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Patient-derived cellular models are a powerful approach to study human disease, especially neurodegenerative diseases, such as Parkinson's disease, where affected primary neurons, e.g., substantia nigra dopaminergic neurons, are almost inaccessible. Starting with a comprehensive generic reconstruction of human metabolism, Recon3D, we generated a high-quality, constraint-based, genome-scale, in silico model of human dopaminergic neuronal metabolism (iDopaNeuro1). It is a synthesis of extensive manual curation of the biochemical literature on neuronal metabolism, together with novel, quantitative, transcriptomic and targeted exometabolomic data from human stem cell-derived, midbrain-specific, dopaminergic neurons in vitro. Thermodynamic constraint-based modelling with iDopaNeuro1 is qualitatively accurate (92% correct) and quantitatively accurate (Spearman rank 0.7) at predicting metabolite secretion or uptake, given quantitative exometabolomic constraints on uptakes, or secretions, respectively. iDopaNeuro1 is also qualitatively accurate at predicting the consequences of metabolic perturbations, e.g., complex I inhibition (Spearman rank 0.69) in a manner consistent with literature on monogenic mitochondrial Parkinson's disease. The iDopaNeuro1 model provides a foundation for a quantitative systems biochemistry approach to metabolic dysfunction in Parkinson's disease. Moreover, the plethora of novel mathematical and computational approaches required to develop it are generalisable to study any other disease associated with metabolic dysfunction.

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