Learning Boolean logic models of signaling networks with ASP

Autores
Videla, Santiago; Guziolowski, Carito; Eduati, Federica; Thiele, Sven; Gebser, Martin; Nicolas, Jacques; Saez Rodriguez, Julio; Schaub, Torsten; Siegel, Anne
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, automated inference of Boolean logical networks from experimental data is a fundamental question in this field. This paper addresses the problem consisting of learning from a prior knowledge network describing causal interactions and phosphorylation activities at a pseudo-steady state, Boolean logic models of immediate-early response in signaling transduction networks. The underlying optimization problem has been so far addressed through mathematical programming approaches and the use of dedicated genetic algorithms. In a recent work we have shown severe limitations of stochastic approaches in this domain and proposed to use Answer Set Programming (ASP), considering a simpler problem setting. Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge networkand the necessity of multi-objective optimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in siliconumerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in Appendices A–B.
Fil: Videla, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquimicas de Buenos Aires; Argentina. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia. Universität Potsdam; Alemania
Fil: Guziolowski, Carito. CNRS. École Centrale de Nantes; Francia
Fil: Eduati, Federica. European Bioinformatics Institute. European Molecular Biology Laboratory; Reino Unido
Fil: Thiele, Sven. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia
Fil: Gebser, Martin. Universität Potsdam; Alemania
Fil: Nicolas, Jacques. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia
Fil: Saez Rodriguez, Julio. European Bioinformatics Institute. European Molecular Biology Laboratory; Reino Unido
Fil: Schaub, Torsten. Universität Potsdam; Alemania
Fil: Siegel, Anne. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia
Materia
Answer Set Programming
Signaling Transduction Networks
Boolean Logic Models
Combinatorial Multi-Objective Optimization
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/14203
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Learning Boolean logic models of signaling networks with ASPVidela, SantiagoGuziolowski, CaritoEduati, FedericaThiele, SvenGebser, MartinNicolas, JacquesSaez Rodriguez, JulioSchaub, TorstenSiegel, AnneAnswer Set ProgrammingSignaling Transduction NetworksBoolean Logic ModelsCombinatorial Multi-Objective Optimizationhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, automated inference of Boolean logical networks from experimental data is a fundamental question in this field. This paper addresses the problem consisting of learning from a prior knowledge network describing causal interactions and phosphorylation activities at a pseudo-steady state, Boolean logic models of immediate-early response in signaling transduction networks. The underlying optimization problem has been so far addressed through mathematical programming approaches and the use of dedicated genetic algorithms. In a recent work we have shown severe limitations of stochastic approaches in this domain and proposed to use Answer Set Programming (ASP), considering a simpler problem setting. Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge networkand the necessity of multi-objective optimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in siliconumerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in Appendices A–B.Fil: Videla, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquimicas de Buenos Aires; Argentina. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia. Universität Potsdam; AlemaniaFil: Guziolowski, Carito. CNRS. École Centrale de Nantes; FranciaFil: Eduati, Federica. European Bioinformatics Institute. European Molecular Biology Laboratory; Reino UnidoFil: Thiele, Sven. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; FranciaFil: Gebser, Martin. Universität Potsdam; AlemaniaFil: Nicolas, Jacques. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; FranciaFil: Saez Rodriguez, Julio. European Bioinformatics Institute. European Molecular Biology Laboratory; Reino UnidoFil: Schaub, Torsten. Universität Potsdam; AlemaniaFil: Siegel, Anne. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; FranciaElsevier Science2015-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/14203Videla, Santiago; Guziolowski, Carito; Eduati, Federica; Thiele, Sven; Gebser, Martin; et al.; Learning Boolean logic models of signaling networks with ASP; Elsevier Science; Theoretical Computer Science; 599; 9-2015; 79-1010304-3975enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0304397514004587info:eu-repo/semantics/altIdentifier/url/http://dx.doi.org/10.1016/j.tcs.2014.06.022info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:12:35Zoai:ri.conicet.gov.ar:11336/14203instacron: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:34982025-09-29 10:12:36.175CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Learning Boolean logic models of signaling networks with ASP
title Learning Boolean logic models of signaling networks with ASP
spellingShingle Learning Boolean logic models of signaling networks with ASP
Videla, Santiago
Answer Set Programming
Signaling Transduction Networks
Boolean Logic Models
Combinatorial Multi-Objective Optimization
title_short Learning Boolean logic models of signaling networks with ASP
title_full Learning Boolean logic models of signaling networks with ASP
title_fullStr Learning Boolean logic models of signaling networks with ASP
title_full_unstemmed Learning Boolean logic models of signaling networks with ASP
title_sort Learning Boolean logic models of signaling networks with ASP
dc.creator.none.fl_str_mv Videla, Santiago
Guziolowski, Carito
Eduati, Federica
Thiele, Sven
Gebser, Martin
Nicolas, Jacques
Saez Rodriguez, Julio
Schaub, Torsten
Siegel, Anne
author Videla, Santiago
author_facet Videla, Santiago
Guziolowski, Carito
Eduati, Federica
Thiele, Sven
Gebser, Martin
Nicolas, Jacques
Saez Rodriguez, Julio
Schaub, Torsten
Siegel, Anne
author_role author
author2 Guziolowski, Carito
Eduati, Federica
Thiele, Sven
Gebser, Martin
Nicolas, Jacques
Saez Rodriguez, Julio
Schaub, Torsten
Siegel, Anne
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Answer Set Programming
Signaling Transduction Networks
Boolean Logic Models
Combinatorial Multi-Objective Optimization
topic Answer Set Programming
Signaling Transduction Networks
Boolean Logic Models
Combinatorial Multi-Objective Optimization
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, automated inference of Boolean logical networks from experimental data is a fundamental question in this field. This paper addresses the problem consisting of learning from a prior knowledge network describing causal interactions and phosphorylation activities at a pseudo-steady state, Boolean logic models of immediate-early response in signaling transduction networks. The underlying optimization problem has been so far addressed through mathematical programming approaches and the use of dedicated genetic algorithms. In a recent work we have shown severe limitations of stochastic approaches in this domain and proposed to use Answer Set Programming (ASP), considering a simpler problem setting. Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge networkand the necessity of multi-objective optimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in siliconumerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in Appendices A–B.
Fil: Videla, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquimicas de Buenos Aires; Argentina. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia. Universität Potsdam; Alemania
Fil: Guziolowski, Carito. CNRS. École Centrale de Nantes; Francia
Fil: Eduati, Federica. European Bioinformatics Institute. European Molecular Biology Laboratory; Reino Unido
Fil: Thiele, Sven. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia
Fil: Gebser, Martin. Universität Potsdam; Alemania
Fil: Nicolas, Jacques. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia
Fil: Saez Rodriguez, Julio. European Bioinformatics Institute. European Molecular Biology Laboratory; Reino Unido
Fil: Schaub, Torsten. Universität Potsdam; Alemania
Fil: Siegel, Anne. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia
description Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, automated inference of Boolean logical networks from experimental data is a fundamental question in this field. This paper addresses the problem consisting of learning from a prior knowledge network describing causal interactions and phosphorylation activities at a pseudo-steady state, Boolean logic models of immediate-early response in signaling transduction networks. The underlying optimization problem has been so far addressed through mathematical programming approaches and the use of dedicated genetic algorithms. In a recent work we have shown severe limitations of stochastic approaches in this domain and proposed to use Answer Set Programming (ASP), considering a simpler problem setting. Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge networkand the necessity of multi-objective optimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in siliconumerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in Appendices A–B.
publishDate 2015
dc.date.none.fl_str_mv 2015-09
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/11336/14203
Videla, Santiago; Guziolowski, Carito; Eduati, Federica; Thiele, Sven; Gebser, Martin; et al.; Learning Boolean logic models of signaling networks with ASP; Elsevier Science; Theoretical Computer Science; 599; 9-2015; 79-101
0304-3975
url http://hdl.handle.net/11336/14203
identifier_str_mv Videla, Santiago; Guziolowski, Carito; Eduati, Federica; Thiele, Sven; Gebser, Martin; et al.; Learning Boolean logic models of signaling networks with ASP; Elsevier Science; Theoretical Computer Science; 599; 9-2015; 79-101
0304-3975
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0304397514004587
info:eu-repo/semantics/altIdentifier/url/http://dx.doi.org/10.1016/j.tcs.2014.06.022
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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