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On correlated reaction sets and coupled reaction sets in metabolic networks

机译:关于代谢网络中的相关反应集和耦合反应集

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Two reactions are in the same "correlated reaction set" (or "Co-Set") if their fluxes are linearly correlated. On the other hand, two reactions are "coupled" if nonzero flux through one reaction implies nonzero flux through the other reaction. Flux correlation analysis has been previously used in the analysis of enzyme dysregulation and enzymopathy, while flux coupling analysis has been used to predict co-expression of genes and to model network evolution. The goal of this paper is to emphasize, through a few examples, that these two concepts are inherently different. In other words, except for the case of full coupling, which implies perfect correlation between two fluxes (R-2 = 1), there are no constraints on Pearson correlation coefficients (CC) in case of any other type of (un) coupling relations. In other words, Pearson CC can take any value between 0 and 1 in other cases. Furthermore, by analyzing genome-scale metabolic networks, we confirm that there are some examples in real networks of bacteria, yeast and human, which approve that flux coupling and flux correlation cannot be used interchangeably.
机译:如果两个反应的通量呈线性相关,则它们在同一“关联反应集”(或“共同集”)中。另一方面,如果通过一个反应的非零通量意味着通过另一个反应的非零通量,则两个反应是“耦合的”。通量相关性分析先前已用于酶失调和酶病的分析,而通量耦合分析已用于预测基因的共表达并为网络进化建模。本文的目的是通过几个例子来强调这两个概念在本质上是不同的。换句话说,除了完全耦合的情况(这意味着两个通量之间具有完美的相关性(R-2 = 1)之外),对于任何其他类型的(非)耦合关系,对皮尔逊相关系数(CC)都没有约束。 。换句话说,在其他情况下,Pearson CC可以采用0到1之间的任何值。此外,通过分析基因组规模的代谢网络,我们确认细菌,酵母和人的真实网络中存在一些实例,这些实例支持通量耦合和通量相关性不能互换使用。

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