With Zika virus still making headlines, pressure is mounting for scientists to develop a working vaccine for this arthropod-borne disease. Outside of high interest pandemics such as these, the world of vaccine manufacturing and preventative medicine often takes a back seat in the public eye. Serious oversights, such as the low 20% success rate of 2014-15 flu shot, can have enormous repercussions to more serious diseases. The 2014-15 flu shot was unsuccessful because of a misprediction of the structure of the outer proteins of the influenza virus. The vaccine encouraged the body to produce antibodies, the heat-seeking missiles of the immune system, that had low specificity to the flu strain at hand. This problem is known as poor antigenic mapping.
Researchers at the University of Wisconsin Madison are developing a novel algorithm in predicting the evolution of influenza’s protein coat using network based computing and genomics. This method involves creating a library of H1N1 and H3N2 genomes, keeping track of their single nucleotide polymorphisms (single base pair mutations) with respect to time, creating a probabilistic state model for the viral genome. This mapping allows for scientists to cluster viruses according to patterns of mutation and therefore anticipate high probability mutagenesis sites. This process, called antigenic cartography, has shown promising results in predicting future surface protein structure of new flu strains and therefore increase our ability to create vaccine stockpiles of pathogens that do not yet exist.
This is one of the many computational evolutionary models rooted in graph theory – DARPA’s PROPHECY project similarly attempts to defeat coming pathogens with predictive modeling rooted in genomic big data.
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