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Prediction of dynamic plasmid production by recombinant escherichia coli fed-batch cultivations with a generalized regression neural network

dc.contributor.authorSilva, T.
dc.contributor.authorLima, P.
dc.contributor.authorRoxo-Rosa, M.
dc.contributor.authorHageman, S.
dc.contributor.authorFonseca, L. P.
dc.contributor.authorCalado, C. R. C.
dc.date.accessioned2022-01-20T14:58:39Z
dc.date.available2022-01-20T14:58:39Z
dc.date.issued2009
dc.description.abstractA generalized regression neural network with external feedback was used to predict plasmid production in a fed-batch cultivation of recombinant Escherichia coli. The neu ral network was built out of the experimental data obtained on a few cultivations, of which the general strategy was based on an initial batch phase followed by an exponen tial feeding phase. The different cultivation conditions used resulted in significant differ ences in bacterial growth and plasmid production. The obtained model allows estimation of the experimental outputs (biomass, glucose, acetate and plasmid) based on the bioreactor starting conditions and the following on-line inputs: feeding rate, dissolved oxygen concentration and bioreactor stirring speed. Therefore, the proposed methodol ogy presents a quick, simple and reliable way to perform on-line feedback prediction of the dynamic behaviour of the complex plasmid production process, based on simple on-line input data obtained directly from the bioreactor control unit and with few cultiva tion experiments for neural network learning.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.issn0352-9568
dc.identifier.urihttp://hdl.handle.net/10400.14/36518
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectNeural networkpt_PT
dc.subjectFed-batch cultivationpt_PT
dc.subjectPlasmid productionpt_PT
dc.titlePrediction of dynamic plasmid production by recombinant escherichia coli fed-batch cultivations with a generalized regression neural networkpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage427pt_PT
oaire.citation.issue4pt_PT
oaire.citation.startPage419pt_PT
oaire.citation.titleChemical and Biochemical Engineering Quarterlypt_PT
oaire.citation.volume23pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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