Yu Tianwei, Bai Yun
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA.
Curr Metabolomics. 2013 Jan 1;1(1):83-91. doi: 10.2174/2213235X11301010084.
Metabolic profiling is the unbiased detection and quantification of low molecular-weight metabolites in a living system. It is rapidly developing in biological and translational research, contributing to disease mechanism elucidation, environmental chemical surveillance, biomarker detection, and health outcome prediction. Recent developments in experimental and computational technology allow more and more known metabolites to be detected and quantified from complex samples. As the coverage of the metabolic network improves, it has become feasible to examine metabolic profiling data from a systems perspective, i.e. interpreting the data and performing statistical inference in the context of pathways and genome-scale metabolic networks. Recently a number of methods have been developed in this area, and much improvement in algorithms and databases are still needed. In this review, we survey some methods for the analysis of metabolic profiling data based on metabolic networks.
代谢谱分析是对生物系统中低分子量代谢物进行无偏向性的检测和定量。它在生物学和转化研究中迅速发展，有助于阐明疾病机制、进行环境化学监测、检测生物标志物以及预测健康结果。实验技术和计算技术的最新进展使得能够从复杂样品中检测和定量越来越多已知的代谢物。随着代谢网络覆盖范围的扩大，从系统角度检查代谢谱数据变得可行，即在代谢途径和基因组规模代谢网络的背景下解释数据并进行统计推断。最近在这一领域已经开发了许多方法，并且在算法和数据库方面仍需要大量改进。在本综述中，我们概述了一些基于代谢网络分析代谢谱数据的方法。
