Box 3 Computational biology

Computational methods have become intrinsic to modern biological research, and their importance can only increase as large-scale methods for data generation become more prominent, as the amount and complexity of the data increase, and as the questions being addressed become more sophisticated. All future biomedical research will integrate computational and experimental components. New computational capabilities will enable the generation of hypotheses and stimulate the development of experimental approaches to test them. The resulting experimental data will, in turn, be used to generate more refined models that will improve overall understanding and increase opportunities for application to disease. The areas of computational biology critical to the future of genomics research include:

New approaches to solving problems, such as the identification of different features in a DNA sequence, the analysis of gene expression and regulation, the elucidation of protein structure and protein -- protein interactions, the determination of the relationship between genotype and phenotype, and the identification of the patterns of genetic variation in populations and the processes that produced those patterns
Reusable software modules to facilitate interoperability
Methods to elucidate the effects of environmental (non-genetic) factors and of gene -- environment interactions on health and disease
New ontologies to describe different data types
Improved database technologies to facilitate the integratio and visualization of different data types, for example, information about pathways, protein structure, gene variation, chemical inhibition and clinical information/phenotypes
Improved knowledge management systems and the standardization of data sets to allow the coalescence of knowledge across disciplines

Last updated: May 21, 2012