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 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 compa

SUBMITTER: Szymanska Z 

PROVIDER: S-EPMC8611353 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature