| Title |
Petri-net-based 2D design of DNA walker circuits
|
|---|---|
| Published in |
Natural Computing, February 2018
|
| DOI | 10.1007/s11047-018-9671-4 |
| Pubmed ID | |
| Authors |
David Gilbert, Monika Heiner, Christian Rohr |
| Abstract |
We consider localised DNA computation, where a DNA strand walks along a binary decision graph to compute a binary function. One of the challenges for the design of reliable walker circuits consists in leakage transitions, which occur when a walker jumps into another branch of the decision graph. We automatically identify leakage transitions, which allows for a detailed qualitative and quantitative assessment of circuit designs, design comparison, and design optimisation. The ability to identify leakage transitions is an important step in the process of optimising DNA circuit layouts where the aim is to minimise the computational error inherent in a circuit while minimising the area of the circuit. Our 2D modelling approach of DNA walker circuits relies on coloured stochastic Petri nets which enable functionality, topology and dimensionality all to be integrated in one two-dimensional model. Our modelling and analysis approach can be easily extended to 3-dimensional walker systems. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 21% |
| Student > Bachelor | 2 | 14% |
| Student > Master | 2 | 14% |
| Student > Doctoral Student | 1 | 7% |
| Researcher | 1 | 7% |
| Other | 2 | 14% |
| Unknown | 3 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 4 | 29% |
| Computer Science | 4 | 29% |
| Chemical Engineering | 1 | 7% |
| Mathematics | 1 | 7% |
| Medicine and Dentistry | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 2 | 14% |
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 04 June 2018.
All research outputs
#13,175,336
of 23,577,654 outputs
Outputs from Natural Computing
#60
of 146 outputs
Outputs of similar age
#158,289
of 331,701 outputs
Outputs of similar age from Natural Computing
#1
of 3 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 146 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 58% 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 331,701 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them