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Mendeley readers
| Title |
Plant genome-scale metabolic reconstruction and modelling
|
|---|---|
| Published in |
Current Opinion in Biotechnology, September 2012
|
| DOI | 10.1016/j.copbio.2012.08.007 |
| Pubmed ID | |
| Authors |
Cristiana Gomes de Oliveira Dal’Molin, Lars Keld Nielsen |
| Abstract |
Genome-scale metabolic reconstructions are used extensively in the study of microbial metabolism and have proven powerful tools to guide rational pathway design of industrial strains. Generation and curation of plant genome-scale metabolic models has proven far more challenging, not the least of which is our incomplete knowledge of compartmentation and organelle transporters in plants. Conversely, the potential value of modelling is far greater when exploring a complex, multi-organelle and multi-tissue metabolism. The first generation of plant genome-scale metabolic reconstructions have proven surprisingly functional and robust as well as capable of predicting many observed complex phenotypes. With further refinement, the application of these models promises to make important contributions to plant biology and metabolic engineering. |
Mendeley readers
The data shown below were compiled from readership statistics for 172 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 2% |
| France | 3 | 2% |
| Germany | 2 | 1% |
| Argentina | 2 | 1% |
| Belgium | 2 | 1% |
| Israel | 2 | 1% |
| Latvia | 1 | <1% |
| Australia | 1 | <1% |
| Turkey | 1 | <1% |
| Other | 7 | 4% |
| Unknown | 147 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 40 | 23% |
| Student > Ph. D. Student | 36 | 21% |
| Student > Master | 29 | 17% |
| Student > Doctoral Student | 12 | 7% |
| Professor | 10 | 6% |
| Other | 27 | 16% |
| Unknown | 18 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 88 | 51% |
| Biochemistry, Genetics and Molecular Biology | 26 | 15% |
| Engineering | 17 | 10% |
| Computer Science | 7 | 4% |
| Chemical Engineering | 3 | 2% |
| Other | 8 | 5% |
| Unknown | 23 | 13% |