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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism
|
|---|---|
| Published in |
Cell Systems, November 2016
|
| DOI | 10.1016/j.cels.2016.10.020 |
| Pubmed ID | |
| Authors |
Hooman Hefzi, Kok Siong Ang, Michael Hanscho, Aarash Bordbar, David Ruckerbauer, Meiyappan Lakshmanan, Camila A. Orellana, Deniz Baycin-Hizal, Yingxiang Huang, Daniel Ley, Veronica S. Martinez, Sarantos Kyriakopoulos, Natalia E. Jiménez, Daniel C. Zielinski, Lake-Ee Quek, Tune Wulff, Johnny Arnsdorf, Shangzhong Li, Jae Seong Lee, Giuseppe Paglia, Nicolas Loira, Philipp N. Spahn, Lasse E. Pedersen, Jahir M. Gutierrez, Zachary A. King, Anne Mathilde Lund, Harish Nagarajan, Alex Thomas, Alyaa M. Abdel-Haleem, Juergen Zanghellini, Helene F. Kildegaard, Bjørn G. Voldborg, Ziomara P. Gerdtzen, Michael J. Betenbaugh, Bernhard O. Palsson, Mikael R. Andersen, Lars K. Nielsen, Nicole Borth, Dong-Yup Lee, Nathan E. Lewis |
| Abstract |
Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production. The models accurately predict growth phenotypes and known auxotrophies in CHO cells. With the models, we quantify the protein synthesis capacity of CHO cells and demonstrate that common bioprocess treatments, such as histone deacetylase inhibitors, inefficiently increase product yield. However, our simulations show that the metabolic resources in CHO are more than three times more efficiently utilized for growth or recombinant protein synthesis following targeted efforts to engineer the CHO secretory pathway. This model will further accelerate CHO cell engineering and help optimize bioprocesses. |
X Demographics
The data shown below were collected from the profiles of 30 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 | 8 | 27% |
| Chile | 2 | 7% |
| Canada | 2 | 7% |
| Denmark | 2 | 7% |
| France | 1 | 3% |
| India | 1 | 3% |
| Germany | 1 | 3% |
| Netherlands | 1 | 3% |
| Brazil | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 10 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 16 | 53% |
| Scientists | 12 | 40% |
| Science communicators (journalists, bloggers, editors) | 2 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 408 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Denmark | 3 | <1% |
| United States | 2 | <1% |
| Australia | 1 | <1% |
| Unknown | 402 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 83 | 20% |
| Researcher | 70 | 17% |
| Student > Bachelor | 51 | 13% |
| Student > Master | 40 | 10% |
| Student > Doctoral Student | 17 | 4% |
| Other | 49 | 12% |
| Unknown | 98 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 125 | 31% |
| Agricultural and Biological Sciences | 69 | 17% |
| Chemical Engineering | 34 | 8% |
| Engineering | 34 | 8% |
| Computer Science | 8 | 2% |
| Other | 28 | 7% |
| Unknown | 110 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 71. 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 28 December 2023.
All research outputs
#605,353
of 25,576,801 outputs
Outputs from Cell Systems
#113
of 987 outputs
Outputs of similar age
#11,547
of 318,337 outputs
Outputs of similar age from Cell Systems
#4
of 37 outputs
Altmetric has tracked 25,576,801 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 987 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 37.0. This one has done well, scoring higher than 88% 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 318,337 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.