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Influenza viruses are a cause of large outbreaks and pandemics with high death tolls. A key obstacle is that flu vaccines have inconsistent performance, in the best cases up to 60% effectiveness, but it can be as low as 10%. Uncovering the hidden pathways of how antibodies (Abs) induced by one influenza strain are effective against another, cross-reaction, is a central vexation for the design of universal flu vaccines. 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. Using a High-Performance Computing cluster, several aspects and parameters in the model were tested. 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 3-D reformulation of the current influenza antibody landscape for the representation and modeling of cross-reactive data. The full code as a testing/simulation platform is freely available here: Supplementary data are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail:


Gustavo Hernandez-Mejia, Esteban A Hernandez-Vargas. Uncovering antibody cross-reaction dynamics in influenza A infections. Bioinformatics (Oxford, England). 2021 Apr 19;37(2):229-235

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PMID: 32730562

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