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    The Neurochemical Mobile: Analysis of Dynamic Interactions between Six Neurotransmitters

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    Date
    2013-05-08
    Author
    Fieni, David
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    Abstract
    A mobile, typically seen in an infant’s crib, is an eloquent representation of the balance between multiple items. The neurochemical mobile is a tool used to visualize the balance between six major neurotransmitters in the human brain. Neurotransmitters are chemical compounds that govern the behavior of the brain. Here, this somewhat abstract model has been converted into a more applicable form. This was done using nonlinear computational systems modeling within the framework of biochemical systems theory, which allows even ill-characterized systems to be mapped into a mathematical representation. The interactions between the individual neurotransmitters and the system are described by the “neurochemical interaction matrix”. This matrix explicitly records whether the reaction between two neurotransmitters is excitatory or inhibitory. These interactions have an effect on the concentrations of the six compounds, which in turn make up the symbolic weight. The various weights are then incorporated into a model of a spring-mass system in order to quantitatively represent overall shifts in balance upon alterations in weights. Simulations show how short- and long-term perturbations in any of the neurotransmitters migrate through the entire system, thereby affecting the balances within the mobile. In cases of short-term alterations, transients are of particular interest, whereas long-term changes shed light on persistently altered, allostatic states, which in mental diseases and sleep disorders could be due to a combination of unfavorable factors, resulting from a specific genetic predisposition, epigenetic effects, disease, or the repeated use of drugs, such as opioids and amphetamines. Based on the research done here, the Voit lab continued this line of investigation and published the paper, “The Neurochemical Mobile with Non-Linear Interaction Matrix: An Exploratory Computational Model” [19].
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    http://hdl.handle.net/1853/46895
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    • Undergraduate Research Option Theses [631]
    • Department of Biomedical Engineering Undergraduate Research Option Theses [169]

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