Müller-Linow Mark, Weckwerth Wolfram, Hütt Marc-Thorsten
Bioinformatics Group, Department of Biology, Darmstadt University of Technology, 64287 Darmstadt, Germany.
BMC Syst Biol. 2007 Sep 24;1:44. doi: 10.1186/1752-0509-1-44.
Metabolic correlation networks are derived from the covariance of metabolites in replicates of metabolomics experiments. They constitute an interesting intermediate between topology (i.e. the system's architecture defined by the set of reactions between metabolites) and dynamics (i.e. the metabolic concentrations observed as fluctuations around steady-state values in the metabolic network).
Here we analyze, how such a correlation network changes over time, and compare the relative positions of metabolites in the correlation networks with those in established metabolic networks derived from genome databases. We find that network similarity indeed decreases with an increasing time difference between these networks during a day/night course and, counter intuitively, that proximity of metabolites in the correlation network is no indicator of proximity of the metabolites in the metabolic network.
The organizing principles of correlation networks are distinct from those of metabolic reaction maps. Time courses of correlation networks may in the future prove an important data source for understanding these organizing principles.
代谢相关网络源自代谢组学实验复制品中代谢物的协方差。它们构成了拓扑结构(即由代谢物之间的反应集定义的系统架构)和动力学(即作为代谢网络中围绕稳态值的波动而观察到的代谢浓度)之间一个有趣的中间环节。
在此我们分析了这样一个相关网络如何随时间变化,并将相关网络中代谢物的相对位置与从基因组数据库衍生的既定代谢网络中的代谢物相对位置进行比较。我们发现,在昼夜过程中,随着这些网络之间时间差的增加,网络相似性确实会降低,而且与直觉相反的是,相关网络中代谢物的接近程度并非代谢网络中代谢物接近程度的指标。
相关网络的组织原则与代谢反应图的组织原则不同。相关网络的时间进程未来可能会成为理解这些组织原则的重要数据源。