Stochastic multi-scale models of competition within heterogeneous cellular populations

We propose a modelling framework to analyse the stochastic behaviour of heterogeneous, multi-scale cellular populations. We illustrate our methodology with a particular example in which we study a population with an oxygen-regulated proliferation rate. Our formulation is based on an age-dependent st...

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Autores: Cruz Moreno, Roberto de la, Guerrero, Pilar|||0000-0002-5522-7339, Spill, Fabian, Alarcón Cor, Tomás|||0000-0002-8566-3676
Tipo de documento: artigo
Data de publicação:2016
País:España
Recursos:Universitat Autònoma de Barcelona
Repositório:Dipòsit Digital de Documents de la UAB
Idioma:inglês
OAI Identifier:oai:ddd.uab.cat:185936
Acesso em linha:https://ddd.uab.cat/record/185936
https://dx.doi.org/urn:doi:10.1016/j.jtbi.2016.07.028
Access Level:Acceso aberto
Palavra-chave:Multi-scale modelling
Stochastic population dynamics
Cell cycle
Radiotherapy
Scaling laws
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spelling Stochastic multi-scale models of competition within heterogeneous cellular populationsSimulation methods and mean-field analysisCruz Moreno, Roberto de laGuerrero, Pilar|||0000-0002-5522-7339Spill, FabianAlarcón Cor, Tomás|||0000-0002-8566-3676Multi-scale modellingStochastic population dynamicsCell cycleRadiotherapyScaling lawsWe propose a modelling framework to analyse the stochastic behaviour of heterogeneous, multi-scale cellular populations. We illustrate our methodology with a particular example in which we study a population with an oxygen-regulated proliferation rate. Our formulation is based on an age-dependent stochastic process. Cells within the population are characterised by their age (i.e. time elapsed since they were born). The age-dependent (oxygen-regulated) birth rate is given by a stochastic model of oxygen-dependent cell cycle progression. Once the birth rate is determined, we formulate an age-dependent birth-and-death process, which dictates the time evolution of the cell population. The population is under a feedback loop which controls its steady state size (carrying capacity): cells consume oxygen which in turn fuels cell proliferation. We show that our stochastic model of cell cycle progression allows for heterogeneity within the cell population induced by stochastic effects. Such heterogeneous behaviour is reflected in variations in the proliferation rate. Within this set-up, we have established three main results. First, we have shown that the age to the G1/S transition, which essentially determines the birth rate, exhibits a remarkably simple scaling behaviour. Besides the fact that this simple behaviour emerges from a rather complex model, this allows for a huge simplification of our numerical methodology. A further result is the observation that heterogeneous populations undergo an internal process of quasi-neutral competition. Finally, we investigated the effects of cell-cycle-phase dependent therapies (such as radiation therapy) on heterogeneous populations. In particular, we have studied the case in which the population contains a quiescent sub-population. Our mean-field analysis and numerical simulations confirm that, if the survival fraction of the therapy is too high, rescue of the quiescent population occurs. This gives rise to emergence of resistance to therapy since the rescued population is less sensitive to therapy. 22016-01-0120162016-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/185936https://dx.doi.org/urn:doi:10.1016/j.jtbi.2016.07.028reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengMinisterio de Economía y Competitividad https://doi.org/10.13039/501100003329 MTM2015-71509-C2-1-RMinisterio de Economía y Competitividad https://doi.org/10.13039/501100003329 MDM-2014-0445Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2014/SGR-1307open accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:1859362026-06-06T12:50:31Z
dc.title.none.fl_str_mv Stochastic multi-scale models of competition within heterogeneous cellular populations
Simulation methods and mean-field analysis
title Stochastic multi-scale models of competition within heterogeneous cellular populations
spellingShingle Stochastic multi-scale models of competition within heterogeneous cellular populations
Cruz Moreno, Roberto de la
Multi-scale modelling
Stochastic population dynamics
Cell cycle
Radiotherapy
Scaling laws
title_short Stochastic multi-scale models of competition within heterogeneous cellular populations
title_full Stochastic multi-scale models of competition within heterogeneous cellular populations
title_fullStr Stochastic multi-scale models of competition within heterogeneous cellular populations
title_full_unstemmed Stochastic multi-scale models of competition within heterogeneous cellular populations
title_sort Stochastic multi-scale models of competition within heterogeneous cellular populations
dc.creator.none.fl_str_mv Cruz Moreno, Roberto de la
Guerrero, Pilar|||0000-0002-5522-7339
Spill, Fabian
Alarcón Cor, Tomás|||0000-0002-8566-3676
author Cruz Moreno, Roberto de la
author_facet Cruz Moreno, Roberto de la
Guerrero, Pilar|||0000-0002-5522-7339
Spill, Fabian
Alarcón Cor, Tomás|||0000-0002-8566-3676
author_role author
author2 Guerrero, Pilar|||0000-0002-5522-7339
Spill, Fabian
Alarcón Cor, Tomás|||0000-0002-8566-3676
author2_role author
author
author
dc.subject.none.fl_str_mv Multi-scale modelling
Stochastic population dynamics
Cell cycle
Radiotherapy
Scaling laws
topic Multi-scale modelling
Stochastic population dynamics
Cell cycle
Radiotherapy
Scaling laws
description We propose a modelling framework to analyse the stochastic behaviour of heterogeneous, multi-scale cellular populations. We illustrate our methodology with a particular example in which we study a population with an oxygen-regulated proliferation rate. Our formulation is based on an age-dependent stochastic process. Cells within the population are characterised by their age (i.e. time elapsed since they were born). The age-dependent (oxygen-regulated) birth rate is given by a stochastic model of oxygen-dependent cell cycle progression. Once the birth rate is determined, we formulate an age-dependent birth-and-death process, which dictates the time evolution of the cell population. The population is under a feedback loop which controls its steady state size (carrying capacity): cells consume oxygen which in turn fuels cell proliferation. We show that our stochastic model of cell cycle progression allows for heterogeneity within the cell population induced by stochastic effects. Such heterogeneous behaviour is reflected in variations in the proliferation rate. Within this set-up, we have established three main results. First, we have shown that the age to the G1/S transition, which essentially determines the birth rate, exhibits a remarkably simple scaling behaviour. Besides the fact that this simple behaviour emerges from a rather complex model, this allows for a huge simplification of our numerical methodology. A further result is the observation that heterogeneous populations undergo an internal process of quasi-neutral competition. Finally, we investigated the effects of cell-cycle-phase dependent therapies (such as radiation therapy) on heterogeneous populations. In particular, we have studied the case in which the population contains a quiescent sub-population. Our mean-field analysis and numerical simulations confirm that, if the survival fraction of the therapy is too high, rescue of the quiescent population occurs. This gives rise to emergence of resistance to therapy since the rescued population is less sensitive to therapy.
publishDate 2016
dc.date.none.fl_str_mv 2
2016-01-01
2016
2016-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/185936
https://dx.doi.org/urn:doi:10.1016/j.jtbi.2016.07.028
url https://ddd.uab.cat/record/185936
https://dx.doi.org/urn:doi:10.1016/j.jtbi.2016.07.028
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad https://doi.org/10.13039/501100003329 MTM2015-71509-C2-1-R
Ministerio de Economía y Competitividad https://doi.org/10.13039/501100003329 MDM-2014-0445
Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2014/SGR-1307
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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