Network inference from time-resolved metabolomics data

Metabolism is the whole of all chemical processes in a living organism. These processes ensure that the organism grows and is resistant to changes in the environment. Metabolites are grouped in sequences of subsequent metabolic processes, called metabolic pathways, which are connected to each other in a large network. The study of metabolic pathways is important for various disciplines in scientific research and industry, including medicine, pharmacy and food industry. In this thesis, metabolic pathways are studied by using data of metabolite concentrations which are measured at different time points. Time plays an important role in the study of metabolism because metabolic processes are very dynamic. Chapter 2 examines mathematical methods for discovering new metabolic pathways. From the comparison of the requirements of the mathematical methods with current laboratory practice, we can conclude that mathematical and experimental methods are not consistent with each other. In chapters 3 and 4 we compare metabolic pathways under different conditions. In chapter 5 we search for biological principles that ensure that certain metabolic processes change due to adaptation of the organism to the environment, while other necessary processes are maintained. The thesis concludes with recommendations for future research (chapter 6).


Diana Hendrickx
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Published in: 
PhD thesis
Date of publication: 
April, 2013
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