Abstract:
The objective of this study is the investigation of the effects of Parkinson’s disease on human brain metabolism by using computational systems biology approach. A new brain metabolic model originated from Çakır et al., (2007) is substantially improved by expanding the lumped reactions in the model into elementary reactions and adding several new reactions existed in the literature. In addition, available gene information of each reaction based on HumanCyc is included in the developed brain model to be used in transcriptome data analysis. The new brain model comprises 630 reactions (571 internal, 59 exchange) and 524 metabolites (465 internal, 59 external) with the 670 genes. The applicability of the new brain model is tested by predicting resting (healthy) condition fluxes by using flux balance analysis. Since estimated flux results are consistent with the resting state, the new brain model is used in prediction of fluxes by using constraint-based modeling in hypoxia and Parkinson’s disease. Flux changes in these two cases are computed by using minimization of metabolic adjustment (MOMA) and regulatory on / off minimization (ROOM) approaches. Hypoxia results are in agreement with the results in Çakır et al., (2007). Many of the metabolic fluxes in Parkinson’s disease are found to be almost the same as in resting state. Apart from constraint based modeling, integrative modeling of the transcriptome data and brain metabolism is performed by using different approaches. Reporter metabolite analysis (RMA) is applied in Parkinson’s disease and other neurological diseases. Significant changes are estimated in glycolysis, oxidative phosphorylation and ATPase pathway, isoleucine, and methionine metabolism in Parkinson’s disease by using reporter pathway analysis. Lastly, transcription factor binding site analysis is performed for the reporter metabolites in Parkinson’s disease by grouping up-regulated and down-regulated genes. SOX17, FOXF2, PBX1, Mycn, and Myb are identified as over-represented transcription factors with a high frequency among many others, pointing to these transcription factors as putative regulators of Parkinson’s disease.
