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Quasi-cellular systems: stochastic simulation analysis at nanoscale range

Overview of attention for article published in BMC Bioinformatics, April 2013
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Title
Quasi-cellular systems: stochastic simulation analysis at nanoscale range
Published in
BMC Bioinformatics, April 2013
DOI 10.1186/1471-2105-14-s7-s7
Pubmed ID
Authors

Lorenzo Calviello, Pasquale Stano, Fabio Mavelli, Pier Luigi Luisi, Roberto Marangoni

Abstract

The wet-lab synthesis of the simplest forms of life (minimal cells) is a challenging aspect in modern synthetic biology. Quasi-cellular systems able to produce proteins directly from DNA can be obtained by encapsulating the cell-free transcription/translation system PURESYSTEM(PS) in liposomes. It is possible to detect the intra-vesicle protein production using DNA encoding for GFP and monitoring the fluorescence emission over time. The entrapment of solutes in small-volume liposomes is a fundamental open problem. Stochastic simulation is a valuable tool in the study of biochemical reaction at nanoscale range. QDC (Quick Direct-Method Controlled), a stochastic simulation software based on the well-known Gillespie's SSA algorithm, was used. A suitable model formally describing the PS reactions network was developed, to predict, from inner species concentrations (very difficult to measure in small-volumes), the resulting fluorescence signal (experimentally observable).

X Demographics

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Portugal 1 3%
Unknown 30 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 25%
Researcher 5 16%
Student > Master 5 16%
Student > Bachelor 4 13%
Student > Doctoral Student 2 6%
Other 5 16%
Unknown 3 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 38%
Biochemistry, Genetics and Molecular Biology 5 16%
Chemistry 4 13%
Physics and Astronomy 2 6%
Social Sciences 2 6%
Other 2 6%
Unknown 5 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 18 July 2013.
All research outputs
#18,341,711
of 22,714,025 outputs
Outputs from BMC Bioinformatics
#6,294
of 7,260 outputs
Outputs of similar age
#148,492
of 196,443 outputs
Outputs of similar age from BMC Bioinformatics
#113
of 120 outputs
Altmetric has tracked 22,714,025 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,260 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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We're also able to compare this research output to 120 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.