Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Modeling the dopaminergic nerve terminal.

J B Justice1, L C Nicolaysen, A C Michael

  • 1Department of Chemistry, Emory University, Atlanta, GA 30322.

Journal of Neuroscience Methods
|January 1, 1988
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Controlled cortical impact injury affects dopaminergic transmission in the rat striatum.

Journal of neurochemistry·2005
Same author

Novel tropane-based irreversible ligands for the dopamine transporter.

Journal of medicinal chemistry·2001
Same author

Glutamate regulates the spontaneous and evoked release of dopamine in the rat striatum.

Neuroscience·2001
Same author

Reaction of oxidized dopamine with endogenous cysteine residues in the human dopamine transporter.

Journal of neurochemistry·2001
Same author

Pharmacological evidence for the selectivity of in vivo signals obtained with enzyme-based electrochemical sensors.

Journal of neuroscience methods·2001
Same author

Cocaine-induced changes in extracellular dopamine determined by microdialysis in awake squirrel monkeys.

Psychopharmacology·2001
Same journal

Corrigendum to "Human peripheral nerve xenografts and rat peripheral nerve allografts implanted to the striatum: Methodology and initial findings of cell-based therapy" [J. Neurosci. Methods 434 (2026) 110825].

Journal of neuroscience methods·2026
Same journal

Sombor-based graph-theoretic framework for the structural characterization of neuro-metabolic organic acids.

Journal of neuroscience methods·2026
Same journal

Pupil-DLC: an open-source deep learning pipeline for scalable, marker-less tracking of pupil dynamics across conscious and unconscious states.

Journal of neuroscience methods·2026
Same journal

Time as the language of Behavior: events, sequences, patterns and meanings.

Journal of neuroscience methods·2026
Same journal

Detection of cochlear microphonic for differential diagnosis between auditory neuropathy mice and noise-induced sensorineural hearing loss mice.

Journal of neuroscience methods·2026
Same journal

Assessment metrics for pain control in rats: A methodological commentary.

Journal of neuroscience methods·2026
See all related articles

This study presents a novel computational method for modeling neurochemical processes, specifically the dopaminergic nerve terminal. The approach successfully integrates computer simulation and optimization techniques with experimental data to refine the model.

Area of Science:

  • Neuroscience
  • Computational Biology
  • Pharmacology

Background:

  • Neurochemical processes are complex and crucial for brain function.
  • Accurate modeling of these processes aids in understanding neurological disorders and drug mechanisms.
  • Existing models often require refinement with experimental validation.

Purpose of the Study:

  • To develop and evaluate a computational model for neurochemical processes.
  • To simulate the dopaminergic nerve terminal in the rat striatum.
  • To optimize model parameters using diverse experimental data.

Main Methods:

  • Utilized computer simulation to represent neurochemical dynamics.
  • Employed simplex optimization for parameter refinement.
  • Modeled synthesis, storage, release, uptake, and metabolism using non-linear differential equations.

Related Experiment Videos

  • Validated the model against experimental data including radioactivity passage, dopamine decline, and extracellular dopamine changes.

    • Main Results:

    • Successfully developed a dynamic model of the dopaminergic nerve terminal.
    • Optimized model parameters accurately reflected experimental observations.
    • The model demonstrated predictive capabilities for neurochemical system behavior.

    Conclusions:

    • The described method provides a robust framework for developing and validating neurochemical models.
    • Computational modeling combined with experimental data is essential for advancing our understanding of brain function.
    • This approach can be applied to other neurochemical systems and neurological conditions.