Kouskoumvekaki Irene, Panagiotou Gianni
Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark.
J Biomed Biotechnol. 2011;2011. doi: 10.1155/2011/525497. Epub 2010 Sep 28.
Metabolomics is a rapidly evolving discipline that involves the systematic study of endogenous small molecules that characterize the metabolic pathways of biological systems. The study of metabolism at a global level has the potential to contribute significantly to biomedical research, clinical medical practice, as well as drug discovery. In this paper, we present the most up-to-date metabolite and metabolic pathway resources, and we summarize the statistical, and machine-learning tools used for the analysis of data from clinical metabolomics. Through specific applications on cancer, diabetes, neurological and other diseases, we demonstrate how these tools can facilitate diagnosis and identification of potential biomarkers for use within disease diagnosis. Additionally, we discuss the increasing importance of the integration of metabolomics data in drug discovery. On a case-study based on the Human Metabolome Database (HMDB) and the Chinese Natural Product Database (CNPD), we demonstrate the close relatedness of the two data sets of compounds, and we further illustrate how structural similarity with human metabolites could assist in the design of novel pharmaceuticals and the elucidation of the molecular mechanisms of medicinal plants.
代谢组学是一门快速发展的学科,涉及对表征生物系统代谢途径的内源性小分子进行系统研究。在全球层面开展的代谢研究有潜力为生物医学研究、临床医学实践以及药物发现做出重大贡献。在本文中,我们展示了最新的代谢物和代谢途径资源,并总结了用于分析临床代谢组学数据的统计和机器学习工具。通过在癌症、糖尿病、神经疾病及其他疾病方面的具体应用,我们展示了这些工具如何有助于疾病诊断中的潜在生物标志物的诊断和识别。此外,我们讨论了代谢组学数据整合在药物发现中日益增加的重要性。基于人类代谢组数据库(HMDB)和中国天然产物数据库(CNPD)的案例研究,我们展示了这两个化合物数据集的密切相关性,并进一步说明了与人类代谢物的结构相似性如何有助于新型药物的设计以及药用植物分子机制的阐明。