Interpreting scratch assays using pair density dynamics and approximate Bayesian computation

Johnston, Stuart, Simpson, Matthew, McElwain, Sean, Binder, Benjamin J., & Ross, Joshua (2014) Interpreting scratch assays using pair density dynamics and approximate Bayesian computation. Open Biology, 4, pp. 140097-1.

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Quantifying the impact of biochemical compounds on collective cell spreading is an essential element of drug design, with various applications including developing treatments for chronic wounds and cancer. Scratch assays are a technically simple and inexpensive method used to study collective cell spreading; however, most previous interpretations of scratch assays are qualitative and do not provide estimates of the cell diffusivity, D, or the cell proliferation rate,l. Estimating D and l is important for investigating the efficacy of a potential treatment and provides insight into the mechanism through which the potential treatment acts. While a few methods for estimating D and l have been proposed, these previous methods lead to point estimates of D and l, and provide no insight into the uncertainty in these estimates. Here, we compare various types of information that can be extracted from images of a scratch assay, and quantify D and l using discrete computational simulations and approximate Bayesian computation. We show that it is possible to robustly recover estimates of D and l from synthetic data, as well as a new set of experimental data. For the first time, our approach also provides a method to estimate the uncertainty in our estimates of D and l. We anticipate that our approach can be generalized to deal with more realistic experimental scenarios in which we are interested in estimating D and l, as well as additional relevant parameters such as the strength of cell-to-cell adhesion or the strength of cell-to-substrate adhesion.

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4 citations in Scopus
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5 citations in Web of Science®

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ID Code: 76122
Item Type: Journal Article
Refereed: Yes
Keywords: cell motility, cell proliferation, scratch assay, approximate Bayesian computation, cancer, wound healing , pair correlation
DOI: 10.1098/rsob.140097
ISSN: 2046-2441
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > APPLIED MATHEMATICS (010200) > Biological Mathematics (010202)
Divisions: Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution
License, which permits unrestricted use, provided the original
author and source are credited.
Deposited On: 10 Sep 2014 23:18
Last Modified: 11 Sep 2014 22:07

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