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Computational Models for Neurodegenarative Disease

Parkinson's disease (PD) is the second most common neurodegenerative disease and the 14th leading cause of death in the United States. This progressive neurological disorder affects over 1,500,000 patients and ultimately leaves the patients unable to care for themselves. Thus, the disease causes great economic, personal, and societal burden. PD is thought to be the result of a combination of genetic susceptibility and environmental exposure. The generation of toxic species, mitochondrial dysfunction, and oxidative stress have been implicated in the onset of PD. The disease has very complicated etiology and pathogenesis that suggest the utilization of systems biological approaches to understand more comprehensively the underlying mechanisms of dopamine metabolism and its alterations in PD.

Schizophrenia is regarded as “the worst disease affecting mankind” and, with a lifetime prevalence of about 0.7%, is among the top ten causes of disease-related disability in the world. Schizophrenia is a severe and complex mental disorder that causes an enormous societal and financial burden. Our current understanding of the disease is very fragmented, and the disease is still regarded as an enigma even though its main features have been recognized for centuries. When the post-genomic era arrived, high-throughput instruments and methods ushered in an explosion in the generation of large datasets. This rich information began to facilitate the development of mathematical models, and these models are beginning to show the potential of propelling schizophrenia research onto a new, quantitative level. As schizophrenia is a complex disease that involves uncounted biological processes, there is no complete model which covers even the majority of aspects pertaining to schizophrenia. Instead, every currently available model focuses on a certain aspect of the disease.

In the future, mathematical models may be expected to provide valuable guidance in the long-term investigation of complex diseases like schizophrenia and PD. The goal of this project is to develop such models that accompany biological and clinical studies at Emory University and facilitate insight into the underlying mechanisms, early diagnosis, medical treatment, and prevention of PD and schizophrenia.

This work is performed collaboratively with Professor Gary Miller at Emory University.

Selected Recent References:

[1] Voit. E.O., Z. Qi, and G.W. Miller: Modeling complex biological systems, one step at a time. Pharmacopsychiatry 41(Suppl. 1): S78-S84, 2008.

[2] Qi, Z., G. W. Miller, and E. O. Voit: A mathematical model of presynaptic dopamine homeostasis: Implications for schizophrenia. Pharmacopsychiatry 41(Suppl. 1): S89-S98, 2008.

[3] Qi, Z., G. W. Miller, and E. O. Voit: Computational systems analysis of dopamine metabolism. PLoS One 3(6):e2444, 2008.

[4] Qi, Z., G. W. Miller, and E. O. Voit: Computational analysis of determinants of dopamine dysfunction. Synapse 63: 1133-1142, 2009.

[5] Qi, Z., G. W. Miller, and E. O. Voit: Internal state of medium spiny neurons varies in response to different input signals. BMC Systems Biology 4:26, 2010.

[6] Wu, Jialiang, Z. Qi, and E.O. Voit: Investigation of delays and noise in dopamine signaling with hybrid functional Petri nets. In Silico Biol. 10, 0005, 2010.

[7] Qi, Z., G. W. Miller, and E. O. Voit: Computational modeling as a tool for assessing different dopamine hypotheses of schizophrenia. Pharmacopsychiatry 43(Suppl. 1): 550-560, 2010.

[8] Qi, Z., S. Kikuchi, F. Tretter, and E.O. Voit: Effects of dopamine and glutamate on synaptic plasticity: A computational modeling approach for drug abuse as comorbidity in mood disorders. Pharmacopsychiatry 44(Suppl. 1): S62-S75, 2011.

[9] Qi, Z., G.W. Miller, and E.O. Voit. Mathematical Models in Schizophrenia. Chapter 14 in Volume I of: M.S. Ritsner (Ed.):Textbook of Schizophrenia Spectrum Disorders. Springer Verlag, New York, 2011.

[10] Qi, Z., G.W. Miller, and E.O. Voit. Mathematical Models of Dopamine Metabolism in Parkinson's Disease. in Volume P. Wellstead and M. Cloutier (Eds.): The Systems Biology of Parkinson's Disease. Springer Verlag, New York, 2012.

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