This package generates a basic Turing Pattern model using the Schnakenberg System [1], as part of a group project for the SABS DTC Mathematical Biology Course.
We consider a trimolecular reaction that may display limit cycle behaviour:
2A + B ⇌ 3A
A ⇌ M
N → B
M and N are in sufficient excess that their concentration is effectively constant. In addition, the reverse direction in the first reaction occurs far more slowly than the forwards direction, so a simplifying assumption tha tthe reaction in one-directional is taken. We may consider these reactions to be equivalent to standard formation and decay reactions:
2A + B → 3A, ∅ → A, A → ∅, ∅ → B
Which occur with respective rates k1, k2, k3, k4.
This can be represented by the non-dimensional system of ODEs:
| Parameter | Description | Corresponding Stochastic Parameter |
|---|---|---|
| μ | Birth rate of A molcules | k_2 |
| β | Birth rate of B molecules | k_4 |
| κ | Rate of 2A + B → 3A reaction | k_1 |
| α | Death rate of A molcules | k_3 |
β > 0 controls the rate of tranmission, κ > 0 the rate at which exposed individuals become infectious, and γ > 0 the rate at which individuals recover. The model also requires initial conditions for each compartment: S(0), E(0), I(0), and R(0), which represent the initial number of people in each category.
This autocatalytic system is similar to a 'Brusselator', in which B is instead formed from A (at some rate kA ).
In order to see any patterning, a spatial component must be taken into account. Turing patterns are driven by an instability causing diffusion mechanism, which needs to be incorporated into the model.
The ODE system therefore becomes the PDE system:
Diffusive terms can be added into the stochastic model by partitioning the domain into boxes of side length h. In this model we consider a pseudo-2D domain so arrange our boxes on a 2D rectangular lattice, and index these boxes by i,j. Diffusion events are modelled as "reactions" between adjoining boxes:
Running files have 'main' in the file name (such as ode_main.py), and can be used to run simulations and generate visualisations of the system, based on functions defined in the other files. The functionality of each of these files is as follows:
ode_main.py- Solve ODE model with no spatial dependancespatial_main.py- Solve ODE model with spatial dependance in 1Dspatial_main_2d.py- Solve ODE model with spatial dependance in 2Dfd_main.py- Solve ODE model with spatial dependance in 2D, using finite difference method
Further scripts are provided as Jupyter Notebooks (.ipynb) in the Examples/ directory, and used to generate all other figures in the Images/ directory.
This figure displays the formation of a Turing Pattern from a uniform state, with an additional uniform noise distribution. The simulation is nondimensionalised, and so both time and position are in arbitrary units scaled by the rate and diffusion constants.
import workflow as wf
# workflow.py uses sim.py for simulation and vis.py for visualization
sim_1 = wf.Simulation("sim_1") # initializes with parameters from sim_1.txt
sim_1.go() # runs the simulation
sim_1.visualize() # creates gifs
| Population A | Population B |
|---|---|
 |
 |
[1] Schnakenberg, J. (1979). Simple chemical reaction systems with limit cycle behaviour. In Journal of Theoretical Biology (Vol. 81, Issue 3, pp. 389–400). Elsevier BV. https://doi.org/10.1016/0022-5193(79)90042-0