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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, August 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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

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

Citations

dimensions_citation
60 Dimensions

Readers on

mendeley
117 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, August 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 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 6%
United Kingdom 4 3%
Russian Federation 1 <1%
Spain 1 <1%
India 1 <1%
France 1 <1%
Australia 1 <1%
Slovenia 1 <1%
Belgium 1 <1%
Other 1 <1%
Unknown 98 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 41 35%
Researcher 23 20%
Student > Bachelor 13 11%
Student > Master 13 11%
Student > Doctoral Student 7 6%
Other 20 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 32%
Computer Science 23 20%
Biochemistry, Genetics and Molecular Biology 21 18%
Engineering 17 15%
Unspecified 4 3%
Other 14 12%

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
#705,064
of 11,331,318 outputs
Outputs from ACS Synthetic Biology
#128
of 1,005 outputs
Outputs of similar age
#6,588
of 108,060 outputs
Outputs of similar age from ACS Synthetic Biology
#2
of 24 outputs
Altmetric has tracked 11,331,318 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 1,005 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.6. 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 108,060 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 93% of its contemporaries.
We're also able to compare this research output to 24 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 91% of its contemporaries.