Proliferation and competition in discrete biological systems

Area of Science:

• Systems biology
• Computational biology
• Theoretical immunology

Background:

• Collective dynamics in biological systems are often modeled using macroscopic approximations like partial differential equations.
• These macroscopic models may oversimplify or miss emergent phenomena arising from microscopic interactions.

Purpose of the Study:

• To investigate the emergence of collective spatio-temporal objects in biological systems.
• To compare microscopic interaction-based modeling with traditional macroscopic (density-based) approaches.
• To identify optimal modeling strategies for complex biological systems.

Main Methods:

• Simulating elementary interactions between microscopic components of a biological system.
• Using the immune system as a prototype for studying these interactions.
• Comparing simulation results with predictions from partial differential equations.

Main Results:

• Spontaneous emergence of localized complex structures from microscopic noise was observed, even with simple reactions.
• These emergent structures significantly influenced the system's average behavior.
• Simulated systems showed dynamic behavior that differed markedly from predictions of macroscopic models, which suggested system demise.

Conclusions:

• Microscopic simulations reveal emergent collective behaviors not captured by traditional density-based models.
• Hybrid modeling approaches, combining microscopic simulations and continuous methods, are proposed as optimal for accurately representing biological system dynamics.
• Understanding emergent phenomena is crucial for accurate biological system analysis and prediction.