Filling gaps in a metabolic network using expression information
Filling gaps in a metabolic network using expression information
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DOI：
10.1093/bioinformatics/bth930
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发表时间：
2004-08-04
期刊：
影响因子：
5.8
通讯作者：
Church, George M.
中科院分区：
文献类型：
--
作者：
Kharchenko, Peter;Vitkup, Dennis;Church, George M.
动机：新测序和研究良好的生物的代谢模型都包含尚未确定酶的反应。我们提出了一种计算方法，用于识别在部分重建的代谢网络中编码这种缺失的代谢酶的基因。分析：代谢表达放置（MEP）方法取决于代谢网络的共表达属性，并且是序列同源和基因组情境方法互补的目前正在用于识别缺失的代谢基因。 MEP算法预测，在5594个候选者中，在5594个候选中的前50名中，所有已知的酿酒酵母代谢酶编码基因的20％都超过了其酶促功能，并且70％的代谢基因的表达水平显着地在表达数据范围内显着地存在。用过的。
Motivation: The metabolic models of both newly sequenced and well-studied organisms contain reactions for which the enzymes have not been identified yet. We present a computational approach for identifying genes encoding such missing metabolic enzymes in a partially reconstructed metabolic network.Results: The metabolic expression placement (MEP) method relies on the coexpression properties of the metabolic network and is complementary to the sequence homology and genome context methods that are currently being used to identify missing metabolic genes. The MEP algorithm predicts over 20% of all known Saccharomyces cerevisiae metabolic enzyme-encoding genes within the top 50 out of 5594 candidates for their enzymatic function, and 70% of metabolic genes whose expression level has been significantly perturbed across the conditions of the expression dataset used.