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Open Access Research article

Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy

Chen-Ching Lin12, Jen-Tsung Hsiang3, Chia-Yi Wu4, Yen-Jen Oyang2, Hsueh-Fen Juan24* and Hsuan-Cheng Huang1*

Author Affiliations

1 Institute of Biomedical Informatics, Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei 112, Taiwan

2 Graduate Institute of Biomedical Electronics and Bioinformatics, Center for Systems Biology and Bioinformatics, National Taiwan University, Taipei 106, Taiwan

3 Department of Physics, National Dong Hwa University, Hualien 974, Taiwan

4 Department of Life Science, Institute of Molecular and Cellular Biology, National Taiwan University, Taipei 106, Taiwan

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BMC Systems Biology 2010, 4:138  doi:10.1186/1752-0509-4-138

Published: 15 October 2010



Molecular networks represent the backbone of molecular activity within cells and provide opportunities for understanding the mechanism of diseases. While protein-protein interaction data constitute static network maps, integration of condition-specific co-expression information provides clues to the dynamic features of these networks. Dilated cardiomyopathy is a leading cause of heart failure. Although previous studies have identified putative biomarkers or therapeutic targets for heart failure, the underlying molecular mechanism of dilated cardiomyopathy remains unclear.


We developed a network-based comparative analysis approach that integrates protein-protein interactions with gene expression profiles and biological function annotations to reveal dynamic functional modules under different biological states. We found that hub proteins in condition-specific co-expressed protein interaction networks tended to be differentially expressed between biological states. Applying this method to a cohort of heart failure patients, we identified two functional modules that significantly emerged from the interaction networks. The dynamics of these modules between normal and disease states further suggest a potential molecular model of dilated cardiomyopathy.


We propose a novel framework to analyze the interaction networks in different biological states. It successfully reveals network modules closely related to heart failure; more importantly, these network dynamics provide new insights into the cause of dilated cardiomyopathy. The revealed molecular modules might be used as potential drug targets and provide new directions for heart failure therapy.