Uncovering antibody cross-reaction dynamics in influenza A infections

Uncovering the hidden pathways of how antibodies induced by one influenza strain is effective against another, cross-reaction, is the central dogma for the design of universal flu vaccines. Here, we conceive a stochastic model that successfully represents the antibody cross-reactive data from mice infected with H3N2 influenza strains and further validation with cross-reaction data of H1N1 strains. After modifying several aspects and parameters in the model, our computational simulations highlight that changes in time of infection and the B-cells population are relevant, however, the affinity threshold of B-cells between consecutive infections is a necessary condition for the successful Abs cross-reaction. Our results suggest a reformulation in 3-D of the current antibody influenza landscape.