PhysiBoSS 2.0: a Sustainable Integration of Stochastic Boolean and Agent-Based Modelling Frameworks

Miguel Ponce-de-Leon,Arnau Montagud,Vincent Noël,Gerard Pradas,Annika Meert,Emmanuel Barillot,Laurence Calzone,Alfonso Valencia

NPJ systems biology and applications（2023）SCI 2区SCI 1区

Life Science

Cited 3|Views23

Abstract

Cancer progression is a complex phenomenon that spans multiple scales from molecular to cellular and intercellular. Simulations can be used to perturb the underlying mechanisms of those systems and to generate hypotheses on novel therapies. We present a new version of PhysiBoSS, a multiscale modelling framework designed to cover multiple temporal and spatial scales, that improves its integration with PhysiCell, decoupling the cell agent simulations with the internal Boolean model in an easy-to-maintain computational framework. PhysiBoSS 2.0 is a redesign and reimplementation of PhysiBoSS, conceived as an add-on that expands the PhysiCell agent-based functionalities with intracellular cell signalling using MaBoSS having a decoupled, maintainable and model-agnostic design. PhysiBoSS 2.0 successfully reproduces simulations reported in the former version and expands its functionalities such as using user-defined models and cells’ specifications, having mechanistic submodels of substrate internalisation with ODEs and enabling the study of drug synergies. PhysiBoSS 2.0 is open-source and publicly available on GitHub () under the BSD 3-clause license with several repositories of accompanying interoperable tools. Additionally, a nanoHUB tool has been set up to ease the use of PhysiBoSS 2.0 (). ### Competing Interest Statement The authors have declared no competing interest.

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