Modelling non-Markovian dynamics in biochemical reactions
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRN |
Texto Completo: | https://repositorio.ufrn.br/jspui/handle/123456789/29763 |
Resumo: | Background Biochemical reactions are often modelled as discrete-state continuous-time stochastic processes evolving as memoryless Markov processes. However, in some cases, biochemical systems exhibit non-Markovian dynamics. We propose here a methodology for building stochastic simulation algorithms which model more precisely non-Markovian processes in some specific situations. Our methodology is based on Constraint Programming and is implemented by using Gecode, a state-of-the-art framework for constraint solving. Results Our technique allows us to randomly sample waiting times from probability density functions that not necessarily are distributed according to a negative exponential function. In this context, we discuss an important case-study in which the probability density function is inferred from single-molecule experiments that describe the distribution of the time intervals between two consecutive enzymatically catalysed reactions. Noticeably, this feature allows some types of enzyme reactions to be modelled as non-Markovian processes. Conclusions We show that our methodology makes it possible to obtain accurate models of enzymatic reactions that, in specific cases, fit experimental data better than the corresponding Markovian models |
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Chiarugi, DavideFalaschi, MorenoHermith, DianaVega, Carlos Alberto OlarteTorella, Luca2020-07-30T19:43:41Z2020-07-30T19:43:41Z2015CHIARUGI, Davide; FALASCHI, Moreno; HERMITH, Diana; OLARTE, Carlos; TORELLA, Luca. Modelling non-Markovian dynamics in biochemical reactions. BMC Systems Biology, v. 9, p. S8, 2015. Disponível em: https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-9-S3-S8. Acesso em: 29 jul. 2020. https://doi.org/10.1186/1752-0509-9-S3-S81752-0509https://repositorio.ufrn.br/jspui/handle/123456789/2976310.1186/1752-0509-9-S3-S8BMCNon-Markovian dynamicsConstraint programmingBiochemical reactionsModelling non-Markovian dynamics in biochemical reactionsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleBackground Biochemical reactions are often modelled as discrete-state continuous-time stochastic processes evolving as memoryless Markov processes. However, in some cases, biochemical systems exhibit non-Markovian dynamics. We propose here a methodology for building stochastic simulation algorithms which model more precisely non-Markovian processes in some specific situations. Our methodology is based on Constraint Programming and is implemented by using Gecode, a state-of-the-art framework for constraint solving. Results Our technique allows us to randomly sample waiting times from probability density functions that not necessarily are distributed according to a negative exponential function. In this context, we discuss an important case-study in which the probability density function is inferred from single-molecule experiments that describe the distribution of the time intervals between two consecutive enzymatically catalysed reactions. Noticeably, this feature allows some types of enzyme reactions to be modelled as non-Markovian processes. Conclusions We show that our methodology makes it possible to obtain accurate models of enzymatic reactions that, in specific cases, fit experimental data better than the corresponding Markovian modelsengreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNinfo:eu-repo/semantics/openAccessORIGINALModellingNon-MarkovianDynamics_VEGA_2015.pdfModellingNon-MarkovianDynamics_VEGA_2015.pdfapplication/pdf1630398https://repositorio.ufrn.br/bitstream/123456789/29763/1/ModellingNon-MarkovianDynamics_VEGA_2015.pdf01db6202593052fe9a5b0e2ed826fc2aMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81484https://repositorio.ufrn.br/bitstream/123456789/29763/2/license.txte9597aa2854d128fd968be5edc8a28d9MD52TEXTModellingNon-MarkovianDynamics_VEGA_2015.pdf.txtModellingNon-MarkovianDynamics_VEGA_2015.pdf.txtExtracted texttext/plain54118https://repositorio.ufrn.br/bitstream/123456789/29763/3/ModellingNon-MarkovianDynamics_VEGA_2015.pdf.txt12a2f456b8670ede5f97321b2cb544b3MD53THUMBNAILModellingNon-MarkovianDynamics_VEGA_2015.pdf.jpgModellingNon-MarkovianDynamics_VEGA_2015.pdf.jpgGenerated Thumbnailimage/jpeg1710https://repositorio.ufrn.br/bitstream/123456789/29763/4/ModellingNon-MarkovianDynamics_VEGA_2015.pdf.jpgb7da77d19de57ba8f5facc422c6ebd65MD54123456789/297632020-08-02 04:54:44.915oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2020-08-02T07:54:44Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false |
dc.title.pt_BR.fl_str_mv |
Modelling non-Markovian dynamics in biochemical reactions |
title |
Modelling non-Markovian dynamics in biochemical reactions |
spellingShingle |
Modelling non-Markovian dynamics in biochemical reactions Chiarugi, Davide Non-Markovian dynamics Constraint programming Biochemical reactions |
title_short |
Modelling non-Markovian dynamics in biochemical reactions |
title_full |
Modelling non-Markovian dynamics in biochemical reactions |
title_fullStr |
Modelling non-Markovian dynamics in biochemical reactions |
title_full_unstemmed |
Modelling non-Markovian dynamics in biochemical reactions |
title_sort |
Modelling non-Markovian dynamics in biochemical reactions |
author |
Chiarugi, Davide |
author_facet |
Chiarugi, Davide Falaschi, Moreno Hermith, Diana Vega, Carlos Alberto Olarte Torella, Luca |
author_role |
author |
author2 |
Falaschi, Moreno Hermith, Diana Vega, Carlos Alberto Olarte Torella, Luca |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Chiarugi, Davide Falaschi, Moreno Hermith, Diana Vega, Carlos Alberto Olarte Torella, Luca |
dc.subject.por.fl_str_mv |
Non-Markovian dynamics Constraint programming Biochemical reactions |
topic |
Non-Markovian dynamics Constraint programming Biochemical reactions |
description |
Background Biochemical reactions are often modelled as discrete-state continuous-time stochastic processes evolving as memoryless Markov processes. However, in some cases, biochemical systems exhibit non-Markovian dynamics. We propose here a methodology for building stochastic simulation algorithms which model more precisely non-Markovian processes in some specific situations. Our methodology is based on Constraint Programming and is implemented by using Gecode, a state-of-the-art framework for constraint solving. Results Our technique allows us to randomly sample waiting times from probability density functions that not necessarily are distributed according to a negative exponential function. In this context, we discuss an important case-study in which the probability density function is inferred from single-molecule experiments that describe the distribution of the time intervals between two consecutive enzymatically catalysed reactions. Noticeably, this feature allows some types of enzyme reactions to be modelled as non-Markovian processes. Conclusions We show that our methodology makes it possible to obtain accurate models of enzymatic reactions that, in specific cases, fit experimental data better than the corresponding Markovian models |
publishDate |
2015 |
dc.date.issued.fl_str_mv |
2015 |
dc.date.accessioned.fl_str_mv |
2020-07-30T19:43:41Z |
dc.date.available.fl_str_mv |
2020-07-30T19:43:41Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
CHIARUGI, Davide; FALASCHI, Moreno; HERMITH, Diana; OLARTE, Carlos; TORELLA, Luca. Modelling non-Markovian dynamics in biochemical reactions. BMC Systems Biology, v. 9, p. S8, 2015. Disponível em: https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-9-S3-S8. Acesso em: 29 jul. 2020. https://doi.org/10.1186/1752-0509-9-S3-S8 |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufrn.br/jspui/handle/123456789/29763 |
dc.identifier.issn.none.fl_str_mv |
1752-0509 |
dc.identifier.doi.none.fl_str_mv |
10.1186/1752-0509-9-S3-S8 |
identifier_str_mv |
CHIARUGI, Davide; FALASCHI, Moreno; HERMITH, Diana; OLARTE, Carlos; TORELLA, Luca. Modelling non-Markovian dynamics in biochemical reactions. BMC Systems Biology, v. 9, p. S8, 2015. Disponível em: https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-9-S3-S8. Acesso em: 29 jul. 2020. https://doi.org/10.1186/1752-0509-9-S3-S8 1752-0509 10.1186/1752-0509-9-S3-S8 |
url |
https://repositorio.ufrn.br/jspui/handle/123456789/29763 |
dc.language.iso.fl_str_mv |
eng |
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eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
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BMC |
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BMC |
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