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dc.contributor.authorMao, Longfei
dc.date.accessioned2013-11-13T02:04:42Z
dc.date.available2013-11-13T02:04:42Z
dc.date.issued2013
dc.identifier.urihttps://hdl.handle.net/10182/5718
dc.description.abstractMicrobial fuel cells (MFCs) are bioelectrochemcial devices that possess a similar design to a fuel cell, with an anode and a cathode connected through an electrical circuit. Unlike fuel cells, MFCs use microorganisms as biocatalysts to convert organic matter into electrons and protons, of which a portion can be transferred to electrode to generate electricity. Since all microorganisms transfer electrons during metabolism inside the cell, there could potentially be unlimited choices of biocatalyst candidates for MFCs for various applications. However, in reality, the application of MFCs is heavily restricted by their low current outputs. As the performance of an MFC is associated with various factors, MFC research devoted to improve energy output level is a very interdisciplinary field and demands knowledge from a diverse range of scientific areas including microbiology, environmental science and chemical engineering. Because the MFC current is fundamentally produced by the metabolic activity of the microorganisms, it would be desirable to elucidate the innate capacity of the microbial systems to sustain the energy extraction processes in MFCs, and pertinent metabolic pathways and burdens. However, due to the large number of cellular reactions (up to thousands for a eukaryote), no previous study has provided such information regarding the complete metabolic processes inside the cells during an MFC operation. Therefore, to understand the metabolic potential and behaviours at the whole-cell level and obviate the difficulties experienced in reductionist investigative methods, the present PhD thesis employed in silico metabolic engineering techniques to model the optimal metabolic states and flux adjustments of the four selected microbial species for electricity generation. The first part of this thesis is the development of a framework method based on existing constraint-based methods to analyse the impact of microbial electricity generation on the metabolisms of the selected microorganisms at genome-scale. To identify the alternative equivalent solutions and avoid the disturbance of futile cycle in a network mode, we developed a method, flux variability analysis with target flux minimization (FATMIN), which can characterize the metabolic strategies underlying a high current output compatible with reaction stoichiometry and realistic biological constraints. The second part of this thesis is the application of the constraint-based methods to model the four selected microorganisms for current production. The present study mainly considered NADH as the intracellular electron source for MET mode and c-type cytochrome for DET mode. In DET mode, charge is directly transferred from the microorganism to the electrode, and in MET mode a mediator molecule performs the transfer. The results have shown that that S. cerevisiae was the best candidate for MFC use based on its highest potential computed for current output (5.781 for aerobic and 6.193 A/gDW for anaerobic growths) and coulombic efficiency (over 85%). G. sulfurreducen had a potential to achieve the second highest current, up to 2.711 A/gDW for MET mode, 3.710 A/gDW for DET mode and 3.272 A/gDW for a putative mixed MET and DET mode. On the other hand, the maximum current values of 2.368 and 0.792 A/gDW were obtained from MET mode for C. reinhardtii under mixotrophic conditions and from DET mode for Synechocystis sp. PCC 6803 under autotrophic conditions respectively. Subsequently, the present study elucidated the impact of NADH, type-c cytochrome, ferredoxin and quinol-dependent current generation on the metabolisms and possible metabolic pathways that microorganisms can use to resolve the redox disruption arising from the energy extraction. It is expected that these referential data on the four pure cultures to be a good starting point for development of other experimental MFC research. The final part of the thesis is the employment of an optimization algorithm to test the suitability of reaction deletion strategy for improving the electron transfer rates for electricity generation. The results show that the reaction deletion strategy has improved the reducing equivalent regeneration capability for MFC current production in all the cases of G. sulfurreducens, C. reinhardtii and Synechocystis to different extents. Notably, the identified knockouts could effectively produce S. cerevisiae mutants that have elevated the lower limits of the current output to about two-third the theoretical maximum current. Overall, this thesis is the first report of using in silico modelling approach to study the capability and underlying metabolisms of microbial oxidation at the anode. It is expected the knowledge extended by this thesis in the microbial processes for electricity generation will integrate with advance in electrochemical engineering to increase performance of MFCs in future.en
dc.language.isoenen
dc.publisherLincoln Universityen
dc.rights.urihttps://researcharchive.lincoln.ac.nz/page/rights
dc.subjectMFCen
dc.subjectmicrobial fuel cellen
dc.subjectGeobacter sulfurreducensen
dc.subjectChlamydomonas reinhardtiien
dc.subjectSynechocystis sp. PCC 6803en
dc.subjectSaccharomyces cerevisiaeen
dc.subjectbioelectricityen
dc.subjectflux balance analysisen
dc.subjectflux variability analysisen
dc.subjectflux minimizationen
dc.subjectFATMINen
dc.subjectmathematical optimizationen
dc.subjectmetabolic modelen
dc.subjectMetabolic network analysisen
dc.subjectMetabolic network redesignen
dc.subjectmetabolic engineeringen
dc.subjectin silico simulationen
dc.subjectmodel-driven discoveryen
dc.titleFlux balance analysis to model microbial metabolism for electricity generationen
dc.typeThesisen
thesis.degree.grantorLincoln Universityen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen
lu.thesis.supervisorVerwoerd, Wynand Jr
lu.thesis.supervisorKulasiri, Don Jr
lu.contributor.unitDepartment of Wine, Food and Molecular Biosciencesen
dc.subject.anzsrc060104 Cell Metabolismen
dc.subject.anzsrc060102 Bioinformaticsen
dc.subject.anzsrc1003 Industrial Biotechnologyen
dc.subject.anzsrc010202 Biological Mathematicsen
dc.subject.anzsrc06 Biological Sciencesen
dc.subject.anzsrc069999 Biological Sciences not elsewhere classifieden


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