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

 Download zip file   Auto-launch 
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 V1 L6 pyramidal corticothalamic 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 [tuomo.maki-marttunen at tut.fi];
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic 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
hay
models
morphologies
Ca_HVA.mod *
Ca_LVAst.mod *
CaDynamics_E2.mod *
epsp.mod *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod *
NaTa_t.mod *
NaTs2_t.mod *
SK_E2.mod *
SKv3_1.mod *
calcifcurves.py
calcsteadystate.py
collectfig1.py
collectfig2.py
fig1_curves.mat
fig2_curves.mat
findDCshortthreshold.py
mutation_stuff.py *
mytools.py *
runme.sh *
scalings_cs.sav
                            
: this model is built-in to neuron with suffix epsp
: Schaefer et al. 2003

COMMENT
modified from syn2.mod
injected current with exponential rise and decay current defined by
         i = 0 for t < onset and
         i=amp*((1-exp(-(t-onset)/tau0))-(1-exp(-(t-onset)/tau1)))
          for t > onset

	compare to experimental current injection:
 	i = - amp*(1-exp(-t/t1))*(exp(-t/t2))

	-> tau1==t2   tau0 ^-1 = t1^-1 + t2^-1
ENDCOMMENT
					       
INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
	POINT_PROCESS epsp
	RANGE onset, tau0, tau1, imax, i, myv
	NONSPECIFIC_CURRENT i
}
UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(umho) = (micromho)
}

PARAMETER {
	onset=0  (ms)
	tau0=0.2 (ms)
	tau1=3.0 (ms)
	imax=0 	 (nA)
	v	 (mV)
}

ASSIGNED { i (nA)  myv (mV)}

LOCAL   a[2]
LOCAL   tpeak
LOCAL   adjust
LOCAL   amp

BREAKPOINT {
	myv = v
        i = curr(t)
}

FUNCTION myexp(x) {
	if (x < -100) {
	myexp = 0
	}else{
	myexp = exp(x)
	}
}

FUNCTION curr(x) {				
	tpeak=tau0*tau1*log(tau0/tau1)/(tau0-tau1)
	adjust=1/((1-myexp(-tpeak/tau0))-(1-myexp(-tpeak/tau1)))
	amp=adjust*imax
	if (x < onset) {
		curr = 0
	}else{
		a[0]=1-myexp(-(x-onset)/tau0)
		a[1]=1-myexp(-(x-onset)/tau1)
		curr = -amp*(a[0]-a[1])
	}
}

Loading data, please wait...