Pleiotropic effects of SCZ-associated genes (Mäki-Marttunen et al. 2017)

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Accession:187615
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).
Reference:
1 . Mäki-Marttunen T, Lines GT, Edwards AG, Tveito A, Dale AM, Einevoll GT, Andreassen OA (2017) Pleiotropic effects of schizophrenia-associated genetic variants in neuron firing and cardiac pacemaking revealed by computational modeling. Transl Psychiatry 7:5 [PubMed]
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 V1 L6 pyramidal corticothalamic cell; Cardiac atrial cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Sodium; I Calcium; I Potassium; I A, slow; Na/Ca exchanger; I_SERCA; Na/K pump; Kir;
Gap Junctions:
Receptor(s):
Gene(s): Nav1.1 SCN1A; Cav3.3 CACNA1I; Cav1.3 CACNA1D; Cav1.2 CACNA1C;
Transmitter(s):
Simulation Environment: NEURON; MATLAB; Python;
Model Concept(s): Schizophrenia;
Implementer(s): Maki-Marttunen, Tuomo [tuomo.maki-marttunen at tut.fi];
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic cell; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Sodium; I Calcium; I Potassium; I A, slow; Na/Ca exchanger; I_SERCA; Na/K pump; Kir;
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pleiotropy
hay
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scalings_cs.sav
                            
:Reference :Colbert and Pan 2002
:comment: took the NaTa and shifted both activation/inactivation by 6 mv

NEURON	{
	SUFFIX NaTs2_t
	USEION na READ ena WRITE ina
	RANGE gNaTs2_tbar, gNaTs2_t, ina
}

UNITS	{
	(S) = (siemens)
	(mV) = (millivolt)
	(mA) = (milliamp)
}

PARAMETER	{
	gNaTs2_tbar = 0.00001 (S/cm2)
}

ASSIGNED	{
	v	(mV)
	ena	(mV)
	ina	(mA/cm2)
	gNaTs2_t	(S/cm2)
	mInf
	mTau
	mAlpha
	mBeta
	hInf
	hTau
	hAlpha
	hBeta
}

STATE	{
	m
	h
}

BREAKPOINT	{
	SOLVE states METHOD cnexp
	gNaTs2_t = gNaTs2_tbar*m*m*m*h
	ina = gNaTs2_t*(v-ena)
}

DERIVATIVE states	{
	rates()
	m' = (mInf-m)/mTau
	h' = (hInf-h)/hTau
}

INITIAL{
	rates()
	m = mInf
	h = hInf
}

PROCEDURE rates(){
  LOCAL qt
  qt = 2.3^((34-21)/10)

	UNITSOFF
    if(v == -32){
    	v = v+0.0001
    }
		mAlpha = (0.182 * (v- -32))/(1-(exp(-(v- -32)/6)))
		mBeta  = (0.124 * (-v -32))/(1-(exp(-(-v -32)/6)))
		mInf = mAlpha/(mAlpha + mBeta)
		mTau = (1/(mAlpha + mBeta))/qt

    if(v == -60){
      v = v + 0.0001
    }
		hAlpha = (-0.015 * (v- -60))/(1-(exp((v- -60)/6)))
		hBeta  = (-0.015 * (-v -60))/(1-(exp((-v -60)/6)))
		hInf = hAlpha/(hAlpha + hBeta)
		hTau = (1/(hAlpha + hBeta))/qt
	UNITSON
}

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