Improvement of a two - stage fermentation process for docosahexaenoic acid production by Aurantiochytrium limacinum SR21 applying statistical experimental designs and data analysis

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Statistical screening experimental designs were applied to identify the significant culture variables for biomass production of Aurantiochytrium limacinum SR21 and their optimal levels were found using a combination of Artificial Neural Networks, genetic algorithms and graphical analysis. The biomas...

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Detalles Bibliográficos
Otros Autores: Rosa, Silvina Mariana, Soria, Marcelo Abel, Vélez, Carlos Guillermo, Galvagno, Miguel Angel
Formato: Artículo
Lenguaje:Inglés
Materias:
ARTIFICIAL NEURAL NETWORKS
AURANTIOCHYTRIUM
DOCOSAHEXAENOIC ACID
STATISTICAL DESIGNS
TWO-STAGE FERMENTATION
BIOMASS
BIOREACTOR
BIOTECHNOLOGY
CULTURE MEDIUM
EUKARYOTE
FERMENTATION
GROWTH, DEVELOPMENT AND AGING
METABOLISM
METHODOLOGY
MICROBIOLOGY
PHYSIOLOGY
REPRODUCIBILITY
STATISTICAL MODEL
STATISTICS
CULTURE MEDIA
EUKARYOTA
MODELS, STATISTICAL
REPRODUCIBILITY OF RESULTS
STATISTICS AS TOPIC
Acceso en línea:http://ri.agro.uba.ar/files/intranet/articulo/2010Rosa.pdf
LINK AL EDITOR
Aporte de:Registro referencial: Solicitar el recurso aquí
Biblioteca Central (FAUBA) de Universidad de Buenos Aires
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520 |a Statistical screening experimental designs were applied to identify the significant culture variables for biomass production of Aurantiochytrium limacinum SR21 and their optimal levels were found using a combination of Artificial Neural Networks, genetic algorithms and graphical analysis. The biomass value obtained [40.3 g cell dry weight l-1] employing the selected culture conditions agreed with that predicted by the model. Subsequently, two significant culture conditions for docosahexaenoic acid [DHA] production were determined, finding that an inoculum of 10 percent [v/v], obtained from the previous [statistically optimized] stage, should be used in a DHA production medium having a molar C:N ratio of 55:1, to reach a production of 7.8 g DHA l-1 d-1. The production step was thereafter scaled in a 3.5 l bioreactor, and DHA productivity of 3.7 g l-1 d-1 was obtained. This two-stage strategy: statistically optimized inoculum production [fist step] and a DHA production step, is presented for the first time to optimize a bioprocess conducive to the obtention of microbial DHA. 
653 0 |a ARTIFICIAL NEURAL NETWORKS 
653 0 |a AURANTIOCHYTRIUM 
653 0 |a DOCOSAHEXAENOIC ACID 
653 0 |a STATISTICAL DESIGNS 
653 0 |a TWO-STAGE FERMENTATION 
653 0 |a BIOMASS 
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700 1 |9 69519  |a Rosa, Silvina Mariana 
700 1 |9 49057  |a Soria, Marcelo Abel 
700 1 |a Vélez, Carlos Guillermo   |9 33848 
700 1 |9 69520  |a Galvagno, Miguel Angel 
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900 |a ^aRosa^bS.M.^tInstituto de Investigaciones Biotecnológicas, IIB-CONICET, Universidad Nacional de San Martín, Av. Colectora General Paz 5445, [1650] Buenos Aires, Argentina 
900 |a ^aSoria^bM.A.^tDepartamento de Biodiversidad y Biología Experimental, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, [1428] Buenos Aires, Argentina 
900 |a ^aVélez^bC.G.^tCátedra de Microbiología Agrícola, Facultad de Agronomía, Universidad de Buenos Aires, Av. San Martín 4453, [1417] Buenos Aires, Argentina 
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