Computational Biology Application for Influenza Research

New high-throughput methods in biology are producing new patterns which would not be detectable without systematic and automated approaches because of the volume and often noisy nature of the data. data  at  an  unprecedented  rate. The  influenza  surveillance  and  vaccine  strain  selection processes are in a good position to take advantage of these new  methods. Human influenza is a complex pathogen, mostly because of its capacity to vary its surface proteins to escape immune surveillance. There are two main patterns of change. The first, antigenic shift, is the result of a new influenza A subtype  entering  the  human  population,  either  directly  or The second, antigenic drift, is the result of changes in existing human influenza viruses, to  escape  immune  surveillance indirectly  from  birds. Mathematical techniques have been used to identify clusters  in  genetic  data.

Influenza virus is a member of the Orthomyxoviridae family of segmented, negative-strand RNA viruses. Influenza’s eight genomic segments encode 10 or 11 proteins, depending on whether the PB1-F2 ORF is present. Although antiviral drugs and prophylactic vaccines have been developed, the rapid evolution of influenza facilitates the generation of drug-resistant and vaccine-resistant variants; therefore, this virus remains a major public health threat. Each influenza season provides new challenges and uncertainties to both the public as well as the public health community. A comparison of hemagglutinin sequence conservation and epitope coverage information revealed highly conserved protein regions that can be recognized by the human adaptive immune system as possible targets for inducing cross-protective immunity.

  • Comparative genomics
  • Self organizing map
  • Big data analysis and influenza
  • Optofludic techniques in flu detection
  • Mathematical modelling
  • Metabolome profilling technology

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