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

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.

The authors thank Paul Macklin and Randy Heiland for their support with the PhysiCell code and discussions. The authors acknowledge the technical expertise and assistance provided by the Spanish Supercomputing Network (Red Española de Supercomputación), as well as the computer resources used: the LaPalma Supercomputer (RES-BCV-2019-2-0008), located at the Instituto de Astrofísica de Canarias, and MareNostrum4 (RES-BCV-2019-2-0013, RES-BCV-2020-3-0016, RES-BCV-2021-2-0020), located at the Barcelona Supercomputing Center. This work has received funding from the Horizon 2020 projects: INFORE (ID: 825070), PerMedCoE (ID: 951773) and CREXDATA (ID: 101092749). This work has received funding from the Horizon 2020 projects INFORE (ID: 825070) and PerMedCoE (ID: 951773) and from the Horizon Europe project CREXDATA (ID: 101092749). This work was funded in part by the French government under the management of Agence Nationale de la Recherche as part of the “Investissements d’avenir” programme, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute).

Peer Reviewed

Postprint (published version)