Rosato Antonio, Tenori Leonardo, Cascante Marta, De Atauri Carulla Pedro Ramon, Martins Dos Santos Vitor A P, Saccenti Edoardo
Magnetic Resonance Center and Department of Chemistry "Ugo Schiff", University of Florence, Florence, Italy.
Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.
Metabolomics. 2018;14(4):37. doi: 10.1007/s11306-018-1335-y. Epub 2018 Feb 27.
Metabolomics is a well-established tool in systems biology, especially in the top-down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.
This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.
We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.
We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.
Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.
代谢组学是系统生物学中一种成熟的工具,尤其是在自上而下的方法中。代谢组学实验通常会产生探索性研究,这些研究提供了有趣的生物学假设,但很少对这些发现提供机制性解释。有鉴于此,通过部署系统生物学方法可以促进对代谢组学数据的解释。
本综述旨在概述与代谢组学相关的系统生物学方法,并讨论这些方法的一些成功应用。
我们回顾了系统生物学工具在代谢组学领域的最新应用,如网络推断与分析、代谢建模和途径分析。
我们对可用于解决代谢组学问题的系统生物学工具进行了全面概述。还以比较的方式讨论了这些工具的特点和应用结果。
系统生物学增强的代谢组学数据分析可以深入了解产生观察到的代谢谱的分子机制,并增强代谢组学研究的科学影响力。