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Parallel molecular computation on digital data stored in DNA

DNA is an incredibly dense storage medium for digital data. However, computing on the stored information is expensive and slow, requiring rounds of sequencing, in silico computation, and DNA synthesis. Prior work on accessing and modifying data using DNA hybridization or enzymatic reactions had limi...

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Autores principales: Wang, Boya, Wang, Siyuan Stella, Chalk, Cameron, Ellington, Andrew D., Soloveichik, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500265/
https://www.ncbi.nlm.nih.gov/pubmed/37669382
http://dx.doi.org/10.1073/pnas.2217330120
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author Wang, Boya
Wang, Siyuan Stella
Chalk, Cameron
Ellington, Andrew D.
Soloveichik, David
author_facet Wang, Boya
Wang, Siyuan Stella
Chalk, Cameron
Ellington, Andrew D.
Soloveichik, David
author_sort Wang, Boya
collection PubMed
description DNA is an incredibly dense storage medium for digital data. However, computing on the stored information is expensive and slow, requiring rounds of sequencing, in silico computation, and DNA synthesis. Prior work on accessing and modifying data using DNA hybridization or enzymatic reactions had limited computation capabilities. Inspired by the computational power of “DNA strand displacement,” we augment DNA storage with “in-memory” molecular computation using strand displacement reactions to algorithmically modify data in a parallel manner. We show programs for binary counting and Turing universal cellular automaton Rule 110, the latter of which is, in principle, capable of implementing any computer algorithm. Information is stored in the nicks of DNA, and a secondary sequence-level encoding allows high-throughput sequencing-based readout. We conducted multiple rounds of computation on 4-bit data registers, as well as random access of data (selective access and erasure). We demonstrate that large strand displacement cascades with 244 distinct strand exchanges (sequential and in parallel) can use naturally occurring DNA sequence from M13 bacteriophage without stringent sequence design, which has the potential to improve the scale of computation and decrease cost. Our work merges DNA storage and DNA computing, setting the foundation of entirely molecular algorithms for parallel manipulation of digital information preserved in DNA.
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spelling pubmed-105002652023-09-15 Parallel molecular computation on digital data stored in DNA Wang, Boya Wang, Siyuan Stella Chalk, Cameron Ellington, Andrew D. Soloveichik, David Proc Natl Acad Sci U S A Physical Sciences DNA is an incredibly dense storage medium for digital data. However, computing on the stored information is expensive and slow, requiring rounds of sequencing, in silico computation, and DNA synthesis. Prior work on accessing and modifying data using DNA hybridization or enzymatic reactions had limited computation capabilities. Inspired by the computational power of “DNA strand displacement,” we augment DNA storage with “in-memory” molecular computation using strand displacement reactions to algorithmically modify data in a parallel manner. We show programs for binary counting and Turing universal cellular automaton Rule 110, the latter of which is, in principle, capable of implementing any computer algorithm. Information is stored in the nicks of DNA, and a secondary sequence-level encoding allows high-throughput sequencing-based readout. We conducted multiple rounds of computation on 4-bit data registers, as well as random access of data (selective access and erasure). We demonstrate that large strand displacement cascades with 244 distinct strand exchanges (sequential and in parallel) can use naturally occurring DNA sequence from M13 bacteriophage without stringent sequence design, which has the potential to improve the scale of computation and decrease cost. Our work merges DNA storage and DNA computing, setting the foundation of entirely molecular algorithms for parallel manipulation of digital information preserved in DNA. National Academy of Sciences 2023-09-05 2023-09-12 /pmc/articles/PMC10500265/ /pubmed/37669382 http://dx.doi.org/10.1073/pnas.2217330120 Text en Copyright © 2023 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Physical Sciences
Wang, Boya
Wang, Siyuan Stella
Chalk, Cameron
Ellington, Andrew D.
Soloveichik, David
Parallel molecular computation on digital data stored in DNA
title Parallel molecular computation on digital data stored in DNA
title_full Parallel molecular computation on digital data stored in DNA
title_fullStr Parallel molecular computation on digital data stored in DNA
title_full_unstemmed Parallel molecular computation on digital data stored in DNA
title_short Parallel molecular computation on digital data stored in DNA
title_sort parallel molecular computation on digital data stored in dna
topic Physical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500265/
https://www.ncbi.nlm.nih.gov/pubmed/37669382
http://dx.doi.org/10.1073/pnas.2217330120
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