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. 2024 Jun 19:2024:gigabyte128.
doi: 10.46471/gigabyte.128. eCollection 2024.

PhysiCell Studio: a graphical tool to make agent-based modeling more accessible

Randy Heiland  1 , Daniel Bergman  2   3 , Blair Lyons  4 , Grant Waldow  5 , Julie Cass  4 , Heber Lima da Rocha  1 , Marco Ruscone  6   7   8   9 , Vincent Noël  6   7   8 , Paul Macklin  1
Affiliations

PhysiCell Studio: a graphical tool to make agent-based modeling more accessible

Randy Heiland et al. GigaByte. .

Abstract

Defining a multicellular model can be challenging. There may be hundreds of parameters that specify the attributes and behaviors of objects. In the best case, the model will be defined using some format specification - a markup language - that will provide easy model sharing (and a minimal step toward reproducibility). PhysiCell is an open-source, physics-based multicellular simulation framework with an active and growing user community. It uses XML to define a model and, traditionally, users needed to manually edit the XML to modify the model. PhysiCell Studio is a tool to make this task easier. It provides a GUI that allows editing the XML model definition, including the creation and deletion of fundamental objects: cell types and substrates in the microenvironment. It also lets users build their model by defining initial conditions and biological rules, run simulations, and view results interactively. PhysiCell Studio has evolved over multiple workshops and academic courses in recent years, which has led to many improvements. There is both a desktop and cloud version. Its design and development has benefited from an active undergraduate and graduate research program. Like PhysiCell, the Studio is open-source software and contributions from the community are encouraged.

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Conflict of interest statement

The authors declare they have no competing interests.

Figures

Figure 1.
Figure 1.
Portion of the XML configuration file showing elements of a cell definition.
Figure 2.
Figure 2.
View of the basic configuration of a PhysiCell model in PhysiCell Studio.
Figure 3.
Figure 3.
View of the microenvironment configuration of a PhysiCell model in PhysiCell Studio.
Figure 4.
Figure 4.
View of the cell cycle definition of a cancer cell in PhysiCell Studio.
Figure 5.
Figure 5.
View of the secretion configuration of a cancer cell in PhysiCell Studio.
Figure 6.
Figure 6.
View of the definition of cells’ initial conditions of a PhysiCell model in PhysiCell Studio.
Figure 7.
Figure 7.
Enable cells’ initial conditions on the Config Basics tab.
Figure 8.
Figure 8.
View of the definition of user parameters in PhysiCell Studio.
Figure 9.
Figure 9.
View of the simulation log in PhysiCell Studio.
Figure 10.
Figure 10.
Simulation results at 1 h (left) and 2 days, 12 h (right) in PhysiCell Studio.
Figure 11.
Figure 11.
Pressure values on cells (at 2 days, 12 h) in PhysiCell Studio.
Figure 12.
Figure 12.
Jupyter notebook GUI showing some model parameters (left) and simulation results (right).
Figure 13.
Figure 13.
An example of model rules (see ‘Supplementary data’) in PhysiCell Studio.
Figure 14.
Figure 14.
3D plots of the cancer-immune-sample model in PhysiCell Studio. Left: Clipping plane (XY) of a cell population. Right: Slice planes (XY, YZ, XZ) showing projected substrate concentrations.
Figure 15.
Figure 15.
Creating initial conditions for cells.
Figure 16.
Figure 16.
Interface for a boolean intracellular model in PhysiCell Studio.
Figure 17.
Figure 17.
Working with PhysiCell Studio. A video from Session 2 of the PhysiCell Workshop 2023 [37]. https://youtu.be/jkbPP1yDzME.
Figure 18.
Figure 18.
Studio 3D display (left) and the Simularium viewer (running in a web browser) which allows cell types to be hidden (right).
Figure 19.
Figure 19.
ParaView rendering of data from the cancer-immune-sample model in PhysiCell Studio.
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