Exploring B Cell Dynamics and Immunological Feedback in Vaccination: A Computational Approach to Affinity Maturation

The effectiveness of vaccines in preventing viral infections depends on the immune system’s ability to produce antibodies that neutralize the target pathogen. This response is driven by affinity maturation, a dynamic process where B cells undergo cycles of mutation and selection within germinal centers. These cycles promote B cells that progressively bind antigens with higher affinity. The strength and quality of the immune response are influenced by the vaccination regimen, which is shaped by factors such as antigen dose, timing of doses, and antigen composition. Prior encounters with antigens influence both the amount, diversity and affinity of antibodies generated in subsequent responses, playing a crucial role in shaping immunity to future exposures. For example, previous vaccinations may impact how efficiently immune cells capture antigens in later rounds, while existing antibodies can mask viral epitopes, limiting access for new B cells. These competing effects reveal the complex feedback mechanisms arising from immunological memory, which can significantly affect the overall effectiveness of a vaccination regimen.

In this study, we employ computational simulations to model the affinity maturation process in B cells and use sparse regression algorithms to derive simplified dynamical equations from the correspoding time series data. These equations effectively capture the evolution of B cell populations and antibody responses and may be used to gain insights into antigen competition, T cell signaling, and the immunological feedback effects. The resulting models can also offer a framework for optimizing antibody production through control strategies that guide the immune response toward desired outcomes.