Analysis of coding theory based models for initiating protein translation in prokaryotic organisms

Rapid advances in both genomic data acquisition and computational technology has encouraged the development and use of engineering based methods in the field of bioinformatics and computational genomics. Several researchers are encouraging the use of coding theory, specifically error-correction coding, in analyzing genetic data [1]. A goal of current work in this context is to use coding theory based analysis to determine whether regions of the specified genome are protein-producing sequences. Using information theory, coding theory specifically, this work develops a coding theory view of the translation initiation process in prokaryotic organisms, paralleling the translation of messenger RNA into amino acid sequences to the decoding of noisy, convolutionally encoded parity streams. This work presents a genetic algorithms-based method for the design of optimal table-based convolutional coding models for translation initiation sites using Escherichia coli K-12 as the model organism. Sequence and function based convolutional coding models are constructed and applied to prokaryotic organisms of varying taxonomical relation including: Escherichia coli K-12, Salmonella typhimurium LT2, Bacillus subtilis, and Staphylococcus aureus Mu50. Model analysis and results are presented.