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Title: In silico guided metabolic engineering of Aspergillus niger for sustainable organic acid production
Author: Upton, Daniel
ISNI:       0000 0004 7431 8927
Awarding Body: University of York
Current Institution: University of York
Date of Award: 2017
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The filamentous fungus Aspergillus niger is a common mould, and has risen to worldwide impact through industrial fermentation that is the chief source of the world’s citric acid. As well as the high market value chemical citric acid, A. niger fermentation supplies an array of enzymes of biotechnological importance. Industrial fermentation processes continue to be dependent on sucrose-based feed-stocks, and rising energy costs and tightening resources are prompting a shift to more sustainable methods. A. niger fermentation is also a promising platform for the sustainable production of many chemicals that could help solve coming world challenges. A. niger has within it the metabolic potential to achieve these goals, but these can only be realised with more efficient strain development techniques. This project built on the recent systems biology studies of A. niger to create in silico tools that guide rational engineering strategies. A dynamic metabolic model, relevant to the industrial setting of batch fermentation, of A. niger organic acid production was developed and shown to accurately capture physiological characteristics. An empirical characterisation of organic acid fermentation by the wild-type ATCC1015 strain was used to inform the model, and revealed new findings. The onset of citric acid production coincided with a shift to phosphate-limited growth, caused by a rapid phosphate uptake and storage of phosphate as polyphosphate. The role of polyphosphate in organic acid fermentation has not previously been described. The dynamic model was probed by the engineering of seven targets. To better inform further engineering, the genome-scale metabolic network of A. niger was updated and made specific to the ATCC1015 strain, the parent of citric acid producing strains. Finally, a genetic algorithm was developed for in silico evolution of A. niger organic acid production, and applied to suggest strategies for optimising the production of different organic acids.
Supervisor: Wood, Jamie ; McQueen-Mason, Simon Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available