Sneha Rai, Utkarsh Raj and Pritish Kumar Varadwaj* Pages 1745 - 1754 ( 10 )
The conventional way of characterizing a disease consists of correlating clinical symptoms with pathological findings. Although this approach for many years has assisted clinicians in establishing syndromic patterns for pathophenotypes, it has major limitations as it does not consider preclinical disease states and is unable to individualize medicine. Moreover, the complexity of disease biology is the major challenge in the development of effective and safe medicines. Therefore, the process of drug development must consider biological responses in both pathological and physiological conditions. Consequently, a quantitative and holistic systems biology approach could aid in understanding complex biological systems by providing an exceptional platform to integrate diverse data types with molecular as well as pathway information, leading to development of predictive models for complex diseases. Furthermore, an increase in knowledgebase of proteins, genes, metabolites from high-throughput experimental data accelerates hypothesis generation and testing in disease models. The systems biology approach also assists in predicting drug effects, repurposing of existing drugs, identifying new targets, facilitating development of personalized medicine and improving decision making and success rate of new drugs in clinical trials.
Network models, Dynamical models, Biomarkers, Drug repurposing, Drug combinations, CVD.
Division of Biotechnology, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, Department of Bioinformatics & Applied Sciences, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, Department of Bioinformatics & Applied Sciences, Indian Institute of Information Technology, Allahabad, Uttar Pradesh