Thesis (Ph. D.)--Ohio State University, 2008. / Title from first page of PDF file. Includes bibliographical references (p. 105-114).

Thesis (M.S.)--Georgia State University, 2009. / Title from title page (Digital Archive@GSU, viewed July 20, 2010) Andrey Shilnikov, Robert Clewley, Gennady Cymbalyuk, committee co-chairs; Igor Belykh, Vladimir Bondarenko, Mukesh Dhamala, Michael Stewart, committee members. Includes bibliographical references (p. 65-67).

The single largest cause of compound attrition during drug development is due to inadequate tools capable of predicting and identifying protein interactions. Several tools have been developed to explore how a compound interferes with specific pathways. However, these tools lack the potential to chronically monitor the time dependent temporal changes in complex biochemical networks, thus limiting our ability to identify possible secondary signaling pathways that could lead to potential toxicity. To overcome this, we have developed an in silico neuronal-metabolic model by coupling the membrane electrical activity to intracellular biochemical pathways that would enable us to perform non-invasive temporal proteomics. This model is capable of predicting and correlating the changes in cellular signaling, metabolic networks and action potential responses to metabolic perturbation. The neuronal-metabolic model was experimentally validated by performing biochemical and electrophysiological measurements on NG108-15 cells followed by testing its prediction capabilities for pathway analysis. The model accurately predicted the changes in neuronal action potentials and the changes in intracellular biochemical pathways when exposed to metabolic perturbations. NG108-15 cells showed a large effect upon exposure to 2DG compared to cyanide and malonate as these cells have elevated glycolysis. A combinational treatment of 2DG, cyanide and malonate had a much higher and faster effect on the cells. A time-dependent change in neuronal action potentials occurred based on the inhibited pathway. We conclude that the experimentally validated in silico model accurately predicts the changes in neuronal action potential shapes and proteins activities to perturbations, and would be a powerful tool for performing proteomics facilitating drug discovery by using action potential peak shape analysis to determine pathway perturbation from an administered compound.