ArticleBioinformatics (Oxford, England)2019
PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 1 of them a synthesis that pooled it.
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Who cites it
61 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Intelligent tool orchestration for rapid mechanistic model prototyping: MCP servers as AI-biology interfaces.NPJ systems biology and applications · 2026Article
- Mesenchymal to epithelial transition (MET) in cancer progression: insights from logical modeling.Scientific reports · 2026Article
- Leveraging training expertise to build capacity in computational personalised medicine.Bioinformatics advances · 2026Article
- SISTEM: simulation of tumor evolution, metastasis, and DNA-seq data under genotype-driven selection.Bioinformatics (Oxford, England) · 2025Article
- Path-based quantification of activation and repression in Boolean models using BooLEVARD.NPJ systems biology and applications · 2025Article
- Local activation of Cxcl12a signaling controls olfactory placode morphogenesis in zebrafish embryos.iScience · 2025Article
- Article
- Start & Stop: a PhysiCell and PhysiBoSS 2.0 add-on for interactive simulation control.BMC bioinformatics · 2025Article
- Cell populations simulated in silico within SimulCell accurately reproduce the behaviour of experimental cell cultures.NPJ systems biology and applications · 2025Article
- Towards an interpretable deep learning model of cancer.NPJ precision oncology · 2025Review
- A Simple Framework for Agent-Based Modeling with Extracellular Matrix.Bulletin of mathematical biology · 2025Article
- Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation.Cancer biology & therapy · 2024Review
- Building multiscale models with PhysiBoSS, an agent-based modeling tool.Briefings in bioinformatics · 2024Article
- Spatial computational modelling illuminates the role of the tumour microenvironment for treating glioblastoma with immunotherapies.NPJ systems biology and applications · 2024Article
- A comprehensive review of computational cell cycle models in guiding cancer treatment strategies.NPJ systems biology and applications · 2024Review
- Article
- Metabolic symbiosis between oxygenated and hypoxic tumour cells: An agent-based modelling study.PLoS computational biology · 2024Article
- Dynamic Boolean modeling of molecular and cellular interactions in psoriasis predicts drug target candidates.iScience · 2024Article
- PhysiMeSS - a new physiCell addon for extracellular matrix modelling.GigaByte (Hong Kong, China) · 2024Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationDue to the complexity and heterogeneity of multicellular biological systems, mathematical models that take into account cell signalling, cell population behaviour and the extracellular environment are particularly helpful. We present PhysiBoSS, an open source software which combines intracellular signalling using Boolean modelling (MaBoSS) and multicellular behaviour using agent-based modelling (PhysiCell).
resultsPhysiBoSS provides a flexible and computationally efficient framework to explore the effect of environmental and genetic alterations of individual cells at the population level, bridging the critical gap from single-cell genotype to single-cell phenotype and emergent multicellular behaviour. PhysiBoSS thus becomes very useful when studying heterogeneous population response to treatment, mutation effects, different modes of invasion or isomorphic morphogenesis events. To concretely illustrate a potential use of PhysiBoSS, we studied heterogeneous cell fate decisions in response to TNF treatment. We explored the effect of different treatments and the behaviour 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. AVAILABILITY AND IMPLEMENTATION: PhysiBoSS is freely available on GitHub (https://github.com/sysbio-curie/PhysiBoSS), with a Docker image (https://hub.docker.com/r/gletort/physiboss/). It is distributed as open source under the BSD 3-clause license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.