Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systems

Autores
Silberberg, Mauro; Hermjakob, Henning; Malik Sheriff, Rahuman S.; Grecco, Hernan Edgardo
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Motivation: Chemical reaction networks (CRNs) play a pivotal role in diverse fields such as systems biology, biochemistry, chemical engineering, and epidemiology. High-level definitions of CRNs enables to use various simulation approaches, including deterministic and stochastic methods, from the same model. However, existing Python tools for simulation of CRN typically wrap external C/Cþþ libraries for model definition, translation into equations and/or numerically solving them, limiting their extensibility and integration with the broader Python ecosystem. Results: In response, we developed Poincare and SimBio, two novel Python packages for simulation of dynamical systems and CRNs. Poincare serves as a foundation for dynamical systems modeling, while SimBio extends this functionality to CRNs, including support for the Systems Biology Markup Language (SBML). Poincare and SimBio are developed as pure Python packages enabling users to easily extend their simulation capabilities by writing new or leveraging other Python packages. Moreover, this does not compromise the performance, as code can be just-intime compiled with Numba. Our benchmark tests using curated models from the BioModels repository demonstrate that these tools may provide a potentially superior performance advantage compared to other existing tools. In addition, to ensure a user-friendly experience, our packages use standard typed modern Python syntax that provides a seamless integration with integrated development environments. Our Python-centric approach significantly enhances code analysis, error detection, and refactoring capabilities, positioning Poincare and SimBio as valuable tools for the modeling community.
Fil: Silberberg, Mauro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Hermjakob, Henning. European Molecular Biology Laboratory; Alemania
Fil: Malik Sheriff, Rahuman S.. European Molecular Biology Laboratory; Alemania
Fil: Grecco, Hernan Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Materia
modelling
simulation
python
ODE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/264098
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systemsSilberberg, MauroHermjakob, HenningMalik Sheriff, Rahuman S.Grecco, Hernan EdgardomodellingsimulationpythonODEhttps://purl.org/becyt/ford/1.7https://purl.org/becyt/ford/1Motivation: Chemical reaction networks (CRNs) play a pivotal role in diverse fields such as systems biology, biochemistry, chemical engineering, and epidemiology. High-level definitions of CRNs enables to use various simulation approaches, including deterministic and stochastic methods, from the same model. However, existing Python tools for simulation of CRN typically wrap external C/Cþþ libraries for model definition, translation into equations and/or numerically solving them, limiting their extensibility and integration with the broader Python ecosystem. Results: In response, we developed Poincare and SimBio, two novel Python packages for simulation of dynamical systems and CRNs. Poincare serves as a foundation for dynamical systems modeling, while SimBio extends this functionality to CRNs, including support for the Systems Biology Markup Language (SBML). Poincare and SimBio are developed as pure Python packages enabling users to easily extend their simulation capabilities by writing new or leveraging other Python packages. Moreover, this does not compromise the performance, as code can be just-intime compiled with Numba. Our benchmark tests using curated models from the BioModels repository demonstrate that these tools may provide a potentially superior performance advantage compared to other existing tools. In addition, to ensure a user-friendly experience, our packages use standard typed modern Python syntax that provides a seamless integration with integrated development environments. Our Python-centric approach significantly enhances code analysis, error detection, and refactoring capabilities, positioning Poincare and SimBio as valuable tools for the modeling community.Fil: Silberberg, Mauro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Hermjakob, Henning. European Molecular Biology Laboratory; AlemaniaFil: Malik Sheriff, Rahuman S.. European Molecular Biology Laboratory; AlemaniaFil: Grecco, Hernan Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaOxford University Press2024-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/264098Silberberg, Mauro; Hermjakob, Henning; Malik Sheriff, Rahuman S.; Grecco, Hernan Edgardo; Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systems; Oxford University Press; Bioinformatics; 40; 8; 8-2024; 1-61367-4811CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btae465/7723995info:eu-repo/semantics/altIdentifier/doi/10.1093/bioinformatics/btae465info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:23:24Zoai:ri.conicet.gov.ar:11336/264098instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:23:24.847CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systems
title Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systems
spellingShingle Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systems
Silberberg, Mauro
modelling
simulation
python
ODE
title_short Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systems
title_full Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systems
title_fullStr Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systems
title_full_unstemmed Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systems
title_sort Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systems
dc.creator.none.fl_str_mv Silberberg, Mauro
Hermjakob, Henning
Malik Sheriff, Rahuman S.
Grecco, Hernan Edgardo
author Silberberg, Mauro
author_facet Silberberg, Mauro
Hermjakob, Henning
Malik Sheriff, Rahuman S.
Grecco, Hernan Edgardo
author_role author
author2 Hermjakob, Henning
Malik Sheriff, Rahuman S.
Grecco, Hernan Edgardo
author2_role author
author
author
dc.subject.none.fl_str_mv modelling
simulation
python
ODE
topic modelling
simulation
python
ODE
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.7
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Motivation: Chemical reaction networks (CRNs) play a pivotal role in diverse fields such as systems biology, biochemistry, chemical engineering, and epidemiology. High-level definitions of CRNs enables to use various simulation approaches, including deterministic and stochastic methods, from the same model. However, existing Python tools for simulation of CRN typically wrap external C/Cþþ libraries for model definition, translation into equations and/or numerically solving them, limiting their extensibility and integration with the broader Python ecosystem. Results: In response, we developed Poincare and SimBio, two novel Python packages for simulation of dynamical systems and CRNs. Poincare serves as a foundation for dynamical systems modeling, while SimBio extends this functionality to CRNs, including support for the Systems Biology Markup Language (SBML). Poincare and SimBio are developed as pure Python packages enabling users to easily extend their simulation capabilities by writing new or leveraging other Python packages. Moreover, this does not compromise the performance, as code can be just-intime compiled with Numba. Our benchmark tests using curated models from the BioModels repository demonstrate that these tools may provide a potentially superior performance advantage compared to other existing tools. In addition, to ensure a user-friendly experience, our packages use standard typed modern Python syntax that provides a seamless integration with integrated development environments. Our Python-centric approach significantly enhances code analysis, error detection, and refactoring capabilities, positioning Poincare and SimBio as valuable tools for the modeling community.
Fil: Silberberg, Mauro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Hermjakob, Henning. European Molecular Biology Laboratory; Alemania
Fil: Malik Sheriff, Rahuman S.. European Molecular Biology Laboratory; Alemania
Fil: Grecco, Hernan Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
description Motivation: Chemical reaction networks (CRNs) play a pivotal role in diverse fields such as systems biology, biochemistry, chemical engineering, and epidemiology. High-level definitions of CRNs enables to use various simulation approaches, including deterministic and stochastic methods, from the same model. However, existing Python tools for simulation of CRN typically wrap external C/Cþþ libraries for model definition, translation into equations and/or numerically solving them, limiting their extensibility and integration with the broader Python ecosystem. Results: In response, we developed Poincare and SimBio, two novel Python packages for simulation of dynamical systems and CRNs. Poincare serves as a foundation for dynamical systems modeling, while SimBio extends this functionality to CRNs, including support for the Systems Biology Markup Language (SBML). Poincare and SimBio are developed as pure Python packages enabling users to easily extend their simulation capabilities by writing new or leveraging other Python packages. Moreover, this does not compromise the performance, as code can be just-intime compiled with Numba. Our benchmark tests using curated models from the BioModels repository demonstrate that these tools may provide a potentially superior performance advantage compared to other existing tools. In addition, to ensure a user-friendly experience, our packages use standard typed modern Python syntax that provides a seamless integration with integrated development environments. Our Python-centric approach significantly enhances code analysis, error detection, and refactoring capabilities, positioning Poincare and SimBio as valuable tools for the modeling community.
publishDate 2024
dc.date.none.fl_str_mv 2024-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/264098
Silberberg, Mauro; Hermjakob, Henning; Malik Sheriff, Rahuman S.; Grecco, Hernan Edgardo; Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systems; Oxford University Press; Bioinformatics; 40; 8; 8-2024; 1-6
1367-4811
CONICET Digital
CONICET
url http://hdl.handle.net/11336/264098
identifier_str_mv Silberberg, Mauro; Hermjakob, Henning; Malik Sheriff, Rahuman S.; Grecco, Hernan Edgardo; Poincaré and SimBio: a versatile and extensible Python ecosystem for modeling systems; Oxford University Press; Bioinformatics; 40; 8; 8-2024; 1-6
1367-4811
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btae465/7723995
info:eu-repo/semantics/altIdentifier/doi/10.1093/bioinformatics/btae465
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
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instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
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repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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