(A) systems biological study for identifying disease-modifying drug targets in the neuronal molecular network of Alzheimer's disease

Abstract

Alzheimer’s disease is a progressive neurodegeneration disease that accounts for about 80% of dementia. Since the disease was first defined in 1906, numerous drug development studies have been conducted to treat Alzheimer’s disease, but drug development trials have not been suggested clear answers. Alzheimer’s disease develops and progresses through complex interactions inside and outside of cells, and this complexity makes it difficult to develop disease-modifying drugs. To identify disease-modifying drug targets based on the understanding of the molecular regulatory mechanisms of cells related to Alzheimer’s disease, a system-level approach is required which can consider several factors simultaneously. In this study, I explored the disease-modifying drug targets through systems biological analysis of Alzheimer’s disease neurons. I constructed a neuronal molecular regulatory network model of Alzheimer’s disease by integrating literature information, and suggesting single drug targets and combinatorial drug targets based on molecular mechanisms through simulation analysis considering the nonlinear dynamics of the network model. Furthermore, for efficient and feasible drug target search in large-scale network models, I developed a linear approximation model that can predict perturbation effects by linearly approximating the nonlinearity model and proposed drug target candidates using it in the Alzheimer’s disease neuronal molecular network model. The studies conducted in this thesis represented the necessity for identifying disease-modifying drug targets based on dynamic simulation analysis with considering Alzheimer’s disease neurons as a system and is expected to provide a basis for the development of a patient-specific drug target identifying platform.