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Calculation of π and Classification of Self-avoiding Lattices via DNA Configuration

Numerical simulation (e.g. Monte Carlo simulation) is an efficient computational algorithm establishing an integral part in science to understand complex physical and biological phenomena related with stochastic problems. Aside from the typical numerical simulation applications, studies calculating...

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Autores principales: Tandon, Anshula, Kim, Seungjae, Song, Yongwoo, Cho, Hyunjae, Bashar, Saima, Shin, Jihoon, Ha, Tai Hwan, Park, Sung Ha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6381155/
https://www.ncbi.nlm.nih.gov/pubmed/30783171
http://dx.doi.org/10.1038/s41598-019-38699-0
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author Tandon, Anshula
Kim, Seungjae
Song, Yongwoo
Cho, Hyunjae
Bashar, Saima
Shin, Jihoon
Ha, Tai Hwan
Park, Sung Ha
author_facet Tandon, Anshula
Kim, Seungjae
Song, Yongwoo
Cho, Hyunjae
Bashar, Saima
Shin, Jihoon
Ha, Tai Hwan
Park, Sung Ha
author_sort Tandon, Anshula
collection PubMed
description Numerical simulation (e.g. Monte Carlo simulation) is an efficient computational algorithm establishing an integral part in science to understand complex physical and biological phenomena related with stochastic problems. Aside from the typical numerical simulation applications, studies calculating numerical constants in mathematics, and estimation of growth behavior via a non-conventional self-assembly in connection with DNA nanotechnology, open a novel perspective to DNA related to computational physics. Here, a method to calculate the numerical value of π, and way to evaluate possible paths of self-avoiding walk with the aid of Monte Carlo simulation, are addressed. Additionally, experimentally obtained variation of the π as functions of DNA concentration and the total number of trials, and the behaviour of self-avoiding random DNA lattice growth evaluated through number of growth steps, are discussed. From observing experimental calculations of π (π(exp)) obtained by double crossover DNA lattices and DNA rings, fluctuation of π(exp) tends to decrease as either DNA concentration or the number of trials increases. Based upon experimental data of self-avoiding random lattices grown by the three-point star DNA motifs, various lattice configurations are examined and analyzed. This new kind of study inculcates a novel perspective for DNA nanostructures related to computational physics and provides clues to solve analytically intractable problems.
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spelling pubmed-63811552019-02-22 Calculation of π and Classification of Self-avoiding Lattices via DNA Configuration Tandon, Anshula Kim, Seungjae Song, Yongwoo Cho, Hyunjae Bashar, Saima Shin, Jihoon Ha, Tai Hwan Park, Sung Ha Sci Rep Article Numerical simulation (e.g. Monte Carlo simulation) is an efficient computational algorithm establishing an integral part in science to understand complex physical and biological phenomena related with stochastic problems. Aside from the typical numerical simulation applications, studies calculating numerical constants in mathematics, and estimation of growth behavior via a non-conventional self-assembly in connection with DNA nanotechnology, open a novel perspective to DNA related to computational physics. Here, a method to calculate the numerical value of π, and way to evaluate possible paths of self-avoiding walk with the aid of Monte Carlo simulation, are addressed. Additionally, experimentally obtained variation of the π as functions of DNA concentration and the total number of trials, and the behaviour of self-avoiding random DNA lattice growth evaluated through number of growth steps, are discussed. From observing experimental calculations of π (π(exp)) obtained by double crossover DNA lattices and DNA rings, fluctuation of π(exp) tends to decrease as either DNA concentration or the number of trials increases. Based upon experimental data of self-avoiding random lattices grown by the three-point star DNA motifs, various lattice configurations are examined and analyzed. This new kind of study inculcates a novel perspective for DNA nanostructures related to computational physics and provides clues to solve analytically intractable problems. Nature Publishing Group UK 2019-02-19 /pmc/articles/PMC6381155/ /pubmed/30783171 http://dx.doi.org/10.1038/s41598-019-38699-0 Text en © The Author(s) 2019 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Tandon, Anshula
Kim, Seungjae
Song, Yongwoo
Cho, Hyunjae
Bashar, Saima
Shin, Jihoon
Ha, Tai Hwan
Park, Sung Ha
Calculation of π and Classification of Self-avoiding Lattices via DNA Configuration
title Calculation of π and Classification of Self-avoiding Lattices via DNA Configuration
title_full Calculation of π and Classification of Self-avoiding Lattices via DNA Configuration
title_fullStr Calculation of π and Classification of Self-avoiding Lattices via DNA Configuration
title_full_unstemmed Calculation of π and Classification of Self-avoiding Lattices via DNA Configuration
title_short Calculation of π and Classification of Self-avoiding Lattices via DNA Configuration
title_sort calculation of π and classification of self-avoiding lattices via dna configuration
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6381155/
https://www.ncbi.nlm.nih.gov/pubmed/30783171
http://dx.doi.org/10.1038/s41598-019-38699-0
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