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;
% Runs the simulations needed for drawing Figure 1 and 2 and draws the figures.
% Assume scaling files are already calculated.
addpath('..');

cd kharche
runcontrol_kharche;
calcrates_kharche;
cd ..

cd severi
runcontrol_severi;
calcrates_severi;
cd ..

cd hay
% These commands may have to be run on command line in order to include the
% required paths (and parallelization is recommended anyway):
unix('nrnivmodl');
inds = [0 47 65 77 83 89 93 102 105];
for iind = 1:length(inds)
  unix(['python calcsteadystate.py ' num2str(inds(iind))]);      %each iind takes a couple of minutes to finish
  unix(['python findDCshortthreshold.py ' num2str(inds(iind))]); %each iind takes around 20 minutes to finish
  unix(['python calcifcurves.py ' num2str(inds(iind))]);         %each iind takes a couple of hours to finish
end
unix('python collectfig1.py');
unix('python collectfig2.py');
cd ..

cd almog
% These commands may have to be run on command line in order to include the
% required paths (and parallelization is recommended anyway):
unix('nrnivmodl');
inds = [0 47 65 77 83 89 93 102 105];
for iind = 1:length(inds)
  unix(['python calcsteadystate.py ' num2str(inds(iind))]);      %each iind takes around 40 minutes to finish
  unix(['python findDCshortthreshold.py ' num2str(inds(iind))]); %each iind takes around an hour to finish
  unix(['python calcifcurves.py ' num2str(inds(iind))]);         %each iind takes a couple of days to finish
end
unix('python collectfig1.py');
unix('python collectfig2.py');
cd ..

rmpath('..');

close all; drawfig1;
close all; drawfig2;