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Automatic Design of Digital Synthetic Gene Circuits

Overview of attention for article published in PLoS Computational Biology, February 2011
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Title
Automatic Design of Digital Synthetic Gene Circuits
Published in
PLoS Computational Biology, February 2011
DOI 10.1371/journal.pcbi.1001083
Pubmed ID
Authors

Mario A. Marchisio, Jörg Stelling

Abstract

De novo computational design of synthetic gene circuits that achieve well-defined target functions is a hard task. Existing, brute-force approaches run optimization algorithms on the structure and on the kinetic parameter values of the network. However, more direct rational methods for automatic circuit design are lacking. Focusing on digital synthetic gene circuits, we developed a methodology and a corresponding tool for in silico automatic design. For a given truth table that specifies a circuit's input-output relations, our algorithm generates and ranks several possible circuit schemes without the need for any optimization. Logic behavior is reproduced by the action of regulatory factors and chemicals on the promoters and on the ribosome binding sites of biological Boolean gates. Simulations of circuits with up to four inputs show a faithful and unequivocal truth table representation, even under parametric perturbations and stochastic noise. A comparison with already implemented circuits, in addition, reveals the potential for simpler designs with the same function. Therefore, we expect the method to help both in devising new circuits and in simplifying existing solutions.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 177 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 6%
United Kingdom 6 3%
Germany 2 1%
Spain 2 1%
Switzerland 2 1%
Latvia 1 <1%
Canada 1 <1%
China 1 <1%
Brazil 1 <1%
Other 4 2%
Unknown 147 83%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 60 34%
Researcher 38 21%
Student > Bachelor 24 14%
Student > Master 13 7%
Professor 9 5%
Other 25 14%
Unknown 8 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 69 39%
Computer Science 27 15%
Biochemistry, Genetics and Molecular Biology 25 14%
Engineering 24 14%
Environmental Science 5 3%
Other 16 9%
Unknown 11 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 23 September 2017.
All research outputs
#15,739,529
of 25,374,647 outputs
Outputs from PLoS Computational Biology
#6,754
of 8,960 outputs
Outputs of similar age
#91,557
of 118,456 outputs
Outputs of similar age from PLoS Computational Biology
#43
of 67 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
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 118,456 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 67 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.