Investigating Serotonin Dynamics and Simulating Effects of Antidepressants Using Variation in Enzyme Expression

Abstract

Serotonin plays a crucial role in the symptoms of depression, and understanding its dynamics in the brain is of the utmost importance in determining how to mitigate the effects of depression. We investigate a mathematical model presented by Best et al. (2020) that examines serotonin dynamics in the substantia nigra pars reticulata. By incorporating experimental data and a stochastic systems population model, several biological mechanisms and observations are further understood. A populations model is utilized to account for enzymatic expression level variation from 75% to 125% of their base values. When generating the population model, uniform distributions are assumed when simulating maximum velocity values, which correspond to enzyme expression levels. We investigate this assumption and show that it is reasonably insensitive; that is, changes in the distributions used to generate these values do not significantly change the results of the model. We also use the model to simulate the effects of monoamine oxidase inhibitors (MAOIs), one of the first treatments discovered for depression. We then use similar methods to simulate the effects of selective serotonin reuptake inhibitors (SSRIs), the most common antidepressant used today. We demonstrate that low enzyme expression levels of tryptophan hydroxylase and neutral amino acid transporter are most associated with low extracellular serotonin values at the steady state, indicating that these two enzymes may play key roles in predicting which patients may or may not respond to SSRI treatment.