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

Heiland, Randy; Bergman, Daniel; Lyons, Blair; Cass, Julie; Rocha, Heber L.; Ruscone, Marco; Noël, Vincent; Macklin, Paul

doi:10.1101/2023.10.24.563727

Cited by 3 publications

(6 citation statements)

References 19 publications

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“…Previously, custom hand-written code and a high level of technical knowledge were required to implement even basic models. Now, a scientist can easily create, specify, and modify cell behaviors without writing code or hand-edited markup languages, particularly when used in combination of graphical and cloud-based modeling frameworks 94 . In this implementation, it is simple to modulate and apply behaviors to different agents in the system and to build hypotheses into the model.…”

Section: Discussionmentioning

confidence: 99%

“…Similar to our prior work to create cloud-based training materials 98 and cloud-based model dissemination 95 , and inspired by other recent advances on “zero-install” models 137 , we have created a cloud-based version 94,138 of PhysiCell based on the nanoHUB platform 139 . This cloud implementation allows scientists to create, execute, visualize, and explore grammar-based models interactively in a web browser, without need for programming expertise or software setup.…”

Section: Code Availabilitymentioning

confidence: 99%

“…The cloud-hosted model is available at https://nanohub.org/tools/pcstudio. Alternatively, scientists can download the latest release of the PhysiCell Studio 94 desktop application at https://github.com/PhysiCell-Tools/PhysiCell-Studio/releases. Assuming an executable model has been compiled, the Studio allows interactive creation and editing of rules ( Fig.…”

Section: Code Availabilitymentioning

confidence: 99%

“…To load and compile an example named myproject, use: make load PROJ=myproject && make Similar to our prior work to create cloud-based training materials 98 and cloud-based model dissemination 95 , and inspired by other recent advances on "zero-install" models 137 , we have created a cloud-based version 94,138…”

Section: Code Availabilitymentioning

confidence: 99%

See 3 more Smart Citations

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Johnson,

Stein-O’Brien,

Booth

et al. 2023

Preprint

Self Cite

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Cells are fundamental units of life. Recent technical advances have revolutionized our ability to quantify the state and identity of individual cells, and intercellular regulatory programs. However, these static measurements alone are limited in their ability to predict the complex collective behaviors that emerge from populations of many interacting cells over time. Mathematical models have a proven record of successfully predicting the behaviors of dynamic biological systems, e.g., therapeutic responses in cancer. Simulations from these models enable in silico visualization, examination, and refinement of biological models and can be used to generate new hypotheses about cells and their collective behavior. Agent-based modeling is particularly well-suited to studying communities of interacting cells, as it is intuitive to map a single cell to a single agent. Thus, we have developed a conceptual framing (with a reference implementation in the widely-used PhysiCell agent-based modeling framework) that can be initialized directly from single cell and spatial transcriptomic data, and that can be easily populated with interactive rules. Because the expert mathematical and computational knowledge required to build agent-based models has limited their widespread adoption in the biomedical research community, we engineered this framework to specify complex cellular responses to signals (or stimuli) using a single line of human readable text. This plain language text encodes cellular phenotypes and regulatory mechanisms from high throughput data and published literature, using a novel concept of hypothesis grammar. We motivate and fully describe this grammar and its philosophy, and then present a series of five example reference models of tumor growth and response to immunotherapy. Biologically, these examples demonstrate how mathematical models can predict from single cell and spatial transcriptomic data the cellular phenotypes responsible for tumor cell invasion and the simulation of immunotherapy treatment to overcome tumor cell growth. Computationally, these examples are designed to demonstrate how this conceptual framing and software implementation empower interdisciplinary teams to build an agent-based model of their experimental system, levering prior biological knowledge alone or in combination with information from spatial multi-omics technologies. Altogether, this approach provides an interface to bridge biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

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Section: Discussionmentioning

confidence: 99%

Section: Code Availabilitymentioning

confidence: 99%

Section: Code Availabilitymentioning

confidence: 99%

Section: Code Availabilitymentioning

confidence: 99%

See 2 more Smart Citations

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Johnson,

Stein-O’Brien,

Booth

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Supplementary data (including a detailed continuation of the tumor model walk-thru) are available in Zenodo [ 15 ] . Archives of the software are available in Software Heritage [ 47 ] . …”

Section: Data Availabilitymentioning

confidence: 99%

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

Heiland,

Bergman,

Lyons

et al. 2024

Gigabyte

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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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Cell density couples tissue mechanics to control the elongation speed of the body axis

Lu,

Vidigueira,

Jie

et al. 2024

Preprint

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The vertebrate body forms by progressive addition and segmentation of tissues in an anterior to posterior direction. The posterior presomitic mesoderm (pPSM), which receives new cells from the tailbud, produces an expansive force that drives axis elongation (1,2,3). Elongation moves an FGF gradient that controls the boundary placement of new segments in conjunction with the oscillatory genes of the segmentation clock (4). As the period of the segmentation clock is insensitive to body size (5,6) or elongation progress (7,8), the number and size consistency of segments depend on stable, robust elongation. How elongation speed is constrained remains unknown. Here we utilised modeling and tissue force microscopy (9) on chicken embryos to show that cell density of the pPSM dynamically modulates elongation speed in a negative feedback loop. Elongation alters the cell density in the pPSM, which in turn controls progenitor cell influx through the mechanical coupling of body axis tissues. This enables responsive cell dynamics in over- and under-elongated axes that consequently self-adjust speed to achieve long-term robustness in axial length. Our simulations and experiments further suggest that cell density and activity under FGF signalling act synergistically to drive elongation. Our work supports a simple mechanism of morphogenetic speed control where the cell density relates negatively to progress, and positively to force generation.

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

Cited by 3 publications

References 19 publications

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

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

Cell density couples tissue mechanics to control the elongation speed of the body axis

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