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BioMed Research International
Volume 2014 (2014), Article ID 325697, 14 pages
http://dx.doi.org/10.1155/2014/325697
Research Article

MPINet: Metabolite Pathway Identification via Coupling of Global Metabolite Network Structure and Metabolomic Profile

1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
2Department of Mathematics, Heilongjiang Institute of Technology, Harbin 150050, China
3Department of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
4Department of Medical Informatics, Harbin Medical University, Daqing Campus, Daqing 163319, China

Received 1 April 2014; Accepted 18 May 2014; Published 25 June 2014

Academic Editor: Li-Ching Wu

Copyright © 2014 Feng Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

High-throughput metabolomics technology, such as gas chromatography mass spectrometry, allows the analysis of hundreds of metabolites. Understanding that these metabolites dominate the study condition from biological pathway perspective is still a significant challenge. Pathway identification is an invaluable aid to address this issue and, thus, is urgently needed. In this study, we developed a network-based metabolite pathway identification method, MPINet, which considers the global importance of metabolites and the unique character of metabolomic profile. Through integrating the global metabolite functional network structure and the character of metabolomic profile, MPINet provides a more accurate metabolomic pathway analysis. This integrative strategy simultaneously captures the global nonequivalence of metabolites in a pathway and the bias from metabolomic experimental technology. We then applied MPINet to four different types of metabolite datasets. In the analysis of metastatic prostate cancer dataset, we demonstrated the effectiveness of MPINet. With the analysis of the two type 2 diabetes datasets, we show that MPINet has the potentiality for identifying novel pathways related with disease and is reliable for analyzing metabolomic data. Finally, we extensively applied MPINet to identify drug sensitivity related pathways. These results suggest MPINet’s effectiveness and reliability for analyzing metabolomic data across multiple different application fields.