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.
Keywords: Network models, Dynamical models, Biomarkers, Drug repurposing, Drug combinations, CVD.
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