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.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

“…We incorporated two Boolean models into a multiscale simulator that consists of the infection of a patch of lung epithelium by SARS-CoV-2 and the immune cells that are recruited ( 45 ): macrophages, neutrophils, dendritic cells, CD4- and CD8-T-cells. We expanded this simulator with our tool, PhysiBoSS ( 91 ), which incorporates MaBoSS ( 92 ), a tool that stochastically simulates Boolean models, into PhysiCell ( 93 ), a tool that uses agent-based modelling to simulate cells and their surrounding environment, and their interplay. Two Boolean models were used: first, the epithelial apoptosis model was converted from the map to the model using CaSQ ( 33 ) and the C19DMap project ( https://fairdomhub.org/models/712 ) ( 82 ).…”

PhysiCell Studio: a graphical tool to make agent-based modeling more accessible

“…PhysiCell Studio will support intracellular modeling. Currently only a boolean intracellular modeling interface is provided for the PhysiBoSS [ 27 ] add-on, allowing settings edits and specific visualization. See Figure 16 .…”

Dysregulated FGFR3 signaling alters the immune landscape in bladder cancer and presents therapeutic possibilities in an agent-based model

“…Partial differential equation (PDE) modeling, accounting for the spatial context and thereby cell-cell interactions, has been used to study cancer immunotherapies ( 19 ). Agent-based models (ABMs), moreover, provide a modular, mechanistic framework to incorporate these features and further interrogate the dynamic processes that determine tumor evolution and response to therapy ( 20 – 24 ). In particular, they include cell-cell interactions, hybrid modeling of diffusive molecules, and therapeutic interventions ( 25 , 26 ).…”