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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Veröffentlicht in: | Proceedings of the National Academy of Sciences - PNAS 2023-09, Vol.120 (37), p.e2217330120 |
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creator | Wang, Boya Wang, Siyuan Stella Chalk, Cameron Ellington, Andrew D. Soloveichik, David |
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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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. 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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.</description><subject>Algorithms</subject><subject>Biological Sciences</subject><subject>Cellular automata</subject><subject>Deoxyribonucleic acid</subject><subject>Digital data</subject><subject>DNA</subject><subject>DNA biosynthesis</subject><subject>Gene sequencing</subject><subject>Hybridization</subject><subject>Next-generation sequencing</subject><subject>Nucleotide sequence</subject><subject>Phages</subject><subject>Physical Sciences</subject><subject>Random access</subject><issn>0027-8424</issn><issn>1091-6490</issn><issn>1091-6490</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNpdkc1Lw0AQxRdRbP04ew148RI7-70BQUr9hKIe9Lxsk6lGNtm6mwj-96a0KAoDc5gfjzfvEXJC4ZyC5pNV69I5Y1RzDpTBDhlTKGiuRAG7ZAzAdG4EEyNykNI7ABTSwD4Zca1UwQ0bk4snF5336LMmeCx772JWhmbVd66rQ5sNU9Wvded8VrnOZakLEausbrOrh-kR2Vs6n_B4uw_Jy8318-wunz_e3s-m87zkRdHlS1RLLYwwDtnCcCipUBXySvLSCEcNVJJJDUouUEOlF1qhFhoFOFRMSM4PyeVGd9UvGqxKbLvBtF3FunHxywZX27-Xtn6zr-HTUpBDBkoOCmdbhRg-ekydbepUoveuxdAny4yiSghO1-jpP_Q99LEd_ltTEoRiBQzUZEOVMaQUcfnjhoJdV2PX1djfavg34_N_TA</recordid><startdate>20230912</startdate><enddate>20230912</enddate><creator>Wang, Boya</creator><creator>Wang, Siyuan Stella</creator><creator>Chalk, Cameron</creator><creator>Ellington, Andrew D.</creator><creator>Soloveichik, David</creator><general>National Academy of Sciences</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QG</scope><scope>7QL</scope><scope>7QP</scope><scope>7QR</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TK</scope><scope>7TM</scope><scope>7TO</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-1898-4063</orcidid><orcidid>https://orcid.org/0000-0002-2585-4120</orcidid><orcidid>https://orcid.org/0000-0001-8249-6553</orcidid></search><sort><creationdate>20230912</creationdate><title>Parallel molecular computation on digital data stored in DNA</title><author>Wang, Boya ; 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subjects | Algorithms Biological Sciences Cellular automata Deoxyribonucleic acid Digital data DNA DNA biosynthesis Gene sequencing Hybridization Next-generation sequencing Nucleotide sequence Phages Physical Sciences Random access |
title | Parallel molecular computation on digital data stored in DNA |
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