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Biomolecular computers with multiple restriction enzymes

Overview of attention for article published in Genetics and Molecular Biology, October 2017
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Title
Biomolecular computers with multiple restriction enzymes
Published in
Genetics and Molecular Biology, October 2017
DOI 10.1590/1678-4685-gmb-2016-0132
Pubmed ID
Authors

Sebastian Sakowski, Tadeusz Krasinski, Jacek Waldmajer, Joanna Sarnik, Janusz Blasiak, Tomasz Poplawski

Abstract

The development of conventional, silicon-based computers has several limitations, including some related to the Heisenberg uncertainty principle and the von Neumann "bottleneck". Biomolecular computers based on DNA and proteins are largely free of these disadvantages and, along with quantum computers, are reasonable alternatives to their conventional counterparts in some applications. The idea of a DNA computer proposed by Ehud Shapiro's group at the Weizmann Institute of Science was developed using one restriction enzyme as hardware and DNA fragments (the transition molecules) as software and input/output signals. This computer represented a two-state two-symbol finite automaton that was subsequently extended by using two restriction enzymes. In this paper, we propose the idea of a multistate biomolecular computer with multiple commercially available restriction enzymes as hardware. Additionally, an algorithmic method for the construction of transition molecules in the DNA computer based on the use of multiple restriction enzymes is presented. We use this method to construct multistate, biomolecular, nondeterministic finite automata with four commercially available restriction enzymes as hardware. We also describe an experimental applicaton of this theoretical model to a biomolecular finite automaton made of four endonucleases.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 29%
Unspecified 1 14%
Unknown 4 57%
Readers by discipline Count As %
Unspecified 1 14%
Physics and Astronomy 1 14%
Engineering 1 14%
Unknown 4 57%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 November 2017.
All research outputs
#19,594,120
of 24,093,053 outputs
Outputs from Genetics and Molecular Biology
#521
of 726 outputs
Outputs of similar age
#256,260
of 331,801 outputs
Outputs of similar age from Genetics and Molecular Biology
#5
of 10 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 726 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 15th percentile – i.e., 15% of its peers scored the same or lower than it.
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We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.