Innovative In Silico Approaches to Address Avian Flu Using Grid Technology

• Main Authors: BRETON, Vincent, Da Costa, L.A., De Vlieger, Paul, Kim, Y.-M., Maigne, L., Reuillon, Romain, Sarramia, D., H.A.I. Truong, N., Q. U.A.N.G. Nguyen, H., Kim, D., Wu, Y.-T.
• Other Authors: Laboratoire de Physique Corpusculaire - Clermont-Ferrand (LPC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2009
• Subjects: Avian flu; grid; surveillance network; virtual screening; molecular epidemiology; [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]; [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
• Online Access: http://hal.in2p3.fr/in2p3-00441054

The recent years have seen the emergence of diseases which have spread very quickly all around the world either through human travels like SARS or animal migration like avian flu. Among the biggest challenges raised by infectious emerging diseases, one is related to the constant mutation of the viruses which turns them into continuously moving targets for drug and vaccine discovery. Another challenge is related to the early detection and surveillance of the diseases as new cases can appear just anywhere due to the globalization of exchanges and the circulation of people and animals around the earth, as recently demonstrated by the avian flu epidemics. For 3 years now, a collaboration of teams in Europe and Asia has been exploring some innovative in silico approaches to better tackle avian flu taking advantage of the very large computing resources available on international grid infrastructures. Grids were used to study the impact of mutations on the effectiveness of existing drugs against H5N1 and to find potentially new leads active on mutated strains. Grids allow also the integration of distributed data in a completely secured way. The paper proposes new approaches for the integration of existing data sources towards a global surveillance network for molecular epidemiology and in silico drug discovery.