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An End-to-End Workflow for Engineering of Biological Networks from High-Level Specifications.

Overview of attention for article published in ACS Synthetic Biology, July 2012
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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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

blogs
1 blog
twitter
8 tweeters
googleplus
1 Google+ user
q&a
1 Q&A thread

Readers on

mendeley
64 Mendeley
citeulike
1 CiteULike
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Title
An End-to-End Workflow for Engineering of Biological Networks from High-Level Specifications.
Published in
ACS Synthetic Biology, July 2012
DOI 10.1021/sb300030d
Pubmed ID
Authors

Jacob Beal, Ron Weiss, Douglas Densmore, Aaron Adler, Evan Appleton, Jonathan Babb, Swapnil Bhatia, Noah Davidsohn, Traci Haddock, Joseph Loyall, Richard Schantz, Viktor Vasilev, Fusun Yaman

Abstract

We present a workflow for the design and production of biological networks from high-level program specifications. The workflow is based on a sequence of intermediate models that incrementally translate high-level specifications into DNA samples that implement them. We identify algorithms for translating between adjacent models and implement them as a set of software tools, organized into a four-stage toolchain: Specification, Compilation, Part Assignment, and Assembly. The specification stage begins with a Boolean logic computation specified in the Proto programming language. The compilation stage uses a library of network motifs and cellular platforms, also specified in Proto, to transform the program into an optimized Abstract Genetic Regulatory Network (AGRN) that implements the programmed behavior. The part assignment stage assigns DNA parts to the AGRN, drawing the parts from a database for the target cellular platform, to create a DNA sequence implementing the AGRN. Finally, the assembly stage computes an optimized assembly plan to create the DNA sequence from available part samples, yielding a protocol for producing a sample of engineered plasmids with robotics assistance. Our workflow is the first to automate the production of biological networks from a high-level program specification. Furthermore, the workflow's modular design allows the same program to be realized on different cellular platforms simply by swapping workflow configurations. We validated our workflow by specifying a small-molecule sensor-reporter program and verifying the resulting plasmids in both HEK 293 mammalian cells and in E. coli bacterial cells.

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 4 6%
United States 4 6%
Russian Federation 1 2%
Spain 1 2%
India 1 2%
France 1 2%
Slovenia 1 2%
Unknown 51 80%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 33%
Researcher 17 27%
Student > Bachelor 7 11%
Student > Master 5 8%
Professor > Associate Professor 4 6%
Other 10 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 28%
Computer Science 15 23%
Engineering 12 19%
Biochemistry, Genetics and Molecular Biology 8 13%
Unspecified 4 6%
Other 7 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 19 November 2015.
All research outputs
#443,889
of 7,392,066 outputs
Outputs from ACS Synthetic Biology
#81
of 663 outputs
Outputs of similar age
#5,403
of 91,912 outputs
Outputs of similar age from ACS Synthetic Biology
#1
of 22 outputs
Altmetric has tracked 7,392,066 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 663 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. This one has done well, scoring higher than 87% 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 91,912 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.