PhysiCell: an open source physics-based cell simulator for 3-D multicellular systems. 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 and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 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 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 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 and

References in zbMATH (referenced in 10 articles )

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  1. Ciaran Welsh, Jin Xu, Lucian Smith, Matthias König, Kiri Choi, Herbert M. Sauro: libRoadRunner 2.0: A High-Performance SBML Simulation and Analysis Library (2022) arXiv
  2. Leschiera, Emma; Lorenzi, Tommaso; Shen, Shensi; Almeida, Luis; Audebert, Chloe: A mathematical model to study the impact of intra-tumour heterogeneity on anti-tumour CD(8^+) T cell immune response (2022)
  3. Hamis, Sara; Yates, James; Chaplain, Mark A. J.; Powathil, Gibin G.: Targeting cellular DNA damage responses in cancer: an in vitro-calibrated agent-based model simulating monolayer and spheroid treatment responses to ATR-inhibiting drugs (2021)
  4. Lötstedt, Per: Derivation of continuum models from discrete models of mechanical forces in cell populations (2021)
  5. Cooper et al.: Chaste: Cancer, Heart and Soft Tissue Environment (2020) not zbMATH
  6. Jenner, Adrianne L.; Frascoli, Federico; Coster, Adelle C. F.; Kim, Peter S.: Enhancing oncolytic virotherapy: observations from a Voronoi cell-based model (2020)
  7. Mathias, Sonja; Coulier, Adrien; Bouchnita, Anass; Hellander, Andreas: Impact of force function formulations on the numerical simulation of centre-based models (2020)
  8. Nardini, John T.; Lagergren, John H.; Hawkins-Daarud, Andrea; Curtin, Lee; Morris, Bethan; Rutter, Erica M.; Swanson, Kristin R.; Flores, Kevin B.: Learning equations from biological data with limited time samples (2020)
  9. Bouchnita, Anass; Hellander, Stefan; Hellander, Andreas: A 3D multiscale model to explore the role of EGFR overexpression in tumourigenesis (2019)
  10. Wolff, Henri B.; Davidson, Lance A.; Merks, Roeland M. H.: Adapting a plant tissue model to animal development: introducing cell sliding into VirtualLeaf (2019)