Key points are not available for this paper at this time.
The immune system plays a critical role in protecting the human body against various pathogens and diseases. Understanding the complexity and dynamics of the immune system is essential for developing effective therapies and interventions. Agent-based modeling (ABM) has emerged as a powerful tool for simulating and studying the behavior of complex systems, including the immune system. This review examines the advantages, challenges, and applications of ABM in immune system modeling. ABM captures the complexity of immune cell behavior, spatial effects and stochasticity. It has been applied to study immune cell dynamics, immune responses to pathogens, immune cell migration, immunotherapies and immune system disorders. Challenges include parameterization, validation, and computational resource requirements. Future directions involve integrating multi-omics and single-cell data, incorporating machine learning, exploring multi-scale modeling, and developing user-friendly interfaces. ABM holds promise for enhancing our understanding of immune system dynamics and advancing diagnostics and treatments in immunology.
Yousef Jamali (Thu,) studied this question.