Bulletin of Mathematical Biology

, Volume 79, Issue 4, pp 788–827 | Cite as

Bifurcation Analysis of Reaction Diffusion Systems on Arbitrary Surfaces

  • Daljit Singh J. DhillonEmail author
  • Michel C. Milinkovitch
  • Matthias Zwicker
Original Article


In this paper, we present computational techniques to investigate the effect of surface geometry on biological pattern formation. In particular, we study two-component, nonlinear reaction–diffusion (RD) systems on arbitrary surfaces. We build on standard techniques for linear and nonlinear analysis of RD systems and extend them to operate on large-scale meshes for arbitrary surfaces. In particular, we use spectral techniques for a linear stability analysis to characterise and directly compose patterns emerging from homogeneities. We develop an implementation using surface finite element methods and a numerical eigenanalysis of the Laplace–Beltrami operator on surface meshes. In addition, we describe a technique to explore solutions of the nonlinear RD equations using numerical continuation. Here, we present a multiresolution approach that allows us to trace solution branches of the nonlinear equations efficiently even for large-scale meshes. Finally, we demonstrate the working of our framework for two RD systems with applications in biological pattern formation: a Brusselator model that has been used to model pattern development on growing plant tips, and a chemotactic model for the formation of skin pigmentation patterns. While these models have been used previously on simple geometries, our framework allows us to study the impact of arbitrary geometries on emerging patterns.


Reaction diffusion Pattern formation Bifurcation analysis Linear stability analysis Marginal stability analysis Branch tracing Nonlinear PDEs Surface FEMs Large-scale systems Multigrid approach Cross-diffusion 



MCM was supported by grants from the Swiss National Science Foundation (FNSNF, grants 31003A_140785 and SINERGIA CRSII3_132430), and the initiative (project EpiPhysX) for this work. We thank Liana Manukyan from LANE, University of Geneva for 3D scanning a gecko surface and providing us with the point-cloud data. We used Meshlab software for surface reconstruction with this point-cloud. DSD was also supported by the FNSNF grant SINERGIA CRSII3_132430 for this work. He is thankful to his colleague Shihao Wu at CGG, Univ. of Bern for uniform re-sampling of the gecko surface mesh, for level \(L_2\) in Fig. 17. DSD thanks Prof. Dr. T. Wihler at Mathematical Institute, Univ. of Bern for suggesting Deal.II library.


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Copyright information

© Society for Mathematical Biology 2017

Authors and Affiliations

  1. 1.Institute of Computer ScienceUniversity of BernBernSwitzerland
  2. 2.Department of ComputingImperial College LondonLondonUK
  3. 3.Laboratory of Artificial & Natural Evolution (LANE), Department of Genetics and EvolutionUniversity of GenevaGenevaSwitzerland
  4. 4.SIB Swiss Institute of BioinformaticsGenevaSwitzerland

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