Abstract
BackgroundProtein 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.ResultsIn 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. ConclusionsThe 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.
Highlights
Protein translation is a vital cellular process for any living organism
The central dogma of molecular biology describes that the genetic information is transferred from DNA to mRNA through transcription and from mRNA to protein via translation
Toward the goal of understanding how translation machinery functions from a system’s perspective that may enable us to form new theories and make new predictions, it is imperative that we have a better understanding of the structure and properties of protein translation networks. In pursuing such a goal, we previously reported a global analysis of network analysis of Protein Translation Regulatory Networks (PTRN) in yeast [8]
Summary
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. Translation is a vital cellular process in which the information contained in the mRNA sequence is translated into the corresponding protein by the complex translation machinery. Initiation is a series of biochemical reactions leading to the binding of ribosome on the mRNA and the formation of the initiation complex around the start codon This process involves various regulatory proteins (the so-called initiation factors). Codon-specific tRNAs are recruited by the ribosome to grow the polypeptide chain one amino acid at a time while the ribosome moves along the mRNA template (one codon at a time). This process involves various elongation factors and proceeds in a cyclic manner. The newly synthesized peptide chain and eventually the ribosomes themselves are released [2]
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