## User parameters in PhysiCell

As of release 1.4.0, users can add any number of Boolean, integer, double, and string parameters to an XML configuration file. (These are stored by default in ./config/. The default parameter file is ./config/PhysiCell_settings.xml.) These parameters are automatically parsed into a parameters data structure, and accessible throughout a PhysiCell project.

This tutorial will show you the key techniques to use these features. (See the User_Guide for full documentation.) First, let’s create a barebones 2D project by populating the 2D template project. In a terminal shell in your root PhysiCell directory, do this:

make template2D


We will use this 2D project template for the remainder of the tutorial. We assume you already have a working copy of PhysiCell installed, version 1.4.0 or later. (If not, visit the PhysiCell tutorials to find installation instructions for your operating system.)

### User parameters in the XML configuration file

Next, let’s look at the parameter file. In your text editor of choice, open up ./config/PhysiCell_settings.xml, and browse down to <user_parameters>, which will have some sample parameters from the 2D template project.

	<user_parameters>
<random_seed type="int" units="dimensionless">0</random_seed>
<!-- example parameters from the template -->

<!-- motile cell type parameters -->
<motile_cell_persistence_time type="double" units="min">15</motile_cell_persistence_time>
<motile_cell_migration_speed type="double" units="micron/min">0.5</motile_cell_migration_speed>
<motile_cell_apoptosis_rate type="double" units="1/min">0.0</motile_cell_apoptosis_rate>
<motile_cell_relative_cycle_entry_rate type="double" units="dimensionless">0.1</motile_cell_relative_cycle_entry_rate>
</user_parameters>


Notice a few trends:

• Each XML element (tag) under <user_parameters> is a user parameter, whose name is the element name.
• Each variable requires an attribute named “type”, with one of the following four values:
• bool for a Boolean parameter
• int for an integer parameter
• double for a double (floating point) parameter
• string for text string parameter

While we do not encourage it, if no valid type is supplied, PhysiCell will attempt to interpret the parameter as a double.

• Each variable here has an (optional) attribute “units”. PhysiCell does not convert units, but these are helpful for clarity between users and developers. By default, PhysiCell uses minutes for all time units, and microns for all spatial units.
• Then, between the tags, you list the value of your parameter.

Let’s add the following parameters to the configuration file:

• A string parameter called motile_color that sets the color of the motile_cell type in SVG outputs. Please refer to the User Guide (in the documentation folder) for more information on allowed color formats, including rgb values and named colors. Let’s use the value darkorange.
• A double parameter called base_cycle_entry_rate that will give the rate of entry to the S cycle phase from the G1 phase for the default cell type in the code. Let’s use a ridiculously high value of 0.01 min-1.
• A double parameter called base_apoptosis_rate for the default cell type. Let’s set the value at 1e-7 min-1.
• A double parameter that sets the (relative) maximum cell-cell adhesion sensing distance, relative to the cell’s radius. Let’s set it at 2.5 (dimensionless). (The default is 1.25.)
• A bool parameter that enables or disables placing a single motile cell in the initial setup. Let’s set it at true.

If you edit the <user_parameters> to include these, it should look like this:

	<user_parameters>
<random_seed type="int" units="dimensionless">0</random_seed>
<!-- example parameters from the template -->

<!-- motile cell type parameters -->
<motile_cell_persistence_time type="double" units="min">15</motile_cell_persistence_time>
<motile_cell_migration_speed type="double" units="micron/min">0.5</motile_cell_migration_speed>
<motile_cell_apoptosis_rate type="double" units="1/min">0.0</motile_cell_apoptosis_rate>
<motile_cell_relative_cycle_entry_rate type="double" units="dimensionless">0.1</motile_cell_relative_cycle_entry_rate>

<!-- for the tutorial -->
<motile_color type="string" units="dimensionless">darkorange</motile_color>

<base_cycle_entry_rate type="double" units="1/min">0.01</base_cycle_entry_rate>
<base_apoptosis_rate type="double" units="1/min">1e-7</base_apoptosis_rate>

<include_motile_cell type="bool" units="dimensionless">true</include_motile_cell>
</user_parameters>


Let’s compile and run the project.

make
./project2D


At the beginning of the simulation, PhysiCell parses the <user_parameters> block into a global data structure called parameters, with sub-parts bools, ints, doubles, and strings. It displays these loaded parameters at the start of the simulation. Here’s what it looks like:

shell\$  ./project2D
Using config file ./config/PhysiCell_settings.xml ...
User parameters in XML config file:
Bool parameters::
include_motile_cell: 1 [dimensionless]

Int parameters::
random_seed: 0 [dimensionless]

Double parameters::
motile_cell_persistence_time: 15 [min]
motile_cell_migration_speed: 0.5 [micron/min]
motile_cell_apoptosis_rate: 0 [1/min]
motile_cell_relative_cycle_entry_rate: 0.1 [dimensionless]
base_cycle_entry_rate: 0.01 [1/min]
base_apoptosis_rate: 1e-007 [1/min]

String parameters::
motile_color: darkorange [dimensionless]


### Getting parameter values

Within a PhysiCell project, you can access the value of any parameter by either its index or its name, so long as you know its type. Here’s an example of accessing the base_cell_adhesion_distance by its name:

/* this directly accesses the value of the parameter */
double temp = parameters.doubles( "base_cell_adhesion_distance" );
std::cout << temp << std::endl;

/* this streams a formatted output including the parameter name and units */
std::cout << parameters.doubles[ "base_cell_adhesion_distance" ] << std::endl;

std::cout << parameters.doubles["base_cell_adhesion_distance"].name << " "


Notice that accessing by () gets the value of the parameter in a user-friendly way, whereas accessing by [] gets the entire parameter, including its name, value, and units.

You can more efficiently access the parameter by first finding its integer index, and accessing by index:

/* this directly accesses the value of the parameter */
int my_index = parameters.doubles.find_index( "base_cell_adhesion_distance" );
double temp = parameters.doubles( my_index );
std::cout << temp << std::endl;

/* this streams a formatted output including the parameter name and units */
std::cout << parameters.doubles[ my_index ] << std::endl;

std::cout << parameters.doubles[ my_index ].name << " "
<< parameters.doubles[ my_index ].value << " "
<< parameters.doubles[ my_index ].units << std::endl;


Similarly, we can access string and Boolean parameters. For example:

if( parameters.bools("include_motile_cell") == true )
{ std::cout << "I shall include a motile cell." << std::endl; }

int rand_ind = parameters.ints.find_index( "random_seed" );
std::cout << parameters.ints[rand_ind].name << " is at index " << rand_ind << std::endl;

std::cout << "We'll use this nice color: " << parameters.strings( "motile_color" );


### Using the parameters in custom functions

Let’s use these new parameters when setting up the parameter values of the simulation. For this project, all custom code is in ./custom_modules/custom.cpp. Open that source file in your favorite text editor. Look for the function called “create_cell_types“. In the code snipped below, we access the parameter values to set the appropriate parameters in the default cell definition, rather than hard-coding them.

	// add custom data here, if any

/* for the tutorial */
cell_defaults.phenotype.cycle.data.transition_rate(G0G1_index,S_index) =
parameters.doubles("base_cycle_entry_rate");
cell_defaults.phenotype.death.rates[apoptosis_model_index] =
parameters.doubles("base_apoptosis_rate");



Next, let’s change the tissue setup (“setup_tissue“) to check our Boolean variable before placing the initial motile cell.

     // now create a motile cell
/*  remove this conditional for the normal project */
if( parameters.bools("include_motile_cell") == true )
{
pC = create_cell( motile_cell );
pC->assign_position( 15.0, -18.0, 0.0 );
}


Lastly, let’s make use of the string parameter to change the plotting. Search for my_coloring_function and edit the source file to use the new color:

	// if the cell is motile and not dead, paint it black

static std::string motile_color = parameters.strings( "motile_color" );  // tutorial

if( pCell->phenotype.death.dead == false && pCell->type == 1 )
{
output[0] = motile_color;
output[2] = motile_color;
}


Notice the static here: We intend to call this function many, many times. For performance reasons, we don’t want to declare a string, instantiate it with motile_color, pass it to parameters.strings(), and then deallocate it once done. Instead, we store the search statically within the function, so that all future function calls will have access to that search result.

And that’s it! Compile your code, and give it a go.

make
./project2D


This should create a lot of data in the ./output directory, including SVG files that color motile cells as darkorange, like this one below.

Now that this project is parsing the XML file to get parameter values, we don’t need to recompile to change a model parameter. For example, change motile_color to mediumpurple, set motile_cell_migration_speed to 0.25, and set motile_cell_relative_cycle_entry_rate to 2.0. Rerun the code (without compiling):

./project2D


And let’s look at the change in the final SVG output (output00000120.svg):

### More notes on configuration files

You may notice other sections in the XML configuration file. I encourage you to explore them, but the meanings should be evident: you can set the computational domain size, the number of threads (for OpenMP parallelization), and how frequently (and where) data are stored. In future PhysiCell releases, we will continue adding more and more options to these XML files to simplify setup and configuration of PhysiCell models.

When you’re setting your BioFVM / PhysiCell g++ development environment, you’ll need to add the compiler, MSYS, and your text editor (like Notepad++) to your system path. For example, you may need to add folders like these to your system PATH variable:

1. c:\Program Files\mingw-w64\x86_64-5.3.0-win32-seh-rt_v4_rev0\mingw64\bin\
3. C:\MinGW\msys\1.0\bin\

Here’s how to do that in various versions of Windows.

### Windows XP, 7, and 8

First, open up a text editor, and concatenate your three paths into a single block of text, separated by semicolons (;):

2. Type a semicolon, paste in the first path, and append a semicolon. It should look like this:
;c:\Program Files\mingw-w64\x86_64-5.3.0-win32-seh-rt_v4_rev0\mingw64\bin\;
3. Paste in the next path, and append a semicolon. It should look like this:
;c:\Program Files\mingw-w64\x86_64-5.3.0-win32-seh-rt_v4_rev0\mingw64\bin\;C:\Program Files (x86)\Notepad++\;
4. Paste in the last path, and append a semicolon. It should look something like this:
;c:\Program Files\mingw-w64\x86_64-5.3.0-win32-seh-rt_v4_rev0\mingw64\bin\;C:\Program Files (x86)\Notepad++\;c:\MinGW\msys\1.0\bin\;

Lastly, add these paths to the system path:

1. Go the Start Menu, the right-click “This PC” or “My Computer”, and choose “Properties.”
2. Click on “Advanced system settings”
3. Click on “Environment Variables…” in the “Advanced” tab
4. Scroll through the “System Variables” below until you find Path.
5. Select “Path”, then click “Edit…”
6. At the very end of “Variable Value”, paste what you made in Notepad in the prior steps. Make sure to paste at the end of the existing value, rather than overwriting it!
7. Hit OK, OK, and OK to completely exit the “Advanced system settings.”

### Windows 10:

Windows 10 has made it harder to find these settings, but easier to edit them. First, let’s find the system path:

1. At the “run / search / Cortana” box next to the start menu, type “view advanced”, and you should see “view advanced system settings” auto-complete:
2. Click to enter the advanced system settings, then choose environment variables … at the bottom of this box, and scroll down the list of user variables to Path
3. Click on edit, then click New to add a new path. In the new entry (a new line), paste in your first new path (the compiler):
4. Repeat this for the other two paths, then click OK, OK, Apply, OK to apply the new paths and exit.

## Working with PhysiCell MultiCellDS digital snapshots in Matlab

PhysiCell 1.2.1 and later saves data as a specialized MultiCellDS digital snapshot, which includes chemical substrate fields, mesh information, and a readout of the cells and their phenotypes at single simulation time point. This tutorial will help you learn to use the matlab processing files included with PhysiCell.

This tutorial assumes you know (1) how to work at the shell / command line of your operating system, and (2) basic plotting and other functions in Matlab.

### Key elements of a PhysiCell digital snapshot

A PhysiCell digital snapshot (a customized form of the MultiCellDS digital simulation snapshot) includes the following elements saved as XML and MAT files:

1. output12345678.xml : This is the “base” output file, in MultiCellDS format. It includes key metadata such as when the file was created, the software, microenvironment information, and custom data saved at the simulation time. The Matlab files read this base file to find other related files (listed next). Example: output00003696.xml
2. initial_mesh0.mat : This is the computational mesh information for BioFVM at time 0.0. Because BioFVM and PhysiCell do not use moving meshes, we do not save this data at any subsequent time.
3. output12345678_microenvironment0.mat : This saves each biochemical substrate in the microenvironment at the computational voxels defined in the mesh (see above). Example: output00003696_microenvironment0.mat
4. output12345678_cells.mat : This saves very basic cellular information related to BioFVM, including cell positions, volumes, secretion rates, uptake rates, and secretion saturation densities. Example: output00003696_cells.mat
5. output12345678_cells_physicell.mat : This saves extra PhysiCell data for each cell agent, including volume information, cell cycle status, motility information, cell death information, basic mechanics, and any user-defined custom data. Example: output00003696_cells_physicell.mat

These snapshots make extensive use of Matlab Level 4 .mat files, for fast, compact, and well-supported saving of array data. Note that even if you cannot ready MultiCellDS XML files, you can work to parse the .mat files themselves.

### The PhysiCell Matlab .m files

Every PhysiCell distribution includes some matlab functions to work with PhysiCell digital simulation snapshots, stored in the matlab subdirectory. The main ones are:

1. composite_cutaway_plot.m : provides a quick, coarse 3-D cutaway plot of the discrete cells, with different colors for live (red), apoptotic (b), and necrotic (black) cells.
2. read_MultiCellDS_xml.m : reads the “base” PhysiCell snapshot and its associated matlab files.
3. set_MCDS_constants.m : creates a data structure MCDS_constants that has the same constants as PhysiCell_constants.h. This is useful for identifying cell cycle phases, etc.
4. simple_cutaway_plot.m : provides a quick, coarse 3-D cutaway plot of user-specified cells.
5. simple_plot.m : provides, a quick, coarse 3-D plot of the user-specified cells, without a cutaway or cross-sectional clipping plane.

#### A note on GNU Octave

Unfortunately, GNU octave does not include XML file parsing without some significant user tinkering. And one you’re done, it is approximately one order of magnitude slower than Matlab. Octave users can directly import the .mat files described above, but without the helpful metadata in the XML file. We’ll provide more information on the structure of these MAT files in a future blog post. Moreover, we plan to provide python and other tools for users without access to Matlab.

### A sample digital snapshot

We provide a 3-D simulation snapshot from the final simulation time of the cancer-immune example in Ghaffarizadeh et al. (2017, in review) at:

The corresponding SVG cross-section for that time (through = 0 μm) looks like this:

Unzip the sample dataset in any directory, and make sure the matlab files above are in the same directory (or in your Matlab path). If you’re inside matlab:

!unzip 3D_PhysiCell_matlab_sample.zip


MCDS = read_MultiCellDS_xml( 'output00003696.xml');


This will load the mesh, substrates, and discrete cells into the MCDS data structure, and give a basic summary:

Typing ‘MCDS’ and then hitting ‘tab’ (for auto-completion) shows the overall structure of MCDS, stored as metadata, mesh, continuum variables, and discrete cells:

To get simulation metadata, such as the current simulation time, look at MCDS.metadata.current_time

Here, we see that the current simulation time is 30240 minutes, or 21 days. MCDS.metadata.current_runtime gives the elapsed walltime to up to this point: about 53 hours (1.9e5 seconds), including file I/O time to write full simulation data once per 3 simulated minutes after the start of the adaptive immune response.

### Plotting chemical substrates

Let’s make an oxygen contour plot through z = 0 μm. First, we find the index corresponding to this z-value:

k = find( MCDS.mesh.Z_coordinates == 0 );


Next, let’s figure out which variable is oxygen. Type “MCDS.continuum_variables.name”, which will show the array of variable names:

Here, oxygen is the first variable, (index 1). So, to make a filled contour plot:

contourf( MCDS.mesh.X(:,:,k), MCDS.mesh.Y(:,:,k), ...
MCDS.continuum_variables(1).data(:,:,k) , 20 ) ;


Now, let’s set this to a correct aspect ratio (no stretching in x or y), add a colorbar, and set the axis labels, using

axis image
colorbar
xlabel( sprintf( 'x (%s)' , MCDS.metadata.spatial_units) );
ylabel( sprintf( 'y (%s)' , MCDS.metadata.spatial_units) );


Lastly, let’s add an appropriate (time-based) title:

title( sprintf('%s (%s) at t = %3.2f %s, z = %3.2f %s', MCDS.continuum_variables(1).name , ...
MCDS.continuum_variables(1).units , ...
MCDS.mesh.Z_coordinates(k), ...


Here’s the end result:

We can easily export graphics, such as to PNG format:

print( '-dpng' , 'output_o2.png' );


For more on plotting BioFVM data, see the tutorial
at http://www.mathcancer.org/blog/saving-multicellds-data-from-biofvm/

### Plotting cells in space

#### 3-D point cloud

First, let’s plot all the cells in 3D:

plot3( MCDS.discrete_cells.state.position(:,1) , MCDS.discrete_cells.state.position(:,2), ...
MCDS.discrete_cells.state.position(:,3) , 'bo' );


At first glance, this does not look good: some cells are far out of the simulation domain, distorting the automatic range of the plot:

This does not ordinarily happen in PhysiCell (the default cell mechanics functions have checks to prevent such behavior), but this example includes a simple Hookean elastic adhesion model for immune cell attachment to tumor cells. In rare circumstances, an attached tumor cell or immune cell can apoptose on its own (due to its background apoptosis rate),
without “knowing” to detach itself from the surviving cell in the pair. The remaining cell attempts to calculate its elastic velocity based upon an invalid cell position (no longer in memory), creating an artificially large velocity that “flings” it out of the simulation domain. Such cells  are not simulated any further, so this is effectively equivalent to an extra apoptosis event (only 3 cells are out of the simulation domain after tens of millions of cell-cell elastic adhesion calculations). Future versions of this example will include extra checks to prevent this rare behavior.

The plot can simply be fixed by changing the axis:

axis( 1000*[-1 1 -1 1 -1 1] )
axis square


Notice that this is a very difficult plot to read, and very non-interactive (laggy) to rotation and scaling operations. We can make a slightly nicer plot by searching for different cell types and plotting them with different colors:

% make it easier to work with the cell positions;
P = MCDS.discrete_cells.state.position;

% find type 1 cells
ind1 = find( MCDS.discrete_cells.metadata.type == 1 );
% better still, eliminate those out of the simulation domain
ind1 = find( MCDS.discrete_cells.metadata.type == 1 & ...
abs(P(:,1))' < 1000 & abs(P(:,2))' < 1000 & abs(P(:,3))' < 1000 );

% find type 0 cells
ind0 = find( MCDS.discrete_cells.metadata.type == 0 & ...
abs(P(:,1))' < 1000 & abs(P(:,2))' < 1000 & abs(P(:,3))' < 1000 );

%now plot them
P = MCDS.discrete_cells.state.position;
plot3( P(ind0,1), P(ind0,2), P(ind0,3), 'bo' )
hold on
plot3( P(ind1,1), P(ind1,2), P(ind1,3), 'ro' )
hold off
axis( 1000*[-1 1 -1 1 -1 1] )
axis square


However, this isn’t much better. You can use the scatter3 function to gain more control on the size and color of the plotted cells, or even make macros to plot spheres in the cell locations (with shading and lighting), but Matlab is very slow when plotting beyond 103 cells. Instead, we recommend the faster preview functions below for data exploration, and higher-quality plotting (e.g., by POV-ray) for final publication-

#### Fast 3-D cell data previewers

Notice that plot3 and scatter3 are painfully slow for any nontrivial number of cells. We can use a few fast previewers to quickly get a sense of the data. First, let’s plot all the dead cells, and make them red:

clf
simple_plot( MCDS,  MCDS, MCDS.discrete_cells.dead_cells , 'r' )


This function creates a coarse-grained 3-D indicator function (0 if no cells are present; 1 if they are), and plots a 3-D level surface. It is very responsive to rotations and other operations to explore the data. You may notice the second argument is a list of indices: only these cells are plotted. This gives you a method to select cells with specific characteristics when plotting. (More on that below.) If you want to get a sense of the interior structure, use a cutaway plot:

clf
simple_cutaway_plot( MCDS, MCDS, MCDS.discrete_cells.dead_cells , 'r' )


We also provide a fast “composite” cutaway which plots all live cells as red, apoptotic cells as blue (without the cutaway), and all necrotic cells as black:

clf
composite_cutaway_plot( MCDS )


Lastly, we show an improved plot that uses different colors for the immune cells, and Matlab’s “find” function to help set up the indexing:

constants = set_MCDS_constants

% find the type 0 necrotic cells
ind0_necrotic = find( MCDS.discrete_cells.metadata.type == 0 & ...
(MCDS.discrete_cells.phenotype.cycle.current_phase == constants.necrotic_swelling | ...
MCDS.discrete_cells.phenotype.cycle.current_phase == constants.necrotic_lysed | ...
MCDS.discrete_cells.phenotype.cycle.current_phase == constants.necrotic) );

% find the live type 0 cells
ind0_live = find( MCDS.discrete_cells.metadata.type == 0 & ...
(MCDS.discrete_cells.phenotype.cycle.current_phase ~= constants.necrotic_swelling & ...
MCDS.discrete_cells.phenotype.cycle.current_phase ~= constants.necrotic_lysed & ...
MCDS.discrete_cells.phenotype.cycle.current_phase ~= constants.necrotic & ...
MCDS.discrete_cells.phenotype.cycle.current_phase ~= constants.apoptotic) );

clf
% plot live tumor cells red, in cutaway view
simple_cutaway_plot( MCDS, ind0_live , 'r' );
hold on
% plot dead tumor cells black, in cutaway view
simple_cutaway_plot( MCDS, ind0_necrotic , 'k' )
% plot all immune cells, but without cutaway (to show how they infiltrate)
simple_plot( MCDS, ind1, 'g' )
hold off


### A small cautionary note on future compatibility

PhysiCell 1.2.1 uses the <custom> data tag (allowed as part of the MultiCellDS specification) to encode its cell data, to allow a more compact data representation, because the current PhysiCell daft does not support such a formulation, and Matlab is painfully slow at parsing XML files larger than ~50 MB. Thus, PhysiCell snapshots are not yet fully compatible with general MultiCellDS tools, which would by default ignore custom data. In the future, we will make available converter utilities to transform “native” custom PhysiCell snapshots to MultiCellDS snapshots that encode all the cellular information in a more verbose but compatible XML format.

### Closing words and future work

Because Octave is not a great option for parsing XML files (with critical MultiCellDS metadata), we plan to write similar functions to read and plot PhysiCell snapshots in Python, as an open source alternative. Moreover, our lab in the next year will focus on creating further MultiCellDS configuration, analysis, and visualization routines. We also plan to provide additional 3-D functions for plotting the discrete cells and varying color with their properties.

In the longer term, we will develop open source, stand-alone analysis and visualization tools for MultiCellDS snapshots (including PhysiCell snapshots). Please stay tuned!

Tags :

## Coarse-graining discrete cell cycle models

### Introduction

One observation that often goes underappreciated in computational biology discussions is that a computational model is often a model of a model of a model of biology: that is, it’s a numerical approximation (a model) of a mathematical model of an experimental model of a real-life biological system. Thus, there are three big places where a computational investigation can fall flat:

1. The experimental model may be a bad choice for the disease or process (not our fault).
2. Second, the mathematical model of the experimental system may have flawed assumptions (something we have to evaluate).
3. The numerical implementation may have bugs or otherwise be mathematically inconsistent with the mathematical model.

Critically, you can’t use simulations to evaluate the experimental model or the mathematical model until you verify that the numerical implementation is consistent with the mathematical model, and that the numerical solution converges as $$\Delta t$$ and $$\Delta x$$ shrink to zero.

There are numerous ways to accomplish this, but ideally, it boils down to having some analytical solutions to the mathematical model, and comparing numerical solutions to these analytical or theoretical results. In this post, we’re going to walk through the math of analyzing a typical type of discrete cell cycle model.

### Discrete model

Suppose we have a cell cycle model consisting of phases $$P_1, P_2, \ldots P_n$$, where cells in the $$P_i$$ phase progress to the $$P_{i+1}$$ phase after a mean waiting time of $$T_i$$, and cells leaving the $$P_n$$ phase divide into two cells in the $$P_1$$ phase. Assign each cell agent $$k$$ a current phenotypic phase $$S_k(t)$$. Suppose also that each phase $$i$$ has a death rate $$d_i$$, and that cells persist for on average $$T_\mathrm{A}$$ time in the dead state before they are removed from the simulation.

The mean waiting times $$T_i$$ are equivalent to transition rates $$r_i = 1 / T_i$$ (Macklin et al. 2012). Moreover, for any time interval $$[t,t+\Delta t]$$, both are equivalent to a transition probability of
$\mathrm{Prob}\Bigl( S_k(t+\Delta t) = P_{i+1} | S(t) = P_i \Bigr) = 1 – e^{ -r_i \Delta t } \approx r_i \Delta t = \frac{ \Delta t}{ T_i}.$ In many discrete models (especially cellular automaton models) with fixed step sizes $$\Delta t$$, models are stated in terms of transition probabilities $$p_{i,i+1}$$, which we see are equivalent to the work above with $$p_{i,i+1} = r_i \Delta t = \Delta t / T_i$$, allowing us to tie mathematical model forms to biological, measurable parameters. We note that each $$T_i$$ is the average duration of the $$P_i$$ phase.

#### Concrete example: a Ki67 Model

Ki-67 is a nuclear protein that is expressed through much of the cell cycle, including S, G2, M, and part of G1 after division. It is used very commonly in pathology to assess proliferation, particularly in cancer. See the references and discussion in (Macklin et al. 2012). In Macklin et al. (2012), we came up with a discrete cell cycle model to match Ki-67 data (along with cleaved Caspase-3 stains for apoptotic cells). Let’s summarize the key parts here.

Each cell agent $$i$$ has a phase $$S_i(t)$$. Ki67- cells are quiescent (phase $$Q$$, mean duration $$T_\mathrm{Q}$$), and they can enter the Ki67+ $$K_1$$ phase (mean duration $$T_1$$). When $$K_1$$ cells leave their phase, they divide into two Ki67+ daughter cells in the $$K_2$$ phase with mean duration $$T_2$$. When cells exit $$K_2$$, they return to $$Q$$. Cells in any phase can become apoptotic (enter the $$A$$ phase with mean duration $$T_\mathrm{A}$$), with death rate $$r_\mathrm{A}$$.

### Coarse-graining to an ODE model

If each phase $$i$$ has a death rate $$d_i$$, if $$N_i(t)$$ denotes the number of cells in the $$P_i$$ phase at time $$t$$, and if $$A(t)$$ is the number of dead (apoptotic) cells at time $$t$$, then on average, the number of cells in the $$P_i$$ phase at the next time step is given by
$N_i(t+\Delta t) = N_i(t) + N_{i-1}(t) \cdot \left[ \textrm{prob. of } P_{i-1} \rightarrow P_i \textrm{ transition} \right] – N_i(t) \cdot \left[ \textrm{prob. of } P_{i} \rightarrow P_{i+1} \textrm{ transition} \right]$ $– N_i(t) \cdot \left[ \textrm{probability of death} \right]$ By the work above, this is:
$N_i(t+\Delta t) \approx N_i(t) + N_{i-1}(t) r_{i-1} \Delta t – N_i(t) r_i \Delta t – N_i(t) d_i \Delta t ,$ or after shuffling terms and taking the limit as $$\Delta t \downarrow 0$$, $\frac{d}{dt} N_i(t) = r_{i-1} N_{i-1}(t) – \left( r_i + d_i \right) N_i(t).$ Continuing this analysis, we obtain a linear system:
$\frac{d}{dt}{ \vec{N} } = \begin{bmatrix} -(r_1+d_1) & 0 & \cdots & 0 & 2r_n & 0 \\ r_1 & -(r_2+d_2) & 0 & \cdots & 0 & 0 \\ 0 & r_2 & -(r_3+d_3) & 0 & \cdots & 0 \\ & & \ddots & & \\0&\cdots&0 &r_{n-1} & -(r_n+d_n) & 0 \\ d_1 & d_2 & \cdots & d_{n-1} & d_n & -\frac{1}{T_\mathrm{A}} \end{bmatrix}\vec{N} = M \vec{N},$ where $$\vec{N}(t) = [ N_1(t), N_2(t) , \ldots , N_n(t) , A(t) ]$$.

For the Ki67 model above, let $$\vec{N} = [K_1, K_2, Q, A]$$. Then the linear system is
$\frac{d}{dt} \vec{N} = \begin{bmatrix} -\left( \frac{1}{T_1} + r_\mathrm{A} \right) & 0 & \frac{1}{T_\mathrm{Q}} & 0 \\ \frac{2}{T_1} & -\left( \frac{1}{T_2} + r_\mathrm{A} \right) & 0 & 0 \\ 0 & \frac{1}{T_2} & -\left( \frac{1}{T_\mathrm{Q}} + r_\mathrm{A} \right) & 0 \\ r_\mathrm{A} & r_\mathrm{A} & r_\mathrm{A} & -\frac{1}{T_\mathrm{A}} \end{bmatrix} \vec{N} .$
(If we had written $$\vec{N} = [Q, K_1, K_2 , A]$$, then the matrix above would have matched the general form.)

### Some theoretical results

If $$M$$ has eigenvalues $$\lambda_1 , \ldots \lambda_{n+1}$$ and corresponding eigenvectors $$\vec{v}_1, \ldots , \vec{v}_{n+1}$$, then the general solution is given by
$\vec{N}(t) = \sum_{i=1}^{n+1} c_i e^{ \lambda_i t } \vec{v}_i ,$ and if the initial cell counts are given by $$\vec{N}(0)$$ and we write $$\vec{c} = [c_1, \ldots c_{n+1} ]$$, we can obtain the coefficients by solving $\vec{N}(0) = [ \vec{v}_1 | \cdots | \vec{v}_{n+1} ]\vec{c} .$ In many cases, it turns out that all but one of the eigenvalues (say $$\lambda$$ with corresponding eigenvector $$\vec{v}$$) are negative. In this case, all the other components of the solution decay away, and for long times, we have $\vec{N}(t) \approx c e^{ \lambda t } \vec{v} .$ This is incredibly useful, because it says that over long times, the fraction of cells in the $$i^\textrm{th}$$ phase is given by $v_{i} / \sum_{j=1}^{n+1} v_{j}.$

#### Matlab implementation (with the Ki67 model)

First, let’s set some parameters, to make this a little easier and reusable.

parameters.dt = 0.1; % 6 min = 0.1 hours
parameters.time_units = 'hour';
parameters.t_max = 3*24; % 3 days

parameters.K1.duration =  13;
parameters.K1.death_rate = 1.05e-3;
parameters.K1.initial = 0;

parameters.K2.duration = 2.5;
parameters.K2.death_rate = 1.05e-3;
parameters.K2.initial = 0;

parameters.Q.duration = 74.35 ;
parameters.Q.death_rate = 1.05e-3;
parameters.Q.initial = 1000;

parameters.A.duration = 8.6;
parameters.A.initial = 0;


Next, we write a function to read in the parameter values, construct the matrix (and all the data structures), find eigenvalues and eigenvectors, and create the theoretical solution. It also finds the positive eigenvalue to determine the long-time values.

function solution = Ki67_exact( parameters )

% allocate memory for the main outputs

solution.T = 0:parameters.dt:parameters.t_max;
solution.K1 = zeros( 1 , length(solution.T));
solution.K2 = zeros( 1 , length(solution.T));
solution.K = zeros( 1 , length(solution.T));
solution.Q = zeros( 1 , length(solution.T));
solution.A = zeros( 1 , length(solution.T));
solution.Live = zeros( 1 , length(solution.T));
solution.Total = zeros( 1 , length(solution.T));

% allocate memory for cell fractions

solution.AI = zeros(1,length(solution.T));
solution.KI1 = zeros(1,length(solution.T));
solution.KI2 = zeros(1,length(solution.T));
solution.KI = zeros(1,length(solution.T));

% get the main parameters

T1 = parameters.K1.duration;
r1A = parameters.K1.death_rate;

T2 = parameters.K2.duration;
r2A = parameters.K2.death_rate;

TQ = parameters.Q.duration;
rQA = parameters.Q.death_rate;

TA = parameters.A.duration;

% write out the mathematical model:
% d[Populations]/dt = Operator*[Populations]

Operator = [ -(1/T1 +r1A) , 0 , 1/TQ , 0; ...
2/T1 , -(1/T2 + r2A) ,0 , 0; ...
0 , 1/T2 , -(1/TQ + rQA) , 0; ...
r1A , r2A, rQA , -1/TA ];

% eigenvectors and eigenvalues

[V,D] = eig(Operator);
eigenvalues = diag(D);

% save the eigenvectors and eigenvalues in case you want them.

solution.V = V;
solution.D = D;
solution.eigenvalues = eigenvalues;

% initial condition

VecNow = [ parameters.K1.initial ; parameters.K2.initial ; ...
parameters.Q.initial ; parameters.A.initial ] ;
solution.K1(1) = VecNow(1);
solution.K2(1) = VecNow(2);
solution.Q(1) = VecNow(3);
solution.A(1) = VecNow(4);
solution.K(1) = solution.K1(1) + solution.K2(1);
solution.Live(1) = sum( VecNow(1:3) );
solution.Total(1) = sum( VecNow(1:4) );

solution.AI(1) = solution.A(1) / solution.Total(1);
solution.KI1(1) = solution.K1(1) / solution.Total(1);
solution.KI2(1) = solution.K2(1) / solution.Total(1);
solution.KI(1) = solution.KI1(1) + solution.KI2(1);

% now, get the coefficients to write the analytic solution
% [Populations] = c1*V(:,1)*exp( d(1,1)*t) + c2*V(:,2)*exp( d(2,2)*t ) +
%                 c3*V(:,3)*exp( d(3,3)*t) + c4*V(:,4)*exp( d(4,4)*t );

coeff = linsolve( V , VecNow );

% find the (hopefully one) positive eigenvalue.
% eigensolutions with negative eigenvalues decay,
% leaving this as the long-time behavior.

eigenvalues = diag(D);
n = find( real( eigenvalues ) &amp;gt; 0 )
solution.long_time.KI1 = V(1,n) / sum( V(:,n) );
solution.long_time.KI2 = V(2,n) / sum( V(:,n) );
solution.long_time.QI = V(3,n) / sum( V(:,n) );
solution.long_time.AI = V(4,n) / sum( V(:,n) ) ;
solution.long_time.KI = solution.long_time.KI1 + solution.long_time.KI2;

% now, write out the solution at all the times
for i=2:length( solution.T )
% compact way to write the solution
VecExact = real( V*( coeff .* exp( eigenvalues*solution.T(i) ) ) );

solution.K1(i) = VecExact(1);
solution.K2(i) = VecExact(2);
solution.Q(i) = VecExact(3);
solution.A(i) = VecExact(4);
solution.K(i) = solution.K1(i) + solution.K2(i);
solution.Live(i) = sum( VecExact(1:3) );
solution.Total(i) = sum( VecExact(1:4) );

solution.AI(i) = solution.A(i) / solution.Total(i);
solution.KI1(i) = solution.K1(i) / solution.Total(i);
solution.KI2(i) = solution.K2(i) / solution.Total(i);
solution.KI(i) = solution.KI1(i) + solution.KI2(i);
end

return;


Now, let’s run it and see what this thing looks like:

Next, we plot KI1, KI2, and AI versus time (solid curves), along with the theoretical long-time behavior (dashed curves). Notice how well it matches–it’s neat when theory works! 🙂

Some readers may recognize the long-time fractions: KI1 + KI2 = KI = 0.1743, and AI = 0.00833, very close to the DCIS patient values from our simulation study in Macklin et al. (2012) and improved calibration work in Hyun and Macklin (2013).

### Comparing simulations and theory

I wrote a small Matlab program to implement the discrete model: start with 1000 cells in the $$Q$$ phase, and in each time interval $$[t,t+\Delta t]$$, each cell “decides” whether to advance to the next phase, stay in the same phase, or apoptose. If we compare a single run against the theoretical curves, we see hints of a match:

If we average 10 simulations and compare, the match is better:

And lastly, if we average 100 simulations and compare, the curves are very difficult to tell apart:

Even in logarithmic space, it’s tough to tell these apart:

### Code

The following matlab files (available here) can be used to reproduce this post:

Ki67_exact.m
The function defined above to create the exact solution using the eigenvalue/eignvector approach.
Ki67_stochastic.m
Runs a single stochastic simulation, using the supplied parameters.
script.m
Runs the theoretical solution first, creates plots, and then runs the stochastic model 100 times for comparison.

To make it all work, simply run “script” at the command prompt. Please note that it will generate some png files in its directory.

### Closing thoughts

In this post, we showed a nice way to check a discrete model against theoretical behavior–both in short-term dynamics and long-time behavior. The same work should apply to validating many discrete models. However, when you add spatial effects (e.g., a cellular automaton model that won’t proliferate without an empty neighbor site), I wouldn’t expect a match. (But simulating cells that initially have a “salt and pepper”, random distribution should match this for early times.)

Moreover, models with deterministic phase durations (e.g., K1, K2, and A have fixed durations) aren’t consistent with the ODE model above, unless the cells they are each initialized with a random amount of “progress” in their initial phases. (Otherwise, the cells in each phase will run synchronized, and there will be fixed delays before cells transition to other phases.) Delay differential equations better describe such models. However, for long simulation times, the slopes of the sub-populations and the cell fractions should start to better and better match the ODE models.

Now that we have verified that the discrete model is performing as expected, we can have greater confidence in its predictions, and start using those predictions to assess the underlying models. In ODE and PDE models, you often validate the code on simpler problems where you have an analytical solution, and then move on to making simulation predictions in cases where you can’t solve analytically. Similarly, we can now move on to variants of the discrete model where we can’t as easily match ODE theory (e.g., time-varying rate parameters, spatial effects), but with the confidence that the phase transitions are working as they should.

## BioFVM warmup: 2D continuum simulation of tumor growth

Note: This is part of a series of “how-to” blog posts to help new users and developers of BioFVM. See also the guides to setting up a C++ compiler in Windows or OSX.

### What you’ll need

1. A working C++ development environment with support for OpenMP. See these prior tutorials if you need help.
2. A download of BioFVM, available at http://BioFVM.MathCancer.org and http://BioFVM.sf.net. Use Version 1.0.3 or later.
3. Matlab or Octave for visualization. Matlab might be available for free at your university. Octave is open source and available from a variety of sources.

We will implement a basic 2-D model of tumor growth in a heterogeneous microenvironment, with inspiration by glioblastoma models by Kristin Swanson, Russell Rockne and others (e.g., this work), and continuum tumor growth models by Hermann Frieboes, John Lowengrub, and our own lab (e.g., this paper and this paper).

We will model tumor growth driven by a growth substrate, where cells die when the growth substrate is insufficient. The tumor cells will have motility. A continuum blood vasculature will supply the growth substrate, but tumor cells can degrade this existing vasculature. We will revisit and extend this model from time to time in future tutorials.

### Mathematical model

Taking inspiration from the groups mentioned above, we’ll model a live cell density ρ of a relatively low-adhesion tumor cell species (e.g., glioblastoma multiforme). We’ll assume that tumor cells move randomly towards regions of low cell density (modeled as diffusion with motility μ). We’ll assume that that the net birth rate rB is proportional to the concentration of growth substrate σ, which is released by blood vasculature with density b. Tumor cells can degrade the tissue and hence this existing vasculature. Tumor cells die at rate rD when the growth substrate level is too low. We assume that the tumor cell density cannot exceed a max level ρmax. A model that includes these effects is:

$\frac{ \partial \rho}{\partial t} = \mu \nabla^2 \rho + r_B(\sigma)\rho \left( 1 – \frac{ \rho}{\rho_\textrm{max}} \right) – r_D(\sigma) \rho$

$\frac{ \partial b}{\partial t} = – r_\textrm{degrade} \rho b$

$\frac{\partial \sigma}{ \partial t} = D\nabla^2 \sigma – \lambda_a \sigma – \lambda_2 \rho \sigma + r_\textrm{deliv}b \left( \sigma_\textrm{max} – \sigma \right)$
where for the birth and death rates, we’ll use the constitutive relations:
$r_B(\sigma) = r_B \textrm{ max} \left( \frac{\sigma – \sigma_\textrm{min}}{ \sigma_\textrm{ max} – \sigma_\textrm{min} } , 0 \right)$
$r_D(\sigma) = r_D \textrm{ max} \left( \frac{ \sigma_\textrm{min} – \sigma}{\sigma_\textrm{min}} , 0 \right)$

### Mapping the model onto BioFVM

BioFVM solves on a vector u of substrates. We’ll set u = [ρ , b, σ ]. The code expects PDEs of the general form:

$\frac{\partial q}{\partial t} = D\nabla^2 q – \lambda q + S\left( q^* – q \right) – Uq$
So, we determine the decay rate (λ), source function (S), and uptake function (U) for the cell density ρ and the growth substrate σ.

#### Cell density

We first slightly rewrite the PDE:

$\frac{ \partial \rho}{\partial t} = \mu \nabla^2 \rho + r_B(\sigma) \frac{ \rho}{\rho_\textrm{max}} \left( \rho_\textrm{max} – \rho \right) – r_D(\sigma)\rho$
and then try to match to the general form term-by-term. While BioFVM wasn’t intended for solving nonlinear PDEs of this form, we can make it work by quasi-linearizing, with the following functions:
$S = r_B(\sigma) \frac{ \rho }{\rho_\textrm{max}} \hspace{1in} U = r_D(\sigma).$

When implementing this, we’ll evaluate σ and ρ at the previous time step. The diffusion coefficient is μ, and the decay rate is zero. The target or saturation density is ρmax.

#### Growth substrate

Similarly, by matching the PDE for σ term-by-term with the general form, we use:

$S = r_\textrm{deliv}b, \hspace{1in} U = \lambda_2 \rho.$

The diffusion coefficient is D, the decay rate is λ1, and the saturation density is σmax.

#### Blood vessels

Lastly, a term-by-term matching of the blood vessel equation gives the following functions:

$S=0 \hspace{1in} U = r_\textrm{degrade}\rho.$
The diffusion coefficient, decay rate, and saturation density are all zero.

### Implementation in BioFVM

1. Start a project: Create a new directory for your project (I’d recommend “BioFVM_2D_tumor”), and enter the directory. Place a copy of BioFVM (the zip file) into your directory. Unzip BioFVM, and copy BioFVM*.h, BioFVM*.cpp, and pugixml* files into that directory.
2. Copy the matlab visualization files: To help read and plot BioFVM data, we have provided matlab files. Copy all the *.m files from the matlab subdirectory to your project.
3. Copy the empty project: BioFVM Version 1.0.3 or later includes a template project and Makefile to make it easier to get started. Copy the Makefile and template_project.cpp file to your project. Rename template_project.cpp to something useful, like 2D_tumor_example.cpp.
4. Edit the makefile: Open a terminal window and browse to your project. Tailor the makefile to your new project:
notepad++ Makefile

Change the PROGRAM_NAME to 2Dtumor.

Also, rename main to 2D_tumor_example throughout the Makefile.

Lastly, note that if you are using OSX, you’ll probably need to change from “g++” to your installed compiler. See these tutorials.

5. Start adapting 2D_tumor_example.cpp: First, open 2D_tumor_example.cpp:
notepad++ 2D_tumor_example.cpp

Just after the “using namespace BioFVM” section of the code, define useful globals. Here and throughout, new and/or modified code is in blue:

using namespace BioFVM:

// helpful -- have indices for each "species"
int live_cells  = 0;
int blood_vessels = 1;
int oxygen    = 2;

// some globals
double prolif_rate = 1.0 /24.0;
double death_rate = 1.0 / 6; //
double cell_motility = 50.0 / 365.25 / 24.0 ;
// 50 mm^2 / year --> mm^2 / hour
double o2_uptake_rate = 3.673 * 60.0; // 165 micron length scale
double vessel_degradation_rate = 1.0 / 2.0 / 24.0 ;
// 2 days to disrupt tissue

double max_cell_density = 1.0;

double o2_supply_rate = 10.0;
double o2_normoxic  = 1.0;
double o2_hypoxic   = 0.2;

6. Set up the microenvironment: Within main(), make sure we have the right number of substrates, and set them up:
// create a microenvironment, and set units

Microenvironment M;
M.name = "Tumor microenvironment";
M.time_units = "hr";
M.spatial_units = "mm";
M.mesh.units = M.spatial_units;

// set up and add all the densities you plan

M.set_density( 0 , "live cells" , "cells" );
M.add_density( "blood vessels" , "vessels/mm^2" );

// set the properties of the diffusing substrates

M.diffusion_coefficients[live_cells] = cell_motility;

M.diffusion_coefficients[blood_vessels] = 0;
M.diffusion_coefficients[oxygen] = 6.0;

// 1e5 microns^2/min in units mm^2 / hr

M.decay_rates[live_cells] = 0;
M.decay_rates[blood_vessels] = 0;
M.decay_rates[oxygen] = 0.01 * o2_uptake_rate;
// 1650 micron length scale


Notice how our earlier global definitions of “live_cells”, “blood_vessels”, and “oxygen” makes it easier to make sure we’re referencing the correct substrates in lines like these.

7. Resize the domain and test: For this example (and so the code runs very quickly), we’ll work in 2D in a 2 cm × 2 cm domain:
// set the mesh size

double dx = 0.05; // 50 microns
M.resize_space( 0.0 , 20.0 , 0, 20.0 , -dx/2.0, dx/2.0 , dx, dx, dx );


Notice that we use a tissue thickness of dx/2 to use the 3D code for a 2D simulation. Now, let’s test:

make
2Dtumor


Go ahead and cancel the simulation [Control]+C after a few seconds. You should see something like this:

Starting program ...

Microenvironment summary: Tumor microenvironment:

Mesh information:
type: uniform Cartesian
Domain: [0,20] mm x [0,20] mm x [-0.025,0.025] mm
resolution: dx = 0.05 mm
voxels: 160000
voxel faces: 0
volume: 20 cubic mm
Densities: (3 total)
live cells:
units: cells
diffusion coefficient: 0.00570386 mm^2 / hr
decay rate: 0 hr^-1
diffusion length scale: 75523.9 mm

blood vessels:
units: vessels/mm^2
diffusion coefficient: 0 mm^2 / hr
decay rate: 0 hr^-1
diffusion length scale: 0 mm

oxygen:
units: cells
diffusion coefficient: 6 mm^2 / hr
decay rate: 2.2038 hr^-1
diffusion length scale: 1.65002 mm

simulation time: 0 hr (100 hr max)

Using method diffusion_decay_solver__constant_coefficients_LOD_3D (implicit 3-D LOD with Thomas Algorithm) ...

simulation time: 10 hr (100 hr max)
simulation time: 20 hr (100 hr max)

8. Set up initial conditions: We’re going to make a small central focus of tumor cells, and a “bumpy” field of blood vessels.
// set initial conditions
// use this syntax to create a zero vector of length 3
// std::vector<double> zero(3,0.0);

std::vector<double> center(3);
center[0] = M.mesh.x_coordinates[M.mesh.x_coordinates.size()-1] /2.0;
center[1] = M.mesh.y_coordinates[M.mesh.y_coordinates.size()-1] /2.0;
center[2] = 0;

std::vector<double> one( M.density_vector(0).size() , 1.0 );

double pi = 2.0 * asin( 1.0 );

// use this syntax for a parallelized loop over all the
#pragma omp parallel for
for( int i=0; i < M.number_of_voxels() ; i++ )
{
std::vector<double> displacement = M.voxels(i).center – center;
double distance = norm( displacement );

{
M.density_vector(i)[live_cells] = 0.1;
}
M.density_vector(i)[blood_vessels]= 0.5
+ 0.5*cos(0.4* pi * M.voxels(i).center[0])*cos(0.3*pi *M.voxels(i).center[1]);
M.density_vector(i)[oxygen] = o2_normoxic;
}

9. Change to a 2D diffusion solver:
// set up the diffusion solver, sources and sinks

M.diffusion_decay_solver = diffusion_decay_solver__constant_coefficients_LOD_2D;

10. Set the simulation times: We’ll simulate 10 days, with output every 12 hours.
double t = 0.0;
double t_max = 10.0 * 24.0; // 10 days
double dt = 0.1;

double output_interval = 12.0; // how often you save data
double next_output_time = t; // next time you save data

11. Set up the source function:
void supply_function( Microenvironment* microenvironment, int voxel_index, std::vector<double>* write_here )
{
// use this syntax to access the jth substrate write_here
// (*write_here)[j]
// use this syntax to access the jth substrate in voxel voxel_index of microenvironment:
// microenvironment->density_vector(voxel_index)[j]

static double temp1 = prolif_rate / ( o2_normoxic – o2_hypoxic );

(*write_here)[live_cells] =
microenvironment->density_vector(voxel_index)[oxygen];
(*write_here)[live_cells] -= o2_hypoxic;

if( (*write_here)[live_cells] < 0.0 )
{
(*write_here)[live_cells] = 0.0;
}
else
{
(*write_here)[live_cells] = temp1;
(*write_here)[live_cells] *=
microenvironment->density_vector(voxel_index)[live_cells];
}

(*write_here)[blood_vessels] = 0.0;
(*write_here)[oxygen] = o2_supply_rate;
(*write_here)[oxygen] *=
microenvironment->density_vector(voxel_index)[blood_vessels];

return;
}


Notice the use of the static internal variable temp1: the first time this function is called, it declares this helper variable (to save some multiplication operations down the road). The static variable is available to all subsequent calls of this function.

12. Set up the target function (substrate saturation densities):
void supply_target_function( Microenvironment* microenvironment, int voxel_index, std::vector<double>* write_here )
{
// use this syntax to access the jth substrate write_here
// (*write_here)[j]
// use this syntax to access the jth substrate in voxel voxel_index of microenvironment:
// microenvironment->density_vector(voxel_index)[j]

(*write_here)[live_cells] = max_cell_density;
(*write_here)[blood_vessels] =  1.0;
(*write_here)[oxygen] = o2_normoxic;

return;
}

13. Set up the uptake function:
void uptake_function( Microenvironment* microenvironment, int voxel_index,
std::vector<double>* write_here )
{
// use this syntax to access the jth substrate write_here
// (*write_here)[j]
// use this syntax to access the jth substrate in voxel voxel_index of microenvironment:
// microenvironment->density_vector(voxel_index)[j]

(*write_here)[live_cells] = o2_hypoxic;
(*write_here)[live_cells] -=
microenvironment->density_vector(voxel_index)[oxygen];
if( (*write_here)[live_cells] < 0.0 )
{
(*write_here)[live_cells] = 0.0;
}
else
{
(*write_here)[live_cells] *= death_rate;
}

(*write_here)[oxygen] = o2_uptake_rate ;
(*write_here)[oxygen] *=
microenvironment->density_vector(voxel_index)[live_cells];

(*write_here)[blood_vessels] *=
microenvironment->density_vector(voxel_index)[live_cells];

return;
}


And that’s it. The source should be ready to go!

### Using the code

#### Running the code

First, compile and run the code:

make
2Dtumor


The output should look like this.

Starting program …

Microenvironment summary: Tumor microenvironment:

Mesh information:
type: uniform Cartesian
Domain: [0,20] mm x [0,20] mm x [-0.025,0.025] mm
resolution: dx = 0.05 mm
voxels: 160000
voxel faces: 0
volume: 20 cubic mm
Densities: (3 total)
live cells:
units: cells
diffusion coefficient: 0.00570386 mm^2 / hr
decay rate: 0 hr^-1
diffusion length scale: 75523.9 mm

blood vessels:
units: vessels/mm^2
diffusion coefficient: 0 mm^2 / hr
decay rate: 0 hr^-1
diffusion length scale: 0 mm

oxygen:
units: cells
diffusion coefficient: 6 mm^2 / hr
decay rate: 2.2038 hr^-1
diffusion length scale: 1.65002 mm

simulation time: 0 hr (240 hr max)

Using method diffusion_decay_solver__constant_coefficients_LOD_2D (2D LOD with Thomas Algorithm) …

simulation time: 12 hr (240 hr max)
simulation time: 24 hr (240 hr max)
simulation time: 36 hr (240 hr max)
simulation time: 48 hr (240 hr max)
simulation time: 60 hr (240 hr max)
simulation time: 72 hr (240 hr max)
simulation time: 84 hr (240 hr max)
simulation time: 96 hr (240 hr max)
simulation time: 108 hr (240 hr max)
simulation time: 120 hr (240 hr max)
simulation time: 132 hr (240 hr max)
simulation time: 144 hr (240 hr max)
simulation time: 156 hr (240 hr max)
simulation time: 168 hr (240 hr max)
simulation time: 180 hr (240 hr max)
simulation time: 192 hr (240 hr max)
simulation time: 204 hr (240 hr max)
simulation time: 216 hr (240 hr max)
simulation time: 228 hr (240 hr max)
simulation time: 240 hr (240 hr max)
Done!


#### Looking at the data

Now, let’s pop it open in matlab (or octave):

matlab


To load and plot a single time (e.g., the last tim)

!ls *.mat
plot_microenvironment( M );


labels{1} = 'tumor cells';
labels{2} = 'blood vessel density';
labels{3} = 'growth substrate';
plot_microenvironment( M ,labels );


Your output should look a bit like this:

Lastly, you might want to script the code to create and save plots of all the times.

labels{1} = 'tumor cells';
labels{2} = 'blood vessel density';
labels{3} = 'growth substrate';
for i=0:20
t = i*12;
input_file = sprintf( 'output_%3.6f.mat', t );
output_file = sprintf( 'output_%3.6f.png', t );