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Systems immunology: When systems biology meets immunology
Lucía Alfonso-González1,2, Francisco J Fernández1, M Cristina Vega2
1Abvance Biotech SL, Pharmacokinetics, Pharmacodynamics and Drug Metabolism (PPDM), Madrid, Spain.
Frontiers in Immunology
|September 22, 2025
Summary
Systems immunology uses computational approaches like artificial intelligence to understand complex immune networks and diseases. These methods help identify biomarkers, optimize therapies, and guide drug discovery for better clinical impact.
Area of Science:
- Immunology
- Computational Biology
- Pharmacology
Background:
- The immune system's complexity necessitates advanced analytical methods.
- Dysregulation of immune responses leads to various diseases.
- Systems-based approaches integrate diverse data for a holistic understanding.
Purpose of the Study:
- To review key methodologies in systems immunology.
- To highlight applications in disease research and drug discovery.
- To discuss challenges and future directions for clinical translation.
Main Methods:
- Network Pharmacology
- Artificial Intelligence (AI)
- Quantitative Systems Pharmacology (QSP)
- Multi-omics data integration
- Mechanistic modeling
Main Results:
- Systems immunology methods reveal emergent behaviors in immune networks.
- Applications include biomarker identification, therapy optimization, and drug discovery.
- These approaches span autoimmune, inflammatory, and infectious diseases.
Conclusions:
- Computational modeling is crucial for next-generation immunology.
- Addressing challenges in data quality and model validation is key for clinical impact.
- Systems immunology offers a pathway for therapeutic innovation.
Keywords:
artificial intelligencebioinformaticsimmunologymechanistic modelsnetwork pharmacologyquantitative systems pharmacologysystems biologysystems immunologyMore Related Videos
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