Turing patterns in network-organized activator–inhibitor systems

By Hiroya Nakao, Alexander S. Mikhailov. 2008

415 citations recorded by the source corpus.

Differences in diffusion constants of activator and inhibitor species can destabilize biological and chemical processes, leading to the spontaneous emergence of periodic spatial patterns. A general framework now provides the tools for studying such so-called Turing patterns in systems organized in complex networks.

Subfields: Computer Networks and Communications, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/f77599f1414ac104adf5e186693adcec71e569dd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Pedro Jordano
2. Alessandro Vespignani
3. Mark Newman
4. Albert-László Barabási
5. Romualdo Pastor-Satorras
6. José Fernando Ferreira Mendes
7. Sergey N. Dorogovtsev
8. R. Durrett

In Public Health, Environmental and Occupational Health

1. Alessandro Vespignani
2. Mark Newman

Citations in the corpus

• Biodiversity, species interactions and ecological networks in a fragmented world (2012, background): “works create abundance heterogeneities among otherwise equivalent fragments (Mimura and Murray, 1978; Nakao and Mikhailov, 2010; Rietkerk et al., 2004).”
• Networks beyond pairwise interactions: structure and dynamics (2020, neutral): “[344] Hiroya Nakao and Alexander S Mikhailov.”
• Modelling dynamical processes in complex socio-technical systems (2011, background): “BystudyingtheTuringmechanism 80 in systems with heterogeneous connectivity patterns it has been found that the relevant instabilities of the systems are localized in a set of vertices with degree inversely proportional to the characteristic scale of diffusion 81 .”