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 Biological Psychiatry, epub before print
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s): Neocortex V1 pyramidal corticothalamic L6 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):
Implementer(s): Ben-Shalom, Roy [bens.roy at gmail.com];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; I Na,t; I K; I Sodium;
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SCN2A_ASD
Excitability
YoungI1473M
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)

}









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