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

 Download zip file 
Help downloading and running models
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 L5/6 pyramidal GLU 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 [tuomomm at uio.no];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU 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;
/
pleiotropy
almog
cells
BK.mod *
ca_h.mod
ca_r.mod
cad.mod *
epsp.mod *
ih.mod *
kfast.mod
kslow.mod
na.mod
SK.mod *
best.params *
calcifcurves.py
calcsteadystate.py
cc_run.hoc *
collectfig1.py
collectfig2.py
fig1_curves.mat
fig2_curves.mat
findDCshortthreshold.py
main.hoc *
model.hoc *
mosinit.hoc *
mutation_stuff.py
myrun.hoc *
mytools.py *
params.hoc *
runme.sh *
scalings.sav
                            
TITLE K-fast channel from Korngreen and Sakmann 2000
: M.Migliore June 2001

NEURON {
	SUFFIX iA
	USEION k READ ek WRITE ik
        RANGE gbar,ik,ninf,linf,taul,taun
        GLOBAL tq,qq, q10,vmin, vmax,tadj,temp
}


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

}

PARAMETER {
	v (mV)
	celsius		(degC)
	Tscale = 10	(degC)
	gbar=0.0 (pS/um2)
        offn=-47   (mV)
        offl=-66   (mV)
        slon=29   (mV)
        slol=10   (mV)
	qq=5
	tq=-55
	ek      (mV)
	vmin = -120	(mV)
	vmax = 100	(mV)
        temp = 21       (degC)          : original temp
        q10  = 2.3

        offmt = -71 (mV)
        slomt = 59 (mV)
        taummin = 0.34 (ms)
        taumdiff = 0.92 (ms)
        offht = -73 (mV)
        sloht = 23 (mV)
        tauhmin = 8 (ms)
        tauhdiff = 49 (ms)
	
}



STATE {
	n
        l
}

ASSIGNED {
	ik (mA/cm2)
        ninf
        linf      
        taul  (ms)
        taun   (ms)
	tadj
}

INITIAL {
	rates(v)
	n=ninf
	l=linf
}


BREAKPOINT {
	SOLVE states METHOD cnexp
	ik = (1e-4)*gbar*n^4*l*(v-ek)
}


DERIVATIVE states {     : exact when v held constant; integrates over dt step
        rates(v)
        n' = (ninf - n)/taun
        l' =  (linf - l)/taul
}

PROCEDURE rates(v (mV)) { :callable from hoc

        tadj= q10^((celsius-temp)/Tscale)
        ninf = 1/(1 + exp(-(v-offn)/slon))
        linf = 1/(1 + exp((v-offl)/slol))
        taun = (taummin+taumdiff*exp(-((offmt-v)/slomt)^2))/tadj
        taul = (tauhmin+tauhdiff*exp(-((offht-v)/sloht)^2))/tadj
}















Loading data, please wait...