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]
Citations  Citation Browser
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 [tuomo.maki-marttunen at tut.fi];
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;
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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
                            
load_3dcell(CellFileName)
    track_data =0

proc init(){
            finitialize(-70)
            save_dt=dt
            dt=10
            i=0
            for i=1,38 fadvance()
            print t
            dt=save_dt
}


a_soma SomaStimSec = new SectionRef()
apic[11] DendStimSec = new SectionRef()
DendStimSec.sec {nseg=40}

func tfunk(){local Chisq,dumm
        Chisq=0
        ra  = transvec.x(0)
        rm = transvec.x(1)
        c_m = transvec.x(2)
        epas_sim  = transvec.x(3)
        gih_end  = transvec.x(4)
	gih_x2 = transvec.x(5)
        gih_alpha=transvec.x(6)
	gih_start=transvec.x(7)
	ih_q10=transvec.x(8)
	gkslow_start= transvec.x(9)
	gkslow_alpha= transvec.x(10)
	gkslow_beta=transvec.x(11)
	gka_start= transvec.x(12)
	gka_alpha= transvec.x(13)
	gka_beta= transvec.x(14)
	gna_soma = transvec.x(15)
	gna_api = transvec.x(16)
	dist_na = transvec.x(17)
	na_shift1 = transvec.x(18)
	na_shift2 = transvec.x(19)
	pcah_soma = transvec.x(20)
	pcah_api = transvec.x(21)
	dist_cah = transvec.x(22)
	cah_shift=transvec.x(23)
	cah_shifth=transvec.x(24)

	pcar_soma = transvec.x(25)
	pcar_api = transvec.x(26)
	dist_car = transvec.x(27)
        car_shift=transvec.x(28)
	car_shifth=transvec.x(29)
	car_qm=transvec.x(30)	
	
	gsk_soma=transvec.x(31)
	gsk_dend=transvec.x(32)
	dist_sk=transvec.x(33)
	gbk_soma=transvec.x(34)
	gbk_dend=transvec.x(35)
	dist_bk=transvec.x(36)		
        density()
	return Chisq
}