Abstract

Human metabolism, an essential and highly organized process, which is required to run and maintain cellular processes and to respond to shifts in external and internal conditions, can be described as a complex and interconnected network of metabolic pathways. Computational systems biology provides a suitable framework to study the mechanisms and interactions of this network and to address questions that are difficult to reproduce in vitro or in vivo. This dissertation contributes to the development of computational strategies which help to investigate aspects of human metabolism and metabolic-related disorders. In the first part, we introduce mathematical models of folate-mediated one-carbon metabolism in the cytoplasm and subsequently in the nucleus. A hybrid-stochastic framework is applied to investigate the behavior and stability of the complete metabolic network in response to genetic and nutritional factors. We analyse the effect of a common polymorphism of MTHFR, B12 and folate deficiency, as well as the role of the 5-formyltetrahydrofolate futile cycle on network dynamics. Furthermore, we study the impact of multienzyme complex formation and substrate channelling, which are key aspects related to nuclear folate-mediated one-carbon metabolism. Model simulations of the nuclear model highlight the importance of these two factors for normal functioning of the network and further identify folate status and enzyme levels as important influence factors for network dynamics. In the second part, we focus on metabolic syndrome, a highly prevalent cluster of metabolic disorders. We develop a computational workflow based on network analysis to characterise underlying molecular mechanisms of the disorder and to explore possible novel therapeutic strategies by means of drug repurposing. To this end, genetic data, text mining results, drug expression profiles and drug target information are integrated in the setting of tissue-specific background networks and a proximity score based on topological distance and functional similarity measurements is defined to identify potential new therapeutic applications of already approved drugs. A filtering and prioritization analysis allow us to identify ibrutinib, an inhibitor of bruton tyrosine kinase, as the most promising repurposing candidate.