Publication:
Optimizing recombinant mini proinsulin production via response surface method and microbioreactor screening

dc.contributor.coauthorAytekin, Ali Ozhan
dc.contributor.coauthorKati, Ahmet
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.departmentDepartment of Molecular Biology and Genetics
dc.contributor.kuauthorAyan, Esra
dc.contributor.kuauthorDemirci, Hasan
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.contributor.schoolcollegeinstituteCollege of Sciences
dc.date.accessioned2025-12-31T08:23:06Z
dc.date.available2025-12-31
dc.date.issued2025
dc.description.abstractThe increasing demand for efficient recombinant insulin production necessitates the development of scalable, high-yield, and cost-effective bioprocesses. In this study, we engineered a novel mini-proinsulin (nMPI) with enhanced expression properties by shortening the C-peptide and incorporating specific residue substitutions to eliminate the need for enzymatic cleavage. To optimize its production, we applied a hybrid approach combining microscale high-throughput cultivation using the BioLector microbioreactor and statistical modeling via response surface methodology (RSM). Critical medium components were first screened using Plackett-Burman Design (PBD) and refined through Central Composite Design (CDD), identifying glycerol as the most influential factor for yield. Among the four statistically derived formulations, Scenario III demonstrated the highest productivity in the microscale platform (13.00 g/L) and maintained strong performance upon scale-up to a 3-L bioreactor (11.5 g/L). The optimized medium balanced carbon and nitrogen sources to enhance cell viability and maximize protein expression. This study not only confirms the predictive accuracy and scalability of the hybrid optimization system but also introduces a robust production platform for nMPI that can be translated into industrial settings. The workflow presented here can serve as a model for the development of efficient expression systems for complex recombinant proteins in E. coli.
dc.description.fulltextYes
dc.description.harvestedfromManual
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.openaccessgold
dc.description.publisherscopeInternational
dc.description.readpublishN/A
dc.description.sponsoredbyTubitakEuN/A
dc.identifier.doi10.1371/journal.pone.0329319
dc.identifier.eissn1932-6203
dc.identifier.embargoNo
dc.identifier.issue9
dc.identifier.pubmed40920712
dc.identifier.quartileQ2
dc.identifier.scopus2-s2.0-105015392882
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0329319
dc.identifier.urihttps://hdl.handle.net/20.500.14288/31697
dc.identifier.volume20
dc.identifier.wos001568065700020
dc.keywordsMedium optimization
dc.keywordsProtein-production
dc.keywordsEscherichia
dc.keywordsExpression
dc.keywordsInsulin
dc.keywordsDesign
dc.keywordsAccumulation
dc.keywordsStrategies
dc.language.isoeng
dc.publisherPublic Library of Science
dc.relation.affiliationKoç University
dc.relation.collectionKoç University Institutional Repository
dc.relation.ispartofPLOS One
dc.relation.openaccessNo
dc.rightsCopyrighted
dc.subjectScience & Technology
dc.subjectMultidisciplinary Sciences
dc.titleOptimizing recombinant mini proinsulin production via response surface method and microbioreactor screening
dc.typeJournal Article
dspace.entity.typePublication
person.familyNameAyan
person.familyNameDemirci
person.givenNameEsra
person.givenNameHasan
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