Optimality of mutation and selection in germinal centers

Area of Science:

• Immunology
• Computational Biology
• Evolutionary Biology

Background:

• B cell population dynamics in germinal centers are crucial for understanding antibody affinity maturation.
• Existing models conflict with experimental observations, necessitating a refined approach.

Purpose of the Study:

• To develop a coarse-grained model for B cell population development during affinity maturation.
• To identify optimal parameters (mutation rate, selection strength, initial binding) for maximizing antibody affinity improvement.
• To reconcile model predictions with experimental data and explore evolutionary optimization of germinal centers.

Main Methods:

• Developed a coarse-grained computational model to simulate B cell population dynamics.
• Performed comprehensive parameter space analysis to find optimal values for mutation rate and selection strength.
• Compared model outputs with experimental data, including affinity improvements, mutation counts, and hypermutation rates.

Main Results:

• The optimized model accurately reproduces experimental observations, including ~100-fold affinity improvements and the "all or none" phenomenon.
• Optimal mutation rate balances beneficial mutation accumulation with avoidance of deleterious mutations.
• Optimal selection strength balances affinity gain with population bottleneck passage.

Conclusions:

• Germinal centers are evolutionarily optimized for efficient and rapid generation of high-affinity antibodies.
• B cell migration between germinal centers can further enhance affinity maturation.
• The model provides insights into the functional optimization of germinal centers for adaptive immunity.