On finding convolutional code generators for translation initiation of Escherichia Coli K-12

Using error-control coding theory, the translation of mRNA into amino acids can be functionally paralleled to the decoding of noisy, convolutionally encoded data parity streams. The ribosome is modeled as a table-based convolutional decoder. This work attempts to find plausible convolutional code generators for the Escherichia Coli K-12 translation initiation. The g-mask is chosen from the exposed part of the 16S rRNA. The generators are calculated from the g-mask, using an algorithmic approach. The most plausible generators are chosen based on their ability to produce encoded sequences which provide a clear distinction between the translated and non-translated sequences.