Exploratory analysis of protein translation regulatory networks using hierarchical random graphs.
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.
Wu
Daniel D
DD
Drexel University, Philadelphia, Pennsylvania, USA. daniel.wu@drexel.edu
Hu
Xiaohua
X
Park
E K
EK
Wang
Xiaofeng
X
Feng
Jiali
J
Wu
Xindong
X
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D005656:Fungal Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D008390:Markov Chains; D009010:Monte Carlo Method; D014176:Protein Biosynthesis; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000379:methods*; D012333:RNA, Messenger / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D015203:Reproducibility of Results; D015003:Yeasts / Q000235:genetics / Q000378:metabolism
D005656:Fungal Proteins; D012333:RNA, Messenger