Innovative in silico approaches to address avian flu using grid technology

TL;DR

This paper discusses innovative in silico methods leveraging grid computing to study avian flu, focusing on mutation impacts, drug effectiveness, and developing a global surveillance network for molecular epidemiology.

Contribution

It introduces novel grid-based computational approaches for analyzing avian flu mutations, drug efficacy, and integrating data for global disease surveillance.

Findings

Studied mutation effects on drug efficacy.

Identified potential new drug leads.

Developed a framework for global data integration.

Abstract

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 presents how we are currently exploring how to integrate the existing data sources towards a global surveillance network for molecular epidemiology.