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Turing patterns in simplicial complexes

  • Shupeng Gao
  • , Lili Chang
  • , Matjaž Perc
  • , Zhen Wang
  • Northwestern Polytechnical University Xian
  • Shanxi University
  • Shanxi Key Lab. of Mathematical Technique and Big Data Analysis on Disease Control and Prevention
  • University of Maribor
  • China Medical University Taichung
  • Alma Mater Europaea University
  • Complexity Science Hub Vienna
  • Kyung Hee University

Research output: Contribution to journalArticlepeer-review

65 Scopus citations

Abstract

The spontaneous emergence of patterns in nature, such as stripes and spots, can be mathematically explained by reaction-diffusion systems. These patterns are often referred as Turing patterns to honor the seminal work of Alan Turing in the early 1950s. With the coming of age of network science, and with its related departure from diffusive nearest-neighbor interactions to long-range links between nodes, additional layers of complexity behind pattern formation have been discovered, including irregular spatiotemporal patterns. Here we investigate the formation of Turing patterns in simplicial complexes, where links no longer connect just pairs of nodes but can connect three or more nodes. Such higher-order interactions are emerging as a new frontier in network science, in particular describing group interaction in various sociological and biological systems, so understanding pattern formation under these conditions is of the utmost importance. We show that a canonical reaction-diffusion system defined over a simplicial complex yields Turing patterns that fundamentally differ from patterns observed in traditional networks. For example, we observe a stable distribution of Turing patterns where the fraction of nodes with reactant concentrations above the equilibrium point is exponentially related to the average degree of 2-simplexes, and we uncover parameter regions where Turing patterns will emerge only under higher-order interactions, but not under pairwise interactions.

Original languageEnglish
Article number014216
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume107
Issue number1
DOIs
StatePublished - Jan 2023

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