Models that contain the Gene Name : Kv4.3 KCND3

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    Models   Description
1.  A detailed Purkinje cell model (Masoli et al 2015)
The Purkinje cell is one of the most complex type of neuron in the central nervous system and is well known for its massive dendritic tree. The initiation of the action potential was theorized to be due to the high calcium channels presence in the dendritic tree but, in the last years, this idea was revised. In fact, the Axon Initial Segment, the first section of the axon was seen to be critical for the spontaneous generation of action potentials. The model reproduces the behaviours linked to the presence of this fundamental sections and the interplay with the other parts of the neuron.
2.  Cardiac Atrial Cell (Courtemanche et al 1998)
Marc Courtemanche, Rafael J. Ramirez, and Stanley Nattel. Ionic mechanisms underlying human atrial action potential properties insights from a mathematical model Am J Physiol Heart Circ Physiol 1998 275: H301-H321. The implementation of this model in NEURON was contributed by Ingemar Jacobson.
3.  Cerebellar purkinje cell: interacting Kv3 and Na currents influence firing (Akemann, Knopfel 2006)
Purkinje neurons spontaneously generate action potentials in the absence of synaptic drive and thereby exert a tonic, yet plastic, input to their target cells in the deep cerebellar nuclei. Purkinje neurons express two ionic currents with biophysical properties that are specialized for high-frequency firing: resurgent sodium currents and potassium currents mediated by Kv3.3. Numerical simulations indicated that Kv3.3 increases the spontaneous firing rate via cooperation with resurgent sodium currents. We conclude that the rate of spontaneous action potential firing of Purkinje neurons is controlled by the interaction of Kv3.3 potassium currents and resurgent sodium currents. See paper for more and details.
4.  DRG neuron models investigate how ion channel levels regulate firing properties (Zheng et al 2019)
We present computational models for an Abeta-LTMR (low-threshold mechanoreceptor) and a C-LTMR expressing four Na channels and four K channels to investigate how the expression level of Kv1 and Kv4 regulate number of spikes (repetitive firing) and onset latency to action potentials in Abeta-LTMRs and C-LTMRs, respectively.
5.  Effect of voltage sensitive fluorescent proteins on neuronal excitability (Akemann et al. 2009)
"Fluorescent protein voltage sensors are recombinant proteins that are designed as genetically encoded cellular probes of membrane potential using mechanisms of voltage-dependent modulation of fluorescence. Several such proteins, including VSFP2.3 and VSFP3.1, were recently reported with reliable function in mammalian cells. ... Expression of these proteins in cell membranes is accompanied by additional dynamic membrane capacitance, ... We used recordings of sensing currents and fluorescence responses of VSFP2.3 and of VSFP3.1 to derive kinetic models of the voltage-dependent signaling of these proteins. Using computational neuron simulations, we quantitatively investigated the perturbing effects of sensing capacitance on the input/output relationship in two central neuron models, a cerebellar Purkinje and a layer 5 pyramidal neuron. ... ". The Purkinje cell model is included in ModelDB.
6.  INa and IKv4.3 heterogeneity in canine LV myocytes (Flaim et al 2006)
"The roles of sustained components of INa and IKv43 in shaping the action potentials (AP) of myocytes isolated from the canine left ventricle (LV) have not been studied in detail. Here we investigate the hypothesis that these two currents can contribute substantially to heterogeneity of early repolarization and arrhythmic risk.... The resulting simulations illustrate ways in which KChIP2- and Ca2+- dependent control of IKv43 can result in a sustained outward current that can neutralize INaL in a rate- and myocyte subtype-dependent manner. Both these currents appear to play significant roles in modulating AP duration and rate dependence in midmyocardial myocytes. ... By design, these models allow upward integration into organ models or may be used as a basis for further investigations into cellular heterogeneities." See paper for more and details.
7.  Kv4.3, Kv1.4 encoded K(+) channel in heart cells (Greenstein et al 2000) (XPP)
A model of canine I:(to1) (the Ca(2+)-independent transient outward current) is formulated as the combination of Kv4.3 and Kv1.4 currents and is incorporated into an existing canine ventricular myocyte model. Simulations demonstrate strong coupling between L-type Ca(2+) current and I:(Kv4.3) and predict a bimodal relationship between I:(Kv4.3) density and APD whereby perturbations in I:(Kv4.3) density may produce either prolongation or shortening of APD, depending on baseline I:(to1) current level. The model files were submitted by: Dr. Sheng-Nan Wu, Dr. Ruey J. Sung, Ya-Jean Wang and Jiun-Shian Wu e-mail:
8.  Learning intrinsic excitability in Medium Spiny Neurons (Scheler 2014)
"We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function). We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. ... "

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