Agent-Based Models Predict Emergent Behavior of Heterogeneous Cell Populations in Dynamic Microenvironments

Area of Science:

• Computational biology
• Systems biology
• Agent-based modeling

Background:

• Equation-based computational models are widely used but can lack intuitive frameworks for interdisciplinary collaboration.
• Rules-based models offer a more accessible approach to understanding complex biological phenomena.
• Investigating emergent behavior in dynamic microenvironments requires advanced modeling techniques.

Purpose of the Study:

• To develop and demonstrate ARCADE, a multi-scale agent-based model for exploring emergent biological dynamics.
• To investigate the impact of intracellular complexity and microenvironmental factors on cell behavior.
• To provide a computational framework facilitating collaboration between computational and experimental biologists.

Main Methods:

• Development of ARCADE, a multi-scale agent-based modeling framework.
• Simulation of heterogeneous cell agents within dynamic microenvironments.
• In silico case studies focusing on context, competition, and heterogeneity.

Main Results:

• Emergent behaviors differ significantly between colony and tissue contexts.
• Parameter variations in co-cultures exhibit linear, non-linear, and multimodal effects on competition.
• Cellular and population heterogeneity variably influence emergent outcomes.

Conclusions:

• ARCADE provides an intuitive and extensible framework for studying complex biological systems.
• The model elucidates the impact of intracellular complexity and microenvironmental dynamics on emergent behaviors.
• ARCADE supports computational and experimental collaboration for biological discovery in systems like tumor microenvironments and microbiomes.