Phase-field models of tumor growth with angiogenesis
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do LNCC |
Texto Completo: | https://tede.lncc.br/handle/tede/180 |
Resumo: | The development of predictive computational models of tumor initiation, growth, and decline is faced with many formidable challenges. Phenomenological models which attempt to capture the complex interactions of multiple tissue and cellular species must cope with moving interfaces of heterogeneous media and the huge uncertainties of the parameters and their evolution. They must be able to deliver predictions consistent with events that take place at cellular scales, and they must faithfully depict biological mechanisms and events that are known to be associated with various forms of cancer. In the present work, some models for the tumor behavior are presented which fall within the framework of phase-field (or diffuse-interface) models suggested by continuum mixture theory. This framework provides for the simultaneous treatment of interactions of multiple evolving species, such as tumor cells, necrotic cell cores, nutrients, and other cellular and tissue types that exist and interact in living tissue. In the present work, a hybrid phase field ten-species vascular model for the tumor growth is developed, which couples the tumor growth with sprouting through angiogenesis. The model is able to represent the branching of new vessels through coupling a discrete model for which the angiogenesis is started upon pre-defined conditions on the nutrient deprivation in the continuum model. Such conditions are represented by hypoxic cells that release tumor growth factors that ultimately trigger vascular growth. We discuss the numerical approximation of the model using mixed finite elements. We also consider an avascular stochastic six-species tumor growth model derived directly from the hybrid ten-species model. The stochasticity comes from modeling uncertainties in the parameters of the model. We perform a sensitivity analysis to identify the more relevant parameters on the tumor mass growth. The stochastic model is then developed taking into account the uncertainty of the most influential parameter. The numerical approximation of the model using Stochastic Collocation method to treat uncertainties in the nonlinear system is presented. The results of numerous numerical experiments are also presented and discussed. |
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Almeida, Regina Célia Cerqueira deCPF:59472731791http://lattes.cnpq.br/6688041530466410Oden, John TinsleyCPF:216695507http://lattes.cnpq.br/8931182291799997Loula, Abimael Fernando DouradoCPF:24477575734http://lattes.cnpq.br/7315592936477868Coutinho, Alvaro Luiz Gayoso de AzeredoCosta, Michel Iskin da SilveiraCPF:15083900459http://lattes.cnpq.br/3313361232260092Bevilácqua, LuizCPF:19141327700http://lattes.cnpq.br/5898851138882202Godoy, Wesley Augusto CondeCPF:04121251881http://lattes.cnpq.br/0147181014156799CPF:35299650817http://lattes.cnpq.br/2188154440681419Lima, Ernesto Augusto Bueno da Fonseca2015-03-04T18:57:59Z2014-07-092014-04-29https://tede.lncc.br/handle/tede/180The development of predictive computational models of tumor initiation, growth, and decline is faced with many formidable challenges. Phenomenological models which attempt to capture the complex interactions of multiple tissue and cellular species must cope with moving interfaces of heterogeneous media and the huge uncertainties of the parameters and their evolution. They must be able to deliver predictions consistent with events that take place at cellular scales, and they must faithfully depict biological mechanisms and events that are known to be associated with various forms of cancer. In the present work, some models for the tumor behavior are presented which fall within the framework of phase-field (or diffuse-interface) models suggested by continuum mixture theory. This framework provides for the simultaneous treatment of interactions of multiple evolving species, such as tumor cells, necrotic cell cores, nutrients, and other cellular and tissue types that exist and interact in living tissue. In the present work, a hybrid phase field ten-species vascular model for the tumor growth is developed, which couples the tumor growth with sprouting through angiogenesis. The model is able to represent the branching of new vessels through coupling a discrete model for which the angiogenesis is started upon pre-defined conditions on the nutrient deprivation in the continuum model. Such conditions are represented by hypoxic cells that release tumor growth factors that ultimately trigger vascular growth. We discuss the numerical approximation of the model using mixed finite elements. We also consider an avascular stochastic six-species tumor growth model derived directly from the hybrid ten-species model. The stochasticity comes from modeling uncertainties in the parameters of the model. We perform a sensitivity analysis to identify the more relevant parameters on the tumor mass growth. The stochastic model is then developed taking into account the uncertainty of the most influential parameter. The numerical approximation of the model using Stochastic Collocation method to treat uncertainties in the nonlinear system is presented. The results of numerous numerical experiments are also presented and discussed.Modelos matematicos e computacionais sao utilizados na compreensao de fenomenos complexos, sendo aplicados em diversas areas como engenharia, fisica e biologia. Na Medicina tem um importante papel na simulacao do tratamento e evolucao de algumas doencas, entre elas o cancer. O desenvolvimento de modelos computacionais para o crescimento tumoral se depara com desafios formidaveis. Modelos fenomenologicos que tentam capturar as complexas interacoes de multiplos tecidos e especies celulares devem lidar com interfaces em meios heterogeneos e as enormes incertezas dos parametros e suas evolucoes. Eles devem ser capazes de proporcionar predicoes consistentes com eventos que ocorrem em escalas celulares, e devem representar fielmente os mecanismos biologicos associados ao cancer. No presente trabalho, sao apresentados alguns modelos para o crescimento tumoral. Esses modelos inserem-se no ambito de modelos de campo de fase (ou interface difusiva) sugeridos pela teoria mistura. Esta metodologia preve o tratamento simultaneo de interacoes entre multiplos constituintes, como as celulas tumorais, celulas necroticas, nutrientes e outros tipos celulares e teciduais que existem e interagem em tecidos vivos. Neste trabalho, um modelo hibrido de campo de fases, de dez constituintes e desenvolvido para o crescimento tumoral vascular, que acopla o crescimento de tumores com crescimento de novos vasos sanguineos atraves da angiogenese. O modelo é capaz de representar a ramificacao de novos vasos atraves do acoplamento de um modelo discreto, no qual a angiogenese é iniciada mediante condicoes pre-definidas, relacionadas a privacao de nutrientes no modelo macroscopico. Tais condicoes sao representadas por celulas hipoxicas que liberam quimicos reponsaveis por induzir a angiogenese tumoral. A aproximacao numerica do modelo usando elementos finitos mistos é discutida. Considera-se tambem um modelo estocastico avascular de seis constituintes para o crescimento tumoral, derivado diretamente do modelo hibrido de dez constituintes. A estocasticidade vem de incertezas na modelagem dos parametros do modelo. Realiza-se uma analise de sensibilidade para identificar os parametros mais relevantes sobre o crescimento da massa tumoral. O modelo estocastico é entao desenvolvido tendo em conta a incerteza no parametro mais influente. A aproximacao numerica do modelo usando o metodo estocastico de Colocacao para tratar incertezas no sistema nao-linear é apresentada. Os resultados de varios experimentos numericos tambem sao apresentados e discutidos.Made available in DSpace on 2015-03-04T18:57:59Z (GMT). No. of bitstreams: 1 thesis.pdf: 5073087 bytes, checksum: f23ad1a1747577782cd9c9eab7574795 (MD5) Previous issue date: 2014-04-29Conselho Nacional de Desenvolvimento Cientifico e Tecnologicoapplication/pdfhttp://tede-server.lncc.br:8080/retrieve/515/thesis.pdf.jpgporLaboratório Nacional de Computação CientíficaPrograma de Pós-Graduação em Modelagem ComputacionalLNCCBRServiço de Análise e Apoio a Formação de Recursos HumanosCâncer - Modelos matemáticosCancer - Mathematical modelsCNPQ::CIENCIAS DA SAUDE::MEDICINA::CLINICA MEDICA::CANCEROLOGIAPhase-field models of tumor growth with angiogenesisModelos de campo de fases para o crescimento tumoral com angiogênesesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações do LNCCinstname:Laboratório Nacional de Computação Científica (LNCC)instacron:LNCCORIGINALthesis.pdfapplication/pdf5073087http://tede-server.lncc.br:8080/tede/bitstream/tede/180/1/thesis.pdff23ad1a1747577782cd9c9eab7574795MD51THUMBNAILthesis.pdf.jpgthesis.pdf.jpgimage/jpeg3051http://tede-server.lncc.br:8080/tede/bitstream/tede/180/2/thesis.pdf.jpga200238620c0b7618c8cf7bbbd279466MD52tede/1802018-07-04 09:59:45.065oai:tede-server.lncc.br:tede/180Biblioteca Digital de Teses e Dissertaçõeshttps://tede.lncc.br/PUBhttps://tede.lncc.br/oai/requestlibrary@lncc.br||library@lncc.bropendoar:2018-07-04T12:59:45Biblioteca Digital de Teses e Dissertações do LNCC - Laboratório Nacional de Computação Científica (LNCC)false |
dc.title.eng.fl_str_mv |
Phase-field models of tumor growth with angiogenesis |
dc.title.alternative.por.fl_str_mv |
Modelos de campo de fases para o crescimento tumoral com angiogêneses |
title |
Phase-field models of tumor growth with angiogenesis |
spellingShingle |
Phase-field models of tumor growth with angiogenesis Lima, Ernesto Augusto Bueno da Fonseca Câncer - Modelos matemáticos Cancer - Mathematical models CNPQ::CIENCIAS DA SAUDE::MEDICINA::CLINICA MEDICA::CANCEROLOGIA |
title_short |
Phase-field models of tumor growth with angiogenesis |
title_full |
Phase-field models of tumor growth with angiogenesis |
title_fullStr |
Phase-field models of tumor growth with angiogenesis |
title_full_unstemmed |
Phase-field models of tumor growth with angiogenesis |
title_sort |
Phase-field models of tumor growth with angiogenesis |
author |
Lima, Ernesto Augusto Bueno da Fonseca |
author_facet |
Lima, Ernesto Augusto Bueno da Fonseca |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Almeida, Regina Célia Cerqueira de |
dc.contributor.advisor1ID.fl_str_mv |
CPF:59472731791 |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/6688041530466410 |
dc.contributor.advisor-co1.fl_str_mv |
Oden, John Tinsley |
dc.contributor.advisor-co1ID.fl_str_mv |
CPF:216695507 |
dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/8931182291799997 |
dc.contributor.referee1.fl_str_mv |
Loula, Abimael Fernando Dourado |
dc.contributor.referee1ID.fl_str_mv |
CPF:24477575734 |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/7315592936477868 |
dc.contributor.referee2.fl_str_mv |
Coutinho, Alvaro Luiz Gayoso de Azeredo |
dc.contributor.referee3.fl_str_mv |
Costa, Michel Iskin da Silveira |
dc.contributor.referee3ID.fl_str_mv |
CPF:15083900459 |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/3313361232260092 |
dc.contributor.referee4.fl_str_mv |
Bevilácqua, Luiz |
dc.contributor.referee4ID.fl_str_mv |
CPF:19141327700 |
dc.contributor.referee4Lattes.fl_str_mv |
http://lattes.cnpq.br/5898851138882202 |
dc.contributor.referee5.fl_str_mv |
Godoy, Wesley Augusto Conde |
dc.contributor.referee5ID.fl_str_mv |
CPF:04121251881 |
dc.contributor.referee5Lattes.fl_str_mv |
http://lattes.cnpq.br/0147181014156799 |
dc.contributor.authorID.fl_str_mv |
CPF:35299650817 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/2188154440681419 |
dc.contributor.author.fl_str_mv |
Lima, Ernesto Augusto Bueno da Fonseca |
contributor_str_mv |
Almeida, Regina Célia Cerqueira de Oden, John Tinsley Loula, Abimael Fernando Dourado Coutinho, Alvaro Luiz Gayoso de Azeredo Costa, Michel Iskin da Silveira Bevilácqua, Luiz Godoy, Wesley Augusto Conde |
dc.subject.por.fl_str_mv |
Câncer - Modelos matemáticos |
topic |
Câncer - Modelos matemáticos Cancer - Mathematical models CNPQ::CIENCIAS DA SAUDE::MEDICINA::CLINICA MEDICA::CANCEROLOGIA |
dc.subject.eng.fl_str_mv |
Cancer - Mathematical models |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS DA SAUDE::MEDICINA::CLINICA MEDICA::CANCEROLOGIA |
description |
The development of predictive computational models of tumor initiation, growth, and decline is faced with many formidable challenges. Phenomenological models which attempt to capture the complex interactions of multiple tissue and cellular species must cope with moving interfaces of heterogeneous media and the huge uncertainties of the parameters and their evolution. They must be able to deliver predictions consistent with events that take place at cellular scales, and they must faithfully depict biological mechanisms and events that are known to be associated with various forms of cancer. In the present work, some models for the tumor behavior are presented which fall within the framework of phase-field (or diffuse-interface) models suggested by continuum mixture theory. This framework provides for the simultaneous treatment of interactions of multiple evolving species, such as tumor cells, necrotic cell cores, nutrients, and other cellular and tissue types that exist and interact in living tissue. In the present work, a hybrid phase field ten-species vascular model for the tumor growth is developed, which couples the tumor growth with sprouting through angiogenesis. The model is able to represent the branching of new vessels through coupling a discrete model for which the angiogenesis is started upon pre-defined conditions on the nutrient deprivation in the continuum model. Such conditions are represented by hypoxic cells that release tumor growth factors that ultimately trigger vascular growth. We discuss the numerical approximation of the model using mixed finite elements. We also consider an avascular stochastic six-species tumor growth model derived directly from the hybrid ten-species model. The stochasticity comes from modeling uncertainties in the parameters of the model. We perform a sensitivity analysis to identify the more relevant parameters on the tumor mass growth. The stochastic model is then developed taking into account the uncertainty of the most influential parameter. The numerical approximation of the model using Stochastic Collocation method to treat uncertainties in the nonlinear system is presented. The results of numerous numerical experiments are also presented and discussed. |
publishDate |
2014 |
dc.date.available.fl_str_mv |
2014-07-09 |
dc.date.issued.fl_str_mv |
2014-04-29 |
dc.date.accessioned.fl_str_mv |
2015-03-04T18:57:59Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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doctoralThesis |
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publishedVersion |
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https://tede.lncc.br/handle/tede/180 |
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https://tede.lncc.br/handle/tede/180 |
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por |
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por |
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info:eu-repo/semantics/openAccess |
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openAccess |
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Laboratório Nacional de Computação Científica |
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Programa de Pós-Graduação em Modelagem Computacional |
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LNCC |
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BR |
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Serviço de Análise e Apoio a Formação de Recursos Humanos |
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Laboratório Nacional de Computação Científica |
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