Solving gap metabolites and blocked reactions in genome-scale models: application to the metabolic network of Blattabacterium cuenoti

Background:Metabolic reconstruction is the computational-based process that aims to elucidate the network of metabolites interconnected through reactions catalyzed by activities assigned to one or more genes. Reconstructed models may contain inconsistencies that appear as gap metabolites and blocked...

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Detalles Bibliográficos
Autores: Ponce-de-Leon, Miguel, Montero, Francisco J, Peretó, Juli
Tipo de recurso: artículo
Fecha de publicación:2013
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/44495
Acceso en línea:https://hdl.handle.net/20.500.14352/44495
Access Level:acceso abierto
Palabra clave:51:57
595.7
579
Bacteria
genetics
metabolisms
symbiosis
biological model
Biomatemáticas
Insectos
Microbiología (Biología)
2404 Biomatemáticas
2413 Biología de Insectos (Entomología)
2414 Microbiología
Descripción
Sumario:Background:Metabolic reconstruction is the computational-based process that aims to elucidate the network of metabolites interconnected through reactions catalyzed by activities assigned to one or more genes. Reconstructed models may contain inconsistencies that appear as gap metabolites and blocked reactions. Although automatic methods for solving this problem have been previously developed, there are many situations where manual curation is still needed. Results:We introduce a general definition of gap metabolite that allows its detection in a straightforward manner. Moreover, a method for the detection of Unconnected Modules, defined as isolated sets of blocked reactions connected through gap metabolites, is proposed. The method has been successfully applied to the curation of iCG238,the genome-scale metabolic model for the bacterium Blattabacterium cuenoti, obligate endosymbiont of cockroaches. Conclusion:We found the proposed approach to be a valuable tool for the curation of genome-scale metabolic models. The outcome of its application to the genome-scale model B. cuenoti iCG238is a more accurate model ver- sion named as B. cuenoti iMP240