1.6 Biomedical research

Key concepts

• biomedical research
• drug discovery
• docking
• telemedicine
• radiotherapy
• epidemiology

Introduction

Biosciences research provides an exemplar of the dramatic transformation occurring within the context of data rich science. Increased emphasis on biology as an “informational science” focused on genomics has resulted in the production of huge data sets that can only be adequately managed and decoded by using advanced information technologies (ICTs). Researchers studying biology at higher levels of organisation than the genome also rely on ICTs to develop models of cells, tissues, organisms and ecologies, in order to come to terms with complexity. Biomedical science aided by ICTs has made significant advances in areas such as the understanding of disease processes (for instance in heart or cancer modelling) and drug discovery.

Biomedicine consists of various aspects which can benefit from a grid-based approach including the search for new drug targets into the genome and the proteome, identification of single nucleotide polymorphisms (SNPs) relating to drug sensitivity, drug resistance mechanism elucidations as well as epidemiological monitoring of disease outbreaks. Well-identified areas of relevance of the grid paradigm are epidemiology and computer-intensive analysis of geographically distributed medical images. Grids are defined as fully distributed, dynamically reconfigurable, scalable and autonomous infrastructures to provide location independent, pervasive, reliable, secure and efficient access to a coordinated set of services encapsulating and virtualising resource. Their relevance for biomedical research has been investigated within the framework of the HealthGrid initiative (Breton et al. 2005, SHARE Project 2008). Here we focus on the use of e-Research methods in the study of infectious diseases such as flu viruses and malaria and Medical Data Management.

Grid as a surveillance tool for diseases

Epidemiology focused on population-level research requires access to distributed, critically sensitive and heterogeneous data, resulting in overall costly computing processes. The study of flu viruses and their treatment is one notable example of e-Research in biomedical sciences. Recent years have seen the emergence of diseases which have spread very quickly around the world, either through human travel, like SARS and SIV (H1N1), or animal migration, like avian flu (H5N1). Swine Flu has been in the headlines in 2009, officially classified as a “pandemic” by the World Health Organization in response to the virus’s worldwide geographic spread (Neumann, Noda and Kawaoka 2009). International collaboration has involved use of grid computing to model potential circumstances surrounding such extreme outbreaks.