Patterns of non-normality in networked systems

Journal article

Several mechanisms have been proposed to explain the spontaneous generation of self-organised patterns, hypothesised to play a role in the formation of many of the magnificent patterns observed in Nature. In several cases of interest, the system under scrutiny displays a homogeneous equilibrium, which is destabilised via a symmetry breaking instability which reflects the specificity of the problem being inspected. The Turing instability is among the most celebrated paradigms for pattern formation. In its original form, the diffusion constants of the two mobile species need to be quite different from each other for the instability to develop. Unfortunately, this condition limits the applicability of the theory. To overcome this impediment, and with the ambitious long term goal to eventually reconcile theory and experiments, we here propose an alternative mechanism for promoting the onset of pattern. To this end a multi-species reactive model is studied, assuming a generalized transport on a discrete and directed network-like support: the instability is triggered by the non-normality of the embedding network. The non-normal character of the dynamics instigates a short time amplification of the imposed perturbation, thus making the system unstable for a choice of parameters that would yield stability under the conventional scenario. In other words, non-normality promotes the emergence of patterns in cases where a classical linear analysis would not predict them. The importance of our result relies also on the fact that non-normal networks are pervasively found, motivating the general interest of the mechanism here discussed.

Authors: Muolo, R; Asllani, M; Fanelli, D; Maini, P; Carletti, T

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Journal of Theoretical Biology
• Volume: 480
• Pages: 81-91
• Publication date: 2019-07-08
• Acceptance date: 2019-07-08
• DOI: 10.1016/j.jtbi.2019.07.004
• EISSN: 1095-8541
• ISSN: 0022-5193
• Language: English