Computational biology applies computational techniques to problems in molecular biology, genomics, and biophysics. Drawing from biochemistry, biophysics, computer science, mathematics, medicine and health, molecular and cell biology, organismal biology, statistics, and other related disciplines, computational biologists address a wide variety of research questions, among them:
- Sequence and structure comparison and analysis: comparison and prediction of the structure and/or function of genes, RNA and proteins.
- Quantitative structure-activity or structure-property relationships (QSAR & QSPR): Modelling the effects of bioactive substances (important in drug development).
- Phylogenetics: determining the evolutionary relationships among organisms.
- Metagenomics: the identification of microbiomes (communities and networks of microorganisms) from large-scale sampling.
- Genome-wide association studies (GWAS): identification of correlations between genetic variations and various traits, processes, or syndromes (such as diseases).
- Pathway elucidation: Analyzing the interactions among genes, proteins, and other cellular components involved in metabolism, gene expression, or cellular signaling (important in processes such as aging, immunity, and disease, and so in drug development).
- Systems biology: modelling of biological processes at the level of systems and networks, from physiology to organismal interaction to ecology.
- Personalized/precision medicine: Using computational techniques, text-mining and machine learning to develop targeted disease or syndrome therapies.
