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dc.creatorPedrozo, Héctor Alejandro
dc.creatorSchvezov, Carlos Enrique
dc.date.accessioned2023-01-30T13:02:44Z
dc.date.available2023-01-30T13:02:44Z
dc.date.issued2016-11-14
dc.identifier.citationPedrozo, H. A., y Schvezov, C. E. (2016). Genetic algorithm applied to parameter estimation of bacterial growth modeling. Journal of Theoretical Biology. [s/d]: Elsevier, pp. 1-25.es_AR
dc.identifier.otherCCPI-CNyE-A-131
dc.identifier.other9305
dc.identifier.otherJTB-D-16-01015 (Manuscript Number)
dc.identifier.urihttps://hdl.handle.net/20.500.12219/4442
dc.descriptionFil: Pedrozo, Héctor Alejandro. Universidad Nacional de Misiones. Facultad de Ciencias Exactas, Químicas y Naturales. Instituto de Materiales de Misiones; Argentina.es_AR
dc.descriptionFil: Pedrozo, Héctor Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico (Nordeste). Instituto de Materiales de Misiones; Argentina.es_AR
dc.descriptionFil: Schvezov, Carlos Enrique. Universidad Nacional de Misiones. Facultad de Ciencias Exactas, Químicas y Naturales. Instituto de Materiales de Misiones; Argentina.es_AR
dc.descriptionFil: Schvezov, Carlos Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico (Nordeste). Instituto de Materiales de Misiones; Argentina.es_AR
dc.description.abstractPredictive microbiology is nowadays one of the main tools to understand microbial interactions and to assess the quantitative risk in foods. Several models have been developed in order to predict microorganism growth. The resulting model equations for the growth of interacting microorganisms include a number of parameters which must be determined for the specific conditions to be modeled. The most effective method to determine these parameters is inverse engineering. When it is required to fit more than one experimental growth curve simultaneously, the process is more complex since it is necessary to apply a multi- objective optimization procedure. In the present report a genetic algorithm is presented which is applied to obtain the best parameter values of a mechanistic model that permit the construction of the front of Pareto with 50 individuals or phenotypes. The method was applied to the growth of lactic acid bacteria (LAB) and Listeria monocytogenes, resulting in very low errors of 0.23 and 0.25 for the LAB and L. monocytogenes between model and experimental values, respectively. The method is very adequate for application in determining parameter values adjusted by inverse engineering giving very good results.en
dc.formatapplication/pdf
dc.format.extent1.120 MB
dc.language.isoengen
dc.publisherElsevieren
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subjectPredictive microbiologyen
dc.subjectBacterial interactionsen
dc.subjectParameter estimationen
dc.subjectGenetic algorithmen
dc.titleGenetic algorithm applied to parameter estimation of bacterial growth modelingen
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:ar-repo/semantics/artículo
dc.typeinfo:eu-repo/semantics/draft


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