This tutorial is automatically generated from the file test/python/cell_based/tutorials/

In [1]:
# Jupyter notebook specific imports 
import matplotlib as mpl 
from IPython import display 
%matplotlib inline


This tutorial is an example of modelling spheroid growth with a nutrient. It covers:

  • Setting up an off-lattice cell population
  • Setting up a cell cycle model with oxygen dependence
  • Setting up and solving an oxygen transport PDE
  • Setting up a cell killer ## Imports and Setup
In [2]:
import matplotlib.pyplot as plt # Plotting
import numpy as np # Matrix tools
import chaste # The PyChaste module
chaste.init() # Set up MPI
import chaste.cell_based # Contains cell populations
import chaste.mesh # Contains meshes
import chaste.visualization # Visualization tools
import chaste.pde # PDEs 

Test 1 - a 2D mesh-based spheroid

In this test we set up a spheroid with a plentiful supply of oxygen on the boundary and watch it grow over time. Cells can gradually become apoptotic if the oxygen tension is too low.

In [3]:
# Set up the test 

This time we will use on off-lattice MeshBased cell population. Cell centres are joined with springs with a Delauney Triangulation used to identify neighbours. Cell area is given by the dual (Voronoi Tesselation). We start off with a small number of cells. We use a MutableMesh which can change connectivity over time and a HoneycombMeshGenerator to set it up with a simple honeycomb pattern. Here the first and second arguments define the size of the mesh - we have chosen a mesh that is 5 nodes (i.e. cells) wide, and 5 nodes high. The extra '2' argument puts two layers of non-cell elements around the mesh, which help to form a nicer voronoi tesselation for area calculations.

In [4]:
file_handler = chaste.core.OutputFileHandler("Python/TestSpheroidTutorial")
generator = chaste.mesh.HoneycombMeshGenerator(5, 5)
mesh = generator.GetMesh()

We create some cells next, with a stem-like proliferative type. This means they will continually proliferate if there is enough oxygen, similar to how a tumour spheroid may behave.

In [5]:
cells = chaste.cell_based.VecCellPtr()
stem_type = chaste.cell_based.StemCellProliferativeType()
cell_generator = chaste.cell_based.CellsGeneratorSimpleOxygenBasedCellCycleModel_2()
cell_generator.GenerateBasicRandom(cells, mesh.GetNumNodes(), stem_type)

Define when cells become apoptotic

In [6]:
for eachCell in cells:
    cell_cycle_model = eachCell.GetCellCycleModel()
    g1_duration = cell_cycle_model.GetStemCellG1Duration()
    sg2m_duration = cell_cycle_model.GetSG2MDuration()
    rnum = chaste.core.RandomNumberGenerator.Instance().ranf()
    birth_time = -rnum * (g1_duration + sg2m_duration)

Now we have a mesh and a set of cells to go with it, we can create a CellPopulation as before.

In [7]:
cell_population = chaste.cell_based.MeshBasedCellPopulation2_2(mesh, cells)

To view the results of this and the next test in Paraview it is necessary to explicitly generate the required .vtu files.

In [8]:

We then pass in the cell population into an OffLatticeSimulation, and set the output directory and end time.

In [9]:
simulator = chaste.cell_based.OffLatticeSimulation2_2(cell_population)

We ask for output every 12 increments

In [10]:

We define how the springs between cells behave using a force law.

In [11]:
force = chaste.cell_based.GeneralisedLinearSpringForce2_2()

We set up a PDE for oxygen diffusion and consumption by cells, setting the rate of consumption to 0.1

In [12]:
pde = chaste.cell_based.CellwiseSourceEllipticPde2(cell_population, -0.5)

We set a constant amount of oxygen on the edge of the spheroid

In [13]:
bc = chaste.pde.ConstBoundaryCondition2(1.0)
is_neumann_bc = False

Set up a pde modifier to solve the PDE at each simulation time step

In [15]:
pde_modifier = chaste.cell_based.EllipticGrowingDomainPdeModifier2(pde, bc, is_neumann_bc)

As before, we set up a scene modifier for real-time visualization

In [16]:
scene = chaste.visualization.VtkScene2()
nb_manager = chaste.visualization.JupyterNotebookManager()
scene_modifier = chaste.visualization.JupyterSceneModifier2(nb_manager)

Eventually remove apoptotic cells

In [17]:
killer = chaste.cell_based.ApoptoticCellKiller2(cell_population)

To run the simulation, we call Solve(). We can again do a quick rendering of the population at the end of the simulation

In [18]:
# Tear down the test 

Full results can be visualized in Paraview from the file_handler.GetOutputDirectoryFullPath() directory.