A Computational Biochemical Model of Insulin Resistance in Parkinson’s Disease

Hello! I’m Ellie Braatz, a rising senior Neuroscience major and Chemistry minor working in Dr. Randolph Coleman’s lab. This summer I plan on developing a computational model of insulin resistance in Parkinson’s disease. Parkinson’s disease is a neurodegenerative disorder caused by a loss of dopamine-producing neurons in the substantia nigra area of the brain. Symptoms of Parkinson’s disease include motor and behavioral problems such as tremors, rigidity, and an increased risk of dementia, depression, and anxiety. Insulin resistance, caused by a diet high in fats and sugars, is a precursor to type-II diabetes. With an aging population that exhibits an increasing tendency toward obesity, these two conditions are likely to converge more frequently in the near future.

My goal is to identify which biochemical pathways contribute the most to the disease state in order to target them for treatment. Neurodegenerative diseases often stem from multiple failed pathways, and therefore a combination of medications is usually the most effective treatment. I plan on using a qualitative model that I have prepared from the literature to mathematize the reactions through Biochemical Systems Theory (BST). BST uses ordinary differential equations to simulate changes in reaction rates and species concentrations over time.

First I will create a baseline model of the relevant pathways behaving normally to make sure my model is accurate. Then I will introduce Parkinsonian characteristics, and finally insulin resistance. By comparing the reaction rates and species concentrations between the Parkinsonian cell with insulin resistance and the one with normal insulin signaling, I will be able to determine which areas of the disease state are the most affected by a decrease in insulin signaling. Candidate pathways include mitochondrial dysfunction and production of reactive oxygen species, inflammation, and protein misfolding and aggregation, among others.

This research is an important step in identifying which pathways should be the focus of future in vitro, in vivo, and clinical studies. A model of the system allows for much greater flexibility and experimentation in determining which treatment blends will produce the best results because computational modeling is much more time- and cost-efficient than other laboratory research methods.