Pyramidal neurons with mutated SCN2A gene (Nav1.2) (Ben-Shalom et al 2017)

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Accession:223955
Model of pyramidal neurons that either hyper or hypo excitable due to SCN2A mutations. Mutations are taken from patients with ASD or Epilepsy
Reference:
1 . Ben-Shalom R, Keeshen CM, Berrios KN, An JY, Sanders SJ, Bender KJ (2017) Opposing Effects on NaV1.2 Function Underlie Differences Between SCN2A Variants Observed in Individuals With Autism Spectrum Disorder or Infantile Seizures. Biol Psychiatry 82:224-232 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I Na,t; I Sodium; I K;
Gap Junctions:
Receptor(s):
Gene(s): Nav1.2 SCN2A;
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Epilepsy; Autism spectrum disorder;
Implementer(s): Ben-Shalom, Roy [rbenshalom at ucdavis.edu];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; I Na,t; I K; I Sodium;
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SCN2A_ASD
Excitability
YoungT1420MRed
Cad.mod *
CaH.mod *
CaT.mod *
charge.mod *
h.mod *
Kca.mod *
Kv.mod *
Kv1_axonal.mod *
Kv7.mod *
na8st.mod *
na8st1.mod *
nax8st.mod *
nax8st1.mod
28_04_10_num19.hoc *
Cell parameters.hoc *
charge.hoc *
mosinit.hoc *
scn2aExps.hoc
                            
TITLE Kv7-current

COMMENT
	Model reproducing cortical M currents, M.H.P. Kole
ENDCOMMENT

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(pS) = (picosiemens)
	(um) = (micron)


}

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

PARAMETER {	
	dt		(ms)
	v 		(mV)
	vhalf = -48 (mV)				 
	gbar = 20	 (pS/um2)	:0.002 mho/cm2
 }


NEURON {
	SUFFIX Kv7
	USEION k READ ek WRITE ik
	RANGE gbar, ik
}

STATE { m }

ASSIGNED {
	ik (mA/cm2)
	gk (pS/um2)
	ek (mV)	
	
}


INITIAL {
	m=alpha(v)/(beta(v)+alpha(v))
}

BREAKPOINT {
	SOLVE state METHOD cnexp
	ik=(1e-4)*gbar*m*(v-ek)
}

FUNCTION alpha(v(mV)) {
	alpha = 0.00623*exp((v-vhalf)/18.76)	

}

FUNCTION beta(v(mV)) {
	beta = 0.0199*exp(-(v-vhalf)/30.6)			
}

DERIVATIVE state {    

	m' = (1-m)*alpha(v) - m*beta(v)

}