| Summary: | Recent international directives promoting the reduced consumption of fossil fuels have warranted methods for effective carbon recycling. Subsequently, Clostridium autoethanogenum has attracted academic and industrial interest due to its ability to convert syngas components (CO, CO2 & H2) into valuable platform chemicals, including ethanol and 2,3-butanediol. Developing the metabolic conversions catalysed by C. autoethanogenum into an efficient bioprocess requires the accurate prediction of optimal metabolic steady states, which in turn necessitates the construction of a genome-scale model (GSM).
In this work, a genome scale model of C. autoethanogenum has been constructed, experimentally parameterised and validated. Unlike previously published models, optimal flux distributions computed with this network reflect the native product profile of C. autoethanogenum without the requirement for additional constraints. The model has been used to investigate pH- and CO-supply- induced metabolic shifts, uncovering experimentally tested bioprocess conditions which promote ethanol and 2,3-butanediol production. The genes encoding Nfn were identified as knock-out targets for improved ethanol production, however the application of a new thermodynamic analysis reveals difficulties which were confirmed experimentally.
The work presented in this thesis ultimately provides a demonstrably useful tool for metabolic and bioprocess engineering of C. autoethanogenum for platform-chemical production.
|
|---|