Division of Biotechnology, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India.
Department of Biotechnology & Bioinformatics, NIIT University, Neemrana, Rajasthan, India.
Curr Top Med Chem. 2018;18(20):1745-1754. doi: 10.2174/1568026618666181025113226.
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.
传统的疾病特征描述方法包括将临床症状与病理发现相关联。尽管这种方法多年来一直帮助临床医生建立病理表型的综合征模式，但它有很大的局限性，因为它不考虑临床前疾病状态，也无法实现个体化医疗。此外，疾病生物学的复杂性是开发有效和安全药物的主要挑战。因此，药物开发过程必须考虑病理和生理条件下的生物学反应。因此，定量和整体的系统生物学方法可以通过提供一个特殊的平台来整合不同类型的数据与分子和途径信息，从而为复杂疾病开发预测模型，帮助理解复杂的生物系统。此外，从高通量实验数据中增加蛋白质、基因、代谢物的知识库，加速疾病模型中的假设生成和验证。系统生物学方法还有助于预测药物作用、重新利用现有药物、确定新靶点、促进个性化医学的发展，并提高新药临床试验的决策和成功率。
