Running Crypt Simulations With Mutations

This tutorial is automatically generated from TestRunningCryptSimulationsWithMutationsTutorial.hpp at revision 409e06cb314b. Note that the code is given in full at the bottom of the page.

Examples showing how to run crypt simulations with various mutations

Introduction

This tutorial assumes you have already read the tutorial Running Mesh Based Simulations.

In this tutorial we show how Chaste can be used to simulate a cylindrical model of an intestinal crypt with mutations using both mesh and vertex-based simulations. Full details of the computational model can be found in the paper by Osborne et al. (2010) 10.1098/rsta.2010.0173.

As in previous cell-based Chaste tutorials, we begin by including the necessary header files.

#include <cxxtest/TestSuite.h>
#include "CheckpointArchiveTypes.hpp"
#include "SmartPointers.hpp"
#include "AbstractCellBasedTestSuite.hpp"

The next header file defines a helper class for generating cells for crypt simulations.

#include "CryptCellsGenerator.hpp"

The next header file defines a WntCellCycleModel, where the proliferative behaviour of a cell is dependent on the concentration of Wnt at that point in space. Cells proliferate where there is a plentiful level of Wnt and cease proliferation below a given threshold.

#include "SimpleWntCellCycleModel.hpp"

The next header file defines a helper class for generating a suitable triangular mesh for the crypt simulation, such that the cell corresponding to each node is initially in mechanical equilibrium with its neighours and periodic boundary conditions are applied at the left- and right-hand sides of the mesh (hence the “cylindrical”).

#include "CylindricalHoneycombMeshGenerator.hpp"

The next header file defines a CellPopulation class that uses a triangular mesh, and allows for the inclusion of ‘ghost nodes’. These are nodes in the mesh that do not correspond to cells; instead they help ensure that a sensible Delaunay triangulation is generated at each timestep. This is because the triangulation algorithm requires a convex hull.

#include "MeshBasedCellPopulationWithGhostNodes.hpp"

The next header file defines a force law, based on a linear spring, for describing the mechanical interactions between neighbouring cells in the crypt.

#include "GeneralisedLinearSpringForce.hpp"

The next header file defines the class that simulates the evolution of a CellPopulation, specialized to deal with the cylindrical crypt geometry.

#include "CryptSimulation2d.hpp"

The next header file defines a Wnt singleton class, which (if used) deals with the imposed Wnt gradient in our crypt model. This affects cell proliferation in the case where we construct each cell with a WntCellCycleModel.

#include "WntConcentration.hpp"

The next header file defines a cell killer class, which implements sloughing of cells into the lumen once they reach the top of the crypt.

#include "SloughingCellKiller.hpp"

These headers are used for defining and recording mutations.

#include "ApcTwoHitCellMutationState.hpp"
#include "CellMutationStatesCountWriter.hpp"

The final header ensures that this test is only ever run sequentially, not in parallel.

#include "FakePetscSetup.hpp"

Next, we define the test class, which inherits from AbstractCellBasedTestSuite and defines some test methods.

class TestRunningCryptSimulationsWithMutationsTutorial : public AbstractCellBasedTestSuite
{
public:

Test 1: a mesh-based crypt simulation with mutations

In the first test, we demonstrate how to introduce mutations into a simulation of a crypt.

    void TestMeshBasedCryptWithMutations()
    {

Note that time is re-initialized to zero and the random number generator is re-seeded to zero in the AbstractCellBasedTestSuite.

We first create a cylindrical mesh, and get the cell location indices, exactly as before.

        CylindricalHoneycombMeshGenerator generator(6, 9, 2);
        boost::shared_ptr<Cylindrical2dMesh> p_mesh = generator.GetCylindricalMesh();

        std::vector<unsigned> location_indices = generator.GetCellLocationIndices();

We create the cells, using the same method as before. Here, though, we use a SimpleWntCellCycleModel.

        std::vector<CellPtr> cells;
        CryptCellsGenerator<SimpleWntCellCycleModel> cells_generator;
        cells_generator.Generate(cells, p_mesh.get(), location_indices, true);

We now create boost shared pointers to any mutations we wish to use. We need to do this using the CellPropertyRegistry, otherwise the numbers of each type of mutation aren’t correctly tracked. For a list of possible mutations, see subclasses of AbstractCellMutationState. These can be found in the inheritance diagram for AbstractCellMutationState. Each mutation has a different effect on the cell cycle models; see the class documentation for details.

        boost::shared_ptr<AbstractCellProperty> p_state(CellPropertyRegistry::Instance()->Get<ApcTwoHitCellMutationState>());

We create the cell population, as before.

        MeshBasedCellPopulationWithGhostNodes<2> cell_population(*p_mesh, cells, location_indices);

In order to visualize mutant cells and to count how many cells there are of each type we need to use the following command.

        cell_population.AddCellPopulationCountWriter<CellMutationStatesCountWriter>();

We set the height of the crypt. As well as passing this variable into the SloughingCellKiller, we will pass it to the WntConcentration object (see below).

        double crypt_height = 8.0;

When using a SimpleWntCellCycleModel, we need a way of telling each cell what the Wnt concentration is at its location. To do this, we set up a WntConcentration object. Like SimulationTime, WntConcentration is a singleton class, so when instantiated it is accessible from anywhere in the code (and in particular, all cells and cell-cycle models can access it). We need to say what the profile of the Wnt concentation should be up the crypt: here, we say it is LINEAR (linear decreasing from 1 to 0 from the bottom of the crypt to the top). We also need to inform the WntConcentration of the cell population and the height of the crypt.

        WntConcentration<2>::Instance()->SetType(LINEAR);
        WntConcentration<2>::Instance()->SetCellPopulation(cell_population);
        WntConcentration<2>::Instance()->SetCryptLength(crypt_height);

Create a simulator as before (except setting a different output directory).

        CryptSimulation2d simulator(cell_population);
        simulator.SetOutputDirectory("MeshBasedCryptWithMutations");
        simulator.SetSamplingTimestepMultiple(12);
        simulator.SetEndTime(10);

As before, we create a force law and cell killer and pass these objects to the simulator, then call Solve().

        MAKE_PTR(GeneralisedLinearSpringForce<2>, p_linear_force);
        simulator.AddForce(p_linear_force);
        MAKE_PTR_ARGS(SloughingCellKiller<2>, p_killer, (&cell_population, crypt_height));
        simulator.AddCellKiller(p_killer);

        simulator.Solve();

Now we have run the simulation to a steady state (where the initial regular configuration is lost) we select a cell to become mutant. We select one of the cells and set the mutation state to ApcTwoHitCellMutationState (i.e. p_state).

        for (AbstractCellPopulation<2>::Iterator cell_iter = cell_population.Begin();
             cell_iter != cell_population.End();
             ++cell_iter)
        {
            unsigned node_index = cell_population.GetLocationIndexUsingCell(*cell_iter);

            if (node_index == 74) // Chosen from looking at the results from steady state
            {
                cell_iter->SetMutationState(p_state);
            }
        }

We also change the value of the damping constant for mutant cells to be ten times the normal value.

       double normal_damping_constant = cell_population.GetDampingConstantNormal();
       cell_population.SetDampingConstantMutant(10*normal_damping_constant);

Next we reset the end time to some later time.

       simulator.SetEndTime(20);

Run the simulation to the new end time.

       simulator.Solve();

Finally, we must tidy up by destroying the WntConcentration singleton object. This avoids memory leaks occurring.

       WntConcentration<2>::Destroy();
    }
};

To visualize the results, open a new terminal, cd to the Chaste directory, then cd to anim. Then do: java Visualize2dCentreCells $CHASTE_TEST_OUTPUT/MeshBasedCryptWithMutations/results_from_time_0.

These are the results before we add the mutations do: java Visualize2dCentreCells $CHASTE_TEST_OUTPUT/MeshBasedCryptWithMutations/results_from_time_10 to see the results from after the mutation has been added.

We may have to do: javac Visualize2dCentreCells.java beforehand to create the java executable.

In the results folder there is also a file cellmutationstates.dat which tracks the numbers of each mutation type in the simulation. These results are just tab separated columns so may be visualized by using gnuplot, Matlab or similar.

Full code

#include <cxxtest/TestSuite.h>
#include "CheckpointArchiveTypes.hpp"
#include "SmartPointers.hpp"
#include "AbstractCellBasedTestSuite.hpp"

#include "CryptCellsGenerator.hpp"
#include "SimpleWntCellCycleModel.hpp"
#include "CylindricalHoneycombMeshGenerator.hpp"
#include "MeshBasedCellPopulationWithGhostNodes.hpp"
#include "GeneralisedLinearSpringForce.hpp"
#include "CryptSimulation2d.hpp"
#include "WntConcentration.hpp"
#include "SloughingCellKiller.hpp"
#include "ApcTwoHitCellMutationState.hpp"
#include "CellMutationStatesCountWriter.hpp"
#include "FakePetscSetup.hpp"

class TestRunningCryptSimulationsWithMutationsTutorial : public AbstractCellBasedTestSuite
{
public:
    void TestMeshBasedCryptWithMutations()
    {
        CylindricalHoneycombMeshGenerator generator(6, 9, 2);
        boost::shared_ptr<Cylindrical2dMesh> p_mesh = generator.GetCylindricalMesh();

        std::vector<unsigned> location_indices = generator.GetCellLocationIndices();

        std::vector<CellPtr> cells;
        CryptCellsGenerator<SimpleWntCellCycleModel> cells_generator;
        cells_generator.Generate(cells, p_mesh.get(), location_indices, true);

        boost::shared_ptr<AbstractCellProperty> p_state(CellPropertyRegistry::Instance()->Get<ApcTwoHitCellMutationState>());

        MeshBasedCellPopulationWithGhostNodes<2> cell_population(*p_mesh, cells, location_indices);

        cell_population.AddCellPopulationCountWriter<CellMutationStatesCountWriter>();

        double crypt_height = 8.0;

        WntConcentration<2>::Instance()->SetType(LINEAR);
        WntConcentration<2>::Instance()->SetCellPopulation(cell_population);
        WntConcentration<2>::Instance()->SetCryptLength(crypt_height);

        CryptSimulation2d simulator(cell_population);
        simulator.SetOutputDirectory("MeshBasedCryptWithMutations");
        simulator.SetSamplingTimestepMultiple(12);
        simulator.SetEndTime(10);

        MAKE_PTR(GeneralisedLinearSpringForce<2>, p_linear_force);
        simulator.AddForce(p_linear_force);
        MAKE_PTR_ARGS(SloughingCellKiller<2>, p_killer, (&cell_population, crypt_height));
        simulator.AddCellKiller(p_killer);

        simulator.Solve();

        for (AbstractCellPopulation<2>::Iterator cell_iter = cell_population.Begin();
             cell_iter != cell_population.End();
             ++cell_iter)
        {
            unsigned node_index = cell_population.GetLocationIndexUsingCell(*cell_iter);

            if (node_index == 74) // Chosen from looking at the results from steady state
            {
                cell_iter->SetMutationState(p_state);
            }
        }

       double normal_damping_constant = cell_population.GetDampingConstantNormal();
       cell_population.SetDampingConstantMutant(10*normal_damping_constant);

       simulator.SetEndTime(20);

       simulator.Solve();

       WntConcentration<2>::Destroy();
    }
};

Running Contact Inhibition Simulations

Running Delta Notch Simulations

Running Crypt Simulations With Mutations

Examples showing how to run crypt simulations with various mutations#

Introduction#

Test 1: a mesh-based crypt simulation with mutations#

Full code#

Examples showing how to run crypt simulations with various mutations

Introduction

Test 1: a mesh-based crypt simulation with mutations

Full code