Category: tutorial

PhysiCell Tools : python-loader

The newest tool for PhysiCell provides an easy way to load your PhysiCell output data into python for analysis. This builds upon previous work on loading data into MATLAB. A post on that tool can be found at:

PhysiCell stores output data as a MultiCell Digital Snapshot (MultiCellDS) that consists of several files for each time step and is probably stored in your ./output directory. pyMCDS is a python object that is initialized with the .xml file

What you’ll need

Anatomy of a MultiCell Digital Snapshot

Each time PhysiCell’s internal time tracker passes a time step where data is to be saved, it generates a number of files of various types. Each of these files will have a number at the end that indicates where it belongs in the sequence of outputs. All of the files from the first round of output will end in 00000000.* and the second round will be 00000001.* and so on. Let’s say we’re interested in a set of output from partway through the run, the 88th set of output files. The files we care about most from this set consists of:

  • output00000087.xml: This file is the main organizer of the data. It contains an overview of the data stored in the MultiCellDS as well as some actual data including:
    • Metadata about the time and runtime for the current time step
    • Coordinates for the computational domain
    • Parameters for diffusing substrates in the microenvironment
    • Column labels for the cell data
    • File names for the files that contain microenvironment and cell data at this time step
  • output00000087_microenvironment0.mat: This is a MATLAB matrix file that contains all of the data about the microenvironment at this time step
  • output00000087_cells_physicell.mat: This is a MATLAB matrix file that contains all of the tracked information about the individual cells in the model. It tells us things like the cells’ position, volume, secretion, cell cycle status, and user-defined cell parameters.


Using pyMCDS

From the appropriate file in your PhysiCell directory, wherever lives, you can use the data loader in your own scripts or in an interactive session. To start you have to import the pyMCDS class

from pyMCDS import pyMCDS

Loading the data

Data is loaded into python from the MultiCellDS by initializing the pyMCDS object. The initialization function for pyMCDS takes one required and one optional argument.

__init__(xml_file, [output_path = '.'])
    xml_file : string
        String containing the name of the output xml file
    output_path : 
        String containing the path (relative or absolute) to the directory
        where PhysiCell output files are stored

We are interested in reading output00000087.xml that lives in ~/path/to/PhysiCell/output (don’t worry Windows paths work too). We would initialize our pyMCDS object using those names and the actual data would be stored in a member dictionary called data.

mcds = pyMCDS('output00000087.xml', '~/path/to/PhysiCell/output')
# Now our data lives in:

We’ve tried to keep everything organized inside of this dictionary but let’s take a look at what we actually have in here. Of course in real output, there will probably not be a chemical named my_chemical, this is simply there to illustrate how multiple chemicals are handled.


Overview of dictionary-of-dictionaries structure
The data member dictionary is a dictionary of dictionaries whose child dictionaries can be accessed through normal python dictionary syntax.['metadata']['continuum_variables']['my_chemical']

Each of these subdictionaries contains data, we will take a look at exactly what that data is and how it can be accessed in the following sections.


Expanded metadata subdictionary

The metadata dictionary contains information about the time of the simulation as well as units for both times and space. Here and in later sections blue boxes indicate scalars and green boxes indicate strings. We can access each of these things using normal dictionary syntax. We’ve also got access to a helper function get_time() for the common operation of retrieving the simulation time.

>>> mcds.get_time()


Expanded mesh dictionary

The mesh dictionary has a lot more going on than the metadata dictionary. It contains three numpy arrays, indicated by orange boxes, as well as another dictionary. The three arrays contain \(x\), \(y\) and \(z\) coordinates for the centers of the voxels that constiture the computational domain in a meshgrid format. This means that each of those arrays is tensors of rank three. Together they identify the coordinates of each possible point in the space.

In contrast, the arrays in the voxel dictionary are stored linearly. If we know that we care about voxel number 42, we want to use the stuff in the voxels dictionary. If we want to make a contour plot, we want to use the x_coordinates, y_coordinates, and z_coordinates arrays.

# We can extract one of the meshgrid arrays as a numpy array
>>> y_coords =['mesh']['y_coordinates']
>>> y_coords.shape
(75, 75, 75)
>>> y_coords[0, 0, :4]
array([-740., -740., -740., -740.])

# We can also extract the array of voxel centers
>>> centers =['mesh']['voxels']['centers']
>>> centers.shape
(3, 421875)
>>> centers[:, :4]
array([[-740., -720., -700., -680.],
       [-740., -740., -740., -740.],
       [-740., -740., -740., -740.]])

# We have a handy function to quickly extract the components of the full meshgrid
>>> xx, yy, zz = mcds.get_mesh()
>>> yy.shape
(75, 75, 75)
>>> yy[0, 0, :4]
array([-740., -740., -740., -740.])

# We can also use this to return the meshgrid describing an x, y plane
>>> xx, yy = mcds.get_2D_mesh()
>>> yy.shape
(75, 75)


Continuum variables

Expanded microenvironment dictionaries

The continuum_variables dictionary is the most complicated of the four. It contains subdictionaries that we access using the names of each of the chemicals in the microenvironment. In our toy example above, these are oxygen and my_chemical. If our model tracked diffusing oxygen, VEGF, and glucose, then the continuum_variables dictionary would contain a subdirectory for each of them.

For a particular chemical species in the microenvironment we have two more dictionaries called decay_rate and diffusion_coefficient, and a numpy array called data. The diffusion and decay dictionaries each complete the value stored as a scalar and the unit stored as a string. The numpy array contains the concentrations of the chemical in each voxel at this time and is the same shape as the meshgrids of the computational domain stored in the .data[‘mesh’] arrays.

# we need to know the names of the substrates to work with
# this data. We have a function to help us find them.
>>> mcds.get_substrate_names()
['oxygen', 'my_chemical']

# The diffusable chemical dictionaries are messy
# if we need to do a lot with them it might be easier
# to put them into their own instance
>>> oxy_dict =['continuum_variables']['oxygen']
>>> oxy_dict['decay_rate']
{'value': 0.1, 'units': '1/min'}

# What we care about most is probably the numpy 
# array of concentrations
>>> oxy_conc = oxy_dict['data']
>>> oxy_conc.shape
(75, 75, 75)

# Alternatively, we can get the same array with a function
>>> oxy_conc2 = mcds.get_concentrations('oxygen')
>>> oxy_conc2.shape
(75, 75, 75)

# We can also get the concentrations on a plane using the
# same function and supplying a z value to "slice through"
# note that right now the z_value must be an exact match
# for a plane of voxel centers, in the future we may add
# interpolation.
>>> oxy_plane = mcds.get_concentrations('oxygen', z_value=100.0)
>>> oxy_plane.shape
(75, 75)

# we can also find the concentration in a single voxel using the
# position of a point within that voxel. This will give us an
# array of all concentrations at that point.
>>> mcds.get_concentrations_at(x=0., y=550., z=0.)
array([17.94514446,  0.99113448])


Discrete Cells

expanded cells dictionary

The discrete cells dictionary is relatively straightforward. It contains a number of numpy arrays that contain information regarding individual cells.  These are all 1-dimensional arrays and each corresponds to one of the variables specified in the output*.xml file. With the default settings, these are:

  • ID: unique integer that will identify the cell throughout its lifetime in the simulation
  • position(_x, _y, _z): floating point positions for the cell in \(x\), \(y\), and \(z\) directions
  • total_volume: total volume of the cell
  • cell_type: integer label for the cell as used in PhysiCell
  • cycle_model: integer label for the cell cycle model as used in PhysiCell
  • current_phase: integer specification for which phase of the cycle model the cell is currently in
  • elapsed_time_in_phase: time that cell has been in current phase of cell cycle model
  • nuclear_volume: volume of cell nucleus
  • cytoplasmic_volume: volume of cell cytoplasm
  • fluid_fraction: proportion of the volume due to fliud
  • calcified_fraction: proportion of volume consisting of calcified material
  • orientation(_x, _y, _z): direction in which cell is pointing
  • polarity:
  • migration_speed: current speed of cell
  • motility_vector(_x, _y, _z): current direction of movement of cell
  • migration_bias: coefficient for stochastic movement (higher is “more deterministic”)
  • motility_bias_direction(_x, _y, _z): direction of movement bias
  • persistence_time: time in-between direction changes for cell
  • motility_reserved:
# Extracting single variables is just like before
>>> cell_ids =['discrete_cells']['ID']
>>> cell_ids.shape
>>> cell_ids[:4]
array([0., 1., 2., 3.])

# If we're clever we can extract 2D arrays
>>> cell_vec = np.zeros((cell_ids.shape[0], 3))
>>> vec_list = ['position_x', 'position_y', 'position_z']
>>> for i, lab in enumerate(vec_list):
...     cell_vec[:, i] =['discrete_cells'][lab]
array([[ -69.72657128,  -39.02046405, -233.63178904],
       [ -69.84507464,  -22.71693265, -233.59277388],
       [ -69.84891462,   -6.04070516, -233.61816711],
       [ -69.845265  ,   10.80035554, -233.61667313]])

# We can get the list of all of the variables stored in this dictionary
>>> mcds.get_cell_variables()
# We can also get all of the cell data as a pandas DataFrame 
>>> cell_df = mcds.get_cell_df() 
>>> cell_df.head() 
ID     position_x   position_y    position_z total_volume cell_type cycle_model ... 
0.0   - 69.726571  - 39.020464  - 233.631789       2494.0       0.0         5.0 ... 
1.0   - 69.845075  - 22.716933  - 233.592774       2494.0       0.0         5.0 ... 
2.0   - 69.848915  - 6.040705   - 233.618167       2494.0       0.0         5.0 ... 
3.0   - 69.845265    10.800356  - 233.616673       2494.0       0.0         5.0 ... 
4.0   - 69.828161    27.324530  - 233.631579       2494.0       0.0         5.0 ... 

# if we want to we can also get just the subset of cells that
# are in a specific voxel
>>> vox_df = mcds.get_cell_df_at(x=0.0, y=550.0, z=0.0)
>>> vox_df.iloc[:, :5]
             ID  position_x  position_y  position_z  total_volume
26718  228761.0    6.623617  536.709341   -1.282934   2454.814507
52736  270274.0   -7.990034  538.184921    9.648955   1523.386488


These examples will not be made using our toy dataset described above but will instead be made using a single timepoint dataset that can be found at:

Substrate contour plot

One of the big advantages of working with PhysiCell data in python is that we have access to its plotting tools. For the sake of example let’s plot the partial pressure of oxygen throughout the computational domain along the \(z = 0\) plane. Once we’ve loaded our data by initializing a pyMCDS object, we can work entirely within python to produce the plot.

from pyMCDS import pyMCDS
import numpy as np
import matplotlib.pyplot as plt

# load data
mcds = pyMCDS('output00003696.xml', '../output')

# Set our z plane and get our substrate values along it
z_val = 0.00
plane_oxy = mcds.get_concentrations('oxygen', z_slice=z_val)

# Get the 2D mesh for contour plotting
xx, yy = mcds.get_mesh()

# We want to be able to control the number of contour levels so we
# need to do a little set up
num_levels = 21
min_conc = plane_oxy.min()
max_conc = plane_oxy.max()
my_levels = np.linspace(min_conc, max_conc, num_levels)

# set up the figure area and add data layers
fig, ax = plt.subplot()
cs = ax.contourf(xx, yy, plane_oxy, levels=my_levels)
ax.contour(xx, yy, plane_oxy, color='black', levels = my_levels,

# Now we need to add our color bar
cbar1 = fig.colorbar(cs, shrink=0.75)

# Let's put the time in to make these look nice
ax.set_xlabel('x (micron)')
ax.set_ylabel('y (micron)')
ax.set_title('oxygen (mmHg) at t = {:.1f} {:s}, z = {:.2f} {:s}'.format(
oxygen partial pressures over z=0

Adding a cells layer

We can also use pandas to do fairly complex selections of cells to add to our plots. Below we use pandas and the previous plot to add a cells layer.

from pyMCDS import pyMCDS
import numpy as np
import matplotlib.pyplot as plt

# load data
mcds = pyMCDS('output00003696.xml', '../output')

# Set our z plane and get our substrate values along it
z_val = 0.00
plane_oxy = mcds.get_concentrations('oxygen', z_slice=z_val)

# Get the 2D mesh for contour plotting
xx, yy = mcds.get_mesh()

# We want to be able to control the number of contour levels so we
# need to do a little set up
num_levels = 21
min_conc = plane_oxy.min()
max_conc = plane_oxy.max()
my_levels = np.linspace(min_conc, max_conc, num_levels)

# get our cells data and figure out which cells are in the plane
cell_df = mcds.get_cell_df()
ds = mcds.get_mesh_spacing()
inside_plane = (cell_df['position_z'] < z_val + ds) \ & (cell_df['position_z'] > z_val - ds)
plane_cells = cell_df[inside_plane]

# We're going to plot two types of cells and we want it to look nice
colors = ['black', 'grey']
sizes = [20, 8]
labels = ['Alive', 'Dead']

# set up the figure area and add microenvironment layer
fig, ax = plt.subplot()
cs = ax.contourf(xx, yy, plane_oxy, levels=my_levels)

# get our cells of interest
# alive_cells = plane_cells[plane_cells['cycle_model'] < 6]
# dead_cells = plane_cells[plane_cells['cycle_model'] > 6]
# -- for newer versions of PhysiCell
alive_cells = plane_cells[plane_cells['cycle_model'] < 100]
dead_cells = plane_cells[plane_cells['cycle_model'] >= 100]

# plot the cell layer
for i, plot_cells in enumerate((alive_cells, dead_cells)):

# Now we need to add our color bar
cbar1 = fig.colorbar(cs, shrink=0.75)

# Let's put the time in to make these look nice
ax.set_xlabel('x (micron)')
ax.set_ylabel('y (micron)')
ax.set_title('oxygen (mmHg) at t = {:.1f} {:s}, z = {:.2f} {:s}'.format(
ax.legend(loc='upper right')

adding a cell layer to the oxygen plot

Future Direction

The first extension of this project will be timeseries functionality. This will provide similar data loading functionality but for a time series of MultiCell Digital Snapshots instead of simply one point in time.

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PhysiCell Tools : PhysiCell-povwriter

As PhysiCell matures, we are starting to turn our attention to better training materials and an ecosystem of open source PhysiCell tools. PhysiCell-povwriter is is designed to help transform your 3-D simulation results into 3-D visualizations like this one:

PhysiCell-povwriter transforms simulation snapshots into 3-D scenes that can be rendered into still images using POV-ray: an open source software package that uses raytracing to mimic the path of light from a source of illumination to a single viewpoint (a camera or an eye). The result is a beautifully rendered scene (at any resolution you choose) with very nice shading and lighting.

If you repeat this on many simulation snapshots, you can create an animation of your work.

What you’ll need

This workflow is entirely based on open source software:


Building PhysiCell-povwriter

After you clone PhysiCell-povwriter or download its source from a release, you’ll need to compile it. In the project’s root directory, compile the project by:


(If you need to set up a C++ PhysiCell development environment, click here for OSX or here for Windows.)

Next, copy povwriter (povwriter.exe in Windows) to either the root directory of your PhysiCell project, or somewhere in your path. Copy ./config/povwriter-settings.xml to the ./config directory of your PhysiCell project.

Editing resolutions in POV-ray

PhysiCell-povwriter is intended for creating “square” images, but POV-ray does not have any pre-created square rendering resolutions out-of-the-box. However, this is straightforward to fix.

  1. Open POV-Ray
  2. Go to the “tools” menu and select “edit resolution INI file”
  3. At the top of the INI file (which opens for editing in POV-ray), make a new profile:
    [1080x1080, AA]

  4. Make similar profiles (with unique names) to suit your preferences. I suggest one at 480×480 (as a fast preview), another at 2160×2160, and another at 5000×5000 (because they will be absurdly high resolution). For example:
    [2160x2160 no AA]

    You can optionally make more profiles with antialiasing on (which provides some smoothing for areas of high detail), but you’re probably better off just rendering without antialiasing at higher resolutions and the scaling the image down as needed. Also, rendering without antialiasing will be faster.

  5. Once done making profiles, save and exit POV-Ray.
  6. The next time you open POV-Ray, your new resolution profiles will be available in the lefthand dropdown box.

Configuring PhysiCell-povwriter

Once you have copied povwriter-settings.xml to your project’s config file, open it in a text editor. Below, we’ll show the different settings.

Camera settings

	<distance_from_origin units="micron">1500</distance_from_origin>
	<xy_angle>3.92699081699</xy_angle> <!-- 5*pi/4 -->
	<yz_angle>1.0471975512</yz_angle> <!-- pi/3 -->

For simplicity, PhysiCell-POVray (currently) always aims the camera towards the origin (0,0,0), with “up” towards the positive z-axis. distance_from_origin sets how far the camera is placed from the origin. xy_angle sets the angle \(\theta\) from the positive x-axis in the xy-plane. yz_angle sets the angle \(\phi\) from the positive z-axis in the yz-plane. Both angles are in radians.


	<nuclear_offset units="micron">0.1</nuclear_offset>
	<cell_bound units="micron">750</cell_bound>

use_standard_colors (if set to true) uses a built-in “paint-by-numbers” color scheme, where each cell type (identified with an integer) gets XML-defined colors for live, apoptotic, and dead cells. More on this below. If use_standard_colors is set to false, then PhysiCell-povwriter uses the my_pigment_and_finish_function in ./custom_modules/povwriter.cpp to color cells.

The nuclear_offset is a small additional height given to nuclei when cropping to avoid visual artifacts when rendering (which can cause some “tearing” or “bleeding” between the rendered nucleus and cytoplasm). cell_bound is used for leaving some cells out of bound: any cell with |x|, |y|, or |z| exceeding cell_bound will not be rendered. threads is used for parallelizing on multicore processors; note that it only speeds up povwriter if you are converting multiple PhysiCell outputs to povray files.


<save> <!-- done -->
	<folder>output</folder> <!-- use . for root -->

Use folder to tell PhysiCell-povwriter where the data files are stored. Use filebase to tell how the outputs are named. Typically, they have the form output########_cells_physicell.mat; in this case, the filebase is output. Lastly, use time_index to set the output number. For example if your file is output00000182_cells_physicell.mat, then filebase = output and time_index = 182.

Below, we’ll see how to specify ranges of indices at the command line, which would supersede the time_index given here in the XML.

Clipping planes

PhysiCell-povwriter uses clipping planes to help create cutaway views of the simulations. By default, 3 clipping planes are used to cut out an octant of the viewing area.

Recall that a plane can be defined by its normal vector and a point p on the plane. With these, the plane can be defined as all points satisfying

\[  \left( \vec{x} -\vec{p} \right) \cdot \vec{n} = 0 \]

These are then written out as a plane equation

\[ a x + by + cz + d = 0, \]


\[ (a,b,c) = \vec{n} \hspace{.5in} \textrm{ and  } \hspace{0.5in} d = \: – \vec{n} \cdot \vec{p}. \]

As of Version 1.0.0, we are having some difficulties with clipping planes that do not pass through the origin (0,0,0), for which \( d = 0 \).

In the config file, these planes are written as \( (a,b,c,d) \):

<clipping_planes> <!-- done --> 

Note that cells “behind” the plane (where \( ( \vec{x} – \vec{p} ) \cdot \vec{n} \le 0 \)) are rendered, and cells in “front” of the plane (where \( (\vec{x}-\vec{p}) \cdot \vec{n} > 0 \)) are not rendered. Cells that intersect the plane are partially rendered (using constructive geometry via union and intersection commands in POV-ray).

Cell color definitions

Within <cell_color_definitions>, you’ll find multiple <cell_colors> blocks, each of which defines the live, dead, and necrotic colors for a specific cell type (with the type ID indicated in the attribute). These colors are only applied if use_standard_colors is set to true in options. See above.

The live colors are given as two rgb (red,green,blue) colors for the cytoplasm and nucleus of live cells. Each element of this triple can range from 0 to 1, and not from 0 to 255 as in many raw image formats. Next, finish specifies ambient (how much highly-scattered background ambient light illuminates the cell), diffuse (how well light rays can illuminate the surface), and specular (how much of a shiny reflective splotch the cell gets).

See the POV-ray documentation for for information on the finish.

This is repeated to give the apoptotic and necrotic colors for the cell type.

<cell_colors type="0">
		<cytoplasm>.25,1,.25</cytoplasm> <!-- red,green,blue --> 
		<finish>0.05,1,0.1</finish> <!-- ambient,diffuse,specular -->
		<cytoplasm>1,0,0</cytoplasm> <!-- red,green,blue --> 
		<finish>0.05,1,0.1</finish> <!-- ambient,diffuse,specular -->
		<cytoplasm>1,0.5412,0.1490</cytoplasm> <!-- red,green,blue --> 
		<finish>0.01,0.5,0.1</finish> <!-- ambient,diffuse,specular -->

Use multiple cell_colors blocks (each with type corresponding to the integer cell type) to define the colors of multiple cell types.

Using PhysiCell-povwriter

Use by the XML configuration file alone

The simplest syntax:

physicell$ ./povwriter

(Windows users: povwriter or povwriter.exe) will process ./config/povwriter-settings.xml and convert the single indicated PhysiCell snapshot to a .pov file.

If you run POV-writer with the default configuration file in the povwriter structure (with the supplied sample data), it will render time index 3696 from the immunotherapy example in our 2018 PhysiCell Method Paper:

physicell$ ./povwriter

povwriter version 1.0.0

Copyright (c) Paul Macklin 2019, on behalf of the PhysiCell project
OSI License: BSD-3-Clause (see LICENSE.txt)

povwriter : run povwriter with config file ./config/settings.xml

povwriter FILENAME.xml : run povwriter with config file FILENAME.xml

povwriter x:y:z : run povwriter on data in FOLDER with indices from x
to y in incremenets of z

Example: ./povwriter 0:2:10 processes files:
(See the config file to set FOLDER and FILEBASE)

povwriter x1,...,xn : run povwriter on data in FOLDER with indices x1,...,xn

Example: ./povwriter 1,3,17 processes files:
(Note that there are no spaces.)
(See the config file to set FOLDER and FILEBASE)

Code updates at

Tutorial & documentation at

Using config file ./config/povwriter-settings.xml ...
Using standard coloring function ...
Found 3 clipping planes ...
Found 2 cell color definitions ...
Processing file ./output/output00003696_cells_physicell.mat...
Matrix size: 32 x 66978
Creating file pov00003696.pov for output ...
Writing 66978 cells ...

Done processing all 1 files!

The result is a single POV-ray file (pov00003696.pov) in the root directory.

Now, open that file in POV-ray (double-click the file if you are in Windows), choose one of your resolutions in your lefthand dropdown (I’ll choose 2160×2160 no antialiasing), and click the green “run” button.

You can watch the image as it renders. The result should be a PNG file (named pov00003696.png) that looks like this:

Cancer immunotherapy sample image, at time index 3696

Using command-line options to process multiple times (option #1)

Now, suppose we have more outputs to process. We still state most of the options in the XML file as above, but now we also supply a command-line argument in the form of start:interval:end. If you’re still in the povwriter project, note that we have some more sample data there. Let’s grab and process it:

physicell$ cd output
physicell$ unzip
inflating: output00000000_cells_physicell.mat
inflating: output00000001_cells_physicell.mat
inflating: output00000250_cells_physicell.mat
inflating: output00000300_cells_physicell.mat
inflating: output00000500_cells_physicell.mat
inflating: output00000750_cells_physicell.mat
inflating: output00001000_cells_physicell.mat
inflating: output00001250_cells_physicell.mat
inflating: output00001500_cells_physicell.mat
inflating: output00001750_cells_physicell.mat
inflating: output00002000_cells_physicell.mat
inflating: output00002250_cells_physicell.mat
inflating: output00002500_cells_physicell.mat
inflating: output00002750_cells_physicell.mat
inflating: output00003000_cells_physicell.mat
inflating: output00003250_cells_physicell.mat
inflating: output00003500_cells_physicell.mat
inflating: output00003696_cells_physicell.mat

physicell$ ls

citation and license.txt

Let’s go back to the parent directory and run povwriter:

physicell$ ./povwriter 0:250:3500

povwriter version 1.0.0

Copyright (c) Paul Macklin 2019, on behalf of the PhysiCell project
OSI License: BSD-3-Clause (see LICENSE.txt)

povwriter : run povwriter with config file ./config/settings.xml

povwriter FILENAME.xml : run povwriter with config file FILENAME.xml

povwriter x:y:z : run povwriter on data in FOLDER with indices from x
to y in incremenets of z

Example: ./povwriter 0:2:10 processes files:
(See the config file to set FOLDER and FILEBASE)

povwriter x1,...,xn : run povwriter on data in FOLDER with indices x1,...,xn

Example: ./povwriter 1,3,17 processes files:
(Note that there are no spaces.)
(See the config file to set FOLDER and FILEBASE)

Code updates at

Tutorial & documentation at

Using config file ./config/povwriter-settings.xml ...
Using standard coloring function ...
Found 3 clipping planes ...
Found 2 cell color definitions ...
Matrix size: 32 x 18317
Processing file ./output/output00000000_cells_physicell.mat...
Creating file pov00000000.pov for output ...
Writing 18317 cells ...
Processing file ./output/output00002000_cells_physicell.mat...
Matrix size: 32 x 33551
Creating file pov00002000.pov for output ...
Writing 33551 cells ...
Processing file ./output/output00002500_cells_physicell.mat...
Matrix size: 32 x 43440
Creating file pov00002500.pov for output ...
Writing 43440 cells ...
Processing file ./output/output00001500_cells_physicell.mat...
Matrix size: 32 x 40267
Creating file pov00001500.pov for output ...
Writing 40267 cells ...
Processing file ./output/output00003000_cells_physicell.mat...
Matrix size: 32 x 56659
Creating file pov00003000.pov for output ...
Writing 56659 cells ...
Processing file ./output/output00001000_cells_physicell.mat...
Matrix size: 32 x 74057
Creating file pov00001000.pov for output ...
Writing 74057 cells ...
Processing file ./output/output00003500_cells_physicell.mat...
Matrix size: 32 x 66791
Creating file pov00003500.pov for output ...
Writing 66791 cells ...
Processing file ./output/output00000500_cells_physicell.mat...
Matrix size: 32 x 114316
Creating file pov00000500.pov for output ...
Writing 114316 cells ...

Processing file ./output/output00000250_cells_physicell.mat...
Matrix size: 32 x 75352
Creating file pov00000250.pov for output ...
Writing 75352 cells ...

Processing file ./output/output00002250_cells_physicell.mat...
Matrix size: 32 x 37959
Creating file pov00002250.pov for output ...
Writing 37959 cells ...

Processing file ./output/output00001750_cells_physicell.mat...
Matrix size: 32 x 32358
Creating file pov00001750.pov for output ...
Writing 32358 cells ...

Processing file ./output/output00002750_cells_physicell.mat...
Matrix size: 32 x 49658
Creating file pov00002750.pov for output ...
Writing 49658 cells ...

Processing file ./output/output00003250_cells_physicell.mat...
Matrix size: 32 x 63546
Creating file pov00003250.pov for output ...
Writing 63546 cells ...




Processing file ./output/output00001250_cells_physicell.mat...
Matrix size: 32 x 54771
Creating file pov00001250.pov for output ...
Writing 54771 cells ...




Processing file ./output/output00000750_cells_physicell.mat...
Matrix size: 32 x 97642
Creating file pov00000750.pov for output ...
Writing 97642 cells ...


Done processing all 15 files!

Notice that the output appears a bit out of order. This is normal: povwriter is using 8 threads to process 8 files at the same time, and sending some output to the single screen. Since this is all happening simultaneously, it’s a bit jumbled (and non-sequential). Don’t panic. You should now have created pov00000000.povpov00000250.pov, … , pov00003500.pov.

Now, go into POV-ray, and choose “queue.” Click “Add File” and select all 15 .pov files you just created:

Hit “OK” to let it render all the povray files to create PNG files (pov00000000.png, … , pov00003500.png).

Using command-line options to process multiple times (option #2)

You can also give a list of indices. Here’s how we render time indices 250, 1000, and 2250:

physicell$ ./povwriter 250,1000,2250

povwriter version 1.0.0

Copyright (c) Paul Macklin 2019, on behalf of the PhysiCell project
OSI License: BSD-3-Clause (see LICENSE.txt)

povwriter : run povwriter with config file ./config/settings.xml

povwriter FILENAME.xml : run povwriter with config file FILENAME.xml

povwriter x:y:z : run povwriter on data in FOLDER with indices from x
to y in incremenets of z

Example: ./povwriter 0:2:10 processes files:
(See the config file to set FOLDER and FILEBASE)

povwriter x1,...,xn : run povwriter on data in FOLDER with indices x1,...,xn

Example: ./povwriter 1,3,17 processes files:
(Note that there are no spaces.)
(See the config file to set FOLDER and FILEBASE)

Code updates at

Tutorial & documentation at

Using config file ./config/povwriter-settings.xml ...
Using standard coloring function ...
Found 3 clipping planes ...
Found 2 cell color definitions ...
Processing file ./output/output00002250_cells_physicell.mat...
Matrix size: 32 x 37959
Creating file pov00002250.pov for output ...
Writing 37959 cells ...
Processing file ./output/output00001000_cells_physicell.mat...
Matrix size: 32 x 74057
Creating file pov00001000.pov for output ...
Processing file ./output/output00000250_cells_physicell.mat...
Matrix size: 32 x 75352
Writing 74057 cells ...
Creating file pov00000250.pov for output ...
Writing 75352 cells ...



Done processing all 3 files!

This will create files pov00000250.povpov00001000.pov, and pov00002250.pov. Render them in POV-ray just as before.

Advanced options (at the source code level)

If you set use_standard_colors to false, povwriter uses the function my_pigment_and_finish_function (at the end of  ./custom_modules/povwriter.cpp). Make sure that you set colors.cyto_pigment (RGB) and colors.nuclear_pigment (also RGB). The source file in povwriter has some hinting on how to write this. Note that the XML files saved by PhysiCell have a legend section that helps you do determine what is stored in each column of the matlab file.

Optional postprocessing

Image conversion / manipulation with ImageMagick

Suppose you want to convert the PNG files to JPEGs, and scale them down to 60% of original size. That’s very straightforward in ImageMagick:

physicell$ magick mogrify -format jpg -resize 60% pov*.png

Creating an animated GIF with ImageMagick

Suppose you want to create an animated GIF based on your images. I suggest first converting to JPG (see above) and then using ImageMagick again. Here, I’m adding a 20 ms delay between frames:

physicell$ magick convert -delay 20 *.jpg out.gif

Here’s the result:

Animated GIF created from raytraced still images. (You have to click the image to see the animation.)

Creating a compressed movie with Mencoder

Syntax coming later.

Closing thoughts and future work

In the future, we will probably allow more control over the clipping planes and a bit more debugging on how to handle planes that don’t pass through the origin. (First thoughts: we need to change how we use union and intersection commands in the POV-ray outputs.)

We should also look at adding some transparency for the cells. I’d prefer something like rgba (red-green-blue-alpha), but POV-ray uses filters and transmission, and we want to make sure to get it right.

Lastly, it would be nice to find a balance between the current very simple camera setup and better control.

Thanks for reading this PhysiCell Friday tutorial! Please do give PhysiCell at try (at and read the method paper at PLoS Computational Biology.

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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.

		<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_relative_adhesion type="double" units="dimensionless">0.05</motile_cell_relative_adhesion>
		<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>

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:

		<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_relative_adhesion type="double" units="dimensionless">0.05</motile_cell_relative_adhesion>
		<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>
		<base_cell_adhesion_distance type="double" units="dimensionless">2.5</base_cell_adhesion_distance> 
		<include_motile_cell type="bool" units="dimensionless">true</include_motile_cell>

Viewing the loaded parameters

Let’s compile and run the project.


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_relative_adhesion: 0.05 [dimensionless]
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]
base_cell_adhesion_distance: 2.5 [dimensionless]

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 << " " 
     << parameters.doubles["base_cell_adhesion_distance"].value << " " 
     << parameters.doubles["base_cell_adhesion_distance"].units << std::endl; 

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 */,S_index) = 
	cell_defaults.phenotype.death.rates[apoptosis_model_index] = 
		parameters.doubles("base_cell_adhesion_distance") ); 

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.


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):


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.

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Adding a directory to your Windows path

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\
  2. c:\Program Files (x86)\Notepad++\
  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 (;):

  1. Open notepad ([Windows]+R, notepad)
  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.
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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:


Loading a PhysiCell MultiCellDS digital snapshot

Now, load the snapshot:

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 “”, 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
metadata to get labels:

axis image
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.metadata.current_time , ...
     MCDS.metadata.time_units, ... 
     MCDS.mesh.Z_coordinates(k), ...
     MCDS.metadata.spatial_units ) ); 

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

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:

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:

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:

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) ); 

% 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!

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Coarse-graining discrete cell cycle models


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);    


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:



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

The function defined above to create the exact solution using the eigenvalue/eignvector approach.
Runs a single stochastic simulation, using the supplied parameters.
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.

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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 and 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.

Our modeling task

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; = "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" );
    M.add_density( "oxygen" , "cells" );
    // 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: 


    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
    	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;
    double radius = 1.0;
    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
    // voxels in your mesh:
    #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 );
    	if( distance < radius )
    		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] = 
    	(*write_here)[live_cells] -= o2_hypoxic; 
    	if( (*write_here)[live_cells] < 0.0 )
    		(*write_here)[live_cells] = 0.0; 
    		(*write_here)[live_cells] = temp1; 
    		(*write_here)[live_cells] *= 
    	(*write_here)[blood_vessels] = 0.0; 
    	(*write_here)[oxygen] = o2_supply_rate; 
    	(*write_here)[oxygen] *=  

    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; 
  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] -= 
    	if( (*write_here)[live_cells] < 0.0 ) 
    		(*write_here)[live_cells] = 0.0; 
    		(*write_here)[live_cells] *= death_rate; 
    	(*write_here)[oxygen] = o2_uptake_rate ; 
    	(*write_here)[oxygen] *= 
    	(*write_here)[blood_vessels] = vessel_degradation_rate ; 
    	(*write_here)[blood_vessels] *= 

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

Source files

You can download completed source for this example here:

  1. 2D_tumor_example.cpp
  2. Makefile

Using the code

Running the code

First, compile and run the code:


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

		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)

Looking at the data

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


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

!ls *.mat
M = read_microenvironment( 'output_240.000000.mat' );
plot_microenvironment( M );

To add some labels:

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:

BioFVM 2D warmup -- final output

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 ); 
	M = read_microenvironment( input_file ); 
	plot_microenvironment( M , labels ); 
	print( gcf , '-dpng' , output_file );

What’s next

We’ll continue posting new tutorials on adapting BioFVM to existing and new simulators, as well as guides to new features as we roll them out.
Stay tuned and watch this blog!

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