Optimal quantification of contact inhibition in cell populations

TL;DR

This paper proposes a Bayesian framework to optimize experimental design for accurately quantifying contact inhibition parameters in cell populations, addressing limitations of standard methods.

Contribution

It introduces a reformulated cell proliferation assay and a Bayesian approach to better estimate contact inhibition parameters, specifically growth rate and carrying capacity.

Findings

Optimized experimental design improves parameter estimation accuracy.

Bayesian analysis quantifies uncertainty in contact inhibition parameters.

Reformulated assay captures both low-density growth and contact inhibition effects.

Abstract

Contact inhibition refers to a reduction in the rate of cell migration and/or cell proliferation in regions of high cell density. Under normal conditions contact inhibition is associated with the proper functioning tissues, whereas abnormal regulation of contact inhibition is associated with pathological conditions, such as tumor spreading. Unfortunately, standard mathematical modeling practices mask the importance of parameters that control contact inhibition through scaling arguments. Furthermore, standard experimental protocols are insufficient to quantify the effects of contact inhibition because they focus on data describing early time, low-density dynamics only. Here we use the logistic growth equation as a caricature model of contact inhibition to make recommendations as to how to best mitigate these issues. Taking a Bayesian approach we quantify the trade-off between different features of experimental design and estimates of parameter uncertainty so that we can re-formulate a standard cell proliferation assay to provide estimates of both the low-density intrinsic growth rate, $\lambda$, and the carrying capacity density, $K$, which is a measure of contact inhibition.