Bayesian inference of a non-local proliferation model
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 which are suitable to perform a 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 a posteriori 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 a detailed proof of the well-posedness of the problem and we investigate the convergence of the EBT algorithm applied to solve the equation.
Cite
@article{arxiv.2106.05955,
title = {Bayesian inference of a non-local proliferation model},
author = {Zuzanna Szymańska and Jakub Skrzeczkowski and Błażej Miasojedow and Piotr Gwiazda},
journal= {arXiv preprint arXiv:2106.05955},
year = {2023}
}