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A fundamental challenge for all communication systems, engineered or living, is the problem of achieving efficient, secure, and error-free communication over noisy channels. Information theoretic principals have been used to develop effective coding theory algorithms to successfully transmit information in engineering systems. Living systems also successfully transmit biological information through genetic processes such as replication, transcription, and translation, where the genome of an organism is the contents of the transmission.Decoding of received bit streams is fairly straightforward when the channel encoding algorithms are efficient and known. If the encoding scheme is unknown or part of the data is missing or intercepted, how would one design a viable decoder for the received transmission? For such systems blind reconstruction of the encodingldecoding system would be a vital step in recovering the original message. Communication engineers may not frequently encounter this situation, but for computational biologists and biotechnologist this is an immediate challenge.system for engineered and biological data. Building on Sandia's strengths in discrete mathematics, algorithms, and communication theory, we use linear programming and will use evolutionary computing techniques to construct efficient algorithms for modeling the coding system for The goal of this work is to develop methods for detecting and reconstructing the encoderldecoder 3 minimally errored engineered data stream and genomic regulatory DNA and RNA sequences. The objective for the initial phase of this project is to construct solid parallels between biological literature and fundamental elements of communication theory. In this light, the milestones for FY2003 were focused on defining genetic channel characteristics and providing an initial approximation for key parameters, including coding rate, memory length, and minimum distance values. A secondary objective addressed the question of determining similar parameters for a received, noisy, error-control encoded data set. In addition to these goals, we initiated exploration of algorithmic approaches to determine if a data set could be approximated with an error-control code and performed initial investigations into optimization based methodolgies for extracting the encoding algorithm given the coding rate of an encoded noise-free and noisy data stream.

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