Background: Parkinson's disease is an age-related disease whose pathogenesis is not completely known. Animal models exist for investigating the disease but not all results can be easily transferred to humans. Therefore, mathematical or probabilistic models for the human disease are to be constructed in silico in order to predict specific processes within a cell, such as the dopamine metabolism and transport processes in a neuron.
Results: We present a Systems Biology Markup Language (SBML) model of a whole dopaminergic nerve cell consisting of 139 reactions and 111 metabolites which includes, among others, the dopamine metabolism and transport, oxidative stress, aggregation of α-synuclein (αSYN), lysosomal and proteasomal degradation, and mitophagy. The predictive power of the model was investigated using flux balance analysis for the identification of steady model states. To this end, we performed six experiments: (i) investigation of the normal cell behavior, (ii) increase of O2, (iii) increase of ATP, (iv) influence of neurotoxins, (v) increase of αSYN in the cell, and (vi) increase of dopamine synthesis. The SBML model is available in the BioModels database with identifier MODEL1302200000.
Conclusion: It is possible to simulate the normal behavior of an in vivo nerve cell with the developed model. We show that the model is sensitive for neurotoxins and oxidative stress. Further, an increased level of αSYN induces apoptosis and an increased flux of αSYN to the extracellular space was observed.