Pey Jon, Theodoropoulos Constantinos, Rezola Alberto, Rubio Angel, Cascante Marta, Planes Francisco J
CEIT and TECNUN, University of Navarra, Manuel de Lardizabal, San Sebastian, Spain.
Biosystems. 2011 Aug;105(2):140-6. doi: 10.1016/j.biosystems.2011.04.005. Epub 2011 Apr 22.
The elementary flux modes (EFMs) approach is an efficient computational tool to predict novel metabolic pathways. Elucidating the physiological relevance of EFMs in a particular cellular state is still an open challenge. Different methods have been presented to carry out this task. However, these methods typically use little experimental data, exploiting methodologies where an a priori optimization function is used to deal with the indetermination underlying metabolic networks. Available "omics" data represent an opportunity to refine current methods. In this article we discuss whether (or not) metabolomics data from isotope labeling experiments (ILEs) and EFMs can be integrated into a linear system of equations. Aside from refining current approaches to infer the physiological relevance of EFMs, this question is important for the integration of metabolomics data from ILEs into metabolic networks, which generally involve non-linear relationships. As a result of our analysis, we concluded that in general the concept of EFMs needs to be redefined at the atomic level for the modeling of ILEs. For this purpose, the concept of Elementary Carbon Modes (ECMs) is introduced.
基本通量模式（EFM）方法是预测新代谢途径的一种有效计算工具。阐明EFM在特定细胞状态下的生理相关性仍然是一个悬而未决的挑战。已经提出了不同的方法来完成这项任务。然而，这些方法通常很少使用实验数据，而是利用先验优化函数来处理代谢网络中潜在的不确定性的方法。现有的“组学”数据为改进当前方法提供了契机。在本文中，我们讨论了来自同位素标记实验（ILE）的代谢组学数据和EFM是否可以整合到一个线性方程组中。除了改进当前推断EFM生理相关性的方法外，这个问题对于将来自ILE的代谢组学数据整合到通常涉及非线性关系的代谢网络中也很重要。经过我们的分析，我们得出结论，一般来说，为了对ILE进行建模，需要在原子水平上重新定义EFM的概念。为此，引入了基本碳模式（ECM）的概念。
