Bayesian inference of a non-local proliferation model

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Authors
Szymańska, Zuzanna
Skrzeczkowski, Jakub
Miasojedow, Błażej
Gwiazda, Piotr
Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Royal Society Publishing
License
Creative Commons License
Creative Commons Attribution 4.0 International License
Abstract
From a systems biology perspective, the majority of cancer models, although interesting and providing a qualitative explanation of some problems, have a major disadvantage in that they usually miss a genuine connection with experimental data. Having this in mind, in this paper, we aim at contributing to the improvement of many cancer models which contain a proliferation term. To this end, we propose a new non-local model of cell proliferation. We select data that are suitable to perform Bayesian inference for unknown parameters and we provide a discussion on the range of applicability of the model. Furthermore, we provide proof of the stability of posterior distributions in total variation norm which exploits the theory of spaces of measures equipped with the weighted flat norm. In a companion paper, we provide detailed proof of the well-posedness of the problem and we investigate the convergence of the escalator boxcar train (EBT) algorithm applied to solve the equation.
Description
Keywords
particle method  Bayesian inverse problems  non-local cancer model  proliferation function  stability of posterior distribution  parameter estimation
Citation
Szymańska Z, Skrzeczkowski J, Miasojedow B, Gwiazda P. 2021 Bayesian inference of a non-local proliferation model. R.Soc. Open Sci. 8: 8211279. https://doi.org/10.1098/rsos.211279
URI
https://open.icm.edu.pl/handle/123456789/21280
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