Advances in Mathematical Physics

Volume 2017 (2017), Article ID 9648538, 9 pages

https://doi.org/10.1155/2017/9648538

## Turing Bifurcation and Pattern Formation of Stochastic Reaction-Diffusion System

^{1}College of Information Science and Technology, Donghua University, Shanghai 201620, China^{2}Institute of Applied Mathematics, Xuchang University, Xuchang, Henan 461000, China

Correspondence should be addressed to Zhijie Wang and Jianwei Shen

Received 28 August 2016; Revised 18 November 2016; Accepted 15 December 2016; Published 12 February 2017

Academic Editor: Zhi-Yuan Sun

Copyright © 2017 Qianiqian Zheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Noise is ubiquitous in a system and can induce some spontaneous pattern formations on a spatially homogeneous domain. In comparison to the Reaction-Diffusion System (RDS), Stochastic Reaction-Diffusion System (SRDS) is more complex and it is very difficult to deal with the noise function. In this paper, we have presented a method to solve it and obtained the conditions of how the Turing bifurcation and Hopf bifurcation arise through linear stability analysis of local equilibrium. In addition, we have developed the amplitude equation with a pair of wave vector by using Taylor series expansion, multiscaling, and further expansion in powers of small parameter. Our analysis facilitates finding regions of bifurcations and understanding the pattern formation mechanism of SRDS. Finally, the simulation shows that the analytical results agree with numerical simulation.

#### 1. Introduction

The pattern formation was first investigated and interpreted by Turing sixty years ago [1]. Othmer and Scriven [2] proposed that the Turing instability which is initially stable steady-state of a dynamical system can become unstable if we consider diffusion in the system. It is also possible in network-organized systems which is important for understanding of multicellular morphogenesis. Recently Turing bifurcation, amplitude equation, and secondary bifurcation have become more significant to study the pattern formation [3–5]; Lee and Cho found that the shape and type of Turing patterns depend on dynamical parameters and external periodic forcing [6]. Moreover, Peña and Pérez-García showed that slightly squeezed hexagons are locally stable in a full range of distorted angles [7]. The domain coarsening process is strongly affected by the spatial separation between groups created by the Turing pattern formation process [8] and the robustness problem is also investigated [9]. The effects of cross-diffusion, the phenomenon in which a gradient in the concentration of one species induces the change of other species, on pattern formation in Reaction-Diffusion Systems have been discussed in many theoretical papers [10]. Fanelli et al. [11] showed that cross-diffusion can destabilize uniform equilibrium which is stable for the kinetic and self-diffusion reaction systems. On the other hand, cross-diffusion can also stabilize a uniform equilibrium which is stable for the kinetic system but unstable for the self-diffusion reaction system [12]. In conclusion, spatial patterns in Reaction-Diffusion Systems have attracted the interest of experimentalists and theorists during the last few decades. However, until now, no general theoretical analysis has been proposed for the possible role of noise in dissipative pattern formation.

Noise is a ubiquitous phenomenon in nature and is always deemed to play a very important role in natural synthetic system [13]. Coherence resonance and stochastic resonance in a noise-driven gene network regulated by small RNA [14, 15]. Viney and Reece [16] treated noise as adaptive and suggested that applying evolutionary rigour to the study of noise is necessary to fully understand organismal phenotypes. Scarsoglio et al. [17] presented different stochastic mechanisms of spatial pattern formation with a variable as noise-induced phenomena. Hori and Hara provided a mechanistic basis of Turing pattern formation that is induced by intrinsic noise and derived an efficient computation tool to examine the spatial power spectrum of the intrinsic noise [18]. Sun et al. [19] revealed that noise can make the regular circle pattern to be a target wave-like pattern by numerical simulations. A stochastic version of the Brusselator model is proposed and studied via the system size expansion [20] and the mesoscopic equations governing the dynamics were derived and used to special models [21]. Many studies have been presented in these research areas [22–28], as practice shows that theory on Turing bifurcation and pattern formation in dynamical system was rarely studied.

It is known that amplitude equation is not only a promising tool to investigate the RDS but also the main focus of the pattern dynamics [29, 30]. However, the amplitude equation is a complex process [31], and only a few systems have been chosen in the past for amplitude equation [32–35]. In this paper, we studied pattern selection of amplitude equation with a pair of wave vector by using the standard multiple scale analysis [36, 37]. Previously, the researchers did not take into account the effect of noise when deriving the amplitude equation but we will include it.

Besides the study of patterns, it can offer useful information on the underlying processes causing possible changes in the system. In order to better understand the reaction diffusion model, first, we proposed to study the pattern formation with noise based on the theory. In this paper, we obtained some interesting results explaining biological mechanism in a modified system. Moreover, we also investigated the relationship between the Reaction-Diffusion System and noise, revealing how the dynamics of the model regulation is affected by noise which provides a way to investigate the mechanism of pattern formation.

The paper is organized as follows. In Section 2, we present the general reaction diffusion with noise and derive the condition of Hopf bifurcation and Turing bifurcation. In Section 3, we derive the amplitude equation from Reaction-Diffusion System with noise. In Section 4, we utilize an example to illustrate the application of these ideas and using simulations validate theoretical results and present some interesting pattern dynamical phenomena. Finally, we summarize our results and conclude.

#### 2. Turing Bifurcation with SRDS

Since we know that noise plays an important role in the nonlinear systems, some promising results have been presented [11, 12, 32]. However, most people investigated noise by simulation and seldom put forward the theoretical conclusion, especially on pattern formation. In this paper, we study the effect of noise on pattern formation by deriving the Turing bifurcation, to know how it affects the pattern formation. The general diffusion form with noise is as follows:where is the noise and is the Laplace operator; , and , are diffusion parameters and noise magnitude, respectively.

For convenience, we just consider , , , as random variable in this system. In order to obtain the stability of this spatially uniform solution, we consider a perturbation of the form in the following:

In the convergence domain, we can obtain the linear system of stochastic system as (3) at which satisfy , .where the matrix is the partial derivative of , at and , .

For convenience, we can get the linearized system governing the dynamics of is defined bywhere the coefficient matrix is given bywhereIn the standard way, we assume that take the form asand get the characteristic equation from system (4) as follows:Finally, we solve the characteristic equation and obtain the eigenvalues where

Based on the bifurcation theory, we obtain new conditions of bifurcation with noise.

(1) Hopf bifurcation occurs in the Reaction-Diffusion System (3) which should satisfy the following critical conditions here:(i),(ii),(iii) is not a constant.

(2) Turing bifurcation (diffusion-driven instability) occurs in the Reaction-Diffusion System (3) which should satisfy the following conditions here:(i),(ii),(iii),(iv).And the critical condition where is

#### 3. Amplitude Equation with a Pair of Wave Vector

For a modified model [38] with the external stimulus , the following is obtained:

In this paper, we expanded (12) at equilibrium by using the Taylor expansion and then we truncated the expansion at third order; it is found that only third order will be included and higher order will not affect the amplitude equation in the process. And it can be written as In the following, we use multiple scale analysis to derive the amplitude equations with a pair of wave vector when . Denote as the controlled parameters. When the controlled parameter is larger than the critical value of Turing point, the solutions of the systems (13) can be expanded as

Close to onset , one has that .

Based on the center manifold near the Turing bifurcation point, it can be concluded that amplitude satisfies .

From the standard multiple scale analysis, up to the third order in the perturbations, the spatiotemporal evolution of the amplitudes can be described as Due to spatial translational symmetry, we have the following equation:

Comparing (15) with (16) and from the center manifold theory, we know that amplitude equation does not include the amplitude with unstable mode. As a result, we have the following equations:

In the following, we will give the expressions of , , and . Let system (13) be written as whereis the variable,is the linear operator, andis the nonlinear term, where and .

We need to investigate the dynamical behavior when is close to , and then we expand as where is a small enough parameter. We expand and as the series form of :and in the Appendix.

Linear operator can be expanded asand and in the Appendix.

Letand is a dependent variable. For the derivation of time, we have that

The solutions of systems (13) have the following form:

This expression implies that the bases of the solutions have nothing to do with time and the amplitude is a variable that changes slowly. As a result, it can be written generally as the following equation:Substituting the above equations into (24) and expanding (24) according to different orders of , we can obtain three equations as follows:and , in the Appendix.

We first consider the case of the first order of . Since is the linear operator of the system close to the onset, is the linear combination of the eigenvectors that corresponds to the zero eigenvalue since that

Let by assuming ; then, where in the Appendix and is the amplitude of the mode .

Now, we consider the case of the second order of . According to the Fredholm solubility condition [32], the vector function of the right hand of the above equation must be orthogonal with the zero eigenvectors of operator . And the zero eigenvectors of adjoint operator are and in the Appendix.

It can be obtained from the orthogonality condition that

By using the same methods, we deduce and coefficients in the Appendix.

For the case of the third order of , replace , , , and by their expression and , in the Appendix. Using the Fredholm solubility condition again, we can obtainAnd then we substitute system (28) and (33) into (24) to simplify [32]; we obtain the expressions of the coefficients of , , and in the Appendix.

And

So the equation of amplitude is as follows:Here, we will investigate the dynamics of amplitude equation by using the linear stability analysis [30, 32] and study the different pattern. The dynamical systems (38) possess two kinds of solution as follows.(i)The stationary solution is stable for and unstable for .(ii)The solution is only unstable for .

#### 4. Simulation

As the examples of Reaction-Diffusion System with noise, we use the following:where , , , and , and obtain the characteristic equation at . Here, we denote , , , , and and get the critical value of Hopf bifurcation when and Turing bifurcation when based on the bifurcation theory in Section 2.

The model is simulated numerically in two spatial dimensions and employ the zero-flux boundary conditions in (39). We set time step and space step as 0.02 and 1, respectively. The bifurcation space divide the space into four domains (Figure 1(a)). On bottom domain, locating below two bifurcation spaces, the system lies in the steady state (Figure 3(d)). The middle domain are regions of pure Turing and pure Hopf in stabilities (Figures 3(b) and 3(c)). On the top, two bifurcation spaces interact (Figure 3(a)). It is found that noise contribute to Turing bifurcation and Hopf bifurcation.