Parkinson's disease (PD) is a neurodegenerative disorder caused by loss of dopaminergic neurons in Substantia Nigra pars compacta (SNc). Although the exact cause of the cell death is not clear, the hypothesis that metabolic deficiency is a key facor has been gaining attention in the recent years. In the present study, we investigate this hypothesis using a multi-scale computational model of the subsystem of the basal ganglia comprising Subthalamic Nucleus (STN), Globus Pallidus externa (GPe) and SNc. The model is a multiscale model in that interactions among the three nuclei are simulated using more abstract Izhikevich neuron models, while the molecular pathways involved in cell death of SNc neurons are simulated in terms of detailed chemical kinetics. Simulation results obtained from the proposed model showed that energy deficiencies occurring at cellular and network levels could precipitate the excitotoxic loss of SNc neurons in PD. At the subcellular level, the models show how calcium elevation leads to apoptosis of SNc neurons. The therapeutic effects of several neuroprotective interventions are also simulated in the model. From neuroprotective studies, it was clear that glutamate inhibition and apoptotic signal blocker therapies were able to halt the progression of SNc cell loss when compared to other therapeutic interventions, which only slows down the progression of SNc cell loss. Deficiency Izhikevich (Spiking) Neuronal Model (STN, GPe) The Izhikevich neuronal models are capable of exhibiting biologically realistic firing patterns, at a relatively low computational expense (Izhikevich, 2003). The proposed model of HEM consists of GPe and STN neurons are modeled as Izhikevich spiking neuron model, where the Izhikevich parameters are adopted from the literature (Mandali et al., 2015;Michmizos and Nikita, 2011). Based on the anatomical data of rat basal ganglia, the neuronal population sizes in the model are selected (Arbuthnott and Wickens, 2007;Oorschot, 1996).The external bias current ( ) was adjusted to match the firing rate of nuclei with published data (Tripathy et al., 2015).