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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Hybrid dynamic/static method for large-scale simulation of metabolism
|
|---|---|
| Published in |
Theoretical Biology and Medical Modelling, October 2005
|
| DOI | 10.1186/1742-4682-2-42 |
| Pubmed ID | |
| Authors |
Katsuyuki Yugi, Yoichi Nakayama, Ayako Kinoshita, Masaru Tomita |
| Abstract |
Many computer studies have employed either dynamic simulation or metabolic flux analysis (MFA) to predict the behaviour of biochemical pathways. Dynamic simulation determines the time evolution of pathway properties in response to environmental changes, whereas MFA provides only a snapshot of pathway properties within a particular set of environmental conditions. However, owing to the large amount of kinetic data required for dynamic simulation, MFA, which requires less information, has been used to manipulate large-scale pathways to determine metabolic outcomes. |
X Demographics
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 2 | 3% |
| Germany | 2 | 3% |
| United States | 2 | 3% |
| United Kingdom | 1 | 1% |
| India | 1 | 1% |
| Japan | 1 | 1% |
| New Zealand | 1 | 1% |
| Unknown | 70 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 18 | 23% |
| Researcher | 18 | 23% |
| Student > Bachelor | 7 | 9% |
| Student > Master | 7 | 9% |
| Student > Doctoral Student | 5 | 6% |
| Other | 15 | 19% |
| Unknown | 10 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 33 | 41% |
| Biochemistry, Genetics and Molecular Biology | 12 | 15% |
| Computer Science | 6 | 8% |
| Engineering | 5 | 6% |
| Chemical Engineering | 3 | 4% |
| Other | 8 | 10% |
| Unknown | 13 | 16% |
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 21 July 2012.
All research outputs
#17,638,663
of 22,671,366 outputs
Outputs from Theoretical Biology and Medical Modelling
#209
of 287 outputs
Outputs of similar age
#55,373
of 59,047 outputs
Outputs of similar age from Theoretical Biology and Medical Modelling
#4
of 4 outputs
Altmetric has tracked 22,671,366 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one is in the 27th percentile – i.e., 27% 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 59,047 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.