BioFVM: an efficient, parallelized diffusive transport solver for 3-D biological simulations

Ghaffarizadeh, Ahmadreza; Friedman, Samuel H.; Macklin, Paul

doi:10.1093/bioinformatics/btv730

Cited by 100 publications

(182 citation statements)

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“…and their interaction with the cells (uptake, secretion, etc.) is also provided by PhysiCell's BioFVM module [48]. Beyond secretion and uptake of diffusing substrates, PhysiCell has implemeted key phenotypic behaviors needed for our target problems in multicellular systems biology: cell volumetric growth, adhesion, "repulsion", directed and random motility, cell cycle progression, and apoptotic and necrotic death processes.…”

Section: Physicellmentioning

confidence: 99%

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PhysiBoSS: a multi-scale agent based modelling framework integrating physical dimension and cell signalling

Letort

Montagud

Stoll

et al. 2018

Preprint

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Due to the complexity of biological systems, their heterogeneity, and the internal regulation of each cell and its surrounding, mathematical models that take into account cell signalling, cell population behaviour and the extracellular environment are particularly helpful to understand such complex systems. However, very few of these tools, freely available and computationally efficient, are currently available. To fill this gap, we present here our open-source software, PhysiBoSS, which is built on two available software packages that focus on different scales: intracellular signalling using continuous-time markovian Boolean modelling (MaBoSS) and multicellular behaviour using agent-based modelling (PhysiCell).The multi-scale feature of PhysiBoSS -its agent-based structure and the possibility to integrate any Boolean network to it -provide a flexible and computationally efficient framework to study heterogeneous cell population growth in diverse experimental set-ups. This tool allows one to explore the effect of environmental and genetic alterations of individual cells at the population level, bridging the critical gap from genotype to phenotype. PhysiBoSS thus becomes very useful when studying population response to treatment, mutations effects, cell modes of invasion or isomorphic morphogenesis events.To illustrate potential use of PhysiBoSS, we studied heterogeneous cell fate decisions in response to TNF treatment in a 2-D cell population and in a tumour cell 3-D spheroid. We explored the effect of different treatment regimes and the behaviour and selection of several resistant mutants. We highlighted the importance of spatial information on the population dynamics by considering the effect of competition for resources like oxygen. PhysiBoSS is freely available on GitHub (https://github.com/gletort/PhysiBoSS), and is distributed open source under the BSD 3-clause license. It is compatible with most Unix systems, and a Docker package (https://hub.docker.com/r/gletort/physiboss/) is provided to ease its deployment in other systems.

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Section: Physicellmentioning

confidence: 99%

“…• BioFVM, a module of PhysiCell software that handles the simulation of one or more diffusing environmental entities [48]. It simulates diffusion, degradation and source of diffusible entities in the extra-cellular matrix (ECM) (Fig 1, green).…”

Section: Physibossmentioning

confidence: 99%

PhysiBoSS: a multi-scale agent based modelling framework integrating physical dimension and cell signalling

Letort

Montagud

Stoll

et al. 2018

Preprint

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“…PhysiCell-EMEWS There have been multiple projects utilizing agentbased/hybrid modeling of tumors and their local environments [34,35,36,37]. Review of this work and our own has led to the following list of key elements needed to systematically investigate cancer-immune dynamics across high-dimensional parameter/hypothesis spaces to identify the factors driving immunotherapy failure or success: 1 efficient 3-D simulation of diffusive biotransport of multiple (5 or more) growth substrates and signaling factors on mm 3 -scale tissues, on a single compute node (attained via BioFVM [33]); 2 efficient simulation of 3-D multicellular systems (10 5 or more cells) that account for basic biomechanics, single-cell processes, cell-cell interactions, and flexible cell-scale hypotheses, on a single compute node (attained via PhysiCell [32]); 3 a mechanistic model of an adaptive immune response to a 3-D heterogeneous tumor, on a single compute node (introduced in [32]); 4 efficient, high-throughput computing frameworks that can automate hundreds or thousands of simulations through high-dimensional hypothesis spaces to efficiently investigate the model behavior by distributing them across HPC/HTC resources (attained via EMEWS [31]); and 5 clear metrics to quantitatively compare simulation behaviors, allowing the formulation of a hypothesis optimization problem (see Proposition: hypothesis testing as an optimization problem).…”

Section: -D Cancer Immunology Model Exploration Usingmentioning

confidence: 99%

“…In prior work [33] we developed BioFVM: an open source framework to simulate biological diffusion of multiple chemical substrates (a vector ρ) in 3-D, governed by the vector of partial differential equations (PDEs)…”

Section: Efficient 3-d Multi-substrate Biotransport With Biofvmmentioning

confidence: 99%

“…In this paper, we formulate the requirements for a computational experimental system for systematic, high-throughput hypothesis testing and optimization. We provide an example of how high-throughput hypothesis testing can be applied to the complex problem of tumor-immune interactions using a novel framework that integrates a multiscale mechanistic model development platform-PhysiCell [32] and BioFVM [33]within a computational ME manager-Extreme Model Exploration with Swift (EMEWS) [31].…”

Section: Introductionmentioning

confidence: 99%

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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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Development and application of an agent‐based model for the simulation of the extravasation process of circulating tumor cells

Schneider

Giehl

Baeurle

2023

Numer Methods Biomed Eng

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The primary cause for cancer-related death is metastasis, and although this phenomenon is the hallmark of cancer, it remains poorly understood. Since studies on the underlying mechanisms are still demanding by experimental means prognostic tools based on computer models can be of great value, not only for elucidating metastasis formation but also for assessing the prospective benefits as well as risks of a therapy for patients with advanced cancer. Here, we present an agent-based model (ABM), describing the complete process of platelet-assisted extravasation of circulating tumor cells (CTCs) from the chemoattraction of blood platelets by the CTCs up to the embedding of the CTCs in the epithelial tissue by computational means. From the simulation results, we conclude that the ABM produces results in consistency with experimental observations, which opens new perspectives for the development of computer models for predicting the efficacity of drug-based tumor therapies and assisting precision medicine approaches.agent-based model, circulating tumor cells, computer simulation of platelet-assisted extravasation, metastasis | INTRODUCTIONCancer is far from being a disease of the modern era. From an evolutionary point of view, it is a quite old disease. One of the oldest proven appearances of cancer was found in an Edmontosaurus. 1 However, cancer has developed to one of the diseases with a significant impact on the life expectancy of humans by causing nearly 10 million deaths per year worldwide. 2,3 Nowadays, the success of a cancer treatment is still highly dependent on the tumor stage of a carcinoma. 4 If the tumor is detected early and has not metastasized, the treatment has a higher chance to be successful, since it is still localized. By contrast, a carcinoma in an advanced stage has spread across multiple tissues/organs in the human body, decreasing the likelihood for a successful treatment. 4,5 To combat metastatic spreading, prognostic tools for the prediction of metastasis in combination with personalized strategies would be useful, but are still in an early stage of development. [6][7][8] The process of metastases formation comprises several steps. 9 First, the metastatic cells have to detach from the primary tumor, followed by the intravasation into the vascular system. 10 After the intravasation step, the cells, known

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BioFVM: an efficient, parallelized diffusive transport solver for 3-D biological simulations

Cited by 100 publications

References 7 publications

PhysiBoSS: a multi-scale agent based modelling framework integrating physical dimension and cell signalling

PhysiBoSS: a multi-scale agent based modelling framework integrating physical dimension and cell signalling

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

Development and application of an agent‐based model for the simulation of the extravasation process of circulating tumor cells

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