick [156]. Notice that the transition is not invertible, solutions “without mass” (0, v 0exp(−μt)) of the system (1.41) are mapped into the zero solution of Eq. (1.42).

  10. 10.

    The Schur–Cohn criterion, from 1914/1922, see [205], often attributed to E.I. Jury 1964,is the analogue of the Routh–Hurwitz criterion for discrete time systems.

  11. 11.

    These inequalities imply detA < 1.

  12. 12.

    We use the formula for the real part of the square root of a complex number \(\mathfrak{R}\sqrt{\xi +i\eta } = [\xi +\sqrt{\xi ^{2 } +\eta ^{2}}]^{1/2}/\sqrt{2}\).

  13. 13.

    We underline that here \(\mathbb{R}_{+}^{n}\) is the positive cone in the tangent space. It could be that also the underlying system for x preserves positivity. But such property would not be relevant here.

  14. 14.

    This result was not widely known in 1974 as can be seen from [140]. But in 1982 M. Hirsch published the first paper of a series of six on cooperative and competitive systems [139].

  15. 15.

    In [126], on coupled systems, it is assumed that the small system and the coupled systems have compact global attractors.

  16. 16.

    This section is almost identical with the paper [105].

References

  1. Allee, W.C.: Animal Aggregations. A Study in General Sociology. University of Chicago Press, Chicago (1931)

    Google Scholar 

  2. Allen, C., et al.: Isolation of quiescent and nonquiescent cells from yeast stationary-phase cultures. J. Cell Biol. 174(1), 89–100 (2006). http://www.yeastgenome.org/reference/S000117025/overview

    Article  Google Scholar 

  3. Armstrong, R.A., McGehee, R.: Competitive exclusion. Am. Nat. 115(2), 151–170 (1980)

    Article  MathSciNet  Google Scholar 

  4. Berman, A., Plemmons, R.J.: Nonnegative Matrices in the Mathematical Sciences. Academic, New York (1979)

    MATH  Google Scholar 

  5. Bilinsky, L., Hadeler, K.P.: Quiescence stabilizes predator-prey relations. J. Biol. Dyn. 3(2–3), 196–208 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  6. Collet, P., Eckmann, J.-P., Lanford, O.E.: Universal properties of maps on an interval. Commun. Math. Phys. 76(3), 211–254 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  7. Cross, G.W.: Three types of matrix stability. Linear Algebra Appl. 20(3), 253–263 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  8. Devaney, R.L.: An Introduction to Chaotic Dynamical Systems. Benjamin Cummings, Menlo Park (1986)

    MATH  Google Scholar 

  9. Ermentrout, B., Campbell, S.J., Oster, G.: A model for shell patterns based on neural activity. Veliger 28, 369–388 (1986)

    Google Scholar 

  10. Ewton, D.Z., Hu, J., Vilenchik, M., Deng, X., Luk K.-C., Polonskaia, A., Hoffman, A.-F., Zipf, K., Boylan, J.F., Friedman, E.A.: Inactivation of mirk/dyrk1b kinase targets quiescent pancreatic cancer cells. Mol. Cancer Ther. 10(11), 2104–2114 (2011). https://www.ncbi.nlm.nih.gov/pubmed/21878655

    Article  Google Scholar 

  11. Feigenbaum, M.J.: Quantitative universality for a class of nonlinear transformations. J. Stat. Phys. 19(1), 25–52 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  12. Fürth, R.: Die Brownsche Bewegung bei Berücksichtigung einer Persistenz der Bewegungsrichtung. Mit Anwendungen auf die Bewegung lebender Infusorien. Z. Physik 2, 244–256 (1920)

    Google Scholar 

  13. Gantmacher, F.R.: The Theory of Matrices, vols. 1, 2. Chelsea, New York (1959)

    Google Scholar 

  14. Gierer, A.: The Hydra model - a model for what? Int. J. Dev. Biol. 56, 437–445 (2012)

    Article  Google Scholar 

  15. Gierer, A., Meinhardt, H.: A theory of biological pattern formation. Kybernetik 12, 30–39 (1972)

    Article  MATH  Google Scholar 

  16. Glansdorff, P., Prigogine, I.: Thermodynamic Theory of Structure, Stability and Fluctuations. Wiley, London (1971)

    MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  18. Gonze, D., Abou-Jaoudé, W.: The Goodwin model: Behind the Hill function. PLoS ONE 8(8), e69,573 (2013)

    Article  Google Scholar 

  19. Guckenheimer, J., Holmes, P.: Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields. Applied Mathematical Sciences, vol. 42. Springer, New York (1990)

    Google Scholar 

  20. Hadeler, K.P.: Quiescent phases and stability. Linear Algebra Appl. 428(7), 1620–1627 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  21. Hadeler, K.P.: Quiescent phases and stability in discrete time dynamical systems. Discrete Contin. Dyn. Syst. Ser. B 20(1), 129–152 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  22. Hadeler, K.P., Hillen, T.: Coupled dynamics and quiescent phases. In: Aletti, G., Burger, M., Micheletti, A., Morale, D. (eds.) Math Everywhere, pp. 7–23. Springer, Berlin (2007)

    Chapter  Google Scholar 

  23. Hadeler, K.P., Lewis, M.A.: Spatial dynamics of the diffusive logistic equation with a sedentary compartment. Can. Appl. Math. Q. 10(4), 473–499 (2002)

    MathSciNet  MATH  Google Scholar 

  24. Hadeler, K.P., Lutscher, F.: Quiescent phases with distributed exit times. Discrete Contin. Dyn. Syst. Ser. B 17(3), 849–869 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  25. Hadeler, K.P., Thieme, H.R.: Monotone dependence of the spectral bound on the transition rates in linear compartmental models. J. Math. Biol. 57(5), 697–712 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  26. Hale, J.K.: Diffusive coupling, dissipation, and synchronization. J. Dyn. Differ. Equ. 9(1), 1–52 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  27. Hassard, B.D., Kazarinoff, N.D., Wan, Y.H.: Theory and Applications of Hopf Bifurcation. London Mathematical Society Lecture Notes Series, vol. 41. Cambride University Press, Cambride (1981)

    Google Scholar 

  28. Hazewinkel, M.E.: Trotter product formula. In: Encyclopedia of Mathematics. Springer, Berlin (2001)

    Google Scholar 

  29. Hirsch, M.W.: Systems of differential equations which are competitive or cooperative. I. limit sets. SIAM J. Math. Anal. 13(2), 167–179 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  30. Hirsch, M.W., Smale, S.: Differential Equations, Dynamical Systems, and Linear Algebra. Pure and Applied Mathematics, vol. 60. Academic, New York/London (1974)

    Google Scholar 

  31. Hofbauer, J., Sigmund, K.: The Theory of Evolution and Dynamical Systems. London Mathematical Society Student Texts, vol. 7. Cambridge University Press, Cambridge (1988)

    Google Scholar 

  32. Holmes, E.E.: Are diffusion models too simple? A comparison with telegraph models of invasion. Am. Nat. 142(5), 779–795 (1993)

    Google Scholar 

  33. Horvath, J., Szalai, I., De Kepper, P.: An experimental design method leading to chemical Turing patterns. Science 324, 772–775 (2009)

    Article  Google Scholar 

  34. Johnson, C.R.: Sufficient conditions for D-stability. J. Econ. Theory 9(1), 53–62 (1974)

    Article  MathSciNet  Google Scholar 

  35. Johnson, R., Tesei, A.: On the D-stability problem for real matrices. Boll. UMI 8, 2-B, 299–314 (1999)

    Google Scholar 

  36. Kac, M.: A stochastic model related to the telegrapher’s equation. Reprinted in Rocky Mt. Math. J. 4, 497–509 (1956/1974)

    Google Scholar 

  37. Körös, E., Field, R.J., Noyes, R.M.: Oscillations in chemical systems. II. Thorough analysis of temporal oscillation in the bromate-cerium-malonic acid system. J. Am. Chem. Soc. 94, 8649–8664 (1972)

    Google Scholar 

  38. Kuznetsov, Y.A.: Elements of Applied Bifurcation Theory. Applied Mathematical Sciences, vol. 112, 3rd edn. Springer, New York (2004)

    Google Scholar 

  39. Lasota, A., Mackey, M.C.: Chaos, Fractals, and Noise. Applied Mathematical Sciences, vol. 97. Springer, New York (1994)

    Google Scholar 

  40. Lewis, M.A., Schmitz, G.: Biological invasion of an organism with separate mobile and stationary states: modelling and analysis. Forma 11, 1–25 (1996)

    MathSciNet  MATH  Google Scholar 

  41. Li, T.Y., Yorke, J.A.: Period three implies chaos. Am. Math. Mon. 92, 985–992 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  42. Lin, C.-S., Takagi, W.-M.: Large amplitude stationary solutions to a chemotaxis system. J. Differ. Equ. 72, 1–27 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  43. Logofet, D.: Matrices and Graphs: Stability Problems in Mathematical Ecology. CRC Press, Boca Raton (1993)

    Google Scholar 

  44. Maini, P.K., Woolley, T.E., Baker, R.E., Gaffney, E.A., Lee, S.S.: Turing’s model for biological pattern formation and the robustness problem. Interface Focus 2, 487–496 (2012)

    Article  Google Scholar 

  45. Malik, T., Smith, H.: A resource-based model of microbial quiescence. J. Math. Biol. 53, 231–252 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  46. Marden, M.: The Geometry of the Zeroes of a Polynomial in a Complex Variable. AMS, New York (1949)

    Book  MATH  Google Scholar 

  47. May, R.M., Oster, G.F.: Bifurcations and dynamic complexity in simple ecological models. Am. Nat. 110, 573–599 (1976)

    Article  Google Scholar 

  48. McLachlan, R.I., Quispel, G.R.W.: Splitting methods. Acta Numer. 11, 341–434 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  49. Meinhardt, H.: The Algorithmic Beauty of Sea Shells. Springer, New York (1995)

    Book  MATH  Google Scholar 

  50. Murray, J.D.: Mathematical Biology. Springer, Berlin (1989)

    Book  MATH  Google Scholar 

  51. Murray, J.D., Myerscough, M.R.: Pigmentation pattern formation on snakes. J. Theor. Biol. 149, 39–360 (1991)

    Article  Google Scholar 

  52. Oki, T., Nishimura, K., Kitaura, J., Togami, K., Maehara, A., Izawa, K., Sakaue-Sawano, A., Niida, A., Miyano, S., Aburatani, H., Kiyonari, H., Miyawaki, A., Kitamura, T.: A novel cell-cycle-indicator, mVenus-p27K-, identifies quiescent cells and visualizes G0-G1 transition. Sci. Rep. 6(4), 4012 (2014). doi: 10.1038/srep04012. https://www.ncbi.nlm.nih.gov/pubmed/24500246

  53. Satnoianu, R.A., van den Driessche, P.: Some remarks on matrix stability with applications to Turing instability. Linear Algebra Appl. 398, 69–74 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  54. Sekimura, T., Madzvamuse, A., Wathen, A.J., Maini, P.K.: A model for colour pattern formation in the butterfly wing of Papilio dardanus. Proc. R. Soc. Lond. B 267, 851–859 (2000)

    Article  MATH  Google Scholar 

  55. Sharkovski, A.N.: The reducibility of a continuous function of a real variable and the structure of the stationary points of the corresponding iteration process. Dokl. Akad. Nauk RSR 139, 1067–1070 (1961)

    MathSciNet  Google Scholar 

  56. Smale, S., Williams, R.F.: The qualitative analysis of a difference equation of population growth. J. Math. Biol. 3, 1–4 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  57. Smith, H.: Monotone Dynamical Systems: An Introduction to the Theory of Competitive and Cooperative Systems. Mathematical Surveys and Monographs, vol. 41. AMS, Providence, RI (1995)

    Google Scholar 

  58. Smith, H.L., Waltman, P.: The Theory of the Chemostat. Cambridge University Press, Cambridge (1995)

    Book  MATH  Google Scholar 

  59. Turing, A.M.: The chemical basis of morphogenesis. Philos. Trans. R. Soc. Lond. B 237, 37–72 (1952)

    Article  MathSciNet  Google Scholar 

  60. Tyson, J.: The Belousov-Zhabotinskii Reaction. Lecture Notes in Biomathematics, vol. 10. Springer, Berlin (1980)

    Google Scholar 

  61. Ulam, S.: Monte Carlo calculations in problems of mathematical physics. In: Modern Mathematics for the Engineer: Second Series, pp. 261–281. McGraw-Hill, New York (1961)

    Google Scholar 

  62. van Strien, S., de Melo, W.: One-Dimensional Dynamics. Ergebnisse der Mathematik und ihrer Grenzgebiete, vol. 3. Springer, Berlin (1993)

    Google Scholar 

  63. Volkening, A., Sandstede, B.: Modelling stripe formation in zebra fish: an agent-based approach. J. R. Soc. Interface 12(112), 20 150 812 (2015)

    Google Scholar 

  64. Zaikin, A.N., Zhabotinsky, A.M.: Concentration waves propagating in two-dimensional liquid phase self-oscillating system. Nature 225, 535–537 (1970)

    Article  Google Scholar 

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Hadeler, KP. (2017). Coupling and Quiescence. In: Topics in Mathematical Biology. Lecture Notes on Mathematical Modelling in the Life Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-65621-2_1

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