Integrated dopaminergic neuronal model with reduced intracellular processes and inhibitory autoreceptors

Maell Cullen, KongFatt Wong-Lin

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Dopamine is an important neurotransmitter for multiple brain functions, and dysfunctions of the dopaminergic system are implicated in neurological and neuropsychiatric disorders. Although dopaminergic system has been studied at multiple levels, an integrated and efficient computational model that bridge from molecular to neuronal circuit level is still lacking. In this paper, we aim to develop a realistic yet efficient computational model of dopaminergic pre-synaptic terminal. We first systematically perturb the variables/substrates of an established computational model of dopamine synthesis, release and uptake, and based on their relative dynamical timescales and steady-state changes, approximate and reduce the model into two versions: one for simulating hourly timescale, and another for millisecond timescale. We show that the original and reduced models exhibit rather similar steady and perturbed states, while the reduced models are more computationally efficient and illuminate the underlying key mechanisms. We then incorporate the reduced fast model into a spiking neuronal model that can realistically simulate the spiking behaviour of dopaminergic neurons. In addition, we successfully include autoreceptor-mediated inhibitory current explicitly in the neuronal model. This integrated computational model provides the first step towards an efficient computational platform for realistic multiscale simulation of dopaminergic systems in in silico neuropharmacology.
LanguageEnglish
JournalIET Systems Biology
DOIs
Publication statusPublished - 22 Jul 2015

Fingerprint

Neurons
Brain
Networks (circuits)
Substrates
Dopamine

Keywords

  • Mathematical model
  • dynamical systems
  • neuromodulator
  • neuropharmacology
  • multiple timescales
  • D2 autoreceptors

Cite this

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title = "Integrated dopaminergic neuronal model with reduced intracellular processes and inhibitory autoreceptors",
abstract = "Dopamine is an important neurotransmitter for multiple brain functions, and dysfunctions of the dopaminergic system are implicated in neurological and neuropsychiatric disorders. Although dopaminergic system has been studied at multiple levels, an integrated and efficient computational model that bridge from molecular to neuronal circuit level is still lacking. In this paper, we aim to develop a realistic yet efficient computational model of dopaminergic pre-synaptic terminal. We first systematically perturb the variables/substrates of an established computational model of dopamine synthesis, release and uptake, and based on their relative dynamical timescales and steady-state changes, approximate and reduce the model into two versions: one for simulating hourly timescale, and another for millisecond timescale. We show that the original and reduced models exhibit rather similar steady and perturbed states, while the reduced models are more computationally efficient and illuminate the underlying key mechanisms. We then incorporate the reduced fast model into a spiking neuronal model that can realistically simulate the spiking behaviour of dopaminergic neurons. In addition, we successfully include autoreceptor-mediated inhibitory current explicitly in the neuronal model. This integrated computational model provides the first step towards an efficient computational platform for realistic multiscale simulation of dopaminergic systems in in silico neuropharmacology.",
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