April  2020, 13(4): 1187-1208. doi: 10.3934/dcdss.2020069

Self-organized clusters in diffusive run-and-tumble processes

1. 

Oregon State University, Department of Mathematics, Kidder Hall 368, Corvallis, OR 97331, USA

2. 

St. Olaf College, Department of Mathematics, 1500 St. Olaf Ave., Northfield, MN 55057, USA

3. 

University of Minnesota, School of Mathematics, 206 Church St. S.E., Minneapolis, MN 55455, USA

* Corresponding author: Arnd Scheel

Received  November 2017 Revised  August 2018 Published  April 2019

Fund Project: The authors were partially supported by NSF grant DMS-1612441.

We analyze a simplistic model for run-and-tumble dynamics, motivated by observations of complex spatio-temporal patterns in colonies of myxobacteria. In our model, agents run with fixed speed either left or right, and agents turn with a density-dependent nonlinear turning rate, in addition to diffusive Brownian motion. We show how a very simple nonlinearity in the turning rate can mediate the formation of self-organized stationary clusters and fronts. Phenomenologically, we demonstrate the formation of barriers, where high concentrations of agents at the boundary of a cluster, moving towards the center of a cluster, prevent the agents caught in the cluster from escaping. Mathematically, we analyze stationary solutions in a four-dimensional ODE with a conserved quantity and a reversibility symmetry, using a combination of bifurcation methods, geometric arguments, and numerical continuation. We also present numerical results on the temporal stability of the solutions found here.

Citation: Patrick Flynn, Quinton Neville, Arnd Scheel. Self-organized clusters in diffusive run-and-tumble processes. Discrete and Continuous Dynamical Systems - S, 2020, 13 (4) : 1187-1208. doi: 10.3934/dcdss.2020069
References:
[1]

B. Fiedler and A. Scheel, Spatio-temporal dynamics of reaction-diffusion patterns, Trends in Nonlinear Analysis, 23–152, Springer, Berlin, 2003.

[2]

H. Freistühler and J. Fuhrmann, Nonlinear waves and polarization in diffusive directed particle flow, SIAM J. Appl. Math., 78 (2018), 759-773.  doi: 10.1137/17M1124103.

[3]

J. Fuhrmann, On a Minimal Model for the Initiation of Cell Movement, Dissertation, Universität Heidelberg, 2012. doi: 10.11588/heidok.00013659.

[4]

M. Holzer and A. Scheel, Criteria for pointwise growth and their role in invasion processes, J. Nonlinear Science, 24 (2014), 661-709.  doi: 10.1007/s00332-014-9202-0.

[5]

K. Kang, A. Scheel and A. Stevens, Global phase diagrams for run-and-tumble: Equidistribution, waves, and blowup, Nonlinearity, to appear.

[6]

F. Lutscher and A. Stevens, Emerging patterns in a hyperbolic model for locally interacting cell systems, J. Nonl. Sci., 12 (2002), 619-640.  doi: 10.1007/s00332-002-0510-4.

[7]

J. D. M. RademacherB. Sandstede and A. Scheel, Computing absolute and essential spectra using continuation, Physica D, 229 (2007), 166-183.  doi: 10.1016/j.physd.2007.03.016.

[8]

H. Reichenbach, Rhythmische vorgänge bei der schwarmentfaltung von myxobakterien, Ber. Deutsch. Bot. Ges., 78 (1965), 102.

[9]

B. Sager and D. Kaiser, Intracellular C-signaling and the traveling waves of Myxococcus, Genes and Development, 8 (1994), 2793-2804. 

[10]

B. Sandstede and A. Scheel, Absolute and convective instabilities of waves on unbounded and large bounded domains, Physica D, 145 (2000), 233-277.  doi: 10.1016/S0167-2789(00)00114-7.

[11]

B. Sandstede and A. Scheel, Relative Morse indices, Fredholm indices, and group velocities, Discr. Cont. Dyn. Sys. A, 20 (2008), 139-158.  doi: 10.3934/dcds.2008.20.139.

[12]

A. Scheel and A. Stevens., Wavenumber selection in coupled transport equations., J. Math. Biology, 75 (2017), 1047-1073.  doi: 10.1007/s00285-017-1107-8.

[13]

L. Shimkets and D. Kaiser, Murein components rescue developmental sporulation of Myxococcus xanthus, J. Bacteriol, 152 (1982), 451-461. 

[14]

R. Welch and D. Kaiser, Cell behavior in traveling wave patterns of myxobacteria, PNAS, 18 (2001), 14907-14912. 

show all references

References:
[1]

B. Fiedler and A. Scheel, Spatio-temporal dynamics of reaction-diffusion patterns, Trends in Nonlinear Analysis, 23–152, Springer, Berlin, 2003.

[2]

H. Freistühler and J. Fuhrmann, Nonlinear waves and polarization in diffusive directed particle flow, SIAM J. Appl. Math., 78 (2018), 759-773.  doi: 10.1137/17M1124103.

[3]

J. Fuhrmann, On a Minimal Model for the Initiation of Cell Movement, Dissertation, Universität Heidelberg, 2012. doi: 10.11588/heidok.00013659.

[4]

M. Holzer and A. Scheel, Criteria for pointwise growth and their role in invasion processes, J. Nonlinear Science, 24 (2014), 661-709.  doi: 10.1007/s00332-014-9202-0.

[5]

K. Kang, A. Scheel and A. Stevens, Global phase diagrams for run-and-tumble: Equidistribution, waves, and blowup, Nonlinearity, to appear.

[6]

F. Lutscher and A. Stevens, Emerging patterns in a hyperbolic model for locally interacting cell systems, J. Nonl. Sci., 12 (2002), 619-640.  doi: 10.1007/s00332-002-0510-4.

[7]

J. D. M. RademacherB. Sandstede and A. Scheel, Computing absolute and essential spectra using continuation, Physica D, 229 (2007), 166-183.  doi: 10.1016/j.physd.2007.03.016.

[8]

H. Reichenbach, Rhythmische vorgänge bei der schwarmentfaltung von myxobakterien, Ber. Deutsch. Bot. Ges., 78 (1965), 102.

[9]

B. Sager and D. Kaiser, Intracellular C-signaling and the traveling waves of Myxococcus, Genes and Development, 8 (1994), 2793-2804. 

[10]

B. Sandstede and A. Scheel, Absolute and convective instabilities of waves on unbounded and large bounded domains, Physica D, 145 (2000), 233-277.  doi: 10.1016/S0167-2789(00)00114-7.

[11]

B. Sandstede and A. Scheel, Relative Morse indices, Fredholm indices, and group velocities, Discr. Cont. Dyn. Sys. A, 20 (2008), 139-158.  doi: 10.3934/dcds.2008.20.139.

[12]

A. Scheel and A. Stevens., Wavenumber selection in coupled transport equations., J. Math. Biology, 75 (2017), 1047-1073.  doi: 10.1007/s00285-017-1107-8.

[13]

L. Shimkets and D. Kaiser, Murein components rescue developmental sporulation of Myxococcus xanthus, J. Bacteriol, 152 (1982), 451-461. 

[14]

R. Welch and D. Kaiser, Cell behavior in traveling wave patterns of myxobacteria, PNAS, 18 (2001), 14907-14912. 

Figure 4.  Homoclinic trajectories in phase space (black), their projections onto the $ \rho $-$ \rho' $ plane (red) and the region $ G $ in the $ \rho $-$ \rho' $ plane (blue) for $ \gamma = 1/16 $ and $ \mu = 1 $. The vertical plane $ \rho' = 0 $ is the reversibility plane $ \mathrm{Fix}\,M $; trajectories are symmetric with respect to reflections at this plane. See Section 3.1 for details on how these solutions were found numerically
Figure 1.  The Hamiltonian potential, $ V(w) $, with region spanned by homoclinic (left) and heteroclinic (right) orbit
Figure 2.  Left column, top to bottom: Potentials $ W(\rho,c) $ for cases with homoclinics to $ \rho_- $, $ \rho_+ $, and heteroclinics, respectively. Right column: Associated phase portraits
Figure 3.  Maxima and minima of homoclinic orbits as a function of the background state (left). Plots of density profiles $ u $ (blue, left-traveling), $ v $ (red, right-traveling), and $ \rho = u+v $ (black) for $ \gamma = 0.15 $, $ c = -0.5,-0.234,0.2 $ from top to bottom (right). Note that concentrations of inward traveling populations peak at the boundary of high-density regions
Figure 5.  Plot of the boundaries of $B$ and $G$ in the $\rho$-$\rho'$ plane for $\mu = 1$
Figure 6.  Cluster and gap solutions, with associated phase portraits. Individual plots show the families of solutions as the background state is varied. Different plots correspond to different values of the parameter $ \gamma $. Shown is the actual computational domain, grid sizes vary in $ dx = 0.01\ldots 0.025 $
Figure 7.  Maxima of clusters and minima of gaps in the continuation, plotted against the background state, for sample values of $ \gamma $
Figure 8.  Heteroclinic profiles plotted as $ \gamma $ varies from $ \gamma = 1/13 $ to $ \gamma = 1/6639 $. Plots of $ \rho\sqrt{\gamma/6} $, $ \log(\rho\sqrt{\gamma/6}) $, and $ \rho'/\rho $ exhibit the asymptotically simple structure of the heteroclinic. Bottom left shows the actual computational domain, grid size is $ dx = 0.088 $
Figure 9.  Real part of the spectrum of the heteroclinics (left) and the two background concentrations of the heteroclinics in black (right); numerically the maximum and minimum of $ \rho(x) $, and the region where the corresponding constant solutions are stable in pink. Computations here use grid size from the previous heteroclinic continuation
Figure 10.  Spectra of clusters and gaps as functions of the background $ \rho_\infty $, for various values of $ \gamma $. Note that eigenvalues with positive real parts exist for gaps and clusters with large or small $ \rho_\infty $, that is, near small-amplitude or heteroclinic limit, respectively
Figure 11.  Cluster instability (left) and gap instability (right). Time evolution of perturbation of a stationary profile in the direction of the unstable eigenvector. Shown are space-time plots for $ u $ and $ v $ (top row), shape of the most unstable eigenfunction (middle row), and snapshots of time evolution for $ u $ (red), $ v $ (blue), and $ u+v $ (black). Parameter values are $ \gamma = 1/64 $, $ \rho_\infty = 1.3115 $ (left) and $ \rho_\infty = 18.3805 $ (right)
Figure 12.  Instability of cluster boundaries (left), showing space-time plots for $ u $ and $ v $, top row, profile of the leading eigenfunction, and time snapshots, for $ \gamma = 1/13.81 $. Stable clusters ($ \gamma = 1/64 $, $ \rho = 1.3072 $) and cluster boundaries ($ \gamma = 1/21.41 $) on the right, with leading eigenfunction corresponding to mass change (cluster) and translation (cluster boundary), respectively
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