MultiCellDS: a community-developed standard for curating microenvironment-dependent multicellular data

Friedman, Samuel H.; Anderson, Alexander R.A.; Bortz, David M.; Fletcher, Alexander G.; Frieboes, Hermann B.; Ghaffarizadeh, Ahmadreza; Grimes, David Robert; Hawkins-Daarud, Andrea; Hoehme, Stefan; Juarez, EF; Kesselman, Carl; Merks, R. R. M. H.; Mumenthaler, SM; Pk, Newton; Norton, Kerri-Ann; Rawat, Rishi R.; Rockne, RC; Ruderman, Daniel; Scott, Jacob G.; Ss, Sindi; Sparks, J. William; Swanson, Kristin R.; Agus, DB; Macklin, Paul

doi:10.1101/090456

Cited by 12 publications

(27 citation statements)

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“…Also, the generalized description of hypotheses is not yet fully mature. Standards have emerged to describe molecularscale systems biology (generally systems of ODEs) as SBML [49], and more recently to express multicellular biology as MultiCellDS [39,40], but cell-cell interaction rules will likely require a different description, such as by using elements of the cell behavior ontology (CBO) [51].…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…In [1], we developed a 3-D agent-based model by extending BioFVM's basic agents (discrete cell-like agents with static positions, which could secrete and consume chemical substrates in the BioFVM environment) to create extensible software cell agents. Each cell has an independent, hierarchically-organized phenotype (the cell's behavioral state and parameters) [39,40], user-settable function pointers to define hypotheses on the cell's phenotype, volume changes, cell cycling or death, mechanics, orientation, and motility, and user-customizable data. The cells' function pointers can be changed at any time in the simulation, allowing dynamical cell behavior and even switching between cell types.…”

Section: Efficient 3-d Multicellular Simulationsmentioning

confidence: 99%

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High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

Ozik

Collier

Wozniak

et al. 2017

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Cancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous systems. Therapies act on this cancer-host system, sometimes with unexpected results. Systematic investigation of mechanistic models could help identify the factors driving a treatment's success or failure, but exploring mechanistic models over highdimensional parameter spaces is computationally challenging. In this paper, we introduce a high throughput computing (HTC) framework that integrates a mechanistic 3-D multicellular simulator (PhysiCell) with an extreme-scale model exploration platform (EMEWS) to investigate high-dimensional parameter spaces. We show early results in adapting PhysiCell-EMEWS to 3-D cancer immunotherapy and show insights on therapeutic failure. We describe a PhysiCell-EMEWS workflow for high-throughput cancer hypothesis testing, where thou-sands of mechanistic simulations are compared against data-driven error metrics to perform hypothesis optimization. We close by discussing novel applications to synthetic multicellular systems for cancer therapy.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

Section: Efficient 3-d Multicellular Simulationsmentioning

confidence: 99%

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

Ozik

Collier

Wozniak

et al. 2017

Preprint

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“…47 PhysiCell aims to balance computational speed, built-in standard functionality, 48 flexibility, and codebase simplicity. It includes a built-in library of standardized cell 49 cycle and cell death models co-developed with biologists and modelers here and in the 50 MultiCellDS standardization process [14,15], force-based cell-cell interaction mechanics, 51 motility, and volume regulation. Users can replace any of these built-in models with 52 their own, and they can dynamically assign custom functions to any agent at any time.…”

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confidence: 99%

“…(See Cell 401 cycling.) We used a simpler "live cells" cycle model [14] Hanging drop spheroids (HDS)-a 3-D cell culture model where a small cluster or 422 aggregate of tumor cells is suspended in a drop of growth medium by surface tension-are 423 increasingly used to approximate 3-D in vivo growth conditions [31]. Unlike traditional 424 2-D monolayer experiments, HDSs allow scientists to investigate the impact of substrate 425 gradients on tumor growth, particularly oxygen gradients.…”

mentioning

confidence: 99%

“…Software limitations and potential solutions PhysiCell is intended to function as a 666 modular engine and to interact with standardized data (e.g., MultiCellDS [14]), but it has 667 not yet fully implemented MultiCellDS import and export. Graphical simulation design 668 tools, data visualization tools, and analysis tools are needed to widen its accessibility 669 beyond seasoned C++ programmers and reduce its learning curve.…”

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confidence: 99%

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PhysiCell: an Open Source Physics-Based Cell Simulator for 3-D Multicellular Systems

Ghaffarizadeh

Friedman

Mumenthaler

et al. 2016

Preprint

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Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal "virtual laboratory" for such multicellular systems simulates both the biochemical microenvironment (the "stage") and many mechanically and biochemically interacting cells (the "players" upon the stage).PhysiCell-physics-based multicellular simulator-is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility "out of the box." The C++ code has minimal dependencies, making it simple to maintain across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 10 5 -10 6 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes.We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We also demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a "cellular cargo delivery" system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new sub-models, capabilities, and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net. This paper introducesPhysiCell: an open source, agent-based model for 3-D multicellular simulations. It includes a standard library of sub-models for cell fluid and solid volume changes, cycle progression, apoptosis, necrosis, mechanics, and motility. PhysiCell is directly coupled to a biotransport solver to simulate many diffusing substrates and cell-secreted signals. Each cell can dynamically update its phenotype based on its microenvironmental conditions. Users can customize or replace the included sub-models.PhysiCell runs on a variety of platforms (Linux, OSX, and Windows) with few software dependencies. Its computational cost scales linearly in the number of cells. It is feasible to simulate 500,000 cells on quad-core desktop workstations, and millions of cells on single HPC compute nodes. We demonstrat...

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Correlating nuclear morphometric patterns with estrogen receptor status in breast cancer pathologic specimens

et al. 2018

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In this pilot study, we introduce a machine learning framework to identify relationships between cancer tissue morphology and hormone receptor pathway activation in breast cancer pathology hematoxylin and eosin (H&E)-stained samples. As a proof-of-concept, we focus on predicting clinical estrogen receptor (ER) status—defined as greater than one percent of cells positive for estrogen receptor by immunohistochemistry staining—from spatial arrangement of nuclear features. Our learning pipeline segments nuclei from H&E images, extracts their position, shape and orientation descriptors, and then passes them to a deep neural network to predict ER status. After training on 57 tissue cores of invasive ductal carcinoma (IDC), our pipeline predicted ER status in an independent test set of patient samples (AUC ROC = 0.72, 95%CI = 0.55–0.89, n = 56). This proof of concept shows that machine-derived descriptors of morphologic histology patterns can be correlated to signaling pathway status. Unlike other deep learning approaches to pathology, our system uses deep neural networks to learn spatial relationships between pre-defined biological features, which improves the interpretability of the system and sheds light on the features the neural network uses to predict ER status. Future studies will correlate morphometry to quantitative measures of estrogen receptor status and, ultimately response to hormonal therapy.

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MultiCellDS: a community-developed standard for curating microenvironment-dependent multicellular data

Cited by 12 publications

References 94 publications

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

PhysiCell: an Open Source Physics-Based Cell Simulator for 3-D Multicellular Systems

Correlating nuclear morphometric patterns with estrogen receptor status in breast cancer pathologic specimens

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