| Title |
FOCAL: an experimental design tool for systematizing metabolic discoveries and model development
|
|---|---|
| Published in |
Genome Biology, December 2012
|
| DOI | 10.1186/gb-2012-13-12-r116 |
| Pubmed ID | |
| Authors |
Christopher J Tervo, Jennifer L Reed |
| Abstract |
Current computational tools can generate and improve genome-scale models based on existing data; however, for many organisms, the data needed to test and refine such models are not available. To facilitate model development, we created the forced coupling algorithm, FOCAL, to identify genetic and environmental conditions such that a reaction becomes essential for an experimentally measurable phenotype. This reaction's conditional essentiality can then be tested experimentally to evaluate whether network connections occur or to create strains with desirable phenotypes. FOCAL allows network connections to be queried, which improves our understanding of metabolism and accuracy of developed models. |
X Demographics
The data shown below were collected from the profiles of 5 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 Kingdom | 3 | 60% |
| Japan | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 60% |
| Science communicators (journalists, bloggers, editors) | 1 | 20% |
| Members of the public | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 6% |
| Germany | 1 | 2% |
| Unknown | 58 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 17 | 27% |
| Student > Master | 14 | 22% |
| Student > Ph. D. Student | 12 | 19% |
| Student > Bachelor | 4 | 6% |
| Student > Doctoral Student | 3 | 5% |
| Other | 7 | 11% |
| Unknown | 6 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 33% |
| Engineering | 14 | 22% |
| Biochemistry, Genetics and Molecular Biology | 8 | 13% |
| Computer Science | 6 | 10% |
| Chemical Engineering | 1 | 2% |
| Other | 4 | 6% |
| Unknown | 9 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 13 December 2012.
All research outputs
#14,913,921
of 25,373,627 outputs
Outputs from Genome Biology
#3,897
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Outputs of similar age
#167,783
of 286,275 outputs
Outputs of similar age from Genome Biology
#34
of 42 outputs
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