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Modeling Structure-Function Relationships in Synthetic DNA Sequences using Attribute Grammars

Overview of attention for article published in PLoS Computational Biology, October 2009
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

blogs
1 blog
twitter
2 X users
wikipedia
1 Wikipedia page

Citations

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30 Dimensions

Readers on

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82 Mendeley
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6 CiteULike
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1 Connotea
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Title
Modeling Structure-Function Relationships in Synthetic DNA Sequences using Attribute Grammars
Published in
PLoS Computational Biology, October 2009
DOI 10.1371/journal.pcbi.1000529
Pubmed ID
Authors

Yizhi Cai, Matthew W. Lux, Laura Adam, Jean Peccoud

Abstract

Recognizing that certain biological functions can be associated with specific DNA sequences has led various fields of biology to adopt the notion of the genetic part. This concept provides a finer level of granularity than the traditional notion of the gene. However, a method of formally relating how a set of parts relates to a function has not yet emerged. Synthetic biology both demands such a formalism and provides an ideal setting for testing hypotheses about relationships between DNA sequences and phenotypes beyond the gene-centric methods used in genetics. Attribute grammars are used in computer science to translate the text of a program source code into the computational operations it represents. By associating attributes with parts, modifying the value of these attributes using rules that describe the structure of DNA sequences, and using a multi-pass compilation process, it is possible to translate DNA sequences into molecular interaction network models. These capabilities are illustrated by simple example grammars expressing how gene expression rates are dependent upon single or multiple parts. The translation process is validated by systematically generating, translating, and simulating the phenotype of all the sequences in the design space generated by a small library of genetic parts. Attribute grammars represent a flexible framework connecting parts with models of biological function. They will be instrumental for building mathematical models of libraries of genetic constructs synthesized to characterize the function of genetic parts. This formalism is also expected to provide a solid foundation for the development of computer assisted design applications for synthetic biology.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 6%
United Kingdom 2 2%
France 2 2%
Australia 1 1%
Brazil 1 1%
Portugal 1 1%
Iran, Islamic Republic of 1 1%
Germany 1 1%
China 1 1%
Other 1 1%
Unknown 66 80%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 28%
Student > Ph. D. Student 19 23%
Student > Bachelor 10 12%
Professor 7 9%
Professor > Associate Professor 5 6%
Other 13 16%
Unknown 5 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 46%
Computer Science 11 13%
Engineering 8 10%
Biochemistry, Genetics and Molecular Biology 8 10%
Chemical Engineering 2 2%
Other 9 11%
Unknown 6 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 02 September 2022.
All research outputs
#3,195,526
of 25,707,225 outputs
Outputs from PLoS Computational Biology
#2,810
of 9,024 outputs
Outputs of similar age
#11,219
of 107,561 outputs
Outputs of similar age from PLoS Computational Biology
#12
of 55 outputs
Altmetric has tracked 25,707,225 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,024 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.3. This one has gotten more attention than average, scoring higher than 68% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 107,561 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 55 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.