ArticleNPJ systems biology and applications2023
PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks.
Article in NPJ systems biology and applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep representation learning for temporal inference in cancer omics: a systematic literature review.Briefings in bioinformatics · 2026Pooled it
- Intelligent tool orchestration for rapid mechanistic model prototyping: MCP servers as AI-biology interfaces.NPJ systems biology and applications · 2026Article
- Experimentally calibrated multiscale model predicts schedule dependent drug combination effects.NPJ systems biology and applications · 2026Article
- Path-based quantification of activation and repression in Boolean models using BooLEVARD.NPJ systems biology and applications · 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
- A Simple Framework for Agent-Based Modeling with Extracellular Matrix.Bulletin of mathematical biology · 2025Article
- Challenges and opportunities for digital twins in precision medicine from a complex systems perspective.NPJ digital medicine · 2025Review
- Simulation approach for common female cancers: a brief review.Frontiers in oncology · 2025Review
- Building multiscale models with PhysiBoSS, an agent-based modeling tool.Briefings in bioinformatics · 2024Article
- Integrating mechanism-based T cell phenotypes into a model of tumor-immune cell interactions.APL bioengineering · 2024Article
- Kiphynet: an online network simulation tool connecting cellular kinetics and physiological transport.Metabolomics : Official journal of the Metabolomic Society · 2024Article
- A comprehensive review of computational cell cycle models in guiding cancer treatment strategies.NPJ systems biology and applications · 2024Review
- Article
- Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist.Frontiers in cell and developmental biology · 2024Review
- PhysiCell Studio: a graphical tool to make agent-based modeling more accessible.GigaByte (Hong Kong, China) · 2024Article
- PhysiMeSS - a new physiCell addon for extracellular matrix modelling.GigaByte (Hong Kong, China) · 2024Article
- Dysregulated FGFR3 signaling alters the immune landscape in bladder cancer and presents therapeutic possibilities in an agent-based model.Frontiers in immunology · 2024Article
- PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks.NPJ systems biology and applications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
In systems biology, mathematical models and simulations play a crucial role in understanding complex biological systems. Different modelling frameworks are employed depending on the nature and scales of the system under study. For instance, signalling and regulatory networks can be simulated using Boolean modelling, whereas multicellular systems can be studied using agent-based modelling. Herein, we present PhysiBoSS 2.0, a hybrid agent-based modelling framework that allows simulating signalling and regulatory networks within individual cell agents. PhysiBoSS 2.0 is a redesign and reimplementation of PhysiBoSS 1.0 and was conceived as an add-on that expands the PhysiCell functionalities by enabling the simulation of intracellular cell signalling using MaBoSS while keeping a decoupled, maintainable and model-agnostic design. PhysiBoSS 2.0 also expands the set of functionalities offered to the users, including custom models and cell specifications, mechanistic submodels of substrate internalisation and detailed control over simulation parameters. Together with PhysiBoSS 2.0, we introduce PCTK, a Python package developed for handling and processing simulation outputs, and generating summary plots and 3D renders. PhysiBoSS 2.0 allows studying the interplay between the microenvironment, the signalling pathways that control cellular processes and population dynamics, suitable for modelling cancer. We show different approaches for integrating Boolean networks into multi-scale simulations using strategies to study the drug effects and synergies in models of cancer cell lines and validate them using experimental data. PhysiBoSS 2.0 is open-source and publicly available on GitHub with several repositories of accompanying interoperable tools.
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Registered trials
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.