## 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 :

## Building a Cellular Automaton Model Using BioFVM

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

### What you’ll need

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 3-D cellular automaton model of tumor growth in a well-mixed fluid, containing oxygen pO2 (mmHg) and a drug c (e.g., doxorubicin, μM), inspired by modeling by Alexander Anderson, Heiko Enderling, Jan PoleszczukGibin Powathil, and others. (I highly suggest seeking out the sophisticated cellular automaton models at Moffitt’s Integrated Mathematical Oncology program!) This example shows you how to extend BioFVM into a new cellular automaton model. I’ll write a similar post on how to add BioFVM into an existing cellular automaton model, which you may already have available.

Tumor growth will be driven by oxygen availability. Tumor cells can be live, apoptotic (going through energy-dependent cell death, or necrotic (undergoing death from energy collapse). Drug exposure can both trigger apoptosis and inhibit cell cycling. We will model this as growth into a well-mixed fluid, with pO2 = 38 mmHg (about 5% oxygen: a physioxic value) and c = 5 μM.

### Mathematical model

As a cellular automaton model, we will divide 3-D space into a regular lattice of voxels, with length, width, and height of 15 μm. (A typical breast cancer cell has radius around 9-10 μm, giving a typical volume around 3.6×103 μm3. If we make each lattice site have the volume of one cell, this gives an edge length around 15 μm.)

In voxels unoccupied by cells, we approximate a well-mixed fluid with Dirichlet nodes, setting pO2 = 38 mmHg, and initially setting c = 0. Whenever a cell dies, we replace it with an empty automaton, with no Dirichlet node. Oxygen and drug follow the typical diffusion-reaction equations:

$\frac{ \partial \textrm{pO}_2 }{\partial t} = D_\textrm{oxy} \nabla^2 \textrm{pO}_2 – \lambda_\textrm{oxy} \textrm{pO}_2 – \sum_{ \textrm{cells} i} U_{i,\textrm{oxy}} \textrm{pO}_2$

$\frac{ \partial c}{ \partial t } = D_c \nabla^2 c – \lambda_c c – \sum_{\textrm{cells }i} U_{i,c} c$

where each uptake rate is applied across the cell’s volume. We start the treatment by setting c = 5 μM on all Dirichlet nodes at t = 504 hours (21 days). For simplicity, we do not model drug degradation (pharmacokinetics), to approximate the in vitro conditions.

In any time interval [t,tt], each live tumor cell i has a probability pi,D of attempting division, probability pi,A of apoptotic death, and probability pi,N of necrotic death. (For simplicity, we ignore motility in this version.) We relate these to the birth rate bi, apoptotic death rate di,A, and necrotic death rate di,N by the linearized equations (from Macklin et al. 2012):

$\textrm{Prob} \Bigl( \textrm{cell } i \textrm{ becomes apoptotic in } [t,t+\Delta t] \Bigr) = 1 – \textrm{exp}\Bigl( -d_{i,A}(t) \Delta t\Bigr) \approx d_{i,A}\Delta t$

$\textrm{Prob} \Bigl( \textrm{cell } i \textrm{ attempts division in } [t,t+\Delta t] \Bigr) = 1 – \textrm{exp}\Bigl( -b_i(t) \Delta t\Bigr) \approx b_{i}\Delta t$

$\textrm{Prob} \Bigl( \textrm{cell } i \textrm{ becomes necrotic in } [t,t+\Delta t] \Bigr) = 1 – \textrm{exp}\Bigl( -d_{i,N}(t) \Delta t\Bigr) \approx d_{i,N}\Delta t$

Each dead cell has a mean duration Ti,D, which will vary by the type of cell death. Each dead cell automaton has a probability pi,L of lysis (rupture and removal) in any time span [t,t+Δt]. The duration TD is converted to a probability of cell lysis by

$\textrm{Prob} \Bigl( \textrm{dead cell } i \textrm{ lyses in } [t,t+\Delta t] \Bigr) = 1 – \textrm{exp}\Bigl( -\frac{1}{T_{i,D}} \Delta t\Bigr) \approx \frac{ \Delta t}{T_{i,D}}$

##### (Illustrative) parameter values

We use Doxy = 105 μm2/min (Ghaffarizadeh et al. 2016), and we set Ui,oxy = 20 min-1 (to give an oxygen diffusion length scale of about 70 μm, with steeper gradients than our typical 100 μm length scale). We set λoxy = 0.01 min-1 for a 1 mm diffusion length scale in fluid.

We set Dc = 300 μm2/min, and Uc = 7.2×10-3 min-1 (Dc from Weinberg et al. (2007), and Ui,c twice as large as the reference value in Weinberg et al. (2007) to get a smaller diffusion length scale of about 204 μm). We set λc = 3.6×10-5 min-1 to give a drug diffusion length scale of about 2.9 mm in fluid.

We use TD = 8.6 hours for apoptotic cells, and TD = 60 days for necrotic cells (Macklin et al., 2013). However, note that necrotic and apoptotic cells lose volume quickly, so one may want to revise those time scales to match the point where a cell loses 90% of its volume.

#### Functional forms for the birth and death rates

We model pharmacodynamics with an area-under-the-curve (AUC) type formulation. If c(t) is the drug concentration at any cell i‘s location at time t, then let its integrated exposure Ei(t) be

$E_i(t) = \int_0^t c(s) \: ds$

and we model its response with a Hill function

$R_i(t) = \frac{ E_i^h(t) }{ \alpha_i^h + E_i^h(t) },$

where h is the drug’s Hill exponent for the cell line, and α is the exposure for a half-maximum effect.

We model the microenvironment-dependent birth rate by:

$b_i(t) = \left\{ \begin{array}{lr} b_{i,P} \left( 1 – \eta_i R_i(t) \right) & \textrm{ if } \textrm{pO}_{2,P} < \textrm{pO}_2 \\ \\ b_{i,P} \left( \frac{\textrm{pO}_{2}-\textrm{pO}_{2,N}}{\textrm{pO}_{2,P}-\textrm{pO}_{2,N}}\right) \Bigl( 1 – \eta_i R_i(t) \Bigr) & \textrm{ if } \textrm{pO}_{2,N} < \textrm{pO}_2 \le \textrm{pO}_{2,P} \\ \\ 0 & \textrm{ if } \textrm{pO}_2 \le \textrm{pO}_{2,N}\end{array} \right.$

where pO2,P is the physioxic oxygen value (38 mmHg), and pO2,N is a necrotic threshold (we use 5 mmHg), and 0 < η < 1 the drug’s birth inhibition. (A fully cytostatic drug has η = 1.)

We model the microenvironment-dependent apoptosis rate by:

$d_{i,A}(t) = d_{i,A}^* + \Bigl( d_{i,A}^\textrm{max} – d_{i,A}^* \Bigr) R_i(t)$

where di,Amax is the maximum apoptotic death rate. We model the microenvironment-dependent necrosis rate by:

$d_{i,N}(t) = \left\{ \begin{array}{lr} 0 & \textrm{ if } \textrm{pO}_{2,N} < \textrm{pO}_{2} \\ \\ d_{i,N}^* & \textrm{ if } \textrm{pO}_{2} \le \textrm{pO}_{2,N} \end{array}\right.$

for a constant value di,N*.
##### (Illustrative) parameter values

We use bi,P = 0.05 hour-1 (for a 20 hour cell cycle in physioxic conditions), di,A* = 0.01 bi,P, and di,N* = 0.04 hour-1 (so necrotic cells survive around 25 hours in low oxygen conditions).

We set α = 30 μM*hour (so that cells reach half max response after 6 hours’ exposure at a maximum concentration c = 5 μM), h = 2 (for a smooth effect), η = 0.25 (so that the drug is partly cytostatic), and di,Amax = 0.1 hour^-1 (so that cells survive about 10 hours after reaching maximum response).

### Building the Cellular Automaton Model in BioFVM

BioFVM already includes Basic_Agents for cell-based substrate sources and sinks. We can extend these basic agents into full-fledged automata, and then arrange them in a lattice to create a full cellular automata model. Let’s sketch that out now.

#### Extending Basic_Agents to Automata

The main idea here is to define an Automaton class which extends (and therefore includes) the Basic_Agent class. This will give each Automaton full access to the microenvironment defined in BioFVM, including the ability to secrete and uptake substrates. We also make sure each Automaton “knows” which microenvironment it lives in (contains a pointer pMicroenvironment), and “knows” where it lives in the cellular automaton lattice. (More on that in the following paragraphs.)

So, as a schematic (just sketching out the most important members of the class):

class Standard_Data; // define per-cell biological data, such as phenotype,
// cell cycle status, etc..
class Custom_Data; // user-defined custom data, specific to a model.

class Automaton : public Basic_Agent
{
private:
Microenvironment* pMicroenvironment;

CA_Mesh* pCA_mesh;
int voxel_index;

protected:
public:
// neighbor connectivity information
std::vector<Automaton*> neighbors;
std::vector<double> neighbor_weights;

Standard_Data standard_data;
void (*current_state_rule)( Automaton& A , double );

Automaton();
void copy_parameters( Standard_Data& SD  );
void overwrite_from_automaton( Automaton& A );

void set_cellular_automaton_mesh( CA_Mesh* pMesh );
CA_Mesh* get_cellular_automaton_mesh( void ) const;

void set_voxel_index( int );
int get_voxel_index( void ) const;

void set_microenvironment( Microenvironment* pME );
Microenvironment* get_microenvironment( void );

// standard state changes
bool attempt_division( void );
void become_apoptotic( void );
void become_necrotic( void );
void perform_lysis( void );

// things the user needs to define

Custom_Data custom_data;

// use this rule to add custom logic
void (*custom_rule)( Automaton& A , double);
};


So, the Automaton class includes everything in the Basic_Agent class, some Standard_Data (things like the cell state and phenotype, and per-cell settings), (user-defined) Custom_Data, basic cell behaviors like attempting division into an empty neighbor lattice site, and user-defined custom logic that can be applied to any automaton. To avoid lots of switch/case and if/then logic, each Automaton has a function pointer for its current activity (current_state_rule), which can be overwritten any time.

Each Automaton also has a list of neighbor Automata (their memory addresses), and weights for each of these neighbors. Thus, you can distance-weight the neighbors (so that corner elements are farther away), and very generalized neighbor models are possible (e.g., all lattice sites within a certain distance).  When updating a cellular automaton model, such as to kill a cell, divide it, or move it, you leave the neighbor information alone, and copy/edit the information (standard_data, custom_data, current_state_rule, custom_rule). In many ways, an Automaton is just a bucket with a cell’s information in it.

Note that each Automaton also “knows” where it lives (pMicroenvironment and voxel_index), and knows what CA_Mesh it is attached to (more below).

#### Connecting Automata into a Lattice

An automaton by itself is lost in the world–it needs to link up into a lattice organization. Here’s where we define a CA_Mesh class, to hold the entire collection of Automata, setup functions (to match to the microenvironment), and two fundamental operations at the mesh level: copying automata (for cell division), and swapping them (for motility). We have provided two functions to accomplish these tasks, while automatically keeping the indexing and BioFVM functions correctly in sync. Here’s what it looks like:

class CA_Mesh{
private:
Microenvironment* pMicroenvironment;
Cartesian_Mesh* pMesh;

std::vector<Automaton> automata;
std::vector<int> iteration_order;
protected:
public:
CA_Mesh();

// setup to match a supplied microenvironment
void setup( Microenvironment& M );
// setup to match the default microenvironment
void setup( void );

int number_of_automata( void ) const;

void randomize_iteration_order( void );

void swap_automata( int i, int j );
void overwrite_automaton( int source_i, int destination_i );

// return the automaton sitting in the ith lattice site
Automaton& operator[]( int i );

// go through all nodes according to random shuffled order
void update_automata( double dt );
};


So, the CA_Mesh has a vector of Automata (which are never themselves moved), pointers to the microenvironment and its mesh, and a vector of automata indices that gives the iteration order (so that we can sample the automata in a random order). You can easily access an automaton with operator[], and copy the data from one Automaton to another with overwrite_automaton() (e.g, for cell division), and swap two Automata’s data (e.g., for cell migration) with swap_automata().  Finally, calling update_automata(dt) iterates through all the automata according to iteration_order, calls their current_state_rules and custom_rules, and advances the automata by dt.

#### Interfacing Automata with the BioFVM Microenvironment

The setup function ensures that the CA_Mesh is the same size as the Microenvironment.mesh, with same indexing, and that all automata have the correct volume, and dimension of uptake/secretion rates and parameters. If you declare and set up the Microenvironment first, all this is take care of just by declaring a CA_Mesh, as it seeks out the default microenvironment and sizes itself accordingly:

// declare a microenvironment
Microenvironment M;
// do things to set it up -- see prior tutorials
// declare a Cellular_Automaton_Mesh
CA_Mesh CA_model;
// it's already good to go, initialized to empty automata:
CA_model.display();


If you for some reason declare the CA_Mesh fist, you can set it up against the microenvironment:

// declare a CA_Mesh
CA_Mesh CA_model;
// declare a microenvironment
Microenvironment M;
// do things to set it up -- see prior tutorials
// initialize the CA_Mesh to match the microenvironment
CA_model.setup( M );
// it's already good to go, initialized to empty automata:
CA_model.display();


Because each Automaton is in the microenvironment and inherits functions from Basic_Agent, it can secrete or uptake. For example, we can use functions like this one:

void set_uptake( Automaton&amp; A, std::vector<double>& uptake_rates )
{
extern double BioFVM_CA_diffusion_dt;
// update the uptake_rates in the standard_data
A.standard_data.uptake_rates = uptake_rates;
// now, transfer them to the underlying Basic_Agent
*(A.uptake_rates) = A.standard_data.uptake_rates;
// and make sure the internal constants are self-consistent
A.set_internal_uptake_constants( BioFVM_CA_diffusion_dt );
}


A function acting on an automaton can sample the microenvironment to change parameters and state. For example:

void do_nothing( Automaton& A, double dt )
{ return; }

void microenvironment_based_rule( Automaton& A, double dt )
{
// sample the microenvironment
std::vector<double> MS = (*A.get_microenvironment())( A.get_voxel_index() );

// if pO2 < 5 mmHg, set the cell to a necrotic state
if( MS[0] < 5.0 ) { A.become_necrotic(); } // if drug > 5 uM, set the birth rate to zero
if( MS[1] > 5 )
{ A.standard_data.birth_rate = 0.0; }

// set the custom rule to something else
A.custom_rule = do_nothing;

return;
}


#### Implementing the mathematical model in this framework

We give each tumor cell a tumor_cell_rule (using this for custom_rule):

void viable_tumor_rule( Automaton& A, double dt )
{
// If there's no cell here, don't bother.
if( A.standard_data.state_code == BioFVM_CA_empty )
{ return; }

// sample the microenvironment
std::vector<double> MS = (*A.get_microenvironment())( A.get_voxel_index() );

// integrate drug exposure
A.standard_data.integrated_drug_exposure += ( MS[1]*dt );
A.standard_data.drug_response_function_value = pow( A.standard_data.integrated_drug_exposure,
A.standard_data.drug_hill_exponent );
double temp = pow( A.standard_data.drug_half_max_drug_exposure,
A.standard_data.drug_hill_exponent );
temp += A.standard_data.drug_response_function_value;
A.standard_data.drug_response_function_value /= temp;

// update birth rates (which themselves update probabilities)
update_birth_rate( A, MS, dt );
update_apoptotic_death_rate( A, MS, dt );
update_necrotic_death_rate( A, MS, dt );

return;
}


The functional tumor birth and death rates are implemented as:

void update_birth_rate( Automaton& A, std::vector<double>& MS, double dt )
{
static double O2_denominator = BioFVM_CA_physioxic_O2 - BioFVM_CA_necrotic_O2;

A.standard_data.birth_rate = 	A.standard_data.drug_response_function_value;
// response
A.standard_data.birth_rate *= A.standard_data.drug_max_birth_inhibition;
// inhibition*response;
A.standard_data.birth_rate *= -1.0;
// - inhibition*response
A.standard_data.birth_rate += 1.0;
// 1 - inhibition*response
A.standard_data.birth_rate *= viable_tumor_cell.birth_rate;
// birth_rate0*(1 - inhibition*response)

double temp1 = MS[0] ; // O2
temp1 -= BioFVM_CA_necrotic_O2;
temp1 /= O2_denominator;

A.standard_data.birth_rate *= temp1;
if( A.standard_data.birth_rate < 0 )
{ A.standard_data.birth_rate = 0.0; }

A.standard_data.probability_of_division = A.standard_data.birth_rate;
A.standard_data.probability_of_division *= dt;
// dt*birth_rate*(1 - inhibition*repsonse) // linearized probability
return;
}

void update_apoptotic_death_rate( Automaton& A, std::vector<double>& MS, double dt )
{
A.standard_data.apoptotic_death_rate = A.standard_data.drug_max_death_rate;
// max_rate
A.standard_data.apoptotic_death_rate -= viable_tumor_cell.apoptotic_death_rate;
// max_rate - background_rate
A.standard_data.apoptotic_death_rate *= A.standard_data.drug_response_function_value;
// (max_rate-background_rate)*response
A.standard_data.apoptotic_death_rate += viable_tumor_cell.apoptotic_death_rate;
// background_rate + (max_rate-background_rate)*response

A.standard_data.probability_of_apoptotic_death = A.standard_data.apoptotic_death_rate;
A.standard_data.probability_of_apoptotic_death *= dt;
// dt*( background_rate + (max_rate-background_rate)*response ) // linearized probability
return;
}

void update_necrotic_death_rate( Automaton& A, std::vector<double>& MS, double dt )
{
A.standard_data.necrotic_death_rate = 0.0;
A.standard_data.probability_of_necrotic_death = 0.0;

if( MS[0] > BioFVM_CA_necrotic_O2 )
{ return; }

A.standard_data.necrotic_death_rate = perinecrotic_tumor_cell.necrotic_death_rate;
A.standard_data.probability_of_necrotic_death = A.standard_data.necrotic_death_rate;
A.standard_data.probability_of_necrotic_death *= dt;
// dt*necrotic_death_rate

return;
}


And each fluid voxel (Dirichlet nodes) is implemented as the following (to turn on therapy at 21 days):

void fluid_rule( Automaton& A, double dt )
{
static double activation_time = 504;
static double activation_dose = 5.0;
static std::vector<double> activated_dirichlet( 2 , BioFVM_CA_physioxic_O2 );
static bool initialized = false;
if( !initialized )
{
activated_dirichlet[1] = activation_dose;
initialized = true;
}

if( fabs( BioFVM_CA_elapsed_time - activation_time ) < 0.01 ) { int ind = A.get_voxel_index(); if( A.get_microenvironment()->mesh.voxels[ind].is_Dirichlet )
{
A.get_microenvironment()->update_dirichlet_node( ind, activated_dirichlet );
}
}
}


At the start of the simulation, each non-cell automaton has its custom_rule set to fluid_rule, and each tumor cell Automaton has its custom_rule set to viable_tumor_rule. Here’s how:

void setup_cellular_automata_model( Microenvironment& M, CA_Mesh& CAM )
{
// Fill in this environment

std::vector<double> tumor_center( 3, 0.0 );

std::vector<double> dirichlet_value( 2 , 1.0 );
dirichlet_value[0] = 38; //physioxia
dirichlet_value[1] = 0; // drug

for( int i=0 ; i < M.number_of_voxels() ;i++ )
{
std::vector<double> displacement( 3, 0.0 );
displacement = M.mesh.voxels[i].center;
displacement -= tumor_center;
double r2 = norm_squared( displacement );

if( r2 > tumor_radius_squared ) // well_mixed_fluid
{
CAM[i].copy_parameters( well_mixed_fluid );
CAM[i].custom_rule = fluid_rule;
CAM[i].current_state_rule = do_nothing;
}
else // tumor
{
CAM[i].copy_parameters( viable_tumor_cell );
CAM[i].custom_rule = viable_tumor_rule;
}

}
}


#### Overall program loop

There are two inherent time scales in this problem: cell processes like division and death (happen on the scale of hours), and transport (happens on the order of minutes). We take advantage of this by defining two step sizes:

double BioFVM_CA_dt = 3;
std::string BioFVM_CA_time_units = "hr";
double BioFVM_CA_save_interval = 12;
double BioFVM_CA_max_time = 24*28;
double BioFVM_CA_elapsed_time = 0.0;

double BioFVM_CA_diffusion_dt = 0.05;

std::string BioFVM_CA_transport_time_units = "min";
double BioFVM_CA_diffusion_max_time = 5.0;


Every time the simulation advances by BioFVM_CA_dt (on the order of hours), we run diffusion to quasi-steady state (for BioFVM_CA_diffusion_max_time, on the order of minutes), using time steps of size BioFVM_CA_diffusion time. We performed numerical stability and convergence analyses to determine 0.05 min works pretty well for regular lattice arrangements of cells, but you should always perform your own testing!

Here’s how it all looks, in a main program loop:

BioFVM_CA_elapsed_time = 0.0;
double next_output_time = BioFVM_CA_elapsed_time; // next time you save data

while( BioFVM_CA_elapsed_time < BioFVM_CA_max_time + 1e-10 )
{
// if it's time, save the simulation
if( fabs( BioFVM_CA_elapsed_time - next_output_time ) < BioFVM_CA_dt/2.0 )
{
std::cout << "simulation time: " << BioFVM_CA_elapsed_time << " " << BioFVM_CA_time_units
<< " (" << BioFVM_CA_max_time << " " << BioFVM_CA_time_units << " max)" << std::endl;
char* filename;
filename = new char [1024];
sprintf( filename, "output_%6f" , next_output_time );
save_BioFVM_cellular_automata_to_MultiCellDS_xml_pugi( filename , M , CA_model ,
BioFVM_CA_elapsed_time );

cell_counts( CA_model );
delete [] filename;
next_output_time += BioFVM_CA_save_interval;
}

// do the cellular automaton step
CA_model.update_automata( BioFVM_CA_dt );
BioFVM_CA_elapsed_time += BioFVM_CA_dt;

// simulate biotransport to quasi-steady state

double t_diffusion = 0.0;
while( t_diffusion < BioFVM_CA_diffusion_max_time + 1e-10 )
{
M.simulate_diffusion_decay( BioFVM_CA_diffusion_dt );
M.simulate_cell_sources_and_sinks( BioFVM_CA_diffusion_dt );
t_diffusion += BioFVM_CA_diffusion_dt;
}
}


### Getting and Running the Code

1. Start a project: Create a new directory for your project (I’d recommend “BioFVM_CA_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.
3. Edit the makefile (if needed): Note that if you are using OSX, you’ll probably need to change from “g++” to your installed compiler. See these tutorials.
4. Test the code: Go to a command line (see previous tutorials), and test:
make
./BioFVM_CA_Example_1


(If you’re on windows, run BioFVM_CA_Example_1.exe.)

### Simulation Result

If you run the code to completion, you will simulate 3 weeks of in vitro growth, followed by a bolus “injection” of drug. The code will simulate one one additional week under the drug. (This should take 5-10 minutes, including full simulation saves every 12 hours.)

In matlab, you can load a saved dataset and check the minimum oxygenation value like this:

MCDS = read_MultiCellDS_xml( 'output_504.000000.xml' );
min(min(min( MCDS.continuum_variables(1).data )))


And then you can start visualizing like this:

contourf( MCDS.mesh.X_coordinates , MCDS.mesh.Y_coordinates , ...
MCDS.continuum_variables(1).data(:,:,33)' ) ;
axis image;
colorbar
xlabel('x (\mum)' , 'fontsize' , 12 );
ylabel( 'y (\mum)' , 'fontsize', 12 );
set(gca, 'fontsize', 12 );
title('Oxygenation (mmHg) at z = 0 \mum', 'fontsize', 14 );
print('-dpng', 'Tumor_o2_3_weeks.png' );
plot_cellular_automata( MCDS , 'Tumor spheroid at 3 weeks');


#### Simulation plots

Here are some plots, showing (left from right) pO2 concentration, a cross-section of the tumor (red = live cells, green = apoptotic, and blue = necrotic), and the drug concentration (after start of therapy):

##### 1 week:

Oxygen- and space-limited growth are restricted to the outer boundary of the tumor spheroid.

##### 2 weeks:

Oxygenation is dipped below 5 mmHg in the center, leading to necrosis.

##### 3 weeks:

As the tumor grows, the hypoxic gradient increases, and the necrotic core grows. The code turns on a constant 5 micromolar dose of doxorubicin at this point

##### Treatment + 12 hours:

The drug has started to penetrate the tumor, triggering apoptotic death towards the outer periphery where exposure has been greatest.

##### Treatment + 24 hours:

The drug profile hasn’t changed much, but the interior cells have now had greater exposure to drug, and hence greater response. Now apoptosis is observed throughout the non-necrotic tumor. The tumor has decreased in volume somewhat.

##### Treatment + 36 hours:

The non-necrotic tumor is now substantially apoptotic. We would require some pharamcokinetic effects (e.g., drug clearance, inactivation, or removal) to avoid the inevitable, presences of a pre-existing resistant strain, or emergence of resistance.

##### Treatment + 48 hours:

By now, almost all cells are apoptotic.

##### Treatment + 60 hours:

The non-necrotic tumor is nearly completed eliminated, leaving a leftover core of previously-necrotic cells (which did not change state in response to the drug–they were already dead!)

### Source files

This file will include the following:

1. BioFVM_cellular_automata.h
2. BioFVM_cellular_automata.cpp
3. BioFVM_CA_example_1.cpp
5. plot_cellular_automata.m
6. Makefile

### What’s next

I plan to update this source code with extra cell motility, and potentially more realistic parameter values. Also, I plan to more formally separate out the example from the generic cell capabilities, so that this source code can work as a bona fide cellular automaton framework.

More immediately, my next tutorial will use the reverse strategy: start with an existing cellular automaton model, and integrate BioFVM capabilities.

## Saving MultiCellDS data from BioFVM

Note: This is part of a series of “how-to” blog posts to help new users and developers of BioFVM

### Introduction

A major initiative for my lab has been MultiCellDS: a standard for multicellular data. The project aims to create model-neutral representations of simulation data (for both discrete and continuum models), which can also work for segmented experimental and clinical data. A single-time output is called a digital snapshot. An interdisciplinary, multi-institutional review panel has been hard at work to nail down the draft standard.

A BioFVM MultiCellDS digital snapshot includes program and user metadata (more information to be included in a forthcoming publication), an output of the microenvironment, and any cells that are secreting or uptaking substrates.

As of Version 1.1.0, BioFVM supports output saved to MultiCellDS XML files. Each download also includes a matlab function for importing MultiCellDS snapshots saved by BioFVM programs. This tutorial will get you going.

BioFVM (finite volume method for biological problems) is an open source code for solving 3-D diffusion of 1 or more substrates. It was recently published as open access in Bioinformatics here:

http://dx.doi.org/10.1093/bioinformatics/btv730

### Working with MultiCellDS in BioFVM programs

We include a MultiCellDS_test.cpp file in the examples directory of every BioFVM download (Version 1.1.0 or later). Create a new project directory, copy the following files to it:

1. BioFVM*.cpp and BioFVM*.h (from the main BioFVM directory)
2. pugixml.* (from the main BioFVM directory)
3. Makefile and MultiCellDS_test.cpp (from the examples directory)

Open the MultiCellDS_test.cpp file to see the syntax as you read the rest of this post.

See earlier tutorials (below) if you have troubles with this.

There are few key bits of metadata. First, the program used for the simulation (all these fields are optional):

// the program name, version, and project website:

// who created the program (if known)
BioFVM_metadata.program.creator.organization = "University of Southern California";
BioFVM_metadata.program.creator.department = "Center for Applied Molecular Medicine";

// (generally peer-reviewed) citation information for the program
BioFVM: an efficient parallelized diffusive transport solver for 3-D biological
simulations, Bioinformatics, 2015. DOI: 10.1093/bioinformatics/btv730.";

// user information: who ran the program
BioFVM_metadata.program.user.department = "U.S.S. Enterprise (NCC 1701)";

// And finally, data citation information (the publication where this simulation snapshot appeared)
an efficient parallelized diffusive transport solver for 3-D biological simulations, Bioinformatics,
2015. DOI: 10.1093/bioinformatics/btv730.";


You can sync the metadata current time, program runtime (wall time), and dimensional units using the following command. (This command is automatically run whenever you use the save command below.)

BioFVM_metadata.sync_to_microenvironment( M );


You can display a basic summary of the metadata via:

BioFVM_metadata.display_information( std::cout );


#### Setting options

By default (to save time and disk space), BioFVM saves the mesh as a Level 3 matlab file, whose location is embedded into the MultiCellDS XML file. You can disable this feature and revert to full XML (e.g., for human-readable cross-model reporting) via:

set_save_biofvm_mesh_as_matlab( false );


Similarly, BioFVM defaults to saving the values of the substrates in a compact Level 3 matlab file. You can override this with:

set_save_biofvm_data_as_matlab( false );


BioFVM by default saves the cell-centered sources and sinks. These take a lot of time to parse because they require very hierarchical data structures. You can disable saving the cells (basic_agents) via:

set_save_biofvm_cell_data( false );


Lastly, when you do save the cells, we default to a customized, minimal matlab format. You can revert to a more standard (but much larger) XML format with:

set_save_biofvm_cell_data_as_custom_matlab( false )


#### Saving a file

Saving the data is very straightforward:

save_BioFVM_to_MultiCellDS_xml_pugi( "sample" , M , current_simulation_time );


Your data will be saved in sample.xml. (Depending upon your options, it may generate several .mat files beginning with “sample”.)

If you’d like the filename to depend upon the simulation time, use something more like this:

double current_simulation_time = 10.347;
char filename_base [1024];
sprintf( &filename_base , "sample_%f", current_simulation_time );
save_BioFVM_to_MultiCellDS_xml_pugi( filename_base , M,
current_simulation_time );


Your data will be saved in sample_10.347000.xml. (Depending upon your options, it may generate several .mat files beginning with “sample_10.347000”.)

#### Compiling and running the program:

Edit the Makefile as below:

PROGRAM_NAME := MCDS_test

all: $(BioFVM_OBJECTS)$(pugixml_OBJECTS) MultiCellDS_test.cpp

$(COMPILE_COMMAND) -o$(PROGRAM_NAME) $(BioFVM_OBJECTS)$(pugixml_OBJECTS) MultiCellDS_test.cpp


If you’re running OSX, you’ll probably need to update the compiler from “g++”. See these tutorials.

Then, at the command prompt:

make
./MCDS_test


On Windows, you’ll need to run without the ./:

make
MCDS_test


### Working with MultiCellDS data in Matlab

MCDS = read_MultiCellDS_xml( 'sample.xml' );


This should take around 30 seconds for larger data files (500,000 to 1,000,000 voxels with a few substrates, and around 250,000 cells). The long execution time is primarily because Matlab is ghastly inefficient at loops over hierarchical data structures. Increasing to 1,000,000 cells requires around 80-90 seconds to parse in matlab.

#### Plotting data in Matlab

##### Plotting the 3-D substrate data

First, let’s do some basic contour and surface plotting:

mid_index = round( length(MCDS.mesh.Z_coordinates)/2 );

contourf( MCDS.mesh.X(:,:,mid_index), ...
MCDS.mesh.Y(:,:,mid_index), ...
MCDS.continuum_variables(2).data(:,:,mid_index) , 20 ) ;
axis image
colorbar
xlabel( sprintf( 'x (%s)' , MCDS.metadata.spatial_units) );
ylabel( sprintf( 'y (%s)' , MCDS.metadata.spatial_units) );
title( sprintf('%s (%s) at t = %f %s, z = %f %s', MCDS.continuum_variables(2).name , ...
MCDS.continuum_variables(2).units , ...
MCDS.mesh.Z_coordinates(mid_index), ...


OR

contourf( MCDS.mesh.X_coordinates , MCDS.mesh.Y_coordinates, ...
MCDS.continuum_variables(2).data(:,:,mid_index) , 20 ) ;
axis image
colorbar
xlabel( sprintf( 'x (%s)' , MCDS.metadata.spatial_units) );
ylabel( sprintf( 'y (%s)' , MCDS.metadata.spatial_units) );
title( sprintf('%s (%s) at t = %f %s, z = %f %s', ...
MCDS.continuum_variables(2).name , ...
MCDS.continuum_variables(2).units , ...
MCDS.mesh.Z_coordinates(mid_index), ...


Here’s a surface plot:

surf( MCDS.mesh.X_coordinates , MCDS.mesh.Y_coordinates, ...
MCDS.continuum_variables(1).data(:,:,mid_index) ) ;
colorbar
axis tight
xlabel( sprintf( 'x (%s)' , MCDS.metadata.spatial_units) );
ylabel( sprintf( 'y (%s)' , MCDS.metadata.spatial_units) );
zlabel( sprintf( '%s (%s)', MCDS.continuum_variables(1).name, ...
MCDS.continuum_variables(1).units ) );
title( sprintf('%s (%s) at t = %f %s, z = %f %s', MCDS.continuum_variables(1).name , ...
MCDS.continuum_variables(1).units , ...
MCDS.mesh.Z_coordinates(mid_index), ...


Finally, here are some more advanced plots. The first is an “exploded” stack of contour plots:

clf
contourslice( MCDS.mesh.X , MCDS.mesh.Y, MCDS.mesh.Z , ...
MCDS.continuum_variables(2).data , [],[], ...
MCDS.mesh.Z_coordinates(1:15:length(MCDS.mesh.Z_coordinates)),20);
view([-45 10]);
axis tight;
xlabel( sprintf( 'x (%s)' , MCDS.metadata.spatial_units) );
ylabel( sprintf( 'y (%s)' , MCDS.metadata.spatial_units) );
zlabel( sprintf( 'z (%s)' , MCDS.metadata.spatial_units) );
title( sprintf('%s (%s) at t = %f %s', ...
MCDS.continuum_variables(2).name , ...
MCDS.continuum_variables(2).units , ...


Next, we show how to use isosurfaces with transparency

clf
patch( isosurface( MCDS.mesh.X , MCDS.mesh.Y, MCDS.mesh.Z, ...
MCDS.continuum_variables(1).data, 1000 ), 'edgecolor', ...
'none', 'facecolor', 'r' , 'facealpha' , 1 );
hold on
patch( isosurface( MCDS.mesh.X , MCDS.mesh.Y, MCDS.mesh.Z, ...
MCDS.continuum_variables(1).data, 5000 ), 'edgecolor', ...
'none', 'facecolor', 'b' , 'facealpha' , 0.7 );
patch( isosurface( MCDS.mesh.X , MCDS.mesh.Y, MCDS.mesh.Z, ...
MCDS.continuum_variables(1).data, 10000 ), 'edgecolor', ...
'none', 'facecolor', 'g' , 'facealpha' , 0.5 );
hold off
camlight
view(3)
axis image
axis tightcamlight lighting gouraud
xlabel( sprintf( 'x (%s)' , MCDS.metadata.spatial_units) );
ylabel( sprintf( 'y (%s)' , MCDS.metadata.spatial_units) );
zlabel( sprintf( 'z (%s)' , MCDS.metadata.spatial_units) );
title( sprintf('%s (%s) at t = %f %s', ...
MCDS.continuum_variables(1).name , ...
MCDS.continuum_variables(1).units , ...


You can get more 3-D volumetric visualization ideas at Matlab’s website. This visualization post at MIT also has some great tips.

##### Plotting the cells

Here is a basic 3-D plot for the cells:

plot3( MCDS.discrete_cells.state.position(:,1) , ...
MCDS.discrete_cells.state.position(:,2) , ...
MCDS.discrete_cells.state.position(:,3) , 'bo' );
view(3)
axis tight
xlabel( sprintf( 'x (%s)' , MCDS.metadata.spatial_units) );
ylabel( sprintf( 'y (%s)' , MCDS.metadata.spatial_units) );
zlabel( sprintf( 'z (%s)' , MCDS.metadata.spatial_units) );
title( sprintf('Cells at t = %f %s', MCDS.metadata.current_time, ...


plot3 is more efficient than scatter3, but scatter3 will give more coloring options. Here is the syntax:

scatter3( MCDS.discrete_cells.state.position(:,1), ...
MCDS.discrete_cells.state.position(:,2), ...
MCDS.discrete_cells.state.position(:,3) , 'bo' );
view(3)
axis tight
xlabel( sprintf( 'x (%s)' , MCDS.metadata.spatial_units) );
ylabel( sprintf( 'y (%s)' , MCDS.metadata.spatial_units) );
zlabel( sprintf( 'z (%s)' , MCDS.metadata.spatial_units) );
title( sprintf('Cells at t = %f %s', MCDS.metadata.current_time, ...


Jan Poleszczuk gives some great insights on plotting many cells in 3D at his blog. I’d recommend checking out his post on visualizing a cellular automaton model. At some point, I’ll update this post with prettier plotting based on his methods.

### What’s next

Future releases of BioFVM will support reading MultiCellDS snapshots (for model initialization).

Matlab is pretty slow at parsing and visualizing large amounts of data. We also plan to include resources for accessing MultiCellDS data in VTK / Paraview and Python.

## Paul Macklin calls for common standards in cancer modeling

At a recent NCI-organized mini-symposium on big data in cancer, Paul Macklin called for new data standards in Multicellular data in simulations, experiments, and clinical science. USC featured the talk (abstract here) and the work at news.usc.edu.

Read the article: http://news.usc.edu/59091/usc-researcher-calls-for-common-standards-in-cancer-modeling/ (Feb. 21, 2014)

## Paul Macklin interviewed at 2013 PSOC Annual Meeting

Paul Macklin gave a plenary talk at the 2013 NIH Physical Sciences in Oncology Annual Meeting. After the talk, he gave an interview to the Pauline Davies at the NIH on the need for data standards and model compatibility in computational and mathematical modeling of cancer. Of particular interest:

Pauline Davies: How would you ever get this standardization? Who would be responsible for saying we want it all reported in this particular way?

Paul Macklin: That’s a good question. It’s a bit of the chicken and the egg problem. Who’s going to come and give you data in your standard if you don’t have a standard? How do you plan a standard without any data? And so it’s a bit interesting. I just think someone needs to step forward and show leadership and try to get a small working group together, and at the end of the day, perfect is the enemy of the good. I think you start small and give it a go, and you add more to your standard as you need it. So maybe version one is, let’s say, how quickly the cells divide, how often they do it, how quickly they die, and what their oxygen level is, and maybe their positions. And that can be version one of this standard and a few of us try it out and see what we can do. I think it really comes down to a starting group of people and a simple starting point, and you grow it as you need it.

Shortly after, the MultiCellDS project was born (using just this strategy above!), with the generous assistance of the Breast Cancer Research Foundation.

Read / Listen to the interview: http://physics.cancer.gov/report/2013report/PaulMacklin.aspx (2013)