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X Demographics
Mendeley readers
Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 38% |
| Aruba | 1 | 13% |
| France | 1 | 13% |
| United Kingdom | 1 | 13% |
| Unknown | 2 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 50% |
| Scientists | 3 | 38% |
| Science communicators (journalists, bloggers, editors) | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 178 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 6 | 3% |
| United Kingdom | 4 | 2% |
| Belgium | 2 | 1% |
| Canada | 1 | <1% |
| Slovenia | 1 | <1% |
| Australia | 1 | <1% |
| India | 1 | <1% |
| Mexico | 1 | <1% |
| Spain | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 159 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 49 | 28% |
| Researcher | 41 | 23% |
| Student > Bachelor | 19 | 11% |
| Student > Master | 18 | 10% |
| Other | 9 | 5% |
| Other | 30 | 17% |
| Unknown | 12 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 49 | 28% |
| Biochemistry, Genetics and Molecular Biology | 42 | 24% |
| Computer Science | 26 | 15% |
| Engineering | 22 | 12% |
| Medicine and Dentistry | 4 | 2% |
| Other | 18 | 10% |
| Unknown | 17 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 20. 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 14 February 2023.
All research outputs
#1,889,427
of 25,377,790 outputs
Outputs from ACS Synthetic Biology
#324
of 2,904 outputs
Outputs of similar age
#10,514
of 163,838 outputs
Outputs of similar age from ACS Synthetic Biology
#5
of 28 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,904 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one has done well, scoring higher than 88% 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 163,838 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 28 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.