Dopamine is one of the central neurotransmitters; its homeostatic concentration is highly maintained through release and re-uptake. Dopamine release is mediated at presynaptic vesicles and controlled by dopamine receptors. Dopamine release regulation by dopamine receptors is mediated by negative feedback. This negative feedback is a function of dopamine receptor occupancy by dopamine or dopamine agonists. Dopamine re-uptake is regulated presynaptically by Dopamine Transporter (DAT).; The object of this study was to develop a computational system to describe and predict the behavior of the complex process of extracellular dopamine concentration control. The uniqueness and advantage of presented modeling work is that it models extracellular dopamine concentration control as a complex of both re-uptake and feedback mechanisms.; Mathematical modeling was based on published pharmacokinetic parameters for dopamine re-uptake and binding in rat striatum, and computational data generated by presented study. The dopamine system modeling was conducted for the following conditions: (1) basal conditions; (2) increased/decreased dopamine release; (3) presence of dopamine agonist/antagonist in the system.; Results of mathematical modeling showed that proposed computational model is successfully predicting outcomes of complex extracellular dopamine concentration control system. It was demonstrated computationally that both re-uptake by dopamine transporter and negative feedback mediated by receptors are necessary components to maintain low, nM, extracellular dopamine concentration under basal conditions. Mathematical modeling demonstrated the critical role of feedback to maintain stable extracellular dopamine concentration under conditions of increased/decreased dopamine release. The model-generated results predicting extracellular dopamine concentration change under conditions equivalent to the presence of dopamine agonist/antagonist were in good accordance with published experimental data.; In addition to the mathematical modeling and computational programming, the value of this thesis work is that it demonstrates how data mining and computational conclusions can be done based on the integration of the results from independent experimental studies. A strong trend in the modern bioscience is a computational analysis based on data generated by biological research. This thesis work is an example of successful biological system modeling based on published experimental data.
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