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Nonlinear manipulation and analysis of large DNA datasets

Information processing functions are essential for organisms to perceive and react to their complex environment, and for humans to analyze and rationalize them. While our brain is extraordinary at processing complex information, winner-take-all, as a type of biased competition is one of the simplest...

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Autores principales: Cui, Meiying, Zhao, Xueping, Reddavide, Francesco V, Gaillez, Michelle Patino, Heiden, Stephan, Mannocci, Luca, Thompson, Michael, Zhang, Yixin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410889/
https://www.ncbi.nlm.nih.gov/pubmed/35947747
http://dx.doi.org/10.1093/nar/gkac672
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author Cui, Meiying
Zhao, Xueping
Reddavide, Francesco V
Gaillez, Michelle Patino
Heiden, Stephan
Mannocci, Luca
Thompson, Michael
Zhang, Yixin
author_facet Cui, Meiying
Zhao, Xueping
Reddavide, Francesco V
Gaillez, Michelle Patino
Heiden, Stephan
Mannocci, Luca
Thompson, Michael
Zhang, Yixin
author_sort Cui, Meiying
collection PubMed
description Information processing functions are essential for organisms to perceive and react to their complex environment, and for humans to analyze and rationalize them. While our brain is extraordinary at processing complex information, winner-take-all, as a type of biased competition is one of the simplest models of lateral inhibition and competition among biological neurons. It has been implemented as DNA-based neural networks, for example, to mimic pattern recognition. However, the utility of DNA-based computation in information processing for real biotechnological applications remains to be demonstrated. In this paper, a biased competition method for nonlinear manipulation and analysis of mixtures of DNA sequences was developed. Unlike conventional biological experiments, selected species were not directly subjected to analysis. Instead, parallel computation among a myriad of different DNA sequences was carried out to reduce the information entropy. The method could be used for various oligonucleotide-encoded libraries, as we have demonstrated its application in decoding and data analysis for selection experiments with DNA-encoded chemical libraries against protein targets.
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spelling pubmed-94108892022-08-26 Nonlinear manipulation and analysis of large DNA datasets Cui, Meiying Zhao, Xueping Reddavide, Francesco V Gaillez, Michelle Patino Heiden, Stephan Mannocci, Luca Thompson, Michael Zhang, Yixin Nucleic Acids Res Synthetic Biology and Bioengineering Information processing functions are essential for organisms to perceive and react to their complex environment, and for humans to analyze and rationalize them. While our brain is extraordinary at processing complex information, winner-take-all, as a type of biased competition is one of the simplest models of lateral inhibition and competition among biological neurons. It has been implemented as DNA-based neural networks, for example, to mimic pattern recognition. However, the utility of DNA-based computation in information processing for real biotechnological applications remains to be demonstrated. In this paper, a biased competition method for nonlinear manipulation and analysis of mixtures of DNA sequences was developed. Unlike conventional biological experiments, selected species were not directly subjected to analysis. Instead, parallel computation among a myriad of different DNA sequences was carried out to reduce the information entropy. The method could be used for various oligonucleotide-encoded libraries, as we have demonstrated its application in decoding and data analysis for selection experiments with DNA-encoded chemical libraries against protein targets. Oxford University Press 2022-08-10 /pmc/articles/PMC9410889/ /pubmed/35947747 http://dx.doi.org/10.1093/nar/gkac672 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Synthetic Biology and Bioengineering
Cui, Meiying
Zhao, Xueping
Reddavide, Francesco V
Gaillez, Michelle Patino
Heiden, Stephan
Mannocci, Luca
Thompson, Michael
Zhang, Yixin
Nonlinear manipulation and analysis of large DNA datasets
title Nonlinear manipulation and analysis of large DNA datasets
title_full Nonlinear manipulation and analysis of large DNA datasets
title_fullStr Nonlinear manipulation and analysis of large DNA datasets
title_full_unstemmed Nonlinear manipulation and analysis of large DNA datasets
title_short Nonlinear manipulation and analysis of large DNA datasets
title_sort nonlinear manipulation and analysis of large dna datasets
topic Synthetic Biology and Bioengineering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410889/
https://www.ncbi.nlm.nih.gov/pubmed/35947747
http://dx.doi.org/10.1093/nar/gkac672
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