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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Global dynamic optimization approach to predict activation in metabolic pathways
|
|---|---|
| Published in |
BMC Systems Biology, January 2014
|
| DOI | 10.1186/1752-0509-8-1 |
| Pubmed ID | |
| Authors |
Gundián M de Hijas-Liste, Edda Klipp, Eva Balsa-Canto, Julio R Banga |
| Abstract |
During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been successfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. |
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 % |
|---|---|---|
| Belarus | 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 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 2% |
| Malaysia | 1 | 1% |
| India | 1 | 1% |
| United Kingdom | 1 | 1% |
| Singapore | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 77 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 26 | 31% |
| Researcher | 17 | 20% |
| Student > Master | 10 | 12% |
| Student > Doctoral Student | 5 | 6% |
| Professor | 4 | 5% |
| Other | 14 | 17% |
| Unknown | 8 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 24 | 29% |
| Biochemistry, Genetics and Molecular Biology | 13 | 15% |
| Computer Science | 10 | 12% |
| Physics and Astronomy | 5 | 6% |
| Mathematics | 5 | 6% |
| Other | 15 | 18% |
| Unknown | 12 | 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 19 January 2014.
All research outputs
#13,373,196
of 23,577,654 outputs
Outputs from BMC Systems Biology
#433
of 1,139 outputs
Outputs of similar age
#160,402
of 308,504 outputs
Outputs of similar age from BMC Systems Biology
#19
of 58 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,139 research outputs from this source. They receive a mean Attention Score of 3.6. This one has gotten more attention than average, scoring higher than 60% 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 308,504 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 58 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.