Innovative in silico approaches to address avian flu using grid technology

Area of Science:

• Virology
• Computational Biology
• Epidemiology

Background:

• Emerging infectious diseases (EIDs) spread rapidly globally via human travel and animal migration.
• Viral mutations present challenges for drug and vaccine development, creating moving targets.
• Globalization and increased circulation of people and animals necessitate robust early detection and surveillance systems for EIDs.

Purpose of the Study:

• To explore innovative in silico approaches for tackling avian flu.
• To leverage international grid infrastructures for enhanced drug discovery and disease surveillance.
• To develop new methods for integrating distributed data for a global molecular epidemiology network.

Main Methods:

• Utilized international grid computing resources for large-scale analysis.
• Studied the impact of viral mutations on existing drug effectiveness against H5N1.
• Investigated new drug leads active against mutated viral strains.
• Developed secure methods for integrating distributed data.

Main Results:

• Demonstrated the utility of grid computing in analyzing drug resistance and identifying potential new drug candidates.
• Showcased the capability of grids to securely integrate diverse and distributed data sources.
• Provided a framework for enhanced in silico drug discovery for avian flu.

Conclusions:

• In silico approaches, powered by grid computing, offer powerful tools for combating emerging infectious diseases like avian flu.
• Grid infrastructures facilitate collaborative research and secure data integration for molecular epidemiology and drug discovery.
• The proposed methods contribute to building a global surveillance network for improved response to future EID outbreaks.