Integration of kinetic models with data from 13C-metabolic flux experiments

Schabort, Willem Petrus Du Toit (2007-12)

Thesis (MSc (Biochemistry))--University of Stellenbosch, 2007.


A detailed mathematical description of all the processes in a cell could be an informative tool for investigating biological function. Detailed kinetic models could be built either by obtaining enzyme kinetic parameters in vitro, or by obtaining them from time series analyses of metabolite data from rapid pulse experiments. A genome scale in vitro enzyme kinetic assay project would be prohibitively laborious with the current technologies. Further, there are still uncertainties about the importance of in vivo effects such as metabolite channelling, spatial effects and molecular crowding which could make in vitro determined parameters invalid. Accordingly, there is much interest in in vivo experiments for kinetic modelling. In vivo experimental methods suffer from a number of technical and even fundamental problems. Technical problems are being solved by more sensitive metabolomics tools and rapid sampling technologies. However, the large number of effectors of each enzyme reaction makes it impossible to obtain models at the level of detail possible with the in vitro method. Ultimately, the solution to building a genome scale Silicon Cell is to make use of both strategies. As metabolomics technologies are rapidly improving, it would thus make sense to follow the parts-based in vitro kinetics methodology, and carry out a detailed accuracy assessment of the model with in vivo experiments. To address the problem of the fundamental limit of information from concentration time-series, other in vivo experiments will have to be carried out as well. 13C-metabolic flux analysis has recently undergone vast improvements with the use of better experimental protocols and powerful algorithms for flux calculation. Incorporation of this type of experiment in the validation protocol is the aim of this thesis, which represents an intermediary step towards using the genome-scale stoichiometric models as platforms for building genome-scale kinetic models. It is illustrated here how kinetic models can be combined with metabolic flux data in a special way which allows correct modelling of boundary conditions and validation using novel concepts. We used 13C-metabolic flux analysis and gas chromatography-mass-spectrometry to measure metabolic fluxes through the central metabolic pathways of the yeast Saccharomyces cerevisiae. This data was integrated with a previously constructed detailed kinetic model of fermentative glycolysis in the yeast to illustrate our approach. Various implications for such data integration with kinetic models were identified and a software program was designed for this purpose.

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