Batch cultivation model for biopolymer production

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dc.contributor.authorTorres-Cerna, C.E.
dc.contributor.authorAlanis, A.Y.
dc.contributor.authorPoblete-Castro, I.
dc.contributor.authorHernandez-Vargas, E.A.
dc.date.accessioned2017-10-26T15:46:15Z
dc.date.available2017-10-26T15:46:15Z
dc.date.issued2017
dc.descriptionIndexación: Scopus.es_CL
dc.description.abstractThis paper presents a mathematical model to evaluate the kinetics of two different Pseudomonas putida strains, wild and mutant-type for the microbial production of polyhydroxyalkanoates (PHAs). Model parameters were estimated to represent adequately experimental data from the batch reactor using the differential evolution algorithm. Based on the mathematical model with the best-fit parameter values, simulations suggested that the high production of PHA by the mutant strain can be attributed not only to the higher production of PHA but also to a reduction in the consumption rate of the substrates of approximately 66 %. Remarkably, the cell growth rate value is higher for the wild type than the mutant type, suggesting that the PHA increase is not only to an increase in the production rate but also to the metabolism of the cells. This mathematical model advances comprehension of the PHA production capacity by P. putida paving the road towards environmentally friendly plastics.es_CL
dc.description.urihttp://silverstripe.fkit.hr/cabeq/assets/Uploads/09-1-17.pdf
dc.identifier.citationChemical and Biochemical Engineering Quarterly. Volume 31, Issue 1, April 2017, Pages 89-99es_CL
dc.identifier.issn0352-9568
dc.identifier.otherDOI: 10.15255/CABEQ.2016.952
dc.identifier.urihttp://repositorio.unab.cl/xmlui/handle/ria/4475
dc.language.isoenes_CL
dc.publisherAssoc. of Chemists and Chemical Engineers of Croatiaes_CL
dc.subjectPseudomonas putidaes_CL
dc.subjectPolyhydroxyalkanoateses_CL
dc.subjectMathematical modelinges_CL
dc.subjectBiopolymerses_CL
dc.subjectBatch cultivation kineticses_CL
dc.titleBatch cultivation model for biopolymer productiones_CL
dc.typeArtículoes_CL
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