Bayesian inference of a non-local proliferation model

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 contributin...

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Autores principales:	Zuzanna Szymańska, Jakub Skrzeczkowski, Błażej Miasojedow, Piotr Gwiazda
Formato:	article
Lenguaje:	EN
Publicado:	The Royal Society 2021
Materias:	particle method Bayesian inverse problems non-local cancer model proliferation function stability of posterior distribution parameter estimation Science Q
Acceso en línea:	https://doaj.org/article/40f1bdf6c40047958372aced8712eb96
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spelling	oai:doaj.org-article:40f1bdf6c40047958372aced8712eb962021-11-24T08:05:47ZBayesian inference of a non-local proliferation model10.1098/rsos.2112792054-5703https://doaj.org/article/40f1bdf6c40047958372aced8712eb962021-11-01T00:00:00Zhttps://royalsocietypublishing.org/doi/10.1098/rsos.211279https://doaj.org/toc/2054-5703From 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.Zuzanna SzymańskaJakub SkrzeczkowskiBłażej MiasojedowPiotr GwiazdaThe Royal Societyarticleparticle methodBayesian inverse problemsnon-local cancer modelproliferation functionstability of posterior distributionparameter estimationScienceQENRoyal Society Open Science, Vol 8, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	particle method Bayesian inverse problems non-local cancer model proliferation function stability of posterior distribution parameter estimation Science Q
spellingShingle	particle method Bayesian inverse problems non-local cancer model proliferation function stability of posterior distribution parameter estimation Science Q Zuzanna Szymańska Jakub Skrzeczkowski Błażej Miasojedow Piotr Gwiazda Bayesian inference of a non-local proliferation model
description	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.
format	article
author	Zuzanna Szymańska Jakub Skrzeczkowski Błażej Miasojedow Piotr Gwiazda
author_facet	Zuzanna Szymańska Jakub Skrzeczkowski Błażej Miasojedow Piotr Gwiazda
author_sort	Zuzanna Szymańska
title	Bayesian inference of a non-local proliferation model
title_short	Bayesian inference of a non-local proliferation model
title_full	Bayesian inference of a non-local proliferation model
title_fullStr	Bayesian inference of a non-local proliferation model
title_full_unstemmed	Bayesian inference of a non-local proliferation model
title_sort	bayesian inference of a non-local proliferation model
publisher	The Royal Society
publishDate	2021
url	https://doaj.org/article/40f1bdf6c40047958372aced8712eb96
work_keys_str_mv	AT zuzannaszymanska bayesianinferenceofanonlocalproliferationmodel AT jakubskrzeczkowski bayesianinferenceofanonlocalproliferationmodel AT błazejmiasojedow bayesianinferenceofanonlocalproliferationmodel AT piotrgwiazda bayesianinferenceofanonlocalproliferationmodel
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Bayesian inference of a non-local proliferation model

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