Biochemically detailed model of LTP and LTD in a cortical spine (Maki-Marttunen et al 2020)

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Accession:260971
"Signalling pathways leading to post-synaptic plasticity have been examined in many types of experimental studies, but a unified picture on how multiple biochemical pathways collectively shape neocortical plasticity is missing. We built a biochemically detailed model of post-synaptic plasticity describing CaMKII, PKA, and PKC pathways and their contribution to synaptic potentiation or depression. We developed a statistical AMPA-receptor-tetramer model, which permits the estimation of the AMPA-receptor-mediated maximal synaptic conductance based on numbers of GluR1s and GluR2s predicted by the biochemical signalling model. We show that our model reproduces neuromodulator-gated spike-timing-dependent plasticity as observed in the visual cortex and can be fit to data from many cortical areas, uncovering the biochemical contributions of the pathways pinpointed by the underlying experimental studies. Our model explains the dependence of different forms of plasticity on the availability of different proteins and can be used for the study of mental disorder-associated impairments of cortical plasticity."
Reference:
1 . Mäki-Marttunen T, Iannella N, Edwards AG, Einevoll GT, Blackwell KT (2020) A unified computational model for cortical post-synaptic plasticity. Elife [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex spiking regular (RS) neuron;
Channel(s): I Calcium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Glutamate; Norephinephrine; Acetylcholine;
Simulation Environment: NEURON; NeuroRD;
Model Concept(s): Long-term Synaptic Plasticity;
Implementer(s): Maki-Marttunen, Tuomo [tuomomm at uio.no];
Search NeuronDB for information about:  I Calcium; Acetylcholine; Norephinephrine; Glutamate;
/
synaptic
NEURON
fitfiles
README.html
#drawfig3.py#
calcconds.py
calcconds_dimerdimer.py
drawfig11.py
drawfig2.py
drawfig3.py
drawfig3_1.py
drawfig4.py
drawfig5.py
drawfig9abc.py
emoo.py
fit_cAMP_withdiss_1d.py
fits_goodparams.mat
fits_goodparams_manyb.mat
fitter_fewer.py
fitter_fewer_check.py
fitter_fewer_check_given.py *
fitter_fewer1withCK_check_given.py *
fitter_manyb_check_given.py
mesh_general.out *
model_nrn_altered_noU.py
model_nrn_altered_noU_extfilename_lowmem_recall.py
model_nrn_altered_noU_noninterp.py
model_nrn_altered_noU_simpleflux_extfilename_lowmem.py
model_nrn_oldCaM_altered_noU.py
model_nrn_oldCaM_altered_noU_extfilename_lowmem_recall.py
model_nrn_oldPKA_altered_noU.py
model_nrn_paired_contnm_var.py
model_nrn_paired_contnm_var_noninterp.py
model_nrn_testPKA_withdiss.py
model_nrn_testPKA_withdiss_williamson_varyrates.py
mytools.py
protocols_many.py
protocols_many_78withoutCK.py
protocols_many_78withoutCK_1withCK.py
reactionGraph.mat
runfig11.sh
runfig2.sh
runfig3_1.sh
runfig3-4.sh
runfig5.sh
runfig9.sh
simsteadystate_flexible.py
simsteadystate_li2020.py
simsteadystate_oldCaM_li2020.py
                            
#drawfig11.py: Draws the figure of model calibration
#Tuomo Maki-Marttunen, 2019-2020
#CC BY 4.0
import matplotlib
matplotlib.use('Agg')
from pylab import *
import scipy.io
import sys
import itertools
from os.path import exists
import mytools
import pickle
from matplotlib.collections import PatchCollection

close("all")
rc('axes',linewidth=0.5)


f,axarr = subplots(12,1)
for iax in range(0,12):
  axarr[iax].spines['top'].set_visible(False)
  axarr[iax].spines['right'].set_visible(False)
  for tick in axarr[iax].xaxis.get_major_ticks() + axarr[iax].yaxis.get_major_ticks():
    tick.label.set_fontsize(5)
  for axis in ['top','bottom','left','right']:
    axarr[iax].spines[axis].set_linewidth(0.5)
  for line in axarr[iax].xaxis.get_ticklines()+axarr[iax].yaxis.get_ticklines():
    line.set_markeredgewidth(0.5)

axnew = []
axarr[0].set_position([0.08,0.8,0.2,0.16])
axarr[1].set_position([0.36,0.8,0.2,0.16])
axarr[2].set_position([0.64,0.8,0.3,0.16]); axnew.append(f.add_axes([0.76, 0.8+0.07, 0.16, 0.09]))

axarr[3].set_position([0.08,0.56,0.16,0.16]) 
axarr[4].set_position([0.32,0.56,0.16,0.16]) 
axarr[5].set_position([0.56,0.56,0.16,0.16]) 
axarr[6].set_position([0.80,0.56,0.16,0.16]) 

axarr[7].set_position([0.08,0.32,0.4,0.16]); axnew.append(f.add_axes([0.14, 0.32+0.07, 0.085, 0.1]))
axarr[8].set_position([0.56,0.32,0.16,0.16])
axarr[9].set_position([0.8,0.32,0.16,0.16])

axarr[10].set_position([0.08,0.08,0.3,0.16])
axarr[11].set_position([0.46,0.08,0.48,0.16]);

axarr[10].set_visible(False)
axarr[11].set_visible(False)

HoffmanData = array([[0.024525077566201113, 0.010894660894660957],
 [0.05026882121429447, 0.007422147744728269],
 [0.10150641893704561, 0.014699995345156758],
 [0.2526954386274699, 0.014841968067774713],
 [0.49522800371345205, 0.02211516082483822],
 [0.970538989520764, 0.03476469766792345],
 [2.019271916170997, 0.05996834706512111],
 [2.9786945720946925, 0.08691058045896738],
 [3.8407459778154385, 0.1174160964483546],
 [4.806380863064389, 0.15508541637573892],
 [10, 0.3863822557370946],
 [14.751329666105498, 0.5943280733603316],
 [19.306977288832496, 0.7269864544058093],
 [38.98603702549072, 0.9009309686729041],
 [57.50958845380086, 0.9529628078015175],
 [98.5159372650316, 0.9888888888888889],
 [145.32410679618488, 0.9997020900246707],
 [193.06977288832496, 0.9907857375599312],
 [407.7459071436882, 0.9962784527300657]]) #Obtained with https://apps.automeris.io/wpd/

for iax in range(0,len(axnew)):
  axnew[iax].spines['top'].set_visible(False)
  axnew[iax].spines['right'].set_visible(False)
  for tick in axnew[iax].xaxis.get_major_ticks() + axnew[iax].yaxis.get_major_ticks():
    tick.label.set_fontsize(5)
  for axis in ['top','bottom','left','right']:
    axnew[iax].spines[axis].set_linewidth(0.25)
  for line in axnew[iax].xaxis.get_ticklines()+axnew[iax].yaxis.get_ticklines():
    line.set_markeredgewidth(0.25)

mesh_input_file = open('mesh_general.out','r')
mesh_firstline = mesh_input_file.readline()
mesh_secondline = mesh_input_file.readline()
mesh_values = mesh_secondline.split()
my_volume = float(mesh_values[-2])*1e-15 #litres
Nskip = 1

def mydraw(ax,species,DATAsets,mydashes=[],colors=[]):
  DATA = []
  tcs = []
  for idata in range(0,len(DATAsets)):    
    DATA.append({})
    for ikey in range(0,len(DATAsets[idata]['headers'])):
      mykey = DATAsets[idata]['headers'][ikey][0:DATAsets[idata]['headers'][ikey].find(' ')]
      DATA[idata][mykey] = DATAsets[idata]['DATA'][ikey]

    times_nrn = DATA[idata]['tvec']
    mytimecourse_nrn = zeros(times_nrn.shape[0])
    spectext = ''
    if type(species) is not list:
      species = [species]
    for ispec in range(0,len(species)):
      specfactor = 1.0
      if len(species[ispec]) > 24:
        mytimecourse_nrn = mytimecourse_nrn + DATA[idata][species[ispec][:24]]
      else:
        mytimecourse_nrn = mytimecourse_nrn + DATA[idata][species[ispec]]
      spectext = spectext+species[ispec]+'+'
      if len(spectext)>30:
        spectext = spectext[0:-1]+'+\n'+'+'
  
    spectext = spectext[0:-1]
    nrnfactor = 1.0
    mycolor = '#000000'
    if len(colors) > idata and type(colors[idata]) is str:
      mycolor = colors[idata]
    if len(colors) > idata and type(colors[idata]) is type(None):
      print "  not plotting idata = "+str(idata)
    else:
      if len(mydashes) > idata and len(mydashes[idata]) > 0:
        ax.plot([x/1000-4000 for x in times_nrn[::Nskip]],mytimecourse_nrn[::Nskip]*1e6*nrnfactor,'k--',dashes=mydashes[idata],color=mycolor,linewidth=1.0)
      else:
        ax.plot([x/1000-4000 for x in times_nrn[::Nskip]],mytimecourse_nrn[::Nskip]*1e6*nrnfactor,'k-',color=mycolor,linewidth=1.0)
    tcs.append(mytimecourse_nrn[::Nskip]*1e6*nrnfactor)
  return tcs

def mydrawmaxes(ax,species,parVals,DATAsetsets,mydashes=[],colors=[]):
  for iset in range(0,len(DATAsetsets)):
    DATAsets = DATAsetsets[iset] 
    maxVals = []
    DATA = []
    nrnfactor = 1.0
    for idata in range(0,len(DATAsets)):    
      DATA.append({})
      for ikey in range(0,len(DATAsets[idata]['headers'])):
        mykey = DATAsets[idata]['headers'][ikey][0:DATAsets[idata]['headers'][ikey].find(' ')]
        DATA[idata][mykey] = DATAsets[idata]['DATA'][ikey]
      mytimecourse_nrn = zeros(DATA[idata]['tvec'].shape[0])
      if type(species) is not list:
        species = [species]
      for ispec in range(0,len(species)):
        specfactor = 1.0
        if len(species[ispec]) > 24:
          mytimecourse_nrn = mytimecourse_nrn + DATA[idata][species[ispec][:24]]
        else:
          mytimecourse_nrn = mytimecourse_nrn + DATA[idata][species[ispec]]
      maxVals.append(max(mytimecourse_nrn)*1e6*nrnfactor)
  
    mycolor = '#000000'
    if len(colors) > iset and type(colors[iset]) is str:
      mycolor = colors[iset]
    if len(mydashes) > iset and len(mydashes[iset]) > 0:
      ax.plot(parVals,maxVals,'k--',dashes=mydashes[iset],color=mycolor,linewidth=1.0)
    else:
      ax.plot(parVals,maxVals,'k-',color=mycolor,linewidth=1.0)


filename_nrn = 'nrn_tstop5000000_tol1e-06_onset4040000.0_n100_freq100.0_dur3.0_flux1900.0_Lflux10.0_Gluflux20.0_AChflux20.0_Ntrains4_trainT4000.0.mat'
assert exists(filename_nrn), filename_nrn+' does not exist'
DATANRN_orig_HFS = scipy.io.loadmat(filename_nrn)
print "loaded "+filename_nrn

########## A ############ (New vs. old membrane-insertion of non-S880-phosphorylated GluR2)
if True:
  filename_nrn = 'nrn_tstop5000000_tol1e-06_Cax1.0_k385-387-389x22.4-22.4-22.4_onset4040000.0_n100_freq100.0_dur3.0_flux1900.0_Lflux10.0_Gluflux20.0_AChflux20.0_Ntrains4_trainT4000.0.mat'
  assert exists(filename_nrn), filename_nrn+' does not exist'
  DATANRN_HFS = scipy.io.loadmat(filename_nrn)
  print "loaded "+filename_nrn

  mydraw(axarr[0],['GluR2_memb', 'GluR2_memb_PKCt', 'GluR2_memb_PKCp', 'GluR2_memb_S880', 'GluR2_memb_S880_PP2A'],[DATANRN_orig_HFS,DATANRN_HFS],[[],[2,2]],['#000000','#808080'])

########## B ############ (old vs. new CaM Ca-binding, CaMCa4 time courses)
if True:
  filename_nrn = 'nrn_oldCaM_tstop5000000_tol1e-06_CaMx0.55_onset4040000.0_n100_freq100.0_dur3.0_flux1900.0_Lflux10.0_Gluflux20.0_AChflux20.0_Ntrains4_trainT4000.0.mat'
  assert exists(filename_nrn), filename_nrn+' does not exist'
  DATANRN_HFS = scipy.io.loadmat(filename_nrn)
  print "loaded "+filename_nrn

  tcs_B = mydraw(axarr[2],['AC1GsaGTPCaMCa4', 'AC1GsaGTPCaMCa4ATP', 'AC1GiaGTPCaMCa4', 'AC1GiaGTPCaMCa4ATP', 'AC1GsaGTPGiaGTPCaMCa4', 'AC1GsGiCaMCa4ATP', 'AC1CaMCa4', 'AC1CaMCa4ATP', 'AC8CaMCa4', 'AC8CaMCa4ATP', 'PDE1CaMCa4', 'PDE1CaMCa4cAMP', 'CaMCa4', 'PP2BCaMCa4', 'CKCaMCa4', 'CKpCaMCa4', 'CKpCaMCa4PP1', 'Ip35PP2BCaMCa4', 'Ip35PP1PP2BCaMCa4', 'PP1PP2BCaMCa4'],[DATANRN_orig_HFS,DATANRN_HFS],[[],[2,2]],['#000000','#808080'])
  maxCa_B = DATANRN_HFS['maxDATA'][1]
  maxCa_orig_B = DATANRN_orig_HFS['maxDATA'][1]
  mydraw(axnew[0],['CaMCa4'],[DATANRN_orig_HFS,DATANRN_HFS],[[],[2,2]],['#000000','#808080'])

########## C ############ (old vs. new CaM Ca-binding, Ca-sensitivity)
if True:
  for iset in range(0,2):
    filename_nrn = 'steadystate_new_li2020_0.0_50.0_0.0_processed.sav' if iset == 0 else 'steadystate_new_oldCaM_li2020_0.0_50.0_0.0_processed.sav'
    mycolor = '#000000' if iset == 0 else '#808080'
    assert exists(filename_nrn), filename_nrn+' does not exist'
    unpicklefile = open(filename_nrn,'r')
    unpickledlist = pickle.load(unpicklefile)
    unpicklefile.close()
    timesAll = unpickledlist[0][0]
    DATA_all_all = unpickledlist[0][3]  
    headers = unpickledlist[0][4]
    CaFluxes = unpickledlist[1]
    print "loaded "+filename_nrn

    species = ['AC1GsaGTPCaMCa4', 'AC1GsaGTPCaMCa4ATP', 'AC1GiaGTPCaMCa4', 'AC1GiaGTPCaMCa4ATP', 'AC1GsaGTPGiaGTPCaMCa4', 'AC1GsGiCaMCa4ATP', 'AC1CaMCa4', 'AC1CaMCa4ATP', 'AC8CaMCa4', 'AC8CaMCa4ATP', 'PDE1CaMCa4', 'PDE1CaMCa4cAMP', 'CaMCa4', 'PP2BCaMCa4', 'CKCaMCa4', 'CKpCaMCa4', 'CKpCaMCa4PP1', 'Ip35PP2BCaMCa4', 'Ip35PP1PP2BCaMCa4', 'PP1PP2BCaMCa4']
    ispecies = [[i for i in range(0,len(headers)) if species[j] in headers[i] and (len(headers[i]) == species[j] or headers[i][len(species[j])] == ' ')][0] for j in range(0,len(species))]

    axarr[1].semilogx([DATA_all_all[i][1]*1e6 for i in range(0,len(CaFluxes))], [sum([DATA_all_all[i][ispec]*1e6 for ispec in ispecies]) for i in range(0,len(CaFluxes))],'k-',color=mycolor,linewidth=1.0,mew=0.9,ms=3.0)
    axarr[1].set_xlim([3e1,3e6])
    axarr[1].set_yticks([0,20000,40000])
  axarr[1].plot(HoffmanData[:,0]*1e3, HoffmanData[:,1]*50000, 'r.',ms=1.4,mew=1.0,fillstyle='full')

  #axarr[1].plot(1e6*maxCa_B, max(tcs_B[0]), 'k.')
  #axarr[1].plot(1e6*maxCa_orig_B, max(tcs_B[1]), 'k.', color='#808080')
  #axarr[1].set_xlim([0,10000])


########## D ############ (fit the PKCp activation) 
if True:
  filenames_nrn = ['nrn_tstop5000000_tol1e-06_Cax1.0_k411x1.0_onset4040000.0_n900_freq5.0_dur3.0_flux1900.0_Lflux10.0_Gluflux20.0_AChflux20.0_Ntrains1_trainT100000.0.mat', #fitted value
                   'nrn_tstop5000000_tol1e-06_Cax1.0_k411x0.1_onset4040000.0_n900_freq5.0_dur3.0_flux1900.0_Lflux10.0_Gluflux20.0_AChflux20.0_Ntrains1_trainT100000.0.mat', #slower PKCp activation (close to original)
                   'nrn_tstop5000000_tol1e-06_Cax1.0_k411x0.3_onset4040000.0_n900_freq5.0_dur3.0_flux1900.0_Lflux10.0_Gluflux20.0_AChflux20.0_Ntrains1_trainT100000.0.mat', #slower PKCp activation
                   'nrn_tstop5000000_tol1e-06_Cax1.0_k411x3.0_onset4040000.0_n900_freq5.0_dur3.0_flux1900.0_Lflux10.0_Gluflux20.0_AChflux20.0_Ntrains1_trainT100000.0.mat', #faster PKCp activation
                   'nrn_tstop5000000_tol1e-06_Cax1.0_k411x10.0_onset4040000.0_n900_freq5.0_dur3.0_flux1900.0_Lflux10.0_Gluflux20.0_AChflux20.0_Ntrains1_trainT100000.0.mat']#faster PKCp activation
  DATANRNs = []
  for ifile in range(0,len(filenames_nrn)):
    filename_nrn = filenames_nrn[ifile]
    assert exists(filename_nrn), filename_nrn+' does not exist'
    DATANRNs.append(scipy.io.loadmat(filename_nrn))
    print "loaded "+filename_nrn
  GluR2_S880_tcs = mydraw(axarr[3],['GluR2_S880','GluR2_S880_PP2A','GluR2_memb_S880','GluR2_memb_S880_PP2A'],DATANRNs,[[],[3,1],[],[],[2,2]],[None,None,None,None,None]) # (just load, don't draw)
  xs = [3,1,2,4,5]
  kfvals = [0.0005*x for x in [1.0,0.1,0.3,3.0,10.0]]
  for i in range(0,5):
    axarr[3].bar(xs[i],GluR2_S880_tcs[i][-1]/270.,edgecolor='#808080' if i != 0 else '#000000',facecolor='#ffffff') ############# D ################# (amount of GluR2 S880 after LFS (270 nM is the total concentration of GluR2))
    axarr[3].text(xs[i]-0.07,0.04+GluR2_S880_tcs[i*(i<3)][-1]/270,'k$_f$ = '+str(kfvals[i]),fontsize=6,rotation='vertical',va='bottom')
  axarr[3].set_xticks([])
  axarr[3].set_ylim([0,1.6])

########## E -- G ############
  filenames_nrn = ['nrn_tstop5000000_tol1e-06_Cax1.0_k411x1.0_onset4040000.0_n900_freq5.0_dur3.0_flux1900.0_Lflux10.0_Gluflux20.0_AChflux20.0_Ntrains1_trainT100000.0.mat', #fitted value
                   'nrn_tstop5000000_tol1e-06_Cax1.0_k411x0.3_onset4040000.0_n900_freq5.0_dur3.0_flux1900.0_Lflux10.0_Gluflux20.0_AChflux20.0_Ntrains1_trainT100000.0.mat', #slower PKCp activation
                   'nrn_tstop5000000_tol1e-06_Cax1.0_k411x3.0_onset4040000.0_n900_freq5.0_dur3.0_flux1900.0_Lflux10.0_Gluflux20.0_AChflux20.0_Ntrains1_trainT100000.0.mat'] #faster PKCp activation
  DATANRNs = []
  for ifile in range(0,len(filenames_nrn)):
    filename_nrn = filenames_nrn[ifile]
    assert exists(filename_nrn), filename_nrn+' does not exist'
    DATANRNs.append(scipy.io.loadmat(filename_nrn))
    print "loaded "+filename_nrn
  mydraw(axarr[4],['PKCt'],DATANRNs,[[],[3,1],[1,3]],['#000000','#808080','#808080']) ############# E ################# (PKCt)
  mydraw(axarr[5],['PKCp'],DATANRNs,[[],[3,1],[1,3]],['#000000','#808080','#808080']) ############# F ################# (PKCp)
  GluR2_S880_tcs = mydraw(axarr[6],['GluR2_S880','GluR2_S880_PP2A','GluR2_memb_S880','GluR2_memb_S880_PP2A'],DATANRNs,[[],[3,1],[1,3]],['#000000','#808080','#808080']) ############# G ################# (GluR2 S880 time course)

f.savefig('fig11.eps')
########## H ############
if True:
  A = scipy.io.loadmat('cAMP_withdiss_test_tstop22000_tol1e-08_onset800.0_n1_freq1.0_dur16000.0_flux0.64.mat')
  if A['times'].shape[1] == 1:
    A['times'] = A['times'].T
  OBJECTIVE_ts = [1000+1000*i for i in range(0,18)]
  OBJECTIVE_DATA = [mytools.interpolate(A['times'][0],A['tcDATA'][0]+A['tcDATA'][1],OBJECTIVE_ts), #Time course of cAMP                                                                                              
                    mytools.interpolate(A['times'][0],A['tcDATA'][5],OBJECTIVE_ts)] #Time course of PKAc                                                                                         
  ks = [0.4e9, 1.0e9, 1.6e9, 2.2e9, 2.8e9]
  fs = []

  polygon = Polygon(array([[1000,17000,17000,1000],[0,0,100,100]]).T, True)
  p = PatchCollection([polygon], cmap=matplotlib.cm.jet)
  p.set_facecolor('#E0E0E0')
  p.set_edgecolor('none')
  axarr[7].add_collection(p)

  mydashes = [[2,2],[1,3],[1,1],[1,3,2,2],[3,1]]
  for ik in range(0,len(ks)):
    unpicklefile = open('run_cAMP_withdiss_flux0.64_k'+str(ks[ik])+'_data.sav','r')
    unpickledlist = pickle.load(unpicklefile)
    unpicklefile.close()

    timesAll = unpickledlist[0]
    timeCoursesAll = unpickledlist[1]
    mydict = {}
    objs = []
    imeas = [0, 5] # cAMP and PKAc traces
    for iobjective in range(0,len(OBJECTIVE_DATA)):
      objs_thisobj = []
      times = timesAll[0]
      if times.shape[1] == 1:
        times = times.T
      timeCourses = mytools.interpolate(times[0],timeCoursesAll[0][imeas[iobjective]],OBJECTIVE_ts)
      mydict['f'+str(iobjective)] = sum(abs(array(timeCourses)-array(OBJECTIVE_DATA[iobjective])))
    fs.append(mydict['f1'])
    if ik != 2:
      #axarr[7].plot(times[0],1e6*timeCoursesAll[0][imeas[1]],'b--',color='#808080',dashes=mydashes[ik])
      axarr[7].plot(times[0],1e6*timeCoursesAll[0][imeas[1]],'b-',color='#A0A0A0',linewidth=1.0)
      axnew[1].bar(ik,1e6*mydict['f1'],edgecolor='#A0A0A0',facecolor='#A0A0A0')
      print str(ik)+","+str(1e6*mydict['f1'])
    else:
      axarr[7].plot(times[0],1e6*timeCoursesAll[0][imeas[1]],'k-',color='#000000',linewidth=1.0)
      control_data = [times[0],1e6*timeCoursesAll[0][imeas[1]]]
      axnew[1].bar(ik,1e6*mydict['f1'],edgecolor='#000000',facecolor='#000000')
      print str(ik)+","+str(1e6*mydict['f1'])
    axnew[1].text(ik-0.3,(1e6*mydict['f1']*(ik > 0))+40,'k$_f$ = '+"{:.1E}".format(ks[ik]),fontsize=6,rotation='vertical',va='bottom')

  axarr[7].plot(OBJECTIVE_ts,[x*1e6 for x in OBJECTIVE_DATA[1]],'kx',color='#808080')
  axarr[7].plot(control_data[0],control_data[1],'k-',color='#000000',linewidth=1.0)
  axarr[7].set_xlim([-2000,18500])
  axarr[7].set_ylim([0,100])
  axnew[1].set_xticks([])
  axnew[1].set_ylim([0,800])

########## I ############ (old PKA, 4xHFS with Lflux 10.0) (Change to response curve to cAMP in the stub model?)
if True:
  filename_nrn = 'nrn_oldPKA_tstop5000000_tol1e-06_onset4040000.0_n100_freq100.0_dur3.0_flux1900.0_Lflux10.0_Gluflux20.0_AChflux20.0_Ntrains4_trainT4000.0.mat'
  assert exists(filename_nrn), filename_nrn+' does not exist'
  DATANRN_HFS = scipy.io.loadmat(filename_nrn)
  print "loaded "+filename_nrn

  mydraw(axarr[8],['GluR1_S845', 'GluR1_S845_S831', 'GluR1_S845_CKCam', 'GluR1_S845_CKpCam', 'GluR1_S845_CKp', 'GluR1_S845_PKCt', 'GluR1_S845_PKCp', 'GluR1_S845_PP1', 'GluR1_S845_S831_PP1', 'GluR1_S845_PP2B', 'GluR1_S845_S831_PP2B', 'GluR1_memb_S845', 'GluR1_memb_S845_S831', 'GluR1_memb_S845_CKCam', 'GluR1_memb_S845_CKpCam', 'GluR1_memb_S845_CKp', 'GluR1_memb_S845_PKCt', 'GluR1_memb_S845_PKCp', 'GluR1_memb_S845_PP1', 'GluR1_memb_S845_S831_PP1', 'GluR1_memb_S845_PP2B', 'GluR1_memb_S845_S831_PP2B'],[DATANRN_orig_HFS,DATANRN_HFS],[[],[2,2]],['#000000','#808080'])
  
########## J ############ (PKC does not phosphorylate S831)
if True:
  filename_nrn = 'nrn_tstop5000000_tol1e-06_Cax1.0_k166-169-181-184-218-221-233-236x0.0-0.0-0.0-0.0-0.0-0.0-0.0-0.0_onset4040000.0_n100_freq100.0_dur3.0_flux1900.0_Lflux10.0_Gluflux20.0_AChflux20.0_Ntrains4_trainT4000.0.mat'
  assert exists(filename_nrn), filename_nrn+' does not exist'
  DATANRN_HFS = scipy.io.loadmat(filename_nrn)
  print "loaded "+filename_nrn

  mydraw(axarr[9],['GluR1_S831', 'GluR1_S845_S831', 'GluR1_S831_PKAc', 'GluR1_S845_S831_PP1', 'GluR1_S831_PP1', 'GluR1_S845_S831_PP2B', 'GluR1_memb_S831', 'GluR1_memb_S845_S831', 'GluR1_memb_S831_PKAc', 'GluR1_memb_S845_S831_PP1', 'GluR1_memb_S831_PP1', 'GluR1_memb_S845_S831_PP2B'],[DATANRN_orig_HFS,DATANRN_HFS],[[],[2,2]],['#000000','#808080'])
  

for iax in range(0,10):
  pos = axarr[iax].get_position()
  f.text(pos.x0 - 0.06, pos.y1 - 0.02, chr(ord('A')+iax), fontsize=15)

for ax in [axarr[0], axarr[2], axnew[0], axarr[1], axarr[4], axarr[5], axarr[6], axarr[7], axnew[1], axarr[8], axarr[9], axarr[10], axarr[11]]:
  ylab = ax.set_ylabel('(nM)',fontsize = 6)
  if ax == axnew[0]:
    xlab = ax.set_xlabel('time (s)',fontsize = 6)
    ax.xaxis.set_label_coords(0.5, -0.2)
  elif ax == axarr[1]:
    xlab = ax.set_xlabel('flux (part./ms)',fontsize = 6)
  elif ax != axnew[1]:
    xlab = ax.set_xlabel('time (s)',fontsize = 6)

axarr[0].text(mean(axarr[0].get_xlim()), [0.01*x[0]+0.99*x[1] for x in [axarr[0].get_ylim()]][0],'[GluR2 at memb.]', fontsize = 6, ha = 'center')
axarr[1].text(mean(axarr[1].get_xlim()), [0.01*x[0]+0.99*x[1] for x in [axarr[1].get_ylim()]][0],'max. [CaMCa4]', fontsize = 6, ha = 'center')
axarr[2].text(mean(axarr[2].get_xlim()), [-0.04*x[0]+1.04*x[1] for x in [axarr[2].get_ylim()]][0],'[CaMCa4]', fontsize = 6, ha = 'center')
axarr[3].text(mean(axarr[3].get_xlim()), [0.01*x[0]+0.99*x[1] for x in [axarr[3].get_ylim()]][0],'[GluR2 S880p]/[tot. GluR2]', fontsize = 6, ha = 'center')
axarr[4].text(mean(axarr[4].get_xlim()), [0.01*x[0]+0.99*x[1] for x in [axarr[4].get_ylim()]][0],'[PKC tr.]', fontsize = 6, ha = 'center')
axarr[5].text(mean(axarr[5].get_xlim()), [0.01*x[0]+0.99*x[1] for x in [axarr[5].get_ylim()]][0],'[PKC pers.]', fontsize = 6, ha = 'center')
axarr[6].text(mean(axarr[6].get_xlim()), [0.01*x[0]+0.99*x[1] for x in [axarr[6].get_ylim()]][0],'[GluR2 S880p]', fontsize = 6, ha = 'center')
axarr[7].text(mean(axarr[7].get_xlim()), [0.01*x[0]+0.99*x[1] for x in [axarr[7].get_ylim()]][0],'[act. PKA]', fontsize = 6, ha = 'center')
axarr[8].text(mean(axarr[8].get_xlim()), [0.01*x[0]+0.99*x[1] for x in [axarr[8].get_ylim()]][0],'[GluR1 S845p]', fontsize = 6, ha = 'center')
axarr[9].text(mean(axarr[9].get_xlim()), [0.01*x[0]+0.99*x[1] for x in [axarr[9].get_ylim()]][0],'[GluR1 S831p]', fontsize = 6, ha = 'center')

f.savefig('fig11.eps')

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