Team:Valencia Biocampus/Demonstration/Diffusion3

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Revision as of 10:53, 3 October 2013

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Proof of a Group Behavior Diffusion Model from a Random Walk Model

Considerations for the Random Walk:

  • Step lenghts ($l_t$) in the order of a pixel in size. That implies, $ \Delta t $ as small as possible.
  • Perfect Random Walk, with uniform probabilistic distributions either for $ v_t $, $\dot{\theta_t}$ and $\delta$.

Discretizing the whole space into pixels, and assuming, the worm can, either occupy one or not, we can assure that, at each time step, it can only move in four different directions: up, down, right or left from its position. As we considered that each random variable follows an uniform probabilistic distribution, it is equipossible to move in any of these directions, with a probability of $ \frac{1}{4} $ each.
Now, we can compute, the probability that the worm is at position $(x_m,y_m)$ at the iteration $n+1$ as follows:

$$ P_{n+1}(x_m,y_m)\;=\;\frac{1}{4}\;\left(P_{n}(x_m,y_{m+1}) + P_{n}(x_{m+1},y_m) + P_{n}(x_m,y_{m-1}) + P_{n}(x_{m-1},y_m)\right) $$
If we now subtract $ P_{n}(x_m,y_m) $ from both sides:

$$ P_{n+1}(x_m,y_m) - P_{n}(x_m,y_m)\;=\;\frac{1}{4}\;\left(P_{n}(x_m,y_{m+1}) + P_{n}(x_{m+1},y_m) + P_{n}(x_m,y_{m-1}) + P_{n}(x_{m-1},y_m) - 4\;P_{n}(x_m,y_m)\right) $$ $$ P_{n+1}(x_m,y_m) - P_{n}(x_m,y_m)\;=\;\frac{1}{4}\;\left(P_{n}(x_{m+1},y_m) - 2\;P_{n}(x_m,y_m) + P_{n}(x_{m-1},y_m) + P_{n}(x_m,y_{m+1}) - 2\;P_{n}(x_m,y_m) + P_{n}(x_m,y_{m-1})\right) $$
Multiplying and dividing by $q$ (time step) and $h^2$ (space step squared) we then get:

$$ \frac{P_{n+1}(x_m,y_m) - P_{n}(x_m,y_m)}{q}\;=\;\frac{h^2}{4\;q}\;\left(\frac{P_{n}(x_{m+1},y_m) - 2\;P_{n}(x_m,y_m) + P_{n}(x_{m-1},y_m)}{h^2} + \frac{P_{n}(x_m,y_{m+1}) - 2\;P_{n}(x_m,y_m) + P_{n}(x_m,y_{m-1})}{h^2}\right) $$
And, defining the constant $D\;=\;\frac{h^2}{4\;q}$:

$$ \frac{P_{n+1}(x_m,y_m) - P_{n}(x_m,y_m)}{q}\;=\;D\;\left(\frac{P_{n}(x_{m+1},y_m) - 2\;P_{n}(x_m,y_m) + P_{n}(x_{m-1},y_m)}{h^2} + \frac{P_{n}(x_m,y_{m+1}) - 2\;P_{n}(x_m,y_m) + P_{n}(x_m,y_{m-1})}{h^2}\right) $$
That can be approximated as a Diffusion Equation, by recalling the definition for the first and second derivates:

$$\frac{\partial P}{\partial t} = D \; \left(\frac{\partial^2 P}{\partial x^2} + \frac{\partial^2 P}{\partial y^2}\right) $$
Allowed directions

Okopinska A. (2002) Fokker-Planck equation for bistable potential in the optimized expansion. Physical review E, Volume 65, 062101

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