  Nakao and Mikhailov proposed using continuous models (mean-field models) to
study reaction-diffusion systems on networks and the corresponding Turing
patterns. This work aims to show that p-adic analysis is the natural tool to
carry out this program. This is possible due to the fact that the discrete
Laplacian attached to a network turns out to be a particular case of a p-adic
Laplacian. By embedding the graph attached to the system into the field of
p-adic numbers, we construct a continuous p-adic version of a
reaction-diffusion system on a network. The existence and uniqueness of the
Cauchy problems for these systems are established. We show that Turing criteria
for the p-adic continuous reaction-diffusion system remain essentially the same
as in the classical case. However, the properties of the emergent patterns are
very different. The classical Turing patterns consisting of alternating domains
do not occur in the p-adic continuous case. Instead of this, several domains
(clusters) occur. Multistability, that is, coexistence of a number of different
patterns with the same parameter values occurs. Clustering and multistability
have been observed in the computer simulations of large reaction-diffusion
systems on networks. Such behavior can be naturally explained in the p-adic
continuous model, in a rigorous mathematical way, but not in the original
discrete model.
