Abstract
The combination of protrusions and retractions in the movement of polarised cells leads to understand the effect of possible synchronisation between the two ends of the cells. This synchronisation, in turn, could lead to different dynamics such as normal and fractional diffusion. Departing from a stochastic single cell trajectory, where a “memory effect” induces persistent movement, we derive a kinetic-renewal system at the mesoscopic scale. We investigate various scenarios with different levels of complexity, where the two ends of the cell move either independently or with partial or full synchronisation. We study the relevant macroscopic limits where we obtain diffusion, drift-diffusion or fractional diffusion, depending on the initial system. This article clarifies the form of relevant macroscopic equations that describe the possible effects of synchronised movement in cells, and sheds light on the switching between normal and fractional diffusion.




Similar content being viewed by others
Notes
\(\psi (k)\) is the probability of a run of length at least k. I would like to achieve this distribution by independent decisions whether to turn or not (based on \(\text {rand}(1)\)). The probability to continue the run after the first time step is P(1), the probability to continue after the second time step is P(2), etc. The probability that the cell has not turned within the first k time steps is P(1)P(2)...P(k). This is in fact equal to \(\psi (k)\). The formula \(\psi (k) = P(1)P(2)...P(k)\) \(\forall k\) has a unique solution for the probabilities P: \(P(j) = \psi (j)/\psi (j-1)\).
References
Ariel, G., Rabani, A., Benisty, S., Partridge, J.D., Harshey, R.M., Be’Er, A.: Swarming bacteria migrate by Lévy walk. Nat. Commun. 6(1), 1–6 (2015)
Estrada-Rodriguez, G., Gimperlein, H., Painter, K.J.: Fractional Patlak–Keller–Segel equations for chemotactic superdiffusion. SIAM J. Appl. Math. 78(2), 1155–1173 (2018)
Fedotov, S., Korabel, N.: Emergence of lévy walks in systems of interacting individuals. Phys. Rev. E 95(3), 030107 (2017)
Fedotov, S., Tan, A., Zubarev, A.: Persistent random walk of cells involving anomalous effects and random death. Phys. Rev. E 91(4), 042124 (2015)
Ferrari, F.: Some nonlocal operators in the first Heisenberg group. Fractal Fract. 1(1), 15 (2017)
Ferrari, F.: Weyl and Marchaud derivatives: a forgotten history. Mathematics 6(1), 6 (2018)
Focardi, S., Montanaro, P., Pecchioli, E.: Adaptive Lévy walks in foraging fallow deer. PLoS ONE 4(8), e6587 (2009)
Frank, M., Goudon, T.: On a generalized Boltzmann equation for non-classical particle transport. Kinetic Relat. Models 3(3), 395–407 (2010)
Frank, M., Sun, W.: Fractional diffusion limits of non-classical transport equations. Kinetic Relat. Models 11(6), 1503–1526 (2018)
Fricke, G.M., Letendre, K.A., Moses, M.E., Cannon, J.L.: Persistence and adaptation in immunity: T cells balance the extent and thoroughness of search. PLoS Comput. Biol. 12(3), e1004818 (2016)
Harris, T.H., Banigan, E.J., Christian, D.A., Konradt, C., Wojno, E.D.T., Norose, K., Wilson, E.H., John, B., Weninger, W., Luster, A.D., et al.: Generalized lévy walks and the role of chemokines in migration of effector cd8+ t cells. Nature 486(7404), 545–548 (2012)
Huda, S., Weigelin, B., Wolf, K., Tretiakov, K.V., Polev, K., Wilk, G., Iwasa, M., Emami, F.S., Narojczyk, J.W., Banaszak, M., et al.: Lévy-like movement patterns of metastatic cancer cells revealed in microfabricated systems and implicated in vivo. Nat. Commun. 9(1), 1–11 (2018)
Jara, M., Komorowski, T., Olla, S.: Limit theorems for additive functionals of a Markov chain. Ann. Appl. Probab. 19(6), 2270–2300 (2009)
Klafter, J., Sokolov, I.M.: First Steps in Random Walks: From Tools to Applications. Oxford University Press, Oxford (2011)
Korobkova, E., Emonet, T., Vilar, J.M., Shimizu, T.S., Cluzel, P.: From molecular noise to behavioural variability in a single bacterium. Nature 428(6982), 574–578 (2004)
Li, L., Nørrelykke, S.F., Cox, E.C.: Persistent cell motion in the absence of external signals: a search strategy for eukaryotic cells. PLoS ONE 3(5), e2093 (2008)
Loy, N., Preziosi, L.: Kinetic models with non-local sensing determining cell polarization and speed according to independent cues. J. Math. Biol. 80(1), 373–421 (2020)
Loy, N., Preziosi, L.: Modelling physical limits of migration by a kinetic model with non-local sensing. J. Math. Biol. 80(6), 1759–1801 (2020)
Mellet, A., Mischler, S., Mouhot, C.: Fractional diffusion limit for collisional kinetic equations. Arch. Ration. Mech. Anal. 199(2), 493–525 (2011)
Metzler, R., Klafter, J.: The random walk’s guide to anomalous diffusion: a fractional dynamics approach. Phys. Rep. 339(1), 1–77 (2000)
Moussa, A., Perthame, B., Salort, D.: Backward parabolicity, cross-diffusion and Turing instability. J. Nonlinear Sci. 29(1), 139–162 (2019)
NIST Digital Library of Mathematical Functions. http://dlmf.nist.gov/, Release 1.0.14 of 2016-12-21. Olver, F.W.J., Olde Daalhuis, A.B., Lozier, D.W., Schneider, B.I., Boisvert, R.F., Clark, C.W., Miller, B.R., Saunders, B.V., (eds.)
Othmer, H.G., Dunbar, S.R., Alt, W.: Models of dispersal in biological systems. J. Math. Biol. 26(3), 263–298 (1988)
Perthame, B., Yasuda, S.: Stiff-response-induced instability for chemotactic bacteria and flux-limited Keller–Segel equation. Nonlinearity 31(9), 4065–4089 (2018)
Raichlen, D.A., Wood, B.M., Gordon, A.D., Mabulla, A.Z., Marlowe, F.W., Pontzer, H.: Evidence of Lévy walk foraging patterns in human hunter-gatherers. Proc. Natl. Acad. Sci. 111(2), 728–733 (2014)
Reynolds, A., Ceccon, E., Baldauf, C., Karina Medeiros, T., Miramontes, O.: Lévy foraging patterns of rural humans. PLoS ONE 13(6), e0199099 (2018)
Reynolds, A., Santini, G., Chelazzi, G., Focardi, S.: The Weierstrassian movement patterns of snails. R. Soc. Open Sci. 4(6), 160941 (2017)
Reynolds, A.M., Smith, A.D., Menzel, R., Greggers, U., Reynolds, D.R., Riley, J.R.: Displaced honey bees perform optimal scale-free search flights. Ecology 88(8), 1955–1961 (2007)
Sims, D.W., Southall, E.J., Humphries, N.E., Hays, G.C., Bradshaw, C.J., Pitchford, J.W., James, A., Ahmed, M.Z., Brierley, A.S., Hindell, M.A., et al.: Scaling laws of marine predator search behaviour. Nature 451(7182), 1098–1102 (2008)
Sokolov, I.M., Metzler, R.: Towards deterministic equations for Lévy walks: the fractional material derivative. Phys. Rev. E 67(1), 010101 (2003)
Viswanathan, G.M., Afanasyev, V., Buldyrev, S.V., Murphy, E., Prince, P., Stanley, H.E.: Lévy flight search patterns of wandering albatrosses. Nature 381(6581), 413–415 (1996)
Zaburdaev, V., Denisov, S., Klafter, J.: Lévy walks. Rev. Mod. Phys. 87(2), 483 (2015)
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by Alain Goriely.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
B.P. has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 740623). G.E.R. acknowledges the support of the Fondation Sciences Mathématiques de Paris (FSMP) for the postdoctoral fellowship.
Appendix
Appendix
1.1 A Miscellaneous
(I) We compute the escape and arrival rates introduced in (63) by using the characteristic solutions (61) and (62). We start from
which, by using (3) and (62), can be rewritten as:
The last equality is obtained using the change of variables \(k=t-s\) along with the following Taylor expansion:
Analogously we can obtain (64) for \(j_{\alpha ^+}(t,x)\).
(II) Now we aim to derive the expression (88). From (84) we write, after multiplying both sides by \(\bar{\psi }^+\bar{\psi }^-\)
Using the initial conditions \(\alpha ^+_{0_\varepsilon }=\varepsilon ^z\delta (x)\) and \({{\alpha }}^-_0(x)=0\), we obtain (88). Now, we introduce the scaling to (74) and we write
Hence from here we compute
Substituting these three quantities in (102), we arrive at (89).
(III) Finally, we are going to work only with the fractional operators. Following (Ferrari 2017, 2018) we have
The above relation is true if \(\tilde{\alpha }^+,\tilde{\alpha }^-\in C^1(\mathbb {R})\) and \(\tilde{\alpha }^+,\tilde{\alpha }^- =o(|x|^{\mu -2-\epsilon })\), \(x\rightarrow +\infty \) for \(\epsilon >0\) (equivalence between Marchaud derivative and Riemann–Liouville derivative).
Now we are going to use the fact that the sum \(\mathbb {D}_-^{\mu -1}f+\mathbb {D}^{\mu -1}_+f\) gives the fractional Laplace operator in one dimension, also known as the Riesz derivative,
where \(c(1,\frac{\mu -1}{2})\) is a normalisation constant.
Rights and permissions
About this article
Cite this article
Estrada-Rodriguez, G., Perthame, B. Motility Switching and Front–Back Synchronisation in Polarised Cells. J Nonlinear Sci 32, 40 (2022). https://doi.org/10.1007/s00332-022-09791-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00332-022-09791-z