Plants are the most abundant biomass on Earth. Understanding plant metabolism represents a significant, fundamental challenge, requiring the incorporation of many fields of study. However it also provides potentially significant leverage with which to change the world in which we live. The model organism Arabidopsis thaliana is probably the single best under- stood plant system. The aim of this thesis is to use mathematical modelling to investigate to what extent existing knowledge can describe broad, emergent aspects of the behaviour of metabolism in this system, with particular respect to the metabolism of sulfur, and other nutrients, and to gain insight into the consequences of the structure of its metabolic network. Constraint-based modelling approaches provide a framework for modelling large reaction networks. Although they require various simplifications, and assump- tions, they provide a route for the understanding of large metabolic networks, which is not possible through other approaches. Here, a genome scale model of Arabidopsis metabolism is developed to reflect experimental data, and deployed in the study of nutrient stress, and nutrient requirements. This model predicts changes in gene expression in response to stress, and provides insight into the consequences of the metabolic structure on nutrient use efficiency, metabolic flexibility, and the consequences of genetic perturbation.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:716390 |
| Date | January 2016 |
| Creators | Calderwood, Alexander |
| Publisher | University of East Anglia |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://ueaeprints.uea.ac.uk/63533/ |
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