Context-dependent immune regulation: a mechanistic and AI-enabled integrative framework

Area of Science:

• Immunology
• Computational Biology
• Artificial Intelligence

Background:

• The immune system is a complex, dynamic network influenced by numerous factors.
• Systems immunology, mathematical modeling, and simulations have studied these properties.
• A context-dependent approach is crucial for understanding immune system behavior.

Purpose of the Study:

• To connect traditional systems immunology with artificial intelligence (AI) methods.
• To develop a context-centered view of the immune system.
• To propose a hybrid AI-mechanistic framework for immune context analysis.

Main Methods:

• Utilizing context dependence as an organizing principle.
• Integrating AI for context representation and multimodal data integration.
• Combining AI with mechanistic and knowledge-based components.

Main Results:

• Summarizes immune phenomena in cancer, infection, innate immunity, autoimmunity, and genetic variation.
• Describes microenvironmental, metabolic, systemic, and temporal determinants of immune responses.
• Distinguishes capabilities of mechanistic, data-driven, symbolic, and hybrid models.

Conclusions:

• The proposed hybrid framework positions AI as a tool for context representation and integration.
• Mechanistic components preserve biological constraints and testable interventions.
• Emphasizes minimum context annotation, uncertainty-aware integration, and benchmarking.