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

Area of Science:

• Systems Biology
• Computational Biology
• Bioinformatics

Background:

• Mathematical models and simulations are vital for understanding complex biological systems.
• Diverse modeling frameworks exist, including Boolean modeling for networks and agent-based modeling for multicellular systems.

Purpose of the Study:

• Introduce PhysiBoSS 2.0, a hybrid agent-based modeling framework.
• Enable simulation of intracellular cell signaling within individual cell agents.
• Expand PhysiCell functionalities for multi-scale biological simulations.

Main Methods:

• PhysiBoSS 2.0 integrates MaBoSS for intracellular signaling with agent-based modeling.
• The framework is designed as a decoupled, maintainable, and model-agnostic add-on to PhysiCell.
• Includes custom models, cell specifications, substrate internalization submodels, and simulation parameter control.

Main Results:

• PhysiBoSS 2.0 facilitates studying the interplay between microenvironment, signaling pathways, and cell population dynamics.
• Demonstrates integration of Boolean networks into multi-scale simulations for cancer modeling.
• Presents approaches for studying drug effects and synergies in cancer cell line models, validated with experimental data.

Conclusions:

• PhysiBoSS 2.0 offers a powerful tool for multi-scale modeling, particularly for cancer research.
• The accompanying PCTK Python package aids in output processing and visualization.
• PhysiBoSS 2.0 is open-source, promoting accessibility and further development in systems biology.