def test_construct(self):
file_handler = chaste.core.OutputFileHandler(
"Python/TestVertexBasedCellPopulation")
# Set up the mesh
mesh_generator = chaste.mesh.HoneycombVertexMeshGenerator(2, 2)
mesh = mesh_generator.GetMesh()
# Make the cells
proliferative_type = chaste.cell_based.DefaultCellProliferativeType()
cell_generator = chaste.cell_based.CellsGeneratorUniformCellCycleModel_2(
)
cells = cell_generator.GenerateBasicRandom(mesh.GetNumElements(),
proliferative_type)
# Make the cell population
cell_population = chaste.cell_based.VertexBasedCellPopulation2(
mesh, cells)
# Set up the visualizer
scene = chaste.visualization.VtkScene2()
scene.SetCellPopulation(cell_population)
scene.SetSaveAsAnimation(True)
scene.SetOutputFilePath(file_handler.GetOutputDirectoryFullPath() +
"/cell_population")
modifier = chaste.cell_based.VtkSceneModifier2()
modifier.SetVtkScene(scene)
force = chaste.cell_based.NagaiHondaForce2()
target_area_modifier = chaste.cell_based.SimpleTargetAreaModifier2()
target_area_modifier.SetGrowthDuration(1.0)
# Set up the simulation
simulator = chaste.cell_based.OffLatticeSimulation2_2(cell_population)
simulator.SetOutputDirectory("Python/TestVertexBasedCellPopulation")
simulator.SetEndTime(0.2)
simulator.AddForce(force)
simulator.SetSamplingTimestepMultiple(200)
simulator.AddSimulationModifier(modifier)
simulator.AddSimulationModifier(target_area_modifier)
simulator.Solve()
def test_monolayer(self):
# JUPYTER_SETUP
## First, we generate a vertex mesh using a HoneycombVertexMeshGenerator.
generator = chaste.mesh.HoneycombVertexMeshGenerator(5, 15)
mesh = generator.GetMesh()
## Now set up the cells, again we want to avoid proliferation.
differentiated_type = chaste.cell_based.DifferentiatedCellProliferativeType(
)
cell_generator = chaste.cell_based.CellsGeneratorUniformG1GenerationalCellCycleModel_2(
)
cells = cell_generator.GenerateBasicRandom(mesh.GetNumElements(),
differentiated_type)
## Next, create the cell population
cell_population = chaste.cell_based.VertexBasedCellPopulation2(
mesh, cells)
## Pass the cell population into an `OffLatticeSimulation`, and set the output directory, output multiple and end time
simulator = chaste.cell_based.OffLatticeSimulation2_2(cell_population)
simulator.SetOutputDirectory("Python/TestTensileTest")
simulator.SetEndTime(1.0)
simulator.SetSamplingTimestepMultiple(1000)
## Now create a force law
force = chaste.cell_based.NagaiHondaForce2()
simulator.AddForce(force)
## A `NagaiHondaForce` assumes that each cell has a target area. The target areas of cells are used to determine
## pressure forces on each vertex and eventually determine the size of each cell in the simulation.
## In order to assign target areas to cells and update them in each time step we add a `SimpleTargetAreaModifier`
## to the simulation, which inherits from `AbstractTargetAreaModifier`.
growth_modifier = chaste.cell_based.SimpleTargetAreaModifier2()
simulator.AddSimulationModifier(growth_modifier)
## For our tensile test we will fix the bottom of the sheet and subject the top to an applied displacement. We neglect
## fixing lateral degress of freedom for simplicity, since we are using an over-damped mechanical model.
my_point = np.array([0.0, 0.0])
normal = np.array([0.0, -1.0])
bc = chaste.cell_based.AttractingPlaneBoundaryCondition2_2(
cell_population, my_point, normal)
simulator.AddCellPopulationBoundaryCondition(bc)
point = np.array([0.0, 15.5])
normal = np.array([0.0, -1.0])
bc2 = chaste.cell_based.AttractingPlaneBoundaryCondition2_2(
cell_population, point, normal)
simulator.AddCellPopulationBoundaryCondition(bc2)
## We want to displace our top boundary over time. We could write a custom boundary condition class to do this.
## A more simple alternative is to modify the the position of the point describing our boundary plane in `bc2`
## as the simulation progresses. As per earlier tutorials we make a new `SimulationModifier` class to do this.
class BoundaryConditionModifier(
chaste.cell_based.PythonSimulationModifier2):
""" Class for time varying boundary conditions
"""
def __init__(self, boundary_condition):
self.boundary_condition = boundary_condition
self.original_location = boundary_condition.rGetPointOnPlane()
self.velocity = 0.5 # cell lengths per time
super(BoundaryConditionModifier, self).__init__()
def UpdateAtEndOfTimeStep(self, cell_population):
""" Move the boundary upwards at the specified velocity
"""
total_time = chaste.cell_based.SimulationTime.Instance(
).GetTime()
new_location = [
self.original_location[0],
self.original_location[1] + self.velocity * total_time
]
self.boundary_condition.SetPointOnPlane(np.array(new_location))
def SetupSolve(self, cell_population, output_directory):
""" Make sure the cell population is in the correct state at the start of the simulation
"""
cell_population.Update()
bc_modifier = BoundaryConditionModifier(bc2)
simulator.AddSimulationModifier(bc_modifier)
## PyChaste can do simple 3D rendering with VTK. We set up a `VtkScene` so that we can see the population
## evovle in real time.
scene = chaste.visualization.VtkScene2()
scene.SetCellPopulation(cell_population)
# JUPYTER_SHOW_FIRST
scene_modifier = chaste.cell_based.VtkSceneModifier2()
scene_modifier.SetVtkScene(scene)
scene_modifier.SetUpdateFrequency(1000)
simulator.AddSimulationModifier(scene_modifier)
## To run the simulation, we call `Solve()`.
scene.Start()
simulator.Solve()
def test_monolayer(self):
# JUPYTER_SETUP
## First, we generate a vertex mesh. To create a MutableVertexMesh, we can use the HoneycombVertexMeshGenerator.
## This generates a honeycomb-shaped mesh, in which all nodes are equidistant. Here the first and second arguments
## define the size of the mesh - we have chosen a mesh that is 2 elements (i.e. cells) wide, and 2 elements high.
chaste.core.OutputFileHandler(
"Python/TestVertexBasedCellSimulationsTutorial")
generator = chaste.mesh.HoneycombVertexMeshGenerator(2, 2)
mesh = generator.GetMesh()
## Having created a mesh, we now create a std::vector of CellPtrs. To do this, we use the CellsGenerator helper class,
## which is templated over the type of cell model required
## and the dimension. We create an empty vector of cells and pass this into the method along with the mesh.
## The second argument represents the size of that the vector cells should become - one cell for each element,
## the third argument specifies the proliferative type of the cell.
transit_type = chaste.cell_based.TransitCellProliferativeType()
cell_generator = chaste.cell_based.CellsGeneratorUniformG1GenerationalCellCycleModel_2(
)
cells = cell_generator.GenerateBasicRandom(mesh.GetNumElements(),
transit_type)
## Now we have a mesh and a set of cells to go with it, we can create a CellPopulation.
## In general, this class associates a collection of cells with a mesh. For this test, because we have a MutableVertexMesh,
## we use a particular type of cell population called a VertexBasedCellPopulation.
cell_population = chaste.cell_based.VertexBasedCellPopulation2(
mesh, cells)
## We can set up a `VtkScene` to do a quick visualization of the population before running the analysis.
scene = chaste.visualization.VtkScene2()
scene.SetCellPopulation(cell_population)
# JUPYTER_SHOW_FIRST
scene.Start() # JUPYTER_SHOW
## We then pass in the cell population into an `OffLatticeSimulation`, and set the output directory, output multiple and end time
simulator = chaste.cell_based.OffLatticeSimulation2_2(cell_population)
simulator.SetOutputDirectory(
"Python/TestVertexBasedCellSimulationsTutorial")
simulator.SetEndTime(5.0)
## For longer simulations, we may not want to output the results every time step.
## In this case we can use the following method, to print results every 50 time steps instead.
## As the default time step used by the simulator (for vertex based simulations), is 0.02 hours, this method
## will cause the simulator to print results every 6 minutes (i.e. 0.1 hours).
simulator.SetSamplingTimestepMultiple(50)
## We must now create one or more force laws, which determine the mechanics of the vertices of each cell in a cell population.
## For this test, we use one force law, based on the Nagai-Honda mechanics, and pass it to the OffLatticeSimulation.
## For a list of possible forces see subclasses of AbstractForce.
## Note that some of these forces are not compatible with vertex-based simulations see the specific class documentation for details,
## if you try to use an incompatible class then you will receive a warning.
force = chaste.cell_based.NagaiHondaForce2()
simulator.AddForce(force)
## A NagaiHondaForce assumes that each cell has a target area. The target areas of cells are used to determine
## pressure forces on each vertex and eventually determine the size of each cell in the simulation.
## In order to assign target areas to cells and update them in each time step we add a SimpleTargetAreaModifier
## to the simulation, which inherits from AbstractTargetAreaModifier.
growth_modifier = chaste.cell_based.SimpleTargetAreaModifier2()
simulator.AddSimulationModifier(growth_modifier)
## Save snapshot images of the population during the simulation
scene_modifier = chaste.cell_based.VtkSceneModifier2()
scene_modifier.SetVtkScene(scene)
scene_modifier.SetUpdateFrequency(100)
simulator.AddSimulationModifier(scene_modifier)
## To run the simulation, we call `Solve()`. We can again do a quick rendering of the population at the end of the simulation
scene.Start()
simulator.Solve()
scene.End()
## The next two lines are for test purposes only and are not part of this tutorial.
## If different simulation input parameters are being explored the lines should be removed.
self.assertEqual(cell_population.GetNumRealCells(), 7)
self.assertAlmostEqual(
chaste.cell_based.SimulationTime.Instance().GetTime(), 5.0, 6)
def test_periodic_monolayer(self):
# JUPYTER_SETUP
## First, we generate a periodic vertex mesh. To create a Cylindrical2dVertexMesh, we can use the CylindricalHoneycombVertexMeshGenerator.
## This generates a honeycomb-shaped mesh, in which all nodes are equidistant and the right hand side is associated with the left hand side.
## Here the first and second arguments define the size of the mesh - we have chosen a mesh that is
## 4 elements (i.e. cells) wide, and 4 elements high.
generator = chaste.mesh.CylindricalHoneycombVertexMeshGenerator(4, 4)
mesh = generator.GetCylindricalMesh()
## Having created a mesh, we now create a VectorSharedPtrCells. This is exactly the same as the above test.
transit_type = chaste.cell_based.TransitCellProliferativeType()
cell_generator = chaste.cell_based.CellsGeneratorUniformG1GenerationalCellCycleModel_2(
)
cells = cell_generator.GenerateBasicRandom(mesh.GetNumElements(),
transit_type)
## Now we have a mesh and a set of cells to go with it, we can create a CellPopulation. This is also the same as in the above test.
cell_population = chaste.cell_based.VertexBasedCellPopulation2(
mesh, cells)
## We then pass in the cell population into an `OffLatticeSimulation`, and set the output directory, output multiple and end time
simulator = chaste.cell_based.OffLatticeSimulation2_2(cell_population)
simulator.SetOutputDirectory(
"Python/TestPeriodicVertexBasedCellPopulation")
simulator.SetEndTime(1.0)
simulator.SetSamplingTimestepMultiple(50)
## We now make a pointer to an appropriate force and pass it to the OffLatticeSimulation.
force = chaste.cell_based.NagaiHondaForce2()
simulator.AddForce(force)
## We also make a pointer to the target area modifier and add it to the simulator.
growth_modifier = chaste.cell_based.SimpleTargetAreaModifier2()
simulator.AddSimulationModifier(growth_modifier)
## We now create one or more CellPopulationBoundaryConditions, which determine any conditions which each cell in a cell population must satisfy.
## For this test, we use a PlaneBoundaryCondition, and pass it to the OffLatticeSimulation. For a list of possible boundary condition
## see subclasses of AbstractCellPopulationBoundaryCondition.
## Note that some of these boundary conditions are not compatible with vertex-based simulations see the specific class documentation
## for details, if you try to use an incompatible class then you will receive a warning.
## The first step is to define a point on the plane boundary and a normal to the plane.
point = np.array([0.0, 0.0])
normal = np.array([0.0, -1.0])
## We can now make a PlaneBoundaryCondition (passing the point and normal to the plane) and pass it to the OffLatticeSimulation.
bc = chaste.cell_based.PlaneBoundaryCondition2_2(
cell_population, point, normal)
simulator.AddCellPopulationBoundaryCondition(bc)
## We now create one or more CellKillers, which determine how cells are removed from the simulation.
## For this test, we use a PlaneBasedCellKiller, and pass it to the OffLatticeSimulation.
## For a list of possible cell killers see subclasses of AbstractCellKiller.
## The first step is to define a point on the plane boundary and a normal to the plane.
point = np.array([0.0, 3.0])
normal = np.array([0.0, 1.0])
## Finally we now make a PlaneBasedCellKiller (passing the point and normal to the plane) and pass it to the OffLatticeSimulation.
killer = chaste.cell_based.PlaneBasedCellKiller2(
cell_population, point, normal)
simulator.AddCellKiller(killer)
## To run the simulation, we call `Solve()`.
simulator.Solve()
## The next two lines are for test purposes only and are not part of this tutorial.
## If different simulation input parameters are being explored the lines should be removed.
self.assertEqual(cell_population.GetNumRealCells(), 12)
self.assertAlmostEqual(
chaste.cell_based.SimulationTime.Instance().GetTime(), 1.0, 6)
def test_potts_monolayer_cell_sorting(self):
# JUPYTER_SETUP
## First, we generate a `Potts` mesh. To create a `PottsMesh`, we can use the `PottsMeshGenerator`.
## This generates a regular square-shaped mesh, in which all elements are the same size.
## We have chosen an 8 by 8 block of elements each consisting of 4 by 4 ( = 16) lattice sites.
generator = chaste.mesh.PottsMeshGenerator2(50, 8, 4, 50, 8, 4)
mesh = generator.GetMesh()
## Having created a mesh, we now create some cells. To do this, we the `CellsGenerator` helper class,
## as before but this time the third argument is set to make all cells non-proliferative.
differentiated_type = chaste.cell_based.DifferentiatedCellProliferativeType(
)
cell_generator = chaste.cell_based.CellsGeneratorUniformCellCycleModel_2(
)
cells = cell_generator.GenerateBasicRandom(mesh.GetNumElements(),
differentiated_type)
## Before we make a CellPopulation we make a cell label and then assign this label to some randomly chosen cells.
label = chaste.cell_based.CellLabel()
for eachCell in cells:
if (chaste.core.RandomNumberGenerator.Instance().ranf() < 0.5):
eachCell.AddCellProperty(label)
## Now we have a mesh and a set of cells to go with it, we can create a `CellPopulation`.
cell_population = chaste.cell_based.PottsBasedCellPopulation2(
mesh, cells)
## In order to visualize labelled cells we need to use the following command.
cell_population.AddCellWriterCellLabelWriter()
## PyChaste can do simple 3D rendering with VTK. We set up a VtkScene so that we can
## see the population evovle in real time.
scene = chaste.visualization.VtkScene2()
scene.SetCellPopulation(cell_population)
scene.GetCellPopulationActorGenerator().SetShowPottsMeshEdges(True)
# JUPYTER_SHOW_FIRST
scene.Start() # JUPYTER_SHOW
## We then pass in the cell population into an `OffLatticeSimulation`, and set the output directory and end time
simulator = chaste.cell_based.OnLatticeSimulation2(cell_population)
simulator.SetOutputDirectory("Python/TestCellSorting")
simulator.SetEndTime(20.0)
simulator.SetSamplingTimestepMultiple(10)
## We must now create one or more update rules, which determine the Hamiltonian in the Potts simulation.
## For this test, we use two update rules based upon a volume constraint (`VolumeConstraintPottsUpdateRule`) and
## differential adhesion between cells (`DifferentialAdhesionPottsUpdateRule`), set appropriate parameters, and
## pass them to the `OnLatticeSimulation`.
volume_constraint_update_rule = chaste.cell_based.VolumeConstraintPottsUpdateRule2(
)
volume_constraint_update_rule.SetMatureCellTargetVolume(16)
volume_constraint_update_rule.SetDeformationEnergyParameter(0.2)
simulator.AddUpdateRule(volume_constraint_update_rule)
## We repeat the process for any other update rules.
differential_adhesion_update_rule = chaste.cell_based.DifferentialAdhesionPottsUpdateRule2(
)
differential_adhesion_update_rule.SetLabelledCellLabelledCellAdhesionEnergyParameter(
0.16)
differential_adhesion_update_rule.SetLabelledCellCellAdhesionEnergyParameter(
0.11)
differential_adhesion_update_rule.SetCellCellAdhesionEnergyParameter(
0.02)
differential_adhesion_update_rule.SetLabelledCellBoundaryAdhesionEnergyParameter(
0.16)
differential_adhesion_update_rule.SetCellBoundaryAdhesionEnergyParameter(
0.16)
simulator.AddUpdateRule(differential_adhesion_update_rule)
## Set up plotting
scene_modifier = chaste.cell_based.VtkSceneModifier2()
scene_modifier.SetVtkScene(scene)
scene_modifier.SetUpdateFrequency(1000)
simulator.AddSimulationModifier(scene_modifier)
## To run the simulation, we call `Solve()`.
scene.Start()
simulator.Solve()
def test_monolayer(self):
# JUPYTER_SETUP
## First, we generate a Potts mesh. To create a PottsMesh, we can use the PottsMeshGenerator.
## This generates a regular square-shaped mesh, in which all elements are the same size.
## Here the first three arguments specify the domain width; the number of elements across; and the width of elements.
## The second set of three arguments specify the domain height; the number of elements up; and the height of individual elements.
## We have chosen a 2 by 2 block of elements, each consisting of 4 by 4 ( = 16) lattice sites.
chaste.core.OutputFileHandler(
"Python/TestPottsBasedCellSimulationsTutorial")
generator = chaste.mesh.PottsMeshGenerator2(50, 2, 4, 50, 2, 4)
mesh = generator.GetMesh()
## Having created a mesh, we now create a vector of CellPtrs. To do this, we the CellsGenerator helper class,
## which is templated over the type of cell model required and the dimension.
## We create an empty vector of cells and pass this into the method along with the mesh.
## The second argument represents the size of that the vector cells should become - one cell for each element.
## Third argument makes all cells proliferate.
cell_generator = chaste.cell_based.CellsGeneratorUniformCellCycleModel_2(
)
cells = cell_generator.GenerateBasic(mesh.GetNumElements())
## Now we have a mesh and a set of cells to go with it, we can create a CellPopulation.
## In general, this class associates a collection of cells with a mesh. For this test, because we have a PottsMesh,
## we use a particular type of cell population called a PottsBasedCellPopulation.
cell_population = chaste.cell_based.PottsBasedCellPopulation2(
mesh, cells)
## We can set the "Temperature" to be used in the Potts Simulation using the optional command below. The default value is 0.1.
cell_population.SetTemperature(0.1)
## By default the Potts simulation will make 1 sweep over the whole domain per timestep.
## To use a different number of sweeps per timestep use the command.
cell_population.SetNumSweepsPerTimestep(1)
## We can set up a `VtkScene` to do a quick visualization of the population before running the analysis.
scene = chaste.visualization.VtkScene2()
scene.SetCellPopulation(cell_population)
scene.GetCellPopulationActorGenerator().SetShowPottsMeshEdges(True)
# JUPYTER_SHOW_FIRST
scene.Start() # JUPYTER_SHOW
## We then pass in the cell population into an `OffLatticeSimulation`, and set the output directory and end time
simulator = chaste.cell_based.OnLatticeSimulation2(cell_population)
simulator.SetOutputDirectory(
"Python/TestPottsBasedCellSimulationsTutorial")
simulator.SetEndTime(50.0)
## The default timestep is 0.1, but can be changed using the below command. The timestep is used in conjunction with the "Temperature"
## and number of sweeps per timestep to specify the relationship between cell movement and proliferation.
## We also set the simulation to only output every 10 steps i.e. once per hour.
simulator.SetDt(0.1)
simulator.SetSamplingTimestepMultiple(10)
## We must now create one or more update rules, which determine the Hamiltonian in the Potts simulation.
## For this test, we use two update rules based upon a volume constraint (VolumeConstraintPottsUpdateRule)
## and adhesion between cells (AdhesionPottsUpdateRule) and pass them to the OnLatticeSimulation.
## For a list of possible update rules see subclasses of AbstractPottsUpdateRule.
volume_constraint_update_rule = chaste.cell_based.VolumeConstraintPottsUpdateRule2(
)
## Set an appropriate target volume in number of lattice sites. Here we use the default value of 16 lattice sites.
volume_constraint_update_rule.SetMatureCellTargetVolume(16)
## You can also vary the deformation energy parameter.
## The larger the parameter the more cells will try to maintain target volume. Here we use the default value of 0.2.
volume_constraint_update_rule.SetDeformationEnergyParameter(0.2)
## Finally we add the update rule to the simulator.
simulator.AddUpdateRule(volume_constraint_update_rule)
## We repeat the process for any other update rules.
adhesion_update_rule = chaste.cell_based.AdhesionPottsUpdateRule2()
simulator.AddUpdateRule(adhesion_update_rule)
## Save snapshot images of the population during the simulation
scene_modifier = chaste.cell_based.VtkSceneModifier2()
scene_modifier.SetVtkScene(scene)
scene_modifier.SetUpdateFrequency(100)
simulator.AddSimulationModifier(scene_modifier)
## To run the simulation, we call `Solve()`. We can again do a quick rendering of the population at the end of the simulation
scene.Start()
simulator.Solve()
## The next two lines are for test purposes only and are not part of this tutorial.
## If different simulation input parameters are being explored the lines should be removed.
self.assertEqual(cell_population.GetNumRealCells(), 41)
self.assertAlmostEqual(
chaste.cell_based.SimulationTime.Instance().GetTime(), 50.0, 6)
def test_potts_spheroid_cell_sorting(self):
# JUPYTER_SETUP
## First, we generate a Potts mesh. To create a PottsMesh, we can use the PottsMeshGenerator.
## This generates a regular square-shaped mesh, in which all elements are the same size.
## Here the first three arguments specify the domain width; the number of elements across; and the width of elements.
## The second set of three arguments specify the domain height; the number of elements up; and the height of individual elements.
## The third set of three arguments specify the domain depth; the number of elements deep; and the depth of individual elements.
## We have chosen an 4 by 4 by 4 ( = 64) block of elements each consisting of 2 by 2 by 2 ( = 8) lattice sites.
generator = chaste.mesh.PottsMeshGenerator3(10, 4, 2, 10, 4, 2, 10, 4,
2)
mesh = generator.GetMesh()
## Having created a mesh, we now create a VectorSharedPtrCells. To do this, we the CellsGenerator helper class,
## as before but this time the third argument is set to make all cells non-proliferative.
differentiated_type = chaste.cell_based.DifferentiatedCellProliferativeType(
)
cell_generator = chaste.cell_based.CellsGeneratorUniformCellCycleModel_3(
)
cells = cell_generator.GenerateBasicRandom(mesh.GetNumElements(),
differentiated_type)
## As for the 2D case before we make a CellPopulation we make a pointer to a cell label and then assign this label to some randomly chosen cells.
label = chaste.cell_based.CellLabel()
for eachCell in cells:
if (chaste.core.RandomNumberGenerator.Instance().ranf() < 0.5):
eachCell.AddCellProperty(label)
## Now we have a mesh and a set of cells to go with it, we can create a CellPopulation.
cell_population = chaste.cell_based.PottsBasedCellPopulation3(
mesh, cells)
## In order to visualize labelled cells we need to use the following command.
cell_population.AddCellWriterCellLabelWriter()
## We then pass in the cell population into an `OffLatticeSimulation`, and set the output directory and end time
simulator = chaste.cell_based.OnLatticeSimulation3(cell_population)
simulator.SetOutputDirectory("Python/TestPottsBasedCellSorting3D")
simulator.SetEndTime(20.0)
simulator.SetSamplingTimestepMultiple(10)
## We must now create one or more update rules, which determine the Hamiltonian in the Potts simulation.
## Now set the target volume to be appropriate for this 3D simulation.
volume_constraint_update_rule = chaste.cell_based.VolumeConstraintPottsUpdateRule3(
)
volume_constraint_update_rule.SetMatureCellTargetVolume(8)
volume_constraint_update_rule.SetDeformationEnergyParameter(0.2)
simulator.AddUpdateRule(volume_constraint_update_rule)
## We use the same differential adhesion parameters as in the 2D case.
differential_adhesion_update_rule = chaste.cell_based.DifferentialAdhesionPottsUpdateRule3(
)
differential_adhesion_update_rule.SetLabelledCellLabelledCellAdhesionEnergyParameter(
0.16)
differential_adhesion_update_rule.SetLabelledCellCellAdhesionEnergyParameter(
0.11)
differential_adhesion_update_rule.SetCellCellAdhesionEnergyParameter(
0.02)
differential_adhesion_update_rule.SetLabelledCellBoundaryAdhesionEnergyParameter(
0.16)
differential_adhesion_update_rule.SetCellBoundaryAdhesionEnergyParameter(
0.16)
simulator.AddUpdateRule(differential_adhesion_update_rule)
## To run the simulation, we call `Solve()`.
simulator.Solve()
## The next two lines are for test purposes only and are not part of this tutorial.
## If different simulation input parameters are being explored the lines should be removed.
self.assertEqual(cell_population.GetNumRealCells(), 64)
self.assertAlmostEqual(
chaste.cell_based.SimulationTime.Instance().GetTime(), 20.0, 6)
def test_potts_monolayer_cell_sorting(self):
# JUPYTER_SETUP
## First, we generate a Potts mesh. To create a PottsMesh, we can use the PottsMeshGenerator.
## This generates a regular square-shaped mesh, in which all elements are the same size.
## We have chosen an 8 by 8 block of elements each consisting of 4 by 4 ( = 16) lattice sites.
generator = chaste.mesh.PottsMeshGenerator2(50, 8, 4, 50, 8, 4)
mesh = generator.GetMesh()
## Having created a mesh, we now create a VectorSharedPtrCells. To do this, we the CellsGenerator helper class,
## as before but this time the third argument is set to make all cells non-proliferative.
differentiated_type = chaste.cell_based.DifferentiatedCellProliferativeType(
)
cell_generator = chaste.cell_based.CellsGeneratorUniformCellCycleModel_2(
)
cells = cell_generator.GenerateBasicRandom(mesh.GetNumElements(),
differentiated_type)
## Before we make a CellPopulation we make a cell label and then assign this label to some randomly chosen cells.
label = chaste.cell_based.CellLabel()
for eachCell in cells:
if (chaste.core.RandomNumberGenerator.Instance().ranf() < 0.5):
eachCell.AddCellProperty(label)
## Now we have a mesh and a set of cells to go with it, we can create a CellPopulation.
cell_population = chaste.cell_based.PottsBasedCellPopulation2(
mesh, cells)
## In order to visualize labelled cells we need to use the following command.
cell_population.AddCellWriterCellLabelWriter()
## We then pass in the cell population into an `OffLatticeSimulation`, and set the output directory and end time
simulator = chaste.cell_based.OnLatticeSimulation2(cell_population)
simulator.SetOutputDirectory("Python/TestPottsBasedCellSorting")
simulator.SetEndTime(20.0)
simulator.SetSamplingTimestepMultiple(10)
## We must now create one or more update rules, which determine the Hamiltonian in the Potts simulation.
## For this test, we use two update rules based upon a volume constraint (VolumeConstraintPottsUpdateRule) and
## differential adhesion between cells (DifferentialAdhesionPottsUpdateRule), set appropriate parameters, and
## pass them to the OnLatticeSimulation.
volume_constraint_update_rule = chaste.cell_based.VolumeConstraintPottsUpdateRule2(
)
volume_constraint_update_rule.SetMatureCellTargetVolume(16)
volume_constraint_update_rule.SetDeformationEnergyParameter(0.2)
simulator.AddUpdateRule(volume_constraint_update_rule)
## We repeat the process for any other update rules.
differential_adhesion_update_rule = chaste.cell_based.DifferentialAdhesionPottsUpdateRule2(
)
differential_adhesion_update_rule.SetLabelledCellLabelledCellAdhesionEnergyParameter(
0.16)
differential_adhesion_update_rule.SetLabelledCellCellAdhesionEnergyParameter(
0.11)
differential_adhesion_update_rule.SetCellCellAdhesionEnergyParameter(
0.02)
differential_adhesion_update_rule.SetLabelledCellBoundaryAdhesionEnergyParameter(
0.16)
differential_adhesion_update_rule.SetCellBoundaryAdhesionEnergyParameter(
0.16)
simulator.AddUpdateRule(differential_adhesion_update_rule)
## To run the simulation, we call `Solve()`.
simulator.Solve()
## The next two lines are for test purposes only and are not part of this tutorial.
## If different simulation input parameters are being explored the lines should be removed.
self.assertEqual(cell_population.GetNumRealCells(), 64)
self.assertAlmostEqual(
chaste.cell_based.SimulationTime.Instance().GetTime(), 20.0, 6)
