Prediction in immune repertoires

Living systems often attempt to calculate and predict the future state of the environment. Given the stochastic nature of many biological systems how is that possible? Since the functioning of the repertoire relies on statistical properties, statistical analysis is needed to identify responding clones. Using such methods I will describe the repertoire level response to the SARS-CoV-2. I will also 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 epertoire by balancing evidence from new pathogen encounters against past experience of infection to predict and prepare for future threats.

Acknowledgements (optional)
This work was supported by the ERC Consolidator Grant 724208.

Συνεδρία: 
Authors: 
Aleksandra Walczak
Room: 
1
Date: 
Thursday, December 10, 2020 - 12:40 to 13:20

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