Analysis of Coding Theory Based Models for Initiating Protein Translation in Prokaryotic Organisms

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Title: Analysis of Coding Theory Based Models for Initiating Protein Translation in Prokaryotic Organisms
Author: May, Elebeoba Eni
Advisors: Dr. Winser Alexander, Committee Member
Dr. Anne Stomp, Committee Member
Dr. Jeff Thorne, Committee Member
Dr. Donald Bitzer, Committee Co-Chair
Dr. Mladen Vouk, Committee Co-Chair
Abstract: Rapid advances in both genomic data acquisition and computational technology have encouraged the development and use of engineering methods in the field of bioinformatics and computational genomics. Several researchers are encouraging the use of error-correction coding in analyzing genetic data [1, 2]. A goal of current work in this context is to use coding theory 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. The translation of messenger RNA into amino acid sequences is functionally paralleled to the decoding of noisy, convolutionally encoded parity streams. This work presents a genetic algorithms 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. Several categories of error-control codes are explored and compared, including: horizontal versus vertical codes and equal versus unequal error protection (UEP) codes. This work produced convolutional codes with decoding masks having high similarity to the 3' end of the 16S ribosomal RNA. Results show that UEP code models recognize the non-random and Shine-Dalgarno domain better than equal error protection models. But, equal error protection models are more effective error detectors. Testing indicates that function-based models are more likely to distinguish taxonomical differences than sequence-based models. Additional results are presented. Research contributions include: coding theory view of prokaryotic translation initiation, the first table-based, convolutional coding model development and design methodology for prokaryotic translation initiation, the first set of (3,1,4) error-control coding models for translation initiation, comparative analysis of models on prokaryotic organisms of differing taxonomical relatedness, and extension of table-based coding principles to field five.
Date: 2002-08-19
Degree: PhD
Discipline: Electrical Engineering
Computer Engineering

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