Translational bioinformatics approaches for systems and dynamical medicine

Abstract

The exponential growth of experimental and clinical data generated from systematic studies, the complexity in health and diseases, and the request for the establishment of systems models are bringing bioinformatics to the center stage of pharmacogenomics and systems biology. Bioinformatics plays an essential role in bridging the gap among different knowledge domains for the translation of the voluminous data into better diagnosis, prognosis, prevention, and treatment. Bioinformatics is essential in fi nding the spatiotemporal patterns in pharmacogenomics, including the time-series analyses of the associations between genetic structural variations and functional alterations such as drug responses. The elucidation of the cross talks among different systems levels and time scales can contribute to the discovery of accurate and robust biomarkers at various diseases stages for the development of systems and dynamical medicine. Various resources are available for such purposes, including databases and tools supporting “omics” studies such as genomics, proteomics, epigenomics, transcriptomics, metabolomics, lipidomics, pharmacogenomics, and chronomics. The combination of bioinformatics and health informatics methods would provide powerful decision support in both scientifi c and clinical environments. Data integration, data mining, and knowledge discovery (KD) methods would enable the simulation of complex systems and dynamical networks to establish predictive models for achieving predictive, preventive, and personalized medicine.