APPLICATION OF APPROXIMATE BAYESIAN COMPUTATION FOR THE ESTIMATION OF PARAMETERS IN A MODEL FOR THE CALCIUM DYNAMICS IN NEURONS
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Interdisciplinar de Pesquisa em Engenharia |
Texto Completo: | https://periodicos.unb.br/index.php/ripe/article/view/21625 |
Resumo: | Ionic transfer plays an important role in several processes in the human body, in special in the electrophysiology of neurons, where the most important ions are those of potassium, sodium and calcium. The models for the dynamics of potassium and sodium are classical and well established in the literature. On the other hand, several models were proposed for the dynamics of calcium ions, such as those of Dupont and Erneux , 1997and of Dupont and Goldbetter ,1993. In fact, none of the proposed models for calcium dynamics is widely accepted and general to represent phenomena characteristic of anomalous behaviors observed in neurons, related, for example, to epilepsy. Due to the nonlinear character of these models, the values of their parameters strongly affect the predicted responses, like the transient ion concentrations, as well as the dynamics of several state variables, including the electrical current responses in voltage clamp experiments. Approximate Bayesian Computation (ABC) methods have been conceived for inferring posterior distributions where likelihood functions are computationally intractable, too costly to evaluate or not exactly known. In this work, we apply an ABC algorithm based on the Monte Carlo method (Toni et al., 2009) for the estimation of parameters appearing in the Calcium model proposed by Dupont and Goldbetter, 1993. Simulated measurements of the concentration of calcium ions in the cytosol are used for the parameter estimation. |
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APPLICATION OF APPROXIMATE BAYESIAN COMPUTATION FOR THE ESTIMATION OF PARAMETERS IN A MODEL FOR THE CALCIUM DYNAMICS IN NEURONSApproximate Bayesian Computation. Calcium Induced Calcium Release Model. Parameter Estimation.Ionic transfer plays an important role in several processes in the human body, in special in the electrophysiology of neurons, where the most important ions are those of potassium, sodium and calcium. The models for the dynamics of potassium and sodium are classical and well established in the literature. On the other hand, several models were proposed for the dynamics of calcium ions, such as those of Dupont and Erneux , 1997and of Dupont and Goldbetter ,1993. In fact, none of the proposed models for calcium dynamics is widely accepted and general to represent phenomena characteristic of anomalous behaviors observed in neurons, related, for example, to epilepsy. Due to the nonlinear character of these models, the values of their parameters strongly affect the predicted responses, like the transient ion concentrations, as well as the dynamics of several state variables, including the electrical current responses in voltage clamp experiments. Approximate Bayesian Computation (ABC) methods have been conceived for inferring posterior distributions where likelihood functions are computationally intractable, too costly to evaluate or not exactly known. In this work, we apply an ABC algorithm based on the Monte Carlo method (Toni et al., 2009) for the estimation of parameters appearing in the Calcium model proposed by Dupont and Goldbetter, 1993. Simulated measurements of the concentration of calcium ions in the cytosol are used for the parameter estimation.Programa de Pós-Graduação em Integridade de Materiais da Engenharia2017-01-30info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.unb.br/index.php/ripe/article/view/2162510.26512/ripe.v2i16.21625Revista Interdisciplinar de Pesquisa em Engenharia; Vol. 2 No. 16 (2016): STOCHASTIC MODELING AND UNCERTAINTY QUANTIFICATION; 144-158Revista Interdisciplinar de Pesquisa em Engenharia; v. 2 n. 16 (2016): STOCHASTIC MODELING AND UNCERTAINTY QUANTIFICATION; 144-1582447-6102reponame:Revista Interdisciplinar de Pesquisa em Engenhariainstname:Universidade de Brasília (UnB)instacron:UNBenghttps://periodicos.unb.br/index.php/ripe/article/view/21625/19941Copyright (c) 2019 Revista Interdisciplinar de Pesquisa em Engenharia - RIPEinfo:eu-repo/semantics/openAccessC. Carvalho, RaphaelC. Estumano, DiegoR. B. Orlande, HelcioJ. Colaço, Marcelo2019-06-16T03:14:29Zoai:ojs.pkp.sfu.ca:article/21625Revistahttps://periodicos.unb.br/index.php/ripePUBhttps://periodicos.unb.br/index.php/ripe/oaianflor@unb.br2447-61022447-6102opendoar:2019-06-16T03:14:29Revista Interdisciplinar de Pesquisa em Engenharia - Universidade de Brasília (UnB)false |
dc.title.none.fl_str_mv |
APPLICATION OF APPROXIMATE BAYESIAN COMPUTATION FOR THE ESTIMATION OF PARAMETERS IN A MODEL FOR THE CALCIUM DYNAMICS IN NEURONS |
title |
APPLICATION OF APPROXIMATE BAYESIAN COMPUTATION FOR THE ESTIMATION OF PARAMETERS IN A MODEL FOR THE CALCIUM DYNAMICS IN NEURONS |
spellingShingle |
APPLICATION OF APPROXIMATE BAYESIAN COMPUTATION FOR THE ESTIMATION OF PARAMETERS IN A MODEL FOR THE CALCIUM DYNAMICS IN NEURONS C. Carvalho, Raphael Approximate Bayesian Computation. Calcium Induced Calcium Release Model. Parameter Estimation. |
title_short |
APPLICATION OF APPROXIMATE BAYESIAN COMPUTATION FOR THE ESTIMATION OF PARAMETERS IN A MODEL FOR THE CALCIUM DYNAMICS IN NEURONS |
title_full |
APPLICATION OF APPROXIMATE BAYESIAN COMPUTATION FOR THE ESTIMATION OF PARAMETERS IN A MODEL FOR THE CALCIUM DYNAMICS IN NEURONS |
title_fullStr |
APPLICATION OF APPROXIMATE BAYESIAN COMPUTATION FOR THE ESTIMATION OF PARAMETERS IN A MODEL FOR THE CALCIUM DYNAMICS IN NEURONS |
title_full_unstemmed |
APPLICATION OF APPROXIMATE BAYESIAN COMPUTATION FOR THE ESTIMATION OF PARAMETERS IN A MODEL FOR THE CALCIUM DYNAMICS IN NEURONS |
title_sort |
APPLICATION OF APPROXIMATE BAYESIAN COMPUTATION FOR THE ESTIMATION OF PARAMETERS IN A MODEL FOR THE CALCIUM DYNAMICS IN NEURONS |
author |
C. Carvalho, Raphael |
author_facet |
C. Carvalho, Raphael C. Estumano, Diego R. B. Orlande, Helcio J. Colaço, Marcelo |
author_role |
author |
author2 |
C. Estumano, Diego R. B. Orlande, Helcio J. Colaço, Marcelo |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
C. Carvalho, Raphael C. Estumano, Diego R. B. Orlande, Helcio J. Colaço, Marcelo |
dc.subject.por.fl_str_mv |
Approximate Bayesian Computation. Calcium Induced Calcium Release Model. Parameter Estimation. |
topic |
Approximate Bayesian Computation. Calcium Induced Calcium Release Model. Parameter Estimation. |
description |
Ionic transfer plays an important role in several processes in the human body, in special in the electrophysiology of neurons, where the most important ions are those of potassium, sodium and calcium. The models for the dynamics of potassium and sodium are classical and well established in the literature. On the other hand, several models were proposed for the dynamics of calcium ions, such as those of Dupont and Erneux , 1997and of Dupont and Goldbetter ,1993. In fact, none of the proposed models for calcium dynamics is widely accepted and general to represent phenomena characteristic of anomalous behaviors observed in neurons, related, for example, to epilepsy. Due to the nonlinear character of these models, the values of their parameters strongly affect the predicted responses, like the transient ion concentrations, as well as the dynamics of several state variables, including the electrical current responses in voltage clamp experiments. Approximate Bayesian Computation (ABC) methods have been conceived for inferring posterior distributions where likelihood functions are computationally intractable, too costly to evaluate or not exactly known. In this work, we apply an ABC algorithm based on the Monte Carlo method (Toni et al., 2009) for the estimation of parameters appearing in the Calcium model proposed by Dupont and Goldbetter, 1993. Simulated measurements of the concentration of calcium ions in the cytosol are used for the parameter estimation. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-30 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.unb.br/index.php/ripe/article/view/21625 10.26512/ripe.v2i16.21625 |
url |
https://periodicos.unb.br/index.php/ripe/article/view/21625 |
identifier_str_mv |
10.26512/ripe.v2i16.21625 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://periodicos.unb.br/index.php/ripe/article/view/21625/19941 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2019 Revista Interdisciplinar de Pesquisa em Engenharia - RIPE info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2019 Revista Interdisciplinar de Pesquisa em Engenharia - RIPE |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Programa de Pós-Graduação em Integridade de Materiais da Engenharia |
publisher.none.fl_str_mv |
Programa de Pós-Graduação em Integridade de Materiais da Engenharia |
dc.source.none.fl_str_mv |
Revista Interdisciplinar de Pesquisa em Engenharia; Vol. 2 No. 16 (2016): STOCHASTIC MODELING AND UNCERTAINTY QUANTIFICATION; 144-158 Revista Interdisciplinar de Pesquisa em Engenharia; v. 2 n. 16 (2016): STOCHASTIC MODELING AND UNCERTAINTY QUANTIFICATION; 144-158 2447-6102 reponame:Revista Interdisciplinar de Pesquisa em Engenharia instname:Universidade de Brasília (UnB) instacron:UNB |
instname_str |
Universidade de Brasília (UnB) |
instacron_str |
UNB |
institution |
UNB |
reponame_str |
Revista Interdisciplinar de Pesquisa em Engenharia |
collection |
Revista Interdisciplinar de Pesquisa em Engenharia |
repository.name.fl_str_mv |
Revista Interdisciplinar de Pesquisa em Engenharia - Universidade de Brasília (UnB) |
repository.mail.fl_str_mv |
anflor@unb.br |
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1798315226576191488 |