Prediction and learning in immune repertoires

Living systems often attempt to predict the future state of the environment based on the learned statistics of past environments. Given the stochastic nature of many biological systems how is that possible? I will show that even a system as complicated as the immune system has reproducible outcomes. Yet predicting the future state of a complex environment requires weighing the trust in new observations against prior experiences. In this light, I will present a view of the adaptive immune system as a dynamic Bayesian machinery that updates its memory repertoire by balancing evidence from new pathogen encounters against past experience of infection to predict and prepare for future threats.