Metabolite biomarker discovery for metabolic diseases by flux analysis

Liu, Limin; Jiang, Hao; Ching, Wai‐Ki; Vassiliadis, Vassilis S.

doi:10.1109/isb.2012.6314103

Cited by 2 publications

(4 citation statements)

References 36 publications

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“…In this section, we discuss some of our findings by our proposed two approaches: FPC and RRN. For FPC, we have filtered out 5 reactions and all the participating genes in these reactions [ 13 ]. In terms of genes, we have identified 11 significant genes involved in diabetes, "ALDH" and its variants(9 in total), "HSD3B2" and "KHK".…”

Section: Resultsmentioning

confidence: 99%

“…The preliminary version of the paper has been presented in ISB 2012 and published in the corresponding conference proceedings [13]. …”

Section: Acknowledgementsmentioning

confidence: 99%

“…The authors would like to thank the three anonymous referees for their helpful comments and constructive suggestions. The preliminary version of the paper has been presented in ISB 2012 and published in the corresponding conference proceedings [ 13 ].…”

Section: Acknowledgementsmentioning

confidence: 99%

“…In [ 12 ], by profiling human metabolic reactions, a drug-reaction network was established for predicting enzyme targets. Here we develop a computational approach to identify metabolic biomarkers using human metabolic reactions incorporating disease-specific gene-expression data [ 13 ]. Metabolic biomarkers are metabolites demonstrating consistent variation in concentration in disease state; they can be very useful for a diagnostic purpose, see for instance [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Discovery of metabolite biomarkers: flux analysis and reaction-reaction network approach

et al. 2013

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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.

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Section: Resultsmentioning

confidence: 99%

“…The preliminary version of the paper has been presented in ISB 2012 and published in the corresponding conference proceedings [13]. …”

Section: Acknowledgementsmentioning

confidence: 99%

Section: Acknowledgementsmentioning

confidence: 99%