A multi-approach and multi-scale platform to model CD4+ T cells responding to infections

Area of Science:

• Systems immunology
• Computational biology
• Immunological modeling

Background:

• Immune responses involve complex interactions across multiple scales and tissues.
• Understanding these interactions requires integrating diverse biological processes.

Purpose of the Study:

• To develop a modular computational model of CD4+ T cells.
• To integrate processes across different spatial scales and tissues.
• To provide a versatile tool for systems immunology research.

Main Methods:

• Developed a modular model combining logical, constraint-based, agent-based, and ordinary differential equation approaches.
• Utilized a Monte Carlo simulation algorithm for efficient information flow between modules.
• Integrated processes at cellular, population, and systemic levels.

Main Results:

• Successfully reproduced known experimental results, including CD4+ T cell differentiation and metabolic regulation.
• Discovered emergent switch-like and oscillatory behaviors in CD4+ T cells.
• Identified inflamed lymph node retention of naive CD4+ T cells as a key mechanism for emergent behaviors.

Conclusions:

• The modular modeling framework enhances computational performance, versatility, and data integration in immunology.
• The model provides multi-scale insights and predictive power for complex immunological problems.
• This approach advances systems immunology by offering a tool for understanding cellular and molecular mechanisms.