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首页> 外文期刊>BMC Veterinary Research >Discovery of metabolite biomarkers: flux analysis and reaction-reaction network approach
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Discovery of metabolite biomarkers: flux analysis and reaction-reaction network approach

机译：代谢物生物标志物的发现：通量分析和反应-反应网络方法

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BackgroundMetabolism is a vital cellular process, and its malfunction can be a major contributor to many human diseases. Metabolites can serve as a metabolic disease biomarker. An detection of such biomarkers plays a significant role in the study of biochemical reaction and signaling networks. Early research mainly focused on the analysis of the metabolic networks. The issue of integrating metabolite networks with other available biological data to reveal the mechanics of disease-metabolite associations is an important and interesting challenge.ResultsIn this article, we propose two new approaches for the identification of metabolic biomarkers with the incorporation of disease specific gene expression data and the genome-scale human metabolic network. The first approach is to compare the flux interval between the normal and disease sample so as to identify reaction biomarkers. The second one is based on the Reaction-Reaction Network (RRN) to reveal the significant reactions. These two approaches utilize reaction flux obtained by a Linear Programming (LP) based method that can contribute to the discovery of potential novel biomarkers.ConclusionsBiomarker identification is an important issue in studying biochemical reactions and signaling networks. Two efficient and effective computational methods are proposed for the identification of biomarkers in this article. Furthermore, the biomarkers found by our proposed methods are shown to be significant determinants for diabetes.
机译：背景技术新陈代谢是重要的细胞过程，其功能障碍可能是许多人类疾病的主要诱因。代谢物可以作为代谢疾病的生物标志物。此类生物标志物的检测在生化反应和信号网络的研究中起着重要作用。早期研究主要集中在代谢网络的分析上。将代谢物网络与其他可用生物学数据整合以揭示疾病-代谢物关联的机制是一个重要而有趣的挑战。结果在本文中，我们提出了两种结合疾病特异性基因表达来鉴定代谢生物标志物的新方法。数据和基因组规模的人类代谢网络。第一种方法是比较正常样本和疾病样本之间的通量间隔，以识别反应生物标记物。第二个是基于反应反应网络（RRN）来揭示重要的反应。这两种方法利用基于线性规划（LP）的方法获得的反应通量，可有助于发现潜在的新型生物标志物。结论生物标志物的识别是研究生化反应和信号网络的重要问题。本文提出了两种有效的计算方法来鉴定生物标志物。此外，我们提出的方法发现的生物标志物被证明是糖尿病的重要决定因素。

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