Depression is the leading cause of disability in the world. Diagnosis and treatment of depression remain challenging due to the fact that the pathophysiology of the disease is not fully understood. As a result, effective diagnosis and development of efficacious treatments remain elusive. Selective serotonin reuptake inhibitors (SSRIs) are the frontline depression treatment and inhibit the reuptake of serotonin, alleviating the symptoms of depression. SSRI efficacy is suboptimal, and usually takes time for effectiveness. This exemplifies the need for a better understanding of the pathophysiology of depression to improve treatment strategies. This thesis develops computational strategies to study serotonin using fast voltammetry signals. We develop web-based software for rapid and quantitative analysis of fast-scan cyclic-voltammetry data, and artificial neural networks for predicting tonic serotonin concentrations using fast-scan controlled-adsorption voltammetry. We then use our web platform to study how antidepressants with different mechanisms of action affect serotonin dynamics \textit{in vivo} in mice. We find that all increase extracellular serotonin levels, but through different mechanisms. SSRIs (fluoxetine and escitalopram) increase extracellular serotonin by inhibiting serotonin transporters, while reboxetine inhibits norepinephrine transporters, which are also able to reuptake serotonin. Ketamine significantly reduces histamine release, resulting in the disinhibition of serotonin. Finally, we develop a mathematical model that mimics relevant processes involved in serotonin and histamine dynamics, including synthesis, release, reuptake, and autoreceptor modulation. We add a pharmacokinetics and pharmacodynamics model of escitalopram to simulate the effects of the drug on the serotonin dynamics modelled in the system. Extrapolating the PK model to oral chronic dosing, we study potential mechanisms that explain the delayed onset of action and variable clinical efficacy of SSRIs. This work has used computational approaches to improve our understanding of serotonin dynamics in depression and the mechanisms of antidepressants, with potential implications for their efficacy and onset of action.