Cellular metabolism is one of the most fundamental systems for any living organisms, involving thousands of metabolites and reactions that forms large interconnected metabolic networks. Proper and comprehensive understanding of the metabolism in human cells has been a field of research for a long time. One of the key parameters in understanding the metabolism are the metabolic fluxes, which are the rates of conversion of metabolic intermediates. Currently, one of the main approaches for determining these fluxes is metabolic flux analysis (MFA), in which isotope-labelled compounds are introduced into the system and measured. Mathematical models are then used to calculate a prediction of the systems flux configuration. However, the current paradigm of MFA lack established methods for validating that a model can accurately predict quantities for which there are no experimental data. In this study, a model for the central human metabolism was created and evaluated with regards to the model’s ability to predict a validation dataset. Further, an uncertainty analysis of these predictions were performed with a prediction profile likelihood analysis. This study has conclusively shown that MFA models can be validated against experimental data that the model has never seen before. Additionally, such model predictions were shown to be observable with a well determined prediction uncertainty. These results shows that a systematic validation of MFA models is possible. This in turn allows for a greater trust to be placed in the models, and in any conclusions that are based on such models.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-156328 |
| Date | January 2019 |
| Creators | Sundqvist, Nicolas |
| Publisher | Linköpings universitet, Institutionen för medicinsk teknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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