Bayesian inference of a non-local proliferation model

TL;DR

This paper introduces a novel non-local cell proliferation model in cancer systems biology, utilizing Bayesian inference to estimate parameters and analyzing the stability of the posterior distributions.

Contribution

It proposes a new non-local proliferation model, applies Bayesian inference for parameter estimation, and discusses the model's applicability and stability properties.

Findings

Successful Bayesian inference of model parameters

Proof of stability of posterior distributions in total variation norm

Discussion on the model's applicability range

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.