Using In silico models to predict lymphocyte activation and development in a data rich era

Area of Science:

• Immunology and computational biology.
• Focus on multi-scale immune responses.

Background:

• 'Omics datasets provide routine insights into immune responses in health and disease.
• Integrating multi-scale data for computational modeling is challenging.

Purpose of the Study:

• Discuss challenges in multi-scale data integration for computational immunology.
• Highlight recent advances and opportunities in the field.
• Use innate lymphoid cells, specifically natural killer cells, as a model.

Main Methods:

• Review of current literature on multi-scale immune response modeling.
• Analysis of challenges in integrating diverse 'omics data.
• Exploration of computational approaches for cross-scale insights.

Main Results:

• Identified key challenges in multi-scale data integration.
• Discussed recent advancements enabling tractable computational models.
• Highlighted opportunities for future research using natural killer cells as an exemplar.

Conclusions:

• Computational models integrating multi-scale data are crucial for understanding immune responses.
• Advances in data integration and modeling offer new avenues for mechanistic insights.
• Natural killer cells serve as a valuable model for exploring these complex interactions.