Cameron Sharma is the principal investigator of this research.
The purpose of this website is to make a scientific contribution towards the prediction and design of the seasonal flu vaccine using reverse vaccinology. Reverse vaccinology is the science of selecting antigenic candidates and designing vaccines using genomic data. It blends computational biology with the conventional lab-based assays.
FutureFlu has developed a phylogeny and antigenicity associated biostatistics model to predict the future mutations in the Type A influenza viruses (H3N2 and H1N1pdm) and to calculate a candidate vaccine.
Microbial mutations and the host immune response to it is an iterative process. The pathogens mutate to evade the immune system and the host adapts to detect the new strains. Therefore, the model invented here approached the solution from those two opposite ends, 1) virology of the forward-moving virus mutations and 2) the adaptive immunology opposing it.
Fast Fourier Transform (FFT) and its subset, Discrete Cosine Transform (DCT) were used to account for the cycles of H3N2 mutations.
FutureFlu will seek to publish the findings in peer reviewed journals to make those widely accessible.
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