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dc.contributor.authorBrown, Steven Richard
dc.date.accessioned2017-03-01T10:32:02Z
dc.date.issued2016-09-05
dc.description.abstractThe proven ability to ferment Saccharomyces cerevisiae on a large scale presents an attractive target for producing chemicals and fuels from sustainable sources. Efficient and predominant carbon flux through to ethanol is a significant engineering issue in the development of this yeast as a multi-product cell chassis used in biorefineries. In order to evaluate diversion of carbon flux away from ethanol, combinatorial deletions were investigated in genes encoding the six isozymes of alcohol dehydrogenase (ADH), which catalyse the terminal step in ethanol production. The scarless, dominant and counter- selectable amdSYM gene deletion method was optimised for generation of a combinatorial ADH knockout library in an industrially relevant strain of S. cerevisiae. Current understanding of the individual ADH genes fails to fully evaluate genotype-by-genotype and genotype-by-environment interactions: rather, further research of such a complex biological process requires a multivariate mathematical modelling approach. Application of such an approach using the Design of Experiments (DoE) methodology is appraised here as essential for detailed empirical evaluation of complex systems. DoE provided empirical evidence that in S. cerevisiae: i) the ADH2 gene is not associated with producing ethanol under anaerobic culture conditions in combination with 25 g l-1 glucose substrate concentrations; ii) ADH4 is associated with increased ethanol production when the cell is confronted with a zinc-limited [1 μM] environment; and iii) ADH5 is linked with the production of ethanol, predominantly at pH 4.5. A successful metabolic engineering strategy is detailed which increases the product portfolio of S. cerevisiae, currently used for large-scale production of bioethanol. Heterologous expression of the cytochrome P450 fatty acid peroxygenase from Jeotgalicoccus sp., OleTJE, fused to the RhFRED reductase from Rhodococcus sp. NCIMB 978 converted free fatty acid precursors to C13, C15 and C17 alkenes (3.81 ng μl-1 total alkene concentration).en_GB
dc.description.sponsorshipRoyal Dutch Shellen_GB
dc.identifier.urihttp://hdl.handle.net/10871/26158
dc.language.isoenen_GB
dc.publisherUniversity of Exeteren_GB
dc.rights.embargoreasonAt the request of Industrial sponsoren_GB
dc.rights5 year embargoen_GB
dc.subjectDesign of Experimentsen_GB
dc.subjectSaccharomyces cerevisiaeen_GB
dc.subjectMetabolic engineeringen_GB
dc.subjectHydrocarbon biosynthesisen_GB
dc.subjectYeasten_GB
dc.subjectMultivariateen_GB
dc.subjectCarbon fluxen_GB
dc.subjectEthanol metabolismen_GB
dc.subjectAlcohol dehydrogenaseen_GB
dc.subjectOleTen_GB
dc.subjectGenotype by environmenten_GB
dc.subjectRhFREDen_GB
dc.subjectADH knockout libraryen_GB
dc.subjectCombinatorial gene deletionen_GB
dc.subjectMathematical modellingen_GB
dc.subjectEmpirical evaluation of complex systemsen_GB
dc.subjectPLSen_GB
dc.subjectTrade off evaluationen_GB
dc.subjectFatty acid-derived biofuelen_GB
dc.subjectBiofuelen_GB
dc.subjectMinistaten_GB
dc.subjectCEN.PK113-7Den_GB
dc.subjectCell factoryen_GB
dc.subjectAlkeneen_GB
dc.subjectIsozymeen_GB
dc.subjectExperiment designen_GB
dc.subjectData visualisationen_GB
dc.subjectGenome scale modelsen_GB
dc.subjectGenotype by genotypeen_GB
dc.subjectModelen_GB
dc.subjectIndustrially relevant strainen_GB
dc.subjectBiorefineryen_GB
dc.titleA Design of Experiments Approach for Engineering Carbon Metabolism in the Yeast Saccharomyces cerevisiaeen_GB
dc.typeThesis or dissertationen_GB
dc.contributor.advisorAves, Stephen J.
dc.contributor.advisorHoward, Thomas P.
dc.descriptionIn conclusion, the Design of Experiments approach is beneficial to the rigour of scientific method required to evaluate complex biological systems, and its use would be advantageous for all such multivariate research areas.en_GB
dc.publisher.departmentCollege of Life Sciencesen_GB
dc.type.degreetitlePhD in Biological Sciencesen_GB
dc.type.qualificationlevelDoctoralen_GB
dc.type.qualificationnamePhDen_GB


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