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Molecular system for an exponentially fast growing programmable synthetic polymer

In this paper, we demonstrate a molecular system for the first active self-assembly linear DNA polymer that exhibits programmable molecular exponential growth in real time, also the first to implement “internal” parallel insertion that does not rely on adding successive layers to “external” edges fo...

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Autores principales: Dabby, Nadine, Barr, Alan, Chen, Ho-Lin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10338630/
https://www.ncbi.nlm.nih.gov/pubmed/37438350
http://dx.doi.org/10.1038/s41598-023-35720-5
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author Dabby, Nadine
Barr, Alan
Chen, Ho-Lin
author_facet Dabby, Nadine
Barr, Alan
Chen, Ho-Lin
author_sort Dabby, Nadine
collection PubMed
description In this paper, we demonstrate a molecular system for the first active self-assembly linear DNA polymer that exhibits programmable molecular exponential growth in real time, also the first to implement “internal” parallel insertion that does not rely on adding successive layers to “external” edges for growth. Approaches like this can produce enhanced exponential growth behavior that is less limited by volume and external surface interference, for an early step toward efficiently building two and three dimensional shapes in logarithmic time. We experimentally demonstrate the division of these polymers via the addition of a single DNA complex that competes with the insertion mechanism and results in the exponential growth of a population of polymers per unit time. In the supplementary material, we note that an “extension” beyond conventional Turing machine theory is needed to theoretically analyze exponential growth itself in programmable physical systems. Sequential physical Turing Machines that run a roughly constant number of Turing steps per unit time cannot achieve an exponential growth of structure per time. In contrast, the “active” self-assembly model in this paper, computationally equivalent to a Push-Down Automaton, is exponentially fast when implemented in molecules, but is taxonomically less powerful than a Turing machine. In this sense, a physical Push-Down Automaton can be more powerful than a sequential physical Turing Machine, even though the Turing Machine can compute any computable function. A need for an “extended” computational/physical theory arises, described in the supplementary material section S1.
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spelling pubmed-103386302023-07-14 Molecular system for an exponentially fast growing programmable synthetic polymer Dabby, Nadine Barr, Alan Chen, Ho-Lin Sci Rep Article In this paper, we demonstrate a molecular system for the first active self-assembly linear DNA polymer that exhibits programmable molecular exponential growth in real time, also the first to implement “internal” parallel insertion that does not rely on adding successive layers to “external” edges for growth. Approaches like this can produce enhanced exponential growth behavior that is less limited by volume and external surface interference, for an early step toward efficiently building two and three dimensional shapes in logarithmic time. We experimentally demonstrate the division of these polymers via the addition of a single DNA complex that competes with the insertion mechanism and results in the exponential growth of a population of polymers per unit time. In the supplementary material, we note that an “extension” beyond conventional Turing machine theory is needed to theoretically analyze exponential growth itself in programmable physical systems. Sequential physical Turing Machines that run a roughly constant number of Turing steps per unit time cannot achieve an exponential growth of structure per time. In contrast, the “active” self-assembly model in this paper, computationally equivalent to a Push-Down Automaton, is exponentially fast when implemented in molecules, but is taxonomically less powerful than a Turing machine. In this sense, a physical Push-Down Automaton can be more powerful than a sequential physical Turing Machine, even though the Turing Machine can compute any computable function. A need for an “extended” computational/physical theory arises, described in the supplementary material section S1. Nature Publishing Group UK 2023-07-12 /pmc/articles/PMC10338630/ /pubmed/37438350 http://dx.doi.org/10.1038/s41598-023-35720-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Dabby, Nadine
Barr, Alan
Chen, Ho-Lin
Molecular system for an exponentially fast growing programmable synthetic polymer
title Molecular system for an exponentially fast growing programmable synthetic polymer
title_full Molecular system for an exponentially fast growing programmable synthetic polymer
title_fullStr Molecular system for an exponentially fast growing programmable synthetic polymer
title_full_unstemmed Molecular system for an exponentially fast growing programmable synthetic polymer
title_short Molecular system for an exponentially fast growing programmable synthetic polymer
title_sort molecular system for an exponentially fast growing programmable synthetic polymer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10338630/
https://www.ncbi.nlm.nih.gov/pubmed/37438350
http://dx.doi.org/10.1038/s41598-023-35720-5
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