Metabolic pathways synthesis based on ant colony optimization

Autores
Gerard, M. F.; Stegmayer Machado, Georgina S.; Milone, Diego H.
Año de publicación
2019
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
A current challenge in bioinformatics is to discover how to transform particular compounds into specific products. Typically, the common approach is finding the sequence of reactions that relate the specified substrate (source) and product (target) using classical searching algorithms. However, those methods have three main limitations: difficulty in handling large amounts of reactions and compounds; absence of a step that verifies the availability of substrates; and inability to find branched pathways. In [1], we propose a novel ant colony-based algorithm for metabolic pathways synthesis. This algorithm, named Pheromone-Directed Seeker (PhDSeeker), is able to relate several compounds simultaneously by emulating the behavior of real ants while seeking a path between their colony and a source of food. The process is designed to ensure the availability of substrates for every reaction in the solution. Thus, ants explore the set of reactions on each iteration searching for possible pathways to link the compounds. After that, they share information about solutions found by each one and then perform a new search. This process is guided by a cost function that evaluates the availability of substrates, the connection between source and target, and the pathway size.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Colony-based algorithm
PhDSeeker
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/87828

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network_name_str SEDICI (UNLP)
spelling Metabolic pathways synthesis based on ant colony optimizationGerard, M. F.Stegmayer Machado, Georgina S.Milone, Diego H.Ciencias InformáticasColony-based algorithmPhDSeekerA current challenge in bioinformatics is to discover how to transform particular compounds into specific products. Typically, the common approach is finding the sequence of reactions that relate the specified substrate (source) and product (target) using classical searching algorithms. However, those methods have three main limitations: difficulty in handling large amounts of reactions and compounds; absence of a step that verifies the availability of substrates; and inability to find branched pathways. In [1], we propose a novel ant colony-based algorithm for metabolic pathways synthesis. This algorithm, named Pheromone-Directed Seeker (PhDSeeker), is able to relate several compounds simultaneously by emulating the behavior of real ants while seeking a path between their colony and a source of food. The process is designed to ensure the availability of substrates for every reaction in the solution. Thus, ants explore the set of reactions on each iteration searching for possible pathways to link the compounds. After that, they share information about solutions found by each one and then perform a new search. This process is guided by a cost function that evaluates the availability of substrates, the connection between source and target, and the pathway size.Sociedad Argentina de Informática e Investigación Operativa2019-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionResumenhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf1http://sedici.unlp.edu.ar/handle/10915/87828enginfo:eu-repo/semantics/altIdentifier/issn/2451-7585info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/3.0/Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:17:29Zoai:sedici.unlp.edu.ar:10915/87828Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:17:29.877SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Metabolic pathways synthesis based on ant colony optimization
title Metabolic pathways synthesis based on ant colony optimization
spellingShingle Metabolic pathways synthesis based on ant colony optimization
Gerard, M. F.
Ciencias Informáticas
Colony-based algorithm
PhDSeeker
title_short Metabolic pathways synthesis based on ant colony optimization
title_full Metabolic pathways synthesis based on ant colony optimization
title_fullStr Metabolic pathways synthesis based on ant colony optimization
title_full_unstemmed Metabolic pathways synthesis based on ant colony optimization
title_sort Metabolic pathways synthesis based on ant colony optimization
dc.creator.none.fl_str_mv Gerard, M. F.
Stegmayer Machado, Georgina S.
Milone, Diego H.
author Gerard, M. F.
author_facet Gerard, M. F.
Stegmayer Machado, Georgina S.
Milone, Diego H.
author_role author
author2 Stegmayer Machado, Georgina S.
Milone, Diego H.
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Colony-based algorithm
PhDSeeker
topic Ciencias Informáticas
Colony-based algorithm
PhDSeeker
dc.description.none.fl_txt_mv A current challenge in bioinformatics is to discover how to transform particular compounds into specific products. Typically, the common approach is finding the sequence of reactions that relate the specified substrate (source) and product (target) using classical searching algorithms. However, those methods have three main limitations: difficulty in handling large amounts of reactions and compounds; absence of a step that verifies the availability of substrates; and inability to find branched pathways. In [1], we propose a novel ant colony-based algorithm for metabolic pathways synthesis. This algorithm, named Pheromone-Directed Seeker (PhDSeeker), is able to relate several compounds simultaneously by emulating the behavior of real ants while seeking a path between their colony and a source of food. The process is designed to ensure the availability of substrates for every reaction in the solution. Thus, ants explore the set of reactions on each iteration searching for possible pathways to link the compounds. After that, they share information about solutions found by each one and then perform a new search. This process is guided by a cost function that evaluates the availability of substrates, the connection between source and target, and the pathway size.
Sociedad Argentina de Informática e Investigación Operativa
description A current challenge in bioinformatics is to discover how to transform particular compounds into specific products. Typically, the common approach is finding the sequence of reactions that relate the specified substrate (source) and product (target) using classical searching algorithms. However, those methods have three main limitations: difficulty in handling large amounts of reactions and compounds; absence of a step that verifies the availability of substrates; and inability to find branched pathways. In [1], we propose a novel ant colony-based algorithm for metabolic pathways synthesis. This algorithm, named Pheromone-Directed Seeker (PhDSeeker), is able to relate several compounds simultaneously by emulating the behavior of real ants while seeking a path between their colony and a source of food. The process is designed to ensure the availability of substrates for every reaction in the solution. Thus, ants explore the set of reactions on each iteration searching for possible pathways to link the compounds. After that, they share information about solutions found by each one and then perform a new search. This process is guided by a cost function that evaluates the availability of substrates, the connection between source and target, and the pathway size.
publishDate 2019
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dc.language.none.fl_str_mv eng
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Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
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rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/
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