Melanoma cell colony expansion parameters revealed by approximate Bayesian computation

Vo, Brenda N., Drovandi, Christopher C., Pettitt, Anthony N., & Pettet, Graeme J. (2015) Melanoma cell colony expansion parameters revealed by approximate Bayesian computation. PLOS Computational Biology, 11(12), e1004635.

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Abstract

In vitro studies and mathematical models are now being widely used to study the underlying mechanisms driving the expansion of cell colonies. This can improve our understanding of cancer formation and progression. Although much progress has been made in terms of developing and analysing mathematical models, far less progress has been made in terms of understanding how to estimate model parameters using experimental in vitro image-based data. To address this issue, a new approximate Bayesian computation (ABC) algorithm is proposed to estimate key parameters governing the expansion of melanoma cell (MM127) colonies, including cell diffusivity, D, cell proliferation rate, λ, and cell-to-cell adhesion, q, in two experimental scenarios, namely with and without a chemical treatment to suppress cell proliferation. Even when little prior biological knowledge about the parameters is assumed, all parameters are precisely inferred with a small posterior coefficient of variation, approximately 2–12%. The ABC analyses reveal that the posterior distributions of D and q depend on the experimental elapsed time, whereas the posterior distribution of λ does not. The posterior mean values of D and q are in the ranges 226–268 µm2h−1, 311–351 µm2h−1 and 0.23–0.39, 0.32–0.61 for the experimental periods of 0–24 h and 24–48 h, respectively. Furthermore, we found that the posterior distribution of q also depends on the initial cell density, whereas the posterior distributions of D and λ do not. The ABC approach also enables information from the two experiments to be combined, resulting in greater precision for all estimates of D and λ.

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ID Code: 83824
Item Type: Journal Article
Refereed: Yes
Keywords: Approximate Bayesian computation, Sequential Monte Carlo, Cell diffusivity, Cell proliferation, Cell-to-cell adhesion, Discrete model of collective cell spreading, Melanoma cells
DOI: 10.1371/journal.pcbi.1004635
ISSN: 1553-7358
Divisions: Current > Research Centres > ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)
Current > Institutes > Institute for Future Environments
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
Copyright Owner: Copyright 2015 Vo et al.
Copyright Statement: This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Deposited On: 03 May 2015 22:44
Last Modified: 14 Jan 2016 23:35

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