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;
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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
                            
#cp model_nrn_testPKA_williamson_varyrates.py model_nrn_testPKA_withdiss_williamson_varyrates.py
#cp model_nrn_testPKA_williamson.py model_nrn_testPKA_williamson_varyrates.py
#cp model_nrn_testPKA.py model_nrn_testPKA_williamson.py
#cp model_nrn_altered_template.py model_nrn_testPKA.py
#Based on Model C in https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2719611/
from neuron import h, rxd
from pylab import *
from matplotlib import pyplot
import scipy.io
import time
import re
import mytools

h.load_file('stdrun.hoc')

dend = h.Section(name='dend')
dend.L=1
dend.diam=0.79788
cyt = rxd.Region([dend], name='cyt', nrn_region='i')

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
mesh_input_file.close()

Duration = 3000
tolerance = 1e-6
cAMP_input_onset = 800
cAMP_input_N     = 100
cAMP_input_freq  = 100
cAMP_input_dur   = 0.005
cAMP_input_flux  = 600.0
Ntrains        = 1
trainT = 0
extfilename = ''

ks = [1.0]*10
ks[0] = 8.72e9   # PKA + cAMP*4 <-> PKAcAMP4 (forward) #(8.72 e-17 in Williamson et al. 2009)
ks[1] = 0.0       # PKA + cAMP*4 <-> PKAcAMP4 (backward)
ks[4] = 0.00024   # PKAcAMP4 <-> PKAr + PKAc*2 (forward)
ks[5] = 25.5      # PKAcAMP4 <-> PKAr + PKAc*2 (backward)

ks[6] = 0.001        # AMP --> ATP (forward)
ks[7] = 21.66        # PDE4 + cAMP <-> PDE4cAMP (forward)
ks[8] = 0.0034656    # PDE4 + cAMP <-> PDE4cAMP (backward)
ks[9] = 0.017233     # PDE4cAMP --> PDE4 + AMP (forward)

if len(sys.argv) > 1:
  Duration = int(sys.argv[1])
if len(sys.argv) > 2:
  tolerance = float(sys.argv[2])
if len(sys.argv) > 3:
  cAMP_input_onset = float(sys.argv[3])
if len(sys.argv) > 4:
  cAMP_input_N     = int(sys.argv[4])
if len(sys.argv) > 5:
  cAMP_input_freq  = float(sys.argv[5])
if len(sys.argv) > 6:
  cAMP_input_dur   = float(sys.argv[6])
if len(sys.argv) > 7:
  cAMP_input_flux  = float(sys.argv[7])
if len(sys.argv) > 8:
  ks[0] = float(sys.argv[8])
if len(sys.argv) > 9:
  ks[1] = float(sys.argv[9])
if len(sys.argv) > 10:
  extfilename = sys.argv[10]

initvalues = [0.0, 0.0, 0.0, 0.0064, 0.0, 0.0, 0.00098, 2.0, 0.00067, 0.0]

species = ['cAMP', 'PKAcAMP2', 'PKAcAMP4', 'PKA', 'PKAr', 'PKAc', 'AMP', 'ATP','PDE4','PDE4cAMP']
windows = [0, 0, 0, 2, 1, 1, 3, 3, 4, 4]
tolscales = [1.0 for i in range(0,len(species))]

print "my_volume = "+str(my_volume)+" l ?= "+str(dend.L*(dend.diam/2)**2*3.14159265358)+" um3"
specs = []
for ispec in range(0,len(species)):
  specs.append(rxd.Species(cyt, name='spec'+str(ispec), charge=0, initial=initvalues[ispec], atolscale=tolscales[ispec]))
cAMP_flux_rate = rxd.Parameter(cyt, initial=0)

#specs[0] cAMP
#specs[1] PKA2cAMP
#specs[2] PKA4cAMP
#specs[3] PKA
#specs[4] PKAr
#specs[5] PKAc
#specs[6] AMP
#specs[7] ATP
#specs[8] PDE4
#specs[9] PDE4cAMP
reaction1 = rxd.Reaction(specs[3] + specs[0]*4 <> specs[2], ks[0], ks[1])
reaction3 = rxd.Reaction(specs[2] <> specs[4] + specs[5]*2, ks[4]*specs[2], ks[5]*specs[4]*specs[5], custom_dynamics=True)

reaction4 = rxd.Reaction(specs[6] > specs[7], ks[6])
reaction5 = rxd.Reaction(specs[8] + specs[0] <> specs[9], ks[7], ks[8])
reaction6 = rxd.Reaction(specs[9] > specs[7] + specs[8], ks[9])


reaction_cAMP_flux = rxd.Rate(specs[0], cAMP_flux_rate) # cAMP
vec_t = h.Vector()

vecs = []
vec_t = h.Vector()
vec_t.record(h._ref_t)
for ispec in range(0,len(species)):
  vecs.append(h.Vector())
  vecs[ispec].record(specs[ispec].nodes(dend)(0.5)[0]._ref_concentration)

cvode = h.CVode()
cvode.active(1)
hmax = cvode.maxstep(100)
hmin = cvode.minstep(1e-10)
cvode.atol(tolerance)

h.finitialize(-65)
def set_param(param, val):
    param.nodes.value = val
    h.cvode.re_init()

### Set on and off the inputs to the spine
T = 1000./cAMP_input_freq
unow = 1
tnow = 0
for itrain in range(0,Ntrains):
    for istim in range(0,cAMP_input_N):
      tnew = cAMP_input_onset + istim*T + trainT*itrain
      h.cvode.event(tnew, lambda: set_param(cAMP_flux_rate, cAMP_input_flux/6.022e23/my_volume*1e3))
      h.cvode.event(tnew+cAMP_input_dur, lambda: set_param(cAMP_flux_rate, 0))
      tnow = tnew
timenow = time.time()
h.continuerun(Duration)
print "Simulation done in "+str(time.time()-timenow)+" seconds"
def isFlux(t):
  for itrain in range(0,Ntrains):
    for istim in range(0,cAMP_input_N):
      tnew = cAMP_input_onset + istim*T + trainT*itrain
      if t >= tnew and t < tnew+cAMP_input_dur:
        return 1
  return 0
tvec = array(vec_t)
minDT_nonFlux = 20.0
minDT_Flux = 1.0
lastt = -inf
itvec2 = []
for it in range(0,len(tvec)):
  if tvec[it] - lastt > minDT_nonFlux or (isFlux(tvec[it]) and tvec[it] - lastt > minDT_Flux):
    itvec2.append(it)
    lastt = tvec[it]

headers = [ 'tvec', 'cAMP', 'PKAcAMP2', 'PKAcAMP4', 'PKA', 'PKAr', 'PKAc']
if len(extfilename) > 0:
  scipy.io.savemat(extfilename+'.mat', {'tcDATA': array(vecs), 'times': array(tvec)})
else:
  scipy.io.savemat('cAMP_withdiss_testwilliamson_tstop'+str(Duration)+'_tol'+str(tolerance)+'_onset'+str(cAMP_input_onset)+'_n'+str(cAMP_input_N)+'_freq'+str(cAMP_input_freq)+'_dur'+str(cAMP_input_dur)+'_flux'+str(cAMP_input_flux)+'.mat',
    {'tcDATA': array(vecs), 'times': array(tvec)})

if False:
  f,axarr = subplots(1,max(windows)+1)
  for i in range(0,len(vecs)):
    axarr[windows[i]].plot(tvec,array(vecs[i]),label=species[i])
  for i in range(0,len(axarr)):
    axarr[i].legend(fontsize=6)
  if len(extfilename) > 0:
    f.savefig('cAMP_withdiss_testwilliamson_'+extfilename+'.eps')
  else:
    f.savefig('cAMP_withdiss_testwilliamson_tstop'+str(Duration)+'_tol'+str(tolerance)+'_onset'+str(cAMP_input_onset)+'_n'+str(cAMP_input_N)+'_freq'+str(cAMP_input_freq)+'_dur'+str(cAMP_input_dur)+'_flux'+str(cAMP_input_flux)+'.eps')

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