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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Anshula Tandon, Seungjae Kim, Yongwoo Song, Hyunjae Cho, Saima Bashar, Jihoon Shin, Tai Hwan Ha, Sung Ha Park

ABSTRACT

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 (πexp) obtained by double crossover DNA lattices and DNA rings, fluctuation of πexp 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. More... »

PAGES

2252

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-019-38699-0

DOI

http://dx.doi.org/10.1038/s41598-019-38699-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112230613

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30783171


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