Models that contain the Gene Name : Nav1.1 SCN1A

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    Models   Description
1.  A single kinetic model for all human voltage-gated sodium channels (Balbi et al, 2017)
Code for simulating macroscopic currents of sodium channels (Nav1.1. to Nav1.9), by means of a single kinetic model. Intensity-voltage curves, normalized conductance-voltage relationship, steady-state availability and recovery from inactivation are simulated.
2.  Biophysically detailed model of the mouse sino-atrial node cell (Kharche et al. 2011)
This model is developed to study the role of various electrophysiological mechanisms in generating cardiac pacemaking action potentials (APs).The model incorporates membrane ionic currents and intracellular mechanisms contributing to spontaneous mouse SAN APs. The model was validated by testing the functional roles of individual membrane currents in one and multiple parameter analyses.The roles of intracellular Ca2+-handling mechanisms on cardiac pacemaking were also investigated in the model.
3.  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.
4.  Ionic mechanisms of bursting in CA3 pyramidal neurons (Xu and Clancy 2008)
"... We present a single-compartment model of a CA3 hippocampal pyramidal neuron based on recent experimental data. We then use the model to determine the roles of primary depolarizing currents in burst generation. The single compartment model incorporates accurate representations of sodium (Na+) channels (NaV1.1) and T-type calcium (Ca2+) channel subtypes (CaV3.1, CaV3.2, and CaV3.3). Our simulations predict the importance of Na+ and T-type Ca2+ channels in hippocampal pyramidal cell bursting and reveal the distinct contribution of each subtype to burst morphology. We also performed fastslow analysis in a reduced comparable model, which shows that our model burst is generated as a result of the interaction of two slow variables, the T-type Ca2+ channel activation gate and the Ca2+-dependent potassium (K+) channel activation gate. The model reproduces a range of experimentally observed phenomena including afterdepolarizing potentials, spike widening at the end of the burst, and rebound. Finally, we use the model to simulate the effects of two epilepsy-linked mutations: R1648H in NaV1.1 and C456S in CaV3.2, both of which result in increased cellular excitability."
5.  Markov models of SCN1A (NaV1.1) applied to abnormal gating and epilepsy (Clancy and Kass 2004)
"Recently, some forms of idiopathic epilepsy have been causally related to genetic mutations in neuronal ion channels. To understand disease mechanisms, it is crucial to understand how a gene defect can disrupt channel gating, which in turn can affect complex cellular dynamic processes. We develop a theoretical Markovian model of the neuronal Na+ channel NaV1.1 to explore and explain gating mechanisms underlying cellular excitability and physiological and pathophysiological mechanisms of abnormal neuronal excitability in the context of epilepsy. ..."
6.  Modelling platform of the cochlear nucleus and other auditory circuits (Manis & Compagnola 2018)
"Models of the auditory brainstem have been an invaluable tool for testing hypotheses about auditory information processing and for highlighting the most important gaps in the experimental literature. Due to the complexity of the auditory brainstem, and indeed most brain circuits, the dynamic behavior of the system may be difficult to predict without a detailed, biologically realistic computational model. Despite the sensitivity of models to their exact construction and parameters, most prior models of the cochlear nucleus have incorporated only a small subset of the known biological properties. This confounds the interpretation of modelling results and also limits the potential future uses of these models, which require a large effort to develop. To address these issues, we have developed a general purpose, bio-physically detailed model of the cochlear nucleus for use both in testing hypotheses about cochlear nucleus function and also as an input to models of downstream auditory nuclei. The model implements conductance-based Hodgkin-Huxley representations of cells using a Python-based interface to the NEURON simulator. ..."
7.  Models of Na channels from a paper on the PKC control of I Na,P (Baker 2005)
"The tetrodotoxin-resistant (TTX-r) persistent Na(+) current, attributed to Na(V)1.9, was recorded in small (< 25 mum apparent diameter) dorsal root ganglion (DRG) neurones cultured from P21 rats and from adult wild-type and Na(V)1.8 null mice. ... Numerical simulation of the up-regulation qualitatively reproduced changes in sensory neurone firing properties. ..." Note: models of NaV1.8 and NaV1.9 and also persistent and transient Na channels that collectively model Nav 1.1, 1.6, and 1.7 are present in this model.
8.  Pleiotropic effects of SCZ-associated genes (Mäki-Marttunen et al. 2017)
Python and MATLAB scripts for studying the dual effects of SCZ-related genes on layer 5 pyramidal cell firing and sinoatrial node cell pacemaking properties. The study is based on two L5PC models (Hay et al. 2011, Almog & Korngreen 2014) and SANC models (Kharche et al. 2011, Severi et al. 2012).
9.  Schiz.-linked gene effects on intrinsic single-neuron excitability (Maki-Marttunen et al. 2016)
Python scripts for running NEURON simulations that model a layer V pyramidal cell with certain genetic variants implemented. The genes included are obtained from genome-wide association studies of schizophrenia.
10.  SCZ-associated variant effects on L5 pyr cell NN activity and delta osc. (Maki-Marttunen et al 2018)
" … Here, using computational modeling, we show that a common biomarker of schizophrenia, namely, an increase in delta-oscillation power, may be a direct consequence of altered expression or kinetics of voltage-gated ion channels or calcium transporters. Our model of a circuit of layer V pyramidal cells highlights multiple types of schizophrenia-related variants that contribute to altered dynamics in the delta frequency band. Moreover, our model predicts that the same membrane mechanisms that increase the layer V pyramidal cell network gain and response to delta-frequency oscillations may also cause a decit in a single-cell correlate of the prepulse inhibition, which is a behavioral biomarker highly associated with schizophrenia."
11.  Sodium channel mutations causing generalized epilepsy with febrile seizures + (Barela et al. 2006)
A novel mutation, R859C, in the Nav1.1 sodium channel was identified in a 4-generation, 33-member Caucasian family with a clinical presentation consistent with GEFS+. The mutation neutralizes a positively charged arginine in the domain 2 S4 voltage sensor of the Nav1.1 channel Ą subunit. When the mutation was placed in the rat Nav1.1 channel and expressed in Xenopus oocytes, the mutant channel displayed a positive shift in the voltage-dependence of sodium channel activation, slower recovery from slow inactivation, and lower levels of current compared to the wild-type channel. Computational analysis suggests that neurons expressing the mutant channel have higher thresholds for firing a single action potential and for firing multiple action potentials, along with decreased repetitive firing. Therefore, this mutation should lead to decreased neuronal excitability, in contrast to most previous GEFS+ sodium channel mutations that have changes predicted to increase neuronal firing.
12.  Spreading depression model for FHM3 with Nav1.1 mutation (Dahlem et al. 2014)
Familial hemiplegic migraine (FHM) is a rare subtype of migraine with aura. A mutation causing FHM type 3 (FHM3) has been identified in SCN1A encoding the Nav1.1 Na+ channel. This genetic defect affects the inactivation gate. The code describes an extended Hodgkin-Huxley framework with dynamic ion concentrations in a wilde-type and mutant form.
13.  TTX-R Na+ current effect on cell response (Herzog et al 2001)
"Small dorsal root ganglion (DRG) neurons, which include nociceptors, express multiple voltage-gated sodium currents. In addition to a classical fast inactivating tetrodotoxin-sensitive (TTX-S) sodium current, many of these cells express a TTX-resistant (TTX-R) sodium current that activates near -70 mV and is persistent at negative potentials. To investigate the possible contributions of this TTX-R persistent (TTX-RP) current to neuronal excitability, we carried out computer simulations using the Neuron program with TTX-S and -RP currents, fit by the Hodgkin-Huxley model, that closely matched the currents recorded from small DRG neurons. ..." See paper for more and details.

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