Deciphering the genetic regulatory code using an inverse error control coding framework.

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We have found that developing a computational framework for reconstructing error control codes for engineered data and ultimately for deciphering genetic regulatory coding sequences is a challenging and uncharted area that will require advances in computational technology for exact solutions. Although exact solutions are desired, computational approaches that yield plausible solutions would be considered sufficient as a proof of concept to the feasibility of reverse engineering error control codes and the possibility of developing a quantitative model for understanding and engineering genetic regulation. Such evidence would help move the idea of reconstructing error control codes for engineered and biological systems ... continued below

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41 p.

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Rintoul, Mark Daniel; May, Elebeoba Eni; Brown, William Michael; Johnston, Anna Marie & Watson, Jean-Paul March 1, 2005.

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Description

We have found that developing a computational framework for reconstructing error control codes for engineered data and ultimately for deciphering genetic regulatory coding sequences is a challenging and uncharted area that will require advances in computational technology for exact solutions. Although exact solutions are desired, computational approaches that yield plausible solutions would be considered sufficient as a proof of concept to the feasibility of reverse engineering error control codes and the possibility of developing a quantitative model for understanding and engineering genetic regulation. Such evidence would help move the idea of reconstructing error control codes for engineered and biological systems from the high risk high payoff realm into the highly probable high payoff domain. Additionally this work will impact biological sensor development and the ability to model and ultimately develop defense mechanisms against bioagents that can be engineered to cause catastrophic damage. Understanding how biological organisms are able to communicate their genetic message efficiently in the presence of noise can improve our current communication protocols, a continuing research interest. Towards this end, project goals include: (1) Develop parameter estimation methods for n for block codes and for n, k, and m for convolutional codes. Use methods to determine error control (EC) code parameters for gene regulatory sequence. (2) Develop an evolutionary computing computational framework for near-optimal solutions to the algebraic code reconstruction problem. Method will be tested on engineered and biological sequences.

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41 p.

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  • Report No.: SAND2005-1029
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/922758 | External Link
  • Office of Scientific & Technical Information Report Number: 922758
  • Archival Resource Key: ark:/67531/metadc900195

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  • March 1, 2005

Added to The UNT Digital Library

  • Sept. 27, 2016, 1:39 a.m.

Description Last Updated

  • Dec. 9, 2016, 3:41 p.m.

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Rintoul, Mark Daniel; May, Elebeoba Eni; Brown, William Michael; Johnston, Anna Marie & Watson, Jean-Paul. Deciphering the genetic regulatory code using an inverse error control coding framework., report, March 1, 2005; United States. (digital.library.unt.edu/ark:/67531/metadc900195/: accessed September 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.