Algorithmic Approach for finding Convolutional

PONNALA, LALIT Algorithmic Approach for finding Convolutional Code generators for the Translation Initiation of Escherichia coli K-12. (Under the direction of Professor Donald L. Bitzer and Professor Winser E. Alexander). Using error-control coding theory, we parallel the functionality of the translation of mRNA into amino acids to the decoding of noisy parity streams that have been encoded using a convolutional code. This enables us to model the ribosome as a tablebased convolution decoder. In this work, we attempt to find plausible convolutional code generators for the translation initiation of Escherichia coli K-12. We choose the g-mask from the exposed part of the 16S rRNA. We develop an algorithmic approach to calculate the generators from the g-mask. We assign plausibility to the generators based on their ability to produce encoded sequences which exhibit a clear distinction between the translated and non-translated sequences. We also explore the construction of g-masks based on binding patterns, and evaluate the performance of the corresponding generators. Algorithmic Approach for finding Convolutional Code generators for the Translation Initiation of Escherichia coli K-12 by Lalit Ponnala A thesis submitted to the Graduate Faculty of North Carolina State University in partial satisfaction of the requirements for the Degree of Master of Science Department of Electrical and Computer Engineering Raleigh 2003