PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems

Abstract

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 http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net.

Agent‐Based Modeling in Molecular Systems Biology

“…Moreover, ABMs are significantly more computationally expensive compared to ODEs and PDEs. Nevertheless, most ABM simulations could be performed in a few hours and up to a few days on desktop workstations, depending on the size of the system . However, computational efficiency of ABM simulations heavily depends on their implementation.…”

“…However, computational efficiency of ABM simulations heavily depends on their implementation. Efficient implementation of ABM simulations could result in linear (or close to linear) performance scalability . In addition, for significantly large systems, ABMs could be easily parallelized with each computing node handling the calculations associated with a subset of agents.…”

“…Nevertheless, most ABM simulations could be performed in a few hours and up to a few days on desktop workstations, depending on the size of the system. [38,59,86] However, computational efficiency of ABM simulations heavily depends on their implementation. Efficient implementation of ABM simulations could result in linear (or close to linear) performance scalability.…”

“…Efficient implementation of ABM simulations could result in linear (or close to linear) performance scalability. [59,86] In addition, for significantly large systems, ABMs could be easily parallelized with each computing node handling the calculations associated with a subset of agents. Accordingly, several efforts have been made to employ high performance computing (HPC) resources for large scale ABMs.…”

Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data

“…In practice, this assumption may depend on the route of CAR T-cell administration, with intracavitary and intraventricular injections potentially resulting in spatially heterogeneous densities of CAR T or cancer cells, although methods to assess the distribution of CART-cells in vivo remains an open challenge(41,42). To address this limitation, the wellmixed assumption may be relaxed and CAR T-cell killing dynamics interrogated with spatial or agent-based models(43). Another limitation is with regard to the experimental system: the change in electrical impedance measured by the cell index does not differentiate cell detachment from cell killing.…”