When Seeing Isn't Believing: How Math Can Guide Our Interpretation of Measurements and Experiments

Macklin, Paul

doi:10.1016/j.cels.2017.08.005

Cited by 28 publications

(28 citation statements)

References 9 publications

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“…We close by noting that this framework has applications beyond cancer. In general, testing multiscale hypotheses in high throughput is valuable in determining the rules underlying (often puzzling) experimental data, and even to evaluate the limitations of experiments themselves [52,53]. Moreover, we envision that the PhysiCell-EMEWS framework could be used as a multicellular design tool: for any given multicellular design including single-cell and cellcell interaction rules (which map onto hypotheses in this framework), PhysiCell-EMEWS can test the emergent multicellular behavior against the target behavior (the design goal), and iteratively tune the cell rules to achieve the design goal.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

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

Ozik

Collier

Wozniak

et al. 2017

Preprint

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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%

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

Ozik

Collier

Wozniak

et al. 2017

Preprint

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“…Thus, PhysiCell is designed to be a general-purpose toolkit for exploring multicellular systems, and the multicellular behaviors that emerge from a user-implemented set of cell phenotypic rules (i.e., a set of hypotheses). Other potential uses include 3/34 simulation-generated synthetic datasets that investigate the meaning and shortcomings of experimental measurements [18,19]. PhysiCell's design has allowed straightforward deployment on supercomputers for high-throughput simulation studies.…”

Section: Scientific Design Goals and Use Casesmentioning

confidence: 99%

“…We now introduce and release as open source PhysiCell: a mechanistic off-lattice agent-based model that extends BioFVM to simulate the tissue-scale behaviors that emerge from basic biological and biophysical cell processes. simulation-generated synthetic datasets that investigate the meaning and shortcomings of experimental measurements [18,19]. PhysiCell's design has allowed straightforward deployment on supercomputers for high-throughput simulation studies.…”

Section: Introductionmentioning

confidence: 99%

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

Ghaffarizadeh

Friedman

Mumenthaler

et al. 2016

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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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“…(See the excellent recent review by Norton et al 2019 13 , and our own recent work by Ghaffarizadeh et al 14 and Ozik et al 15 .) By adjusting model parameters and simulation rules, we can explore the characteristics of successful and unsuccessful treatments, and learn how the "best policies" vary with a patient's tumour characteristics [15][16][17] .…”

Section: Introduction the Translational Dilemma In Cancermentioning

confidence: 99%

Learning-accelerated Discovery of Immune-Tumour Interactions

Ozik

Collier

Heiland

et al. 2019

Preprint

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We present an integrated framework for enabling dynamic exploration of design spaces for cancer immunotherapies with detailed dynamical simulation models on high-performance computing resources. Our framework combines PhysiCell, an open source agent-based simulation platform for cancer and other multicellular systems, and EMEWS, an open source platform for extreme-scale model exploration. We build an agent-based model of immunosurveillance against heterogeneous tumours, which includes spatial dynamics of stochastic tumour-immune contact interactions. We implement active learning and genetic algorithms using high-performance computing workflows to adaptively sample the model parameter space and iteratively discover optimal cancer regression regions within biological and clinical constraints.

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When Seeing Isn't Believing: How Math Can Guide Our Interpretation of Measurements and Experiments

Abstract: Mathematical thought experiments probe the meaning and pitfalls of experimental measurements and demonstrate that caution is in order when measuring heterogeneity.

Cited by 28 publications

References 9 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

Learning-accelerated Discovery of Immune-Tumour Interactions

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