Probabilistic Analysis of Pattern Formation in Monotonic Self-Assembly.

Inspired by biological systems, self-assembly aims to construct complex structures. It functions through piece-wise, local interactions among component parts and has the potential to produce novel materials and devices at the nanoscale. Algorithmic self-assembly models the product of self-assembly a...

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Main Authors: Tyler G Moore, Max H Garzon, Russell J Deaton
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4589292?pdf=render
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spelling doaj-6ec0750c8d4247308980bfb65244b6202020-11-24T21:09:43ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01109e013798210.1371/journal.pone.0137982Probabilistic Analysis of Pattern Formation in Monotonic Self-Assembly.Tyler G MooreMax H GarzonRussell J DeatonInspired by biological systems, self-assembly aims to construct complex structures. It functions through piece-wise, local interactions among component parts and has the potential to produce novel materials and devices at the nanoscale. Algorithmic self-assembly models the product of self-assembly as the output of some computational process, and attempts to control the process of assembly algorithmically. Though providing fundamental insights, these computational models have yet to fully account for the randomness that is inherent in experimental realizations, which tend to be based on trial and error methods. In order to develop a method of analysis that addresses experimental parameters, such as error and yield, this work focuses on the capability of assembly systems to produce a pre-determined set of target patterns, either accurately or perhaps only approximately. Self-assembly systems that assemble patterns that are similar to the targets in a significant percentage are "strong" assemblers. In addition, assemblers should predominantly produce target patterns, with a small percentage of errors or junk. These definitions approximate notions of yield and purity in chemistry and manufacturing. By combining these definitions, a criterion for efficient assembly is developed that can be used to compare the ability of different assembly systems to produce a given target set. Efficiency is a composite measure of the accuracy and purity of an assembler. Typical examples in algorithmic assembly are assessed in the context of these metrics. In addition to validating the method, they also provide some insight that might be used to guide experimentation. Finally, some general results are established that, for efficient assembly, imply that every target pattern is guaranteed to be assembled with a minimum common positive probability, regardless of its size, and that a trichotomy exists to characterize the global behavior of typical efficient, monotonic self-assembly systems in the literature.http://europepmc.org/articles/PMC4589292?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Tyler G Moore
Max H Garzon
Russell J Deaton
spellingShingle Tyler G Moore
Max H Garzon
Russell J Deaton
Probabilistic Analysis of Pattern Formation in Monotonic Self-Assembly.
PLoS ONE
author_facet Tyler G Moore
Max H Garzon
Russell J Deaton
author_sort Tyler G Moore
title Probabilistic Analysis of Pattern Formation in Monotonic Self-Assembly.
title_short Probabilistic Analysis of Pattern Formation in Monotonic Self-Assembly.
title_full Probabilistic Analysis of Pattern Formation in Monotonic Self-Assembly.
title_fullStr Probabilistic Analysis of Pattern Formation in Monotonic Self-Assembly.
title_full_unstemmed Probabilistic Analysis of Pattern Formation in Monotonic Self-Assembly.
title_sort probabilistic analysis of pattern formation in monotonic self-assembly.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2015-01-01
description Inspired by biological systems, self-assembly aims to construct complex structures. It functions through piece-wise, local interactions among component parts and has the potential to produce novel materials and devices at the nanoscale. Algorithmic self-assembly models the product of self-assembly as the output of some computational process, and attempts to control the process of assembly algorithmically. Though providing fundamental insights, these computational models have yet to fully account for the randomness that is inherent in experimental realizations, which tend to be based on trial and error methods. In order to develop a method of analysis that addresses experimental parameters, such as error and yield, this work focuses on the capability of assembly systems to produce a pre-determined set of target patterns, either accurately or perhaps only approximately. Self-assembly systems that assemble patterns that are similar to the targets in a significant percentage are "strong" assemblers. In addition, assemblers should predominantly produce target patterns, with a small percentage of errors or junk. These definitions approximate notions of yield and purity in chemistry and manufacturing. By combining these definitions, a criterion for efficient assembly is developed that can be used to compare the ability of different assembly systems to produce a given target set. Efficiency is a composite measure of the accuracy and purity of an assembler. Typical examples in algorithmic assembly are assessed in the context of these metrics. In addition to validating the method, they also provide some insight that might be used to guide experimentation. Finally, some general results are established that, for efficient assembly, imply that every target pattern is guaranteed to be assembled with a minimum common positive probability, regardless of its size, and that a trichotomy exists to characterize the global behavior of typical efficient, monotonic self-assembly systems in the literature.
url http://europepmc.org/articles/PMC4589292?pdf=render
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