Understanding and Applying Personalized Therapeutics at Systems Level:Role for Translational Bioinformatics

Qing      Yan

doi:10.2174/187569211795508420

Abstract

One of the most significant barriers to personalized medicine is effective linkage of scientific discoveries from bench to improved therapeutic outcomes at the bedside, and at the level of health systems and services more broadly. Translational bioinformatics is an emerging field that provides powerful new methods to bridge the gaps between biomedical sciences, clinical practice and population sciences. These objectives can be achieved both from the biomedical and the informatics sides. On the biomedical side, translational bioinformatics elucidates the structure-function associations and genotype-phenotype correlations for the identification of patient subgroups for personalized therapeutics. It enables the modeling of systemic interactions, networks, and interrelationships among genes, drugs, tissues, organs, and the environment at various systems levels. Translational bioinformatics methods facilitate the identification of systemsbased biomarkers for accurate diagnosis and prognosis, effective preventive measures and optimal treatments. Resources such as dbSNP, HapMap, and OMIM are particularly useful for analyses at different levels of a complex knowledge system such as personalized medicine. On the informatics side, data and workflow integration methods assist in understanding of the complexity in decision-making processes in both research and clinical settings. These methods can be combined with data mining techniques for pattern recognition and predictive modeling in translational systems biology studies and drug combination therapies. Knowledge discovery and knowledge representation methods can help across the domain barriers and provide effective decision support. The integration of these approaches via translational bioinformatics is timely and essential for understanding and applying personalized therapies at a systems level that is more likely to stand the test of time and rigorous scientific evidence.

Keywords: Biomarkers, data integration, data mining, decision support, personalized medicine, pharmacogenomics, systems biology, translational bioinformatics, Healthcare, Unified Modeling Language