Schiz.-linked gene effects on intrinsic single-neuron excitability (Maki-Marttunen et al. 2016)

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Accession:169457
Python scripts for running NEURON simulations that model a layer V pyramidal cell with certain genetic variants implemented. The genes included are obtained from genome-wide association studies of schizophrenia.
Reference:
1 . Mäki-Marttunen T, Halnes G, Devor A, Witoelar A, Bettella F, Djurovic S, Wang Y, Einevoll GT, Andreassen OA, Dale AM (2016) Functional Effects of Schizophrenia-Linked Genetic Variants on Intrinsic Single-Neuron Excitability: A Modeling Study. Biol Psychiatry Cogn Neurosci Neuroimaging 1:49-59 [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;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I K; I K,leak; I M; I h; I K,Ca; I A, slow; Ca pump;
Gap Junctions:
Receptor(s):
Gene(s): Nav1.1 SCN1A; Nav1.7 SCN9A; Cav3.3 CACNA1I; Cav1.3 CACNA1D; Cav1.2 CACNA1C; Kv2.1 KCNB1; HCN1;
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Coincidence Detection; Active Dendrites; Detailed Neuronal Models; 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 K; I K,leak; I M; I h; I K,Ca; I A, slow; Ca pump;
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Maki-MarttunenEtAl2015
models
morphologies
readme.txt
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 *
collectscalings.py
collectthresholddistalamps.py
drawfig1.py
drawfig2.py
drawfig3.py
drawfig4.py
drawfig5.py
findthresholddistalamps.py
mutation_stuff.py
mytools.py
runcontrols.py
savesynapselocations.py
scalemutations.py
scalings.sav
                            
# collectscalings.py
# A script for collecting the scaling coefficients of each variant into one file, scalings.sav
#
# Tuomo Maki-Marttunen, Jan 2015
# (CC BY)
from pylab import *
import mytools
import pickle
import sys
import mutation_stuff
MT = mutation_stuff.getMT()

theseCoeffsAllAll = []
for icell in range(0,2):
 theseCoeffsAll = []
 theseMutValsAll = []
 theseMutVarsAll = []

 counter = -1
 for igene in range(0,len(MT)):
  theseCoeffsGene = []
  for imut in range(0,len(MT[igene])):
   theseCoeffsMut = []
   nVals = len(MT[igene][imut])*[0]
   thesemutvars = []
   for imutvar in range(0,len(MT[igene][imut])):
     thesemutvars.append(MT[igene][imut][imutvar][0])
     if type(MT[igene][imut][imutvar][1]) is int or type(MT[igene][imut][imutvar][1]) is float:
       MT[igene][imut][imutvar][1] = [MT[igene][imut][imutvar][1]]
     nVals[imutvar] = len(MT[igene][imut][imutvar][1])
   cumprodnVals = cumprod(nVals)
   allmutvars = cumprodnVals[len(MT[igene][imut])-1]*[thesemutvars[:]]
   allmutvals = []
   for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
     allmutvals.append([0]*len(thesemutvars))
   for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
     for imutvar in range(0,len(MT[igene][imut])):
       if imutvar==0:
         allmutvals[iallmutval][imutvar] = MT[igene][imut][imutvar][1][iallmutval%nVals[imutvar]]
       else:
         allmutvals[iallmutval][imutvar] = MT[igene][imut][imutvar][1][(iallmutval/cumprodnVals[imutvar-1])%nVals[imutvar]]
   theseMutValsAll.append(allmutvals[:])  
   theseMutVarsAll.append(allmutvars[:])  
   for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
     counter = counter + 1
     try: # If the scaling has been done, load it
       unpicklefile = open('scalings_cs'+str(icell)+'_'+str(counter)+'.sav', 'r')
       unpickledlist = pickle.load(unpicklefile)
       unpicklefile.close()
       theseCoeffsMut.append(unpickledlist[0])
     except: # Otherwise, just add empty list
       theseCoeffsMut.append([])
   theseCoeffsGene.append(theseCoeffsMut[:])
  theseCoeffsAll.append(theseCoeffsGene[:])
 theseCoeffsAllAll.append(theseCoeffsAll[:])
 
picklelist = [theseCoeffsAllAll,theseMutVarsAll,theseMutValsAll,MT]
file = open('scalings.sav', 'w')
pickle.dump(picklelist,file)
file.close()


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