Mechanistic Computational Modeling Of Brain Metabolism

Extensive manual curation of the biochemical literature, together with transcriptomic and proteomic data are combined with novel algorithms to reconstruct metabolic models specific to certain neuronal sub-types, e.g., dopaminergic neurons. Constraint-based computational modelling is used to model metabolism at genome-scale. Iterative rounds of reconstruction, model prediction and

reconciliation with existing experimental data is used to develop a computational model which is a formal synthesis of current knowledge on neuronal metabolic function. The accuracy of the neuronal metabolic model is tested by comparison with independent experimental data. Computational models of neuronal metabolism are used as an aid to interpret experimental data, to optimally design in vitro experiments with stem-cell derived neurons, to understand the aetiopathogenesis of neurodegenerative disease, e.g., Parkinson’s disease, and to develop new approaches for early diagnosis and treatment of disease.