| Title |
A Bayesian Computational Approach to Explore the Optimal Duration of a Cell Proliferation Assay
|
|---|---|
| Published in |
Bulletin of Mathematical Biology, June 2017
|
| DOI | 10.1007/s11538-017-0311-4 |
| Pubmed ID | |
| Authors |
Alexander P. Browning, Scott W. McCue, Matthew J. Simpson |
| Abstract |
Cell proliferation assays are routinely used to explore how a low-density monolayer of cells grows with time. For a typical cell line with a doubling time of 12 h (or longer), a standard cell proliferation assay conducted over 24 h provides excellent information about the low-density exponential growth rate, but limited information about crowding effects that occur at higher densities. To explore how we can best detect and quantify crowding effects, we present a suite of in silico proliferation assays where cells proliferate according to a generalised logistic growth model. Using approximate Bayesian computation we show that data from a standard cell proliferation assay cannot reliably distinguish between classical logistic growth and more general non-logistic growth models. We then explore, and quantify, the trade-off between increasing the duration of the experiment and the associated decrease in uncertainty in the crowding mechanism. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 40% |
| Student > Bachelor | 1 | 10% |
| Lecturer > Senior Lecturer | 1 | 10% |
| Student > Master | 1 | 10% |
| Researcher | 1 | 10% |
| Other | 0 | 0% |
| Unknown | 2 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 4 | 40% |
| Biochemistry, Genetics and Molecular Biology | 1 | 10% |
| Physics and Astronomy | 1 | 10% |
| Engineering | 1 | 10% |
| Unknown | 3 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 10 July 2017.
All research outputs
#20,433,667
of 22,986,950 outputs
Outputs from Bulletin of Mathematical Biology
#1,003
of 1,103 outputs
Outputs of similar age
#275,129
of 315,513 outputs
Outputs of similar age from Bulletin of Mathematical Biology
#16
of 22 outputs
Altmetric has tracked 22,986,950 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,103 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 1st percentile – i.e., 1% 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 315,513 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.