Medicine and Health

PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks

M. Ponce-de-leon, A. Montagud, et al.

Discover the groundbreaking PhysiBoSS 2.0, a state-of-the-art hybrid agent-based modeling framework that enhances the understanding of multicellular systems and intracellular signaling. Developed by an expert team, including Miguel Ponce-de-Leon and Arnau Montagud from the Barcelona Supercomputing Center, this innovative tool is poised to transform cancer drug research.

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Abstract

This paper presents PhysiBoSS 2.0, a hybrid agent-based modeling framework that integrates Boolean modeling of intracellular signaling with agent-based modeling of multicellular systems. It's a redesigned and reimplemented version of PhysiBoSS 1.0, designed as a PhysiCell add-on for improved maintainability and flexibility. PhysiBoSS 2.0 allows for customized models, substrate internalization submodels, and detailed simulation parameter control. Accompanying it is PCTK, a Python package for processing simulation outputs. The authors demonstrate its application in studying drug effects and synergies in cancer models, validated against experimental data.

Publisher

npj Systems Biology and Applications

Published On

Oct 30, 2023

Authors

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

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