Collective computation and regulation in the immune system

To combat infection by diverse and ever-evolving pathogens vertebrates have evolved an intricate defense machinery consisting of a large population of highly specialized cells -- the adaptive immune system. To provide efficient defense this system adapts dynamically to the pathogens it encounters. How does a regulated response to a pathogenic challenge arise from the proliferation of individual cells? And how should a well-adapting immune system best adjust to a changing pathogenic environment? We have recently made progress on both questions: First, we have shown that the regulation of T cell expansion by changing antigen-levels explains phenomenological scaling laws of how expansion depends on precursor number, antigen affinity, and antigen kinetics. Second, we have developed a Bayesian theory of how a population of immune cells can optimally integrate information from pathogen encounters with prior expectations. Our work demonstrates the power of simple physical models to help unravel the regulatory mechanisms that shape immune dynamics and the computational principles they implement.