PT  - JOURNAL ARTICLE
AU  - Alexander P Browning
AU  - Scott W McCue
AU  - Matthew J Simpson
TI  - A Bayesian computational approach to explore the optimal duration of a cell proliferation assay
AID  - 10.1101/147678
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 147678
4099  - http://biorxiv.org/content/early/2017/06/09/147678.short
4100  - http://biorxiv.org/content/early/2017/06/09/147678.full
AB  - 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 hours (or longer), a standard cell proliferation assay conducted over 24 hours 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.