Help plotting interacting Turing modes

In summary, the biologist is trying to replicate plots from a paper using Mathematica, but is having difficulty understanding the notation for PDEs. He is getting errors when trying to solve the equations. If he could clarify the notation he would be able to replicate the plots.
  • #1
jssdenton72
2
0
Hello, everybody.

I'm trying to replicate plots from

Yang et al. 2002. Spatial resonances and superposition patterns in a reaction-diffusion model with interacting Turing modes. Phys. Rev. 88(20)

using Mathematica, to explore specifically under what parameter values the pattern of Figure 3c is stable.

I am a biologist by training, and the notation of PDEs is a bit beyond me (I am also not sure how to define the initial conditions [perturbations of steady state]), but what I have so far to replicate the plots is (in Mathematica 7):

(* Fixed parameter values used to generate Figure 3c *)

a = 3;
Dx1 = 1.31;
Dy1 = 9.87;
Dx2 = 34;
Dy2 = 344.8999;
b = 6;
\[Alpha] = 1;

(* from equation 1, equations Structured as: Laplacian + interaction + Brusselator *)

e1 = D[c1[x1, y1, x2, y2, t], t] ==
Dx1*(D[c1[x1, y1, t], x1, x1] +
D[c2[x1, y1], y1, y1]) + \[Alpha]*(c3[x2, y2, t] -
c1[x1, y1, t]) +
a - (b + 1)*c1[x1, y1, t] + c1[x1, y1, t]^2*c2[x1, y1, t];

e2 = D[c2[x1, y1, x2, y2, t]] ==
Dy1*(D[c1[x1, y1, t], x1, x1] + D[c2[x1, y1, t], y1, y1]) +
\[Alpha]*(c4[x2, y2, t] - c2[x1, y1, t]) +
b*c1[x1, y1, t] - c1[x1, y1, t]^2*c2[x1, y1, t];

e3 = D[c3[x1, y1, x2, y2, t], t] ==
Dx2*(D[c3[x2, y2, t], x2, x2] +
D[c4[x2, y2, t], y2, y2]) + \[Alpha]*(c1[x1, y1, t] -
c3[x2, y2, t]) +
a - (b + 1)*c3[x2, y2, t] + c3[x2, y2, t]^2*c4[x2, y2, t];

e4 = D[c4[x1, y1, x2, y2, t], t] ==
Dy2*(D[c3[x2, y2, t], x2, x2] +
D[c2[x1, y1, t], y1, y1]) + \[Alpha]*(c2[x1, y1, t] -
c4[x2, y2, t]) + b*c3[x2, y2, t] -
c3[x2, y2, t]^2*c4[x2, y2, t];

InteractingBrusselator = NDSolve[{e1, e2, e3, e4},

{c1, c2, c3, c4}, {x1, x2,y1,y2,t}]

I get several errors, including one that says the equation c1[x1,y1,t] is not a function of all the variables. However, c1 and c2 define the interactions in one layer, and c3 and c4 define interactions in another layer. It is not clear to me how to proceed.

I don't need time dynamics for the equations, just the stable pattern, which should look something like the attached images from the software Ready.

Any help on setting up the system for plotting would be greatly appreciated.

John
 

Attachments

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Last edited:
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  • #2
Usually a function you define in Mathematica has a fixed number of arguments. But you have examples of c1, c2, c3 and c4 with different numbers of arguments.

Code:
{c1[x1, y1, t], c1[x1, y1, x2, y2, t],
 c2[x1, y1], c2[x1, y1, t], c2[x1, y1, x2, y2, t],
 c3[x2, y2, t], c3[x1, y1, x2, y2, t],
 c4[x2, y2, t], c4[x1, y1, x2, y2, t]}

Is there any chance you might be mixing up the numbers of variables that you want to differentiate with the arguments to your function? I'm thinking that based on the warning/error you are getting.

If I'm confused and your code is exactly what you meant then I apologize for my misunderstanding.
 
  • #3
Hi Bill,

I think my issue comes from not understanding the notation of PDEs in Mathematica, in general. The code was as close as I could get, but there is something wrong. As you point out, the c1, c2, etc. do not match up, but how do I define the elements properly for Mathematica to solve it as a PDE?

For example, in equation 1 of the paper attached to my initial post, the equations for the components in the first layer are:

∂x_i / ∂t = D_{x_{i}}∇^2 x_i + \alpha (x_j - x_i) + f(x_i, y_i)
∂y_i / ∂t = D_{y_{i}}∇^2 y_i + \beta (y_j - y_i) + g(x_i, y_i)

where f() and g() are the Brusselator functions

f(x_i, y_i) = a - (1 - b)x + x^2y
g(x_i, y_i) = bx - x^2y

These components interact with one another in one layer, according to the first (Laplacian) and third (Brusselator) elements in the RHS, and interact with the second layer according to the second element (diffusion) on the RHS.

So I am defining c1[], c2[], etc as the functions for x1, x2, etc. It looks to me like the requirement in Mathematica is to specify any entry c1[], c2[] as a function of *all* the variables, but based on the equations, that does not make sense to me...for example, f() and g() are only functions of the variables in the local layer, so this is why I am confused.
 

1. How do interacting Turing modes work?

Interacting Turing modes refer to the phenomenon where two or more Turing patterns interact with each other, resulting in complex and dynamic patterns. This occurs when the diffusion properties of one Turing pattern influences the formation and stability of another pattern, leading to new and intricate patterns.

2. What are some examples of interacting Turing modes in nature?

One example of interacting Turing modes in nature is the formation of patterns on animal fur, such as the stripes on a zebra or the spots on a leopard. These patterns are a result of the interaction between two Turing patterns - one responsible for the base color and the other for the stripes or spots.

3. How can we plot interacting Turing modes in a scientific experiment?

To plot interacting Turing modes in a scientific experiment, we first need to create a system with two or more interacting Turing patterns. This can be done by adjusting the diffusion rates and reaction rates of the substances involved in the patterns. Then, we can use mathematical models and computer simulations to observe and analyze the resulting patterns.

4. What is the significance of studying interacting Turing modes?

Studying interacting Turing modes can help us understand the complexity and diversity of patterns in nature. It can also provide insights into the underlying mechanisms behind pattern formation and how they can be influenced by external factors. This knowledge can have various applications in fields such as biology, chemistry, and materials science.

5. Are there any challenges in studying interacting Turing modes?

Yes, there are several challenges in studying interacting Turing modes, such as the difficulty in accurately replicating natural conditions in a laboratory setting. Additionally, the mathematical models used to simulate these patterns may not always accurately represent the complexity of real-world systems. Therefore, it is important to approach these studies with caution and constantly validate the results with experimental data.

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