State dependent drug binding to sodium channels in the dentate gyrus (Thomas & Petrou 2013)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:149174
A Markov model of sodium channels was developed that includes drug binding to fast inactivated states. This was incorporated into a model of the dentate gyrus to investigate the effects of anti-epileptic drugs on neuron and network properties.
Reference:
1 . Thomas EA, Petrou S (2013) Network-specific mechanisms may explain the paradoxical effects of carbamazepine and phenytoin. Epilepsia 54:1195-202 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell; Axon; Channel/Receptor;
Brain Region(s)/Organism:
Cell Type(s): Dentate gyrus granule GLU cell; Dentate gyrus mossy cell; Dentate gyrus basket cell; Dentate gyrus hilar cell;
Channel(s): I Na,t; I A; I_AHP;
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Ion Channel Kinetics; Epilepsy; Calcium dynamics; Drug binding; Markov-type model;
Implementer(s): Thomas, Evan [evan at evan-thomas.net];
Search NeuronDB for information about:  Dentate gyrus granule GLU cell; GabaA; AMPA; I Na,t; I A; I_AHP; Gaba; Glutamate;
/
ThomasPetrou2013
Fig 4
Data
bgka.mod *
CaBK.mod *
ccanl.mod *
Gfluct2.mod *
gskch.mod *
hyperde3.mod *
ichan2.mod
LcaMig.mod *
nad.mod
nca.mod *
tca.mod *
apchew.m
async.hoc
runall.py
                            
TITLE l-calcium channel
: l-type calcium channel


UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(molar) = (1/liter)
	(mM) = (millimolar)
	FARADAY = 96520 (coul)
	R = 8.3134 (joule/degC)
	KTOMV = .0853 (mV/degC)
}

PARAMETER {
	v (mV)
	celsius 	(degC)
	glcabar		 (mho/cm2)
	ki=.001 (mM)
	cai (mM)
	cao (mM)
        tfa=1
}


NEURON {
	SUFFIX lca
	USEION lca READ elca WRITE ilca VALENCE 2
	USEION ca READ cai, cao VALENCE 2 
        RANGE glcabar, cai, ilca, elca
        GLOBAL minf,matu
}

STATE {
	m
}

ASSIGNED {
	ilca (mA/cm2)
        glca (mho/cm2)
        minf
        matu   (ms)
	elca (mV)   

}

INITIAL {
	rate(v)
	m = minf
	VERBATIM
	cai=_ion_cai;
	ENDVERBATIM
}

BREAKPOINT {
	SOLVE state METHOD cnexp
	glca = glcabar*m*m*h2(cai)
	ilca = glca*ghk(v,cai,cao)

}

FUNCTION h2(cai(mM)) {
	h2 = ki/(ki+cai)
}


FUNCTION ghk(v(mV), ci(mM), co(mM)) (mV) {
        LOCAL nu,f

        f = KTF(celsius)/2
        nu = v/f
        ghk=-f*(1. - (ci/co)*exp(nu))*efun(nu)
}

FUNCTION KTF(celsius (DegC)) (mV) {
        KTF = ((25./293.15)*(celsius + 273.15))
}


FUNCTION efun(z) {
	if (fabs(z) < 1e-4) {
		efun = 1 - z/2
	}else{
		efun = z/(exp(z) - 1)
	}
}

FUNCTION alp(v(mV)) (1/ms) {
	TABLE FROM -150 TO 150 WITH 200
	alp = 15.69*(-1.0*v+81.5)/(exp((-1.0*v+81.5)/10.0)-1.0)
}

FUNCTION bet(v(mV)) (1/ms) {
	TABLE FROM -150 TO 150 WITH 200
	bet = 0.29*exp(-v/10.86)
}

DERIVATIVE state {  
        rate(v)
        m' = (minf - m)/matu
}

PROCEDURE rate(v (mV)) { :callable from hoc
        LOCAL a
        a = alp(v)
        matu = 1/(tfa*(a + bet(v)))
        minf = tfa*a*matu
}