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Exploratory analysis of protein translation regulatory networks using hierarchical random graphs
Journal article   Open access   Peer reviewed

Exploratory analysis of protein translation regulatory networks using hierarchical random graphs

Daniel Wu, Xiaohua Hu, E K Park, Xiaofeng Wang, Jiali Feng and Xindong Wu
BMC bioinformatics, v 11 Suppl 3(3), pp S2-S2
29 Apr 2010
DOI: https://doi.org/10.1186/1471-2105-11-S3-S2
PMID: 20438649
url
https://doi.org/10.1186/1471-2105-11-S3-S2View
Published, Version of Record (VoR) Open

Abstract

ABSTRACT : BACKGROUND: Protein translation is a vital cellular process for any living organism. The availability of interaction databases provides an opportunity for researchers to exploit the immense amount of data in silico such as studying biological networks. There has been an extensive effort using computational methods in deciphering the transcriptional regulatory networks. However, research on translation regulatory networks has caught little attention in the bioinformatics and computational biology community. RESULTS: In this paper, we present an exploratory analysis of yeast protein translation regulatory networks using hierarchical random graphs. We derive a protein translation regulatory network from a protein-protein interaction dataset. Using a hierarchical random graph model, we show that the network exhibits well organized hierarchical structure. In addition, we apply this technique to predict missing links in the network. CONCLUSIONS: The hierarchical random graph mode can be a potentially useful technique for inferring hierarchical structure from network data and predicting missing links in partly known networks. The results from the reconstructed protein translation regulatory networks have potential implications for better understanding mechanisms of translational control from a system's perspective.

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Domestic collaboration
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Web of Science research areas
Biochemical Research Methods
Biotechnology & Applied Microbiology
Mathematical & Computational Biology
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