Simble: simulating realistic B cell receptor repertoires in response to diverse immune scenarios 3595

Abstract Description In response to immunological stimuli, such as infections and vaccinations, B cells undergo an evolutionary process in which they acquire mutations in the B cell receptor (BCR) through somatic hypermutation (SHM) and are selected for antigen binding during affinity maturation. Understanding the underlying mechanisms that drive B cell evolution is critical to advancing treatment and vaccines. BCR repertoire data provides a way to study these processes, but its relationship to B cell evolution is complex. Simulations provide a controlled environment with clear inputs and outputs, offering a direct connection between ground-truth and observations. To explore varied scenarios, we present Simble, an open-source Python package that simulates B cell immune responses using a biologically-realistic model of SHM, affinity maturation, differentiation and migration. It models essential processes including B cell competition for antigen in germinal centers and their differentiation into memory cells and plasma cells. Simble accurately simulates real-world and experimental scenarios such as adoptive transfer experiments, repeat vaccines, and chronic infections like HIV. Users can customize the simulation to explore how tunable parameters—mutation rates, selection pressure, migration patterns, etc.—affect BCR mutation patterns. Simble provides a powerful tool for exploring the effect of germinal center dynamics on BCR repertoires, validating new methods, and improving experimental design. Funding Sources Supported by NIH/NIAID R00AI159302 Topic Categories Computational and Systems Immunology (COMP)