Systems Immunology
Interdisciplinary research field that integrates biology, computational science, mathematics, and engineering to understand the immune system as a complex and dynamic network. The primary goal is to develop computational models that simulate immune processes, predict immune responses, and uncover the principles governing immune function and dysfunction.
The field’s interdisciplinary nature fosters collaboration among immunologists, computational scientists, and clinicians, driving innovations that deepen our understanding of immune systems while addressing pressing health challenges.
Key focus areas include:
Modeling Immune Dynamics
Developing mathematical and computational models to simulate key immune processes such as pathogen recognition, immune cell signaling, cytokine networks, and adaptive immune responses. These models aim to capture the spatiotemporal dynamics of immune components and their interactions.
Predictive Immunology
Leveraging machine learning and AI to predict immune responses in various contexts, including infections, vaccination, autoimmunity, cancer immunotherapy, and transplantation. These predictive tools help refine therapeutic strategies and personalize medical interventions.
Multi-scale Integration
Integrating data from different biological scales, from molecular interactions (e.g., antigen-antibody binding) to whole-organism immune responses, enabling a comprehensive understanding of how local immune events translate into systemic outcomes.
Immune Network Analysis
Applying network science to map and analyze the complex relationships between immune components. This includes studying signaling pathways, gene regulation networks, and intercellular communications to identify critical nodes and potential therapeutic targets.
Big Data in Immunology
Utilizing high-throughput data from genomics, transcriptomics, proteomics, and single-cell technologies to construct and validate computational models. Systems immunology bridges the gap between data generation and actionable insights by making sense of the vast datasets.