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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Dynamic Modeling of Cell Migration and Spreading Behaviors on Fibronectin Coated Planar Substrates and Micropatterned Geometries
|
|---|---|
| Published in |
PLoS Computational Biology, February 2013
|
| DOI | 10.1371/journal.pcbi.1002926 |
| Pubmed ID | |
| Authors |
Min-Cheol Kim, Devin M. Neal, Roger D. Kamm, H. Harry Asada |
| Abstract |
An integrative cell migration model incorporating focal adhesion (FA) dynamics, cytoskeleton and nucleus remodeling, actin motor activity, and lamellipodia protrusion is developed for predicting cell spreading and migration behaviors. This work is motivated by two experimental works: (1) cell migration on 2-D substrates under various fibronectin concentrations and (2) cell spreading on 2-D micropatterned geometries. These works suggest (1) cell migration speed takes a maximum at a particular ligand density (∼1140 molecules/µm(2)) and (2) that strong traction forces at the corners of the patterns may exist due to combined effects exerted by actin stress fibers (SFs). The integrative model of this paper successfully reproduced these experimental results and indicates the mechanism of cell migration and spreading. In this paper, the mechanical structure of the cell is modeled as having two elastic membranes: an outer cell membrane and an inner nuclear membrane. The two elastic membranes are connected by SFs, which are extended from focal adhesions on the cortical surface to the nuclear membrane. In addition, the model also includes ventral SFs bridging two focal adhesions on the cell surface. The cell deforms and gains traction as transmembrane integrins distributed over the outer cell membrane bond to ligands on the ECM surface, activate SFs, and form focal adhesions. The relationship between the cell migration speed and fibronectin concentration agrees with existing experimental data for Chinese hamster ovary (CHO) cell migrations on fibronectin coated surfaces. In addition, the integrated model is validated by showing persistent high stress concentrations at sharp geometrically patterned edges. This model will be used as a predictive model to assist in design and data processing of upcoming microfluidic cell migration assays. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Peru | 1 | 25% |
| Norway | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Science communicators (journalists, bloggers, editors) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 222 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 6 | 3% |
| Spain | 2 | <1% |
| France | 2 | <1% |
| India | 2 | <1% |
| Japan | 2 | <1% |
| Kazakhstan | 1 | <1% |
| Argentina | 1 | <1% |
| Germany | 1 | <1% |
| United Arab Emirates | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 203 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 73 | 33% |
| Researcher | 49 | 22% |
| Student > Master | 20 | 9% |
| Professor | 12 | 5% |
| Student > Doctoral Student | 11 | 5% |
| Other | 38 | 17% |
| Unknown | 19 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 57 | 26% |
| Agricultural and Biological Sciences | 55 | 25% |
| Physics and Astronomy | 26 | 12% |
| Biochemistry, Genetics and Molecular Biology | 16 | 7% |
| Medicine and Dentistry | 7 | 3% |
| Other | 27 | 12% |
| Unknown | 34 | 15% |
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 15 April 2013.
All research outputs
#15,169,543
of 25,374,647 outputs
Outputs from PLoS Computational Biology
#6,528
of 8,960 outputs
Outputs of similar age
#114,570
of 205,217 outputs
Outputs of similar age from PLoS Computational Biology
#90
of 156 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 25th percentile – i.e., 25% 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 205,217 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 156 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.