DC FieldValueLanguage
dc.contributor.authorKuchemüller, Kim Beatrice-
dc.contributor.authorPörtner, Ralf-
dc.contributor.authorMöller, Johannes-
dc.date.accessioned2020-01-28T11:48:26Z-
dc.date.available2020-01-28T11:48:26Z-
dc.date.issued2020-
dc.identifier.citationMethods in Molecular Biology (2095): 235-249 (2020)de_DE
dc.identifier.issn1064-3745de_DE
dc.identifier.urihttp://hdl.handle.net/11420/4614-
dc.description.abstractConventional design of experiments (DoE) methods require expert knowledge about the investigated factors and their boundary values and mostly lead to multiple rounds of time-consuming and costly experiments. The combination of DoE with mathematical process modeling in model-assisted DoE (mDoE) can be used to increase the mechanistic understanding of the process. Furthermore, it is aimed to optimize the processes with respect to a target (e.g., amount of cells, product titer), which also provides new insights into the process. In this chapter, the workflow of mDoE is explained stepwise including corresponding protocols. Firstly, a mathematical process model is adapted to cultivation data of first experimental data or existing knowledge. Secondly, model-assisted simulations are treated in the same way as experimentally derived data and included as responses in statistical DoEs. The DoEs are then evaluated based on the simulated data, and a constrained-based optimization of the experimental space can be conducted. This loop can be repeated several times and significantly reduces the number of experiments in process development.en
dc.language.isoende_DE
dc.relation.ispartofMethods in molecular biologyde_DE
dc.subjectBatchde_DE
dc.subjectComputer-aided methodsde_DE
dc.subjectDoEde_DE
dc.subjectExperimental spacede_DE
dc.subjectFed-batchde_DE
dc.subjectResponse surfacede_DE
dc.titleEfficient optimization of process strategies with model-assisted design of experimentsde_DE
dc.typeinBookde_DE
dc.type.dinibookPart-
dcterms.DCMITypeText-
tuhh.abstract.englishConventional design of experiments (DoE) methods require expert knowledge about the investigated factors and their boundary values and mostly lead to multiple rounds of time-consuming and costly experiments. The combination of DoE with mathematical process modeling in model-assisted DoE (mDoE) can be used to increase the mechanistic understanding of the process. Furthermore, it is aimed to optimize the processes with respect to a target (e.g., amount of cells, product titer), which also provides new insights into the process. In this chapter, the workflow of mDoE is explained stepwise including corresponding protocols. Firstly, a mathematical process model is adapted to cultivation data of first experimental data or existing knowledge. Secondly, model-assisted simulations are treated in the same way as experimentally derived data and included as responses in statistical DoEs. The DoEs are then evaluated based on the simulated data, and a constrained-based optimization of the experimental space can be conducted. This loop can be repeated several times and significantly reduces the number of experiments in process development.de_DE
tuhh.publisher.doi10.1007/978-1-0716-0191-4_13-
tuhh.publication.instituteBioprozess- und Biosystemtechnik V-1de_DE
tuhh.type.opusInBuch (Kapitel / Teil einer Monographie)-
dc.type.driverbookPart-
dc.type.casraiBook Chapter-
tuhh.container.startpage235de_DE
tuhh.container.endpage249de_DE
dc.relation.projectIBÖM04:mDoE-Toolbox2-Neue mDoE Software-Toolbox zur modellgestützten Optimierung biotechnologischer Prozesse-
item.creatorOrcidKuchemüller, Kim Beatrice-
item.creatorOrcidPörtner, Ralf-
item.creatorOrcidMöller, Johannes-
item.openairetypeinBook-
item.openairecristypehttp://purl.org/coar/resource_type/c_3248-
item.fulltextNo Fulltext-
item.mappedtypeinBook-
item.creatorGNDKuchemüller, Kim Beatrice-
item.creatorGNDPörtner, Ralf-
item.creatorGNDMöller, Johannes-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.languageiso639-1en-
crisitem.project.funderBundesministerium für Bildung und Forschung (BMBF)-
crisitem.project.funderid501100002347-
crisitem.project.funderrorid04pz7b180-
crisitem.project.grantno031B0577A-
crisitem.author.deptBioprozess- und Biosystemtechnik V-1-
crisitem.author.deptBioprozess- und Biosystemtechnik V-1-
crisitem.author.deptBioprozess- und Biosystemtechnik V-1-
crisitem.author.orcid0000-0003-1163-9718-
crisitem.author.orcid0000-0001-9546-055X-
crisitem.author.parentorgStudiendekanat Verfahrenstechnik-
crisitem.author.parentorgStudiendekanat Verfahrenstechnik-
crisitem.author.parentorgStudiendekanat Verfahrenstechnik-
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