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Axisymmetric solutions for a chemotaxis model of Multiple Sclerosis

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Abstract

In this paper we study radially symmetric solutions for our recently proposed reaction–diffusion–chemotaxis model of Multiple Sclerosis. Through a weakly nonlinear expansion we classify the bifurcation at the onset and derive the amplitude equations ruling the formation of concentric demyelinating patterns which reproduce the concentric layers observed in Balò sclerosis and in the early phase of Multiple Sclerosis. We present numerical simulations which illustrate and fit the analytical results.

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References

  1. Abdechiri, M., Faez, K., Amindavar, H., Bilotta, E.: The chaotic dynamics of high-dimensional systems. Nonlinear Dyn. 87(4), 2597–2610 (2017)

    Article  MathSciNet  Google Scholar 

  2. Aragón, J., Torres, M., Gil, D., Barrio, R., Maini, P.: Turing patterns with pentagonal symmetry. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 65(5), 051913/1–051913/9 (2002)

    Article  MathSciNet  Google Scholar 

  3. Baló, J.: Encephalitis periaxialis concentrica. Arch. Neurol. Psychiatry 19(2), 242–264 (1928)

    Article  Google Scholar 

  4. Barnett, M., Prineas, J.: Relapsing and remitting multiple sclerosis: pathology of the newly forming lesion. Ann. Neurol. 55(4), 458–468 (2004)

    Article  Google Scholar 

  5. Barnett, M.H., Parratt, J.D.E., Pollard, J.D., Prineas, J.W.: MS: Is it one disease? Int. MS J. 16(2), 57–65 (2009)

    Google Scholar 

  6. Barresi, R., Bilotta, E., Gargano, F., Lombardo, M., Pantano, P., Sammartino, M.: Wavefront invasion for a chemotaxis model of multiple sclerosis. Ricerche Mat. 65(2), 423–434 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  7. Bilotta, E., Pantano, P.: Cellular nonlinear networks meet KdV equation: a newparadigm. Int. J. Bifurc. Chaos 23(1), 1330003 (2013)

    Article  MATH  Google Scholar 

  8. Bilotta, E., Pantano, P., Vena, S.: Speeding up cellular neural network processing ability by embodying memristors. IEEE Trans. Neural Netw. Learn. Syst. 28(5), 1228–1232 (2017)

    Article  Google Scholar 

  9. Bozzini, B., Gambino, G., Lacitignola, D., Lupo, S., Sammartino, M., Sgura, I.: Weakly nonlinear analysis of Turing patterns in a morphochemical model for metal growth. Comput. Math. Appl. 70(8), 1948–1969 (2015)

    Article  MathSciNet  Google Scholar 

  10. Byrne, H.: A weakly nonlinear analysis of a model of avascular solid tumour growth. J. Math. Biol. 39(1), 59–89 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  11. Byrne, H., Matthews, P.: Asymmetric growth of models of avascular solid tumours: exploiting symmetries. IMA J. Math. Appl. Med. Biol. 19(1), 1–29 (2002)

    Article  MATH  Google Scholar 

  12. Chalmers, A., Cohen, A., Bursill, C., Myerscough, M.: Bifurcation and dynamics in a mathematical model of early atherosclerosis: how acute inflammation drives lesion development. J. Math. Biol. 71(6–7), 1451–1480 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  13. Dolak, Y., Schmeiser, C.: The Keller–Segel model with logistic sensitivity function and small diffusivity. SIAM J. Appl. Math. 66(1), 286–308 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  14. Gambino, G., Lombardo, M., Sammartino, M.: A velocity-diffusion method for a Lotka–Volterra system with nonlinear cross and self-diffusion. Appl. Numer. Math. 59(5), 1059–1074 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  15. Gambino, G., Lombardo, M., Sammartino, M.: Turing instability and traveling fronts for a nonlinear reaction-diffusion system with cross-diffusion. Math. Comput. Simul. 82(6), 1112–1132 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  16. Gambino, G., Lombardo, M., Sammartino, M., Sciacca, V.: Turing pattern formation in the Brusselator system with nonlinear diffusion. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 88(4), 042925 (2013)

    Article  Google Scholar 

  17. Gambino, G., Lombardo, M., Sammartino, M.: Turing instability and pattern formation for the Lengyel–Epstein system with nonlinear diffusion. Acta Appl. Math. 132(1), 283–294 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  18. Gambino, G., Lombardo, M., Sammartino, M.: Cross-diffusion-induced subharmonic spatial resonances in a predator–prey system. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 97(1), 012220 (2018)

    Article  Google Scholar 

  19. Hillen, T., Painter, K.: Global existence for a parabolic chemotaxis model with prevention of overcrowding. Adv. Appl. Math. 26(4), 280–301 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  20. Hillen, T., Painter, K.J.: A user’s guide to PDE models for chemotaxis. J. Math. Biol. 58(1–2), 183–217 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  21. Khonsari, R., Calvez, V.: The origins of concentric demyelination: self-organization in the human brain. PLoS ONE 2(1), e150 (2007)

    Article  Google Scholar 

  22. Lassmann, H.: Multiple sclerosis pathology: evolution of pathogenetic concepts. Brain Pathol. 15(3), 217–222 (2005)

    Article  Google Scholar 

  23. Lombardo, M., Barresi, R., Bilotta, E., Gargano, F., Pantano, P., Sammartino, M.: Demyelination patterns in a mathematical model of multiple sclerosis. J. Math. Biol. 75(2), 373–417 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  24. Luca, M., Chavez-Ross, A., Edelstein-Keshet, L., Mogilner, A.: Chemotactic signaling, microglia, and Alzheimer’s disease senile plaques: Is there a connection? Bull. Math. Biol. 65(4), 693–730 (2003)

    Article  MATH  Google Scholar 

  25. Lucchinetti, C., Brück, W., Parisi, J., Scheithauer, B., Rodriguez, M., Lassmann, H.: Heterogeneity of multiple sclerosis lesions: implications for the pathogenesis of demyelination. Ann. Neurol. 47(6), 707–717 (2000)

    Article  Google Scholar 

  26. Morgan, D.S., Kaper, T.J.: Axisymmetric ring solutions of the \(2\)D Gray-Scott model and their destabilization into spots. Phys. D 192(1–2), 33–62 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  27. Penner, K., Ermentrout, B., Swigon, D.: Pattern formation in a model of acute inflammation. SIAM J. Appl. Dyn. Syst. 11(2), 629–660 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  28. Pomeau, Y., Zaleski, S., Manneville, P.: Axisymmetric cellular structures revisited. ZAMP Z. Angew. Math. Phys. 36(3), 367–394 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  29. Quinlan, R., Straughan, B.: Decay bounds in a model for aggregation of microglia: application to Alzheimer’s disease senile plaques. Proc. R. Soc. A Math. Phys. Eng. Sci. 461(2061), 2887–2897 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  30. Rionero, S., Vitiello, M.: Stability and absorbing set of parabolic chemotaxis model of Escherichia coli. Nonlinear Anal. Modell. Control 18(2), 210–226 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  31. Rovira, A., Auger, C., Alonso, J.: Magnetic resonance monitoring of lesion evolution in multiple sclerosis. Ther. Adv. Neurol. Disord. 6(5), 298–310 (2013)

    Article  Google Scholar 

  32. Short, M.B., Bertozzi, A.L., Brantingham, P.J.: Nonlinear patterns in urban crime: hotspots, bifurcations, and suppression. SIAM J. Appl. Dyn. Syst. 9(2), 462–483 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  33. Tindall, M., Maini, P., Porter, S., Armitage, J.: Overview of mathematical approaches used to model bacterial chemotaxis II: bacterial populations. Bull. Math. Biol. 70(6), 1570–1607 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  34. Van Der Valk, P., Amor, S.: Preactive lesions in multiple sclerosis. Curr. Opin. Neurol. 22(3), 207–213 (2009)

    Google Scholar 

  35. van Noort, J., van den Elsen, P., van Horssen, J., Geurts, J., van der Valk, P., Amor, S.: Preactive multiple sclerosis lesions offer novel clues for neuroprotective therapeutic strategies. CNS Neurol. Disord. Drug Targets 10(1), 68–81 (2011)

    Article  Google Scholar 

  36. Wrzosek, D.: Global attractor for a chemotaxis model with prevention of overcrowding. Nonlinear Anal. Theory Methods Appl. 59(8), 1293–1310 (2004)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work has been partially supported by National Group of Mathematical Physics (GNFM-INDAM).

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Correspondence to V. Giunta.

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Dedicated to Professor Tommaso Ruggeri on the occasion of his 70th birthday.

A Derivation of the amplitude equation

A Derivation of the amplitude equation

In this Appendix, we give the details of the derivation of the amplitude equation (14).

At \(O(\eta )\) we get a linear problem whose solution is given by (13). Taking into account this solution, at \(O(\eta ^2)\) we obtain the following linear problem:

$$\begin{aligned} \mathcal {L}^{\chi _c}{} \mathbf w _2=\mathbf {F} \end{aligned}$$
(34)

where \(\mathbf {F}=\frac{\partial A}{\partial T}\varvec{\gamma } J_0(k_c \varrho )+\mathbf {G}_0^{(1)}AJ_0(k_c \varrho )+(\mathbf {G}_0^{(2)}J_0(k_c \varrho )^2+\mathbf {G}_1^{(2)}J_1(k_c \varrho )^2)A^2 \)

and

$$\begin{aligned}&\mathbf {G}_0^{(1)}=\left( \begin{array}{c} -\frac{\chi _1}{2}k_c^2\\ 0 \\ 0 \end{array} \right) ,&\end{aligned}$$
(35)
$$\begin{aligned}&\mathbf {G}_0^{(2)}=\left( \begin{array}{c} -\frac{\chi _1}{4}k_c^2 M+M^2 \\ 0\\ \frac{3}{4}r M N \end{array} \right) ,&\end{aligned}$$
(36)
$$\begin{aligned}&\mathbf {G}_1^{(2)}=\left( \begin{array}{c} \frac{\chi _c}{4}k_c^2 M \\ 0 \\ 0 \end{array} \right) ,&\end{aligned}$$
(37)

By Fredholm alternative theorem, the Eq. (34) admits solutions if and only if \(\langle \mathbf {F}, \varvec{\psi }^{*}\rangle =0\), where

$$\begin{aligned} \varvec{\psi }^{*}=\varvec{\psi }J_0(k_c \varrho ), , \end{aligned}$$
(38)

and

$$\begin{aligned} \varvec{\psi }=\left( \bar{M} , 1 , \bar{N} \right) ^T=\left( \frac{\beta }{\tau (1+k_c^2)} , 1 , \frac{2\delta }{\tau r} \right) ^T\in Ker[(K-k_c^2D^{\chi _c})^{\dag }]. \end{aligned}$$

The solvability condition \( \langle \mathbf {F}, \, \varvec{\psi }^{*}\rangle =0 \) for Eq. (34) leads to (14), where the expression for \(\sigma \) and L are given by:

$$\begin{aligned} \sigma= & {} -\frac{\langle \mathbf {G}_0^{(1)},\varvec{\psi }\rangle }{\langle \varvec{\gamma },\varvec{\psi }\rangle }, \qquad L\nonumber \\= & {} \frac{\langle \mathbf {G}_0^{(2)},\varvec{\psi }\rangle }{\langle \varvec{\gamma },\varvec{\psi }\rangle } \frac{\int _{0}^{R} \varrho \, J_0(k_c \rho )^3 \, d \varrho }{\int _{0}^{R} \varrho \, J_0(k_c \rho )^2 \, d \varrho }+\frac{\langle \mathbf {G}_1^{(2)},\varvec{\psi }\rangle }{\langle \varvec{\gamma },\varvec{\psi }\rangle } \frac{\int _{0}^{R} \varrho \, J_0(k_c \rho ) \, J_1(k_c \rho )^2 \, d \varrho }{\int _{0}^{R} \varrho J_0(k_c \rho )^2 d \varrho } \end{aligned}$$
(39)

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Bilotta, E., Gargano, F., Giunta, V. et al. Axisymmetric solutions for a chemotaxis model of Multiple Sclerosis. Ricerche mat 68, 281–294 (2019). https://doi.org/10.1007/s11587-018-0406-8

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  • DOI: https://doi.org/10.1007/s11587-018-0406-8

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