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
                            
import numpy

protoparams_fixed = { 'Duration': 5260000, 'tolerance': 1e-6, 'Ca_input_onset': 4040000}
# Stimulus protocols:
protoparams_var = {
  'Ca_input_Ns':      [100,      156,      4,        4,        50,       4,        180,      5],
  'Ca_input_freqs':   [100,      312,      100,      100,      0.1,      100,      1,        100],
  'Ca_input_Ntrains': [1,        1,        10,       50,       1,        10,       1,        25],
  'Ca_input_trainTs': [1,        1,        200,      200,      1,        200,      1,        1000],
  'Ca_input_durs':    [3,        3,        3,        3,        3,        3,        15,       3]    #used a standard 3-ms Ca flux for all cases. 900 x 5Hz x 3ms replaced by 180 x 1Hz x 15ms for speed
}

#Measure 0) Ca
#        1) S845-phos GluR1
#        2) S831-phos GluR1
#        3) double-phos GluR1
#        4) membrane-inserted GluR1
#        5) membrane-inserted S831-phos GluR1
#        6) S880-phos GluR2
#        7) membrane-inserted GluR2
#        8) synaptic maximal conductance, which is calculated from 4), 5), and 7)
Measured_species = [ ['Ca'],
                     ['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'],
                     ['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'],
                     ['GluR1_S845_S831', 'GluR1_S845_S831_PP1', 'GluR1_S845_S831_PP2B', 'GluR1_memb_S845_S831', 'GluR1_memb_S845_S831_PP1', 'GluR1_memb_S845_S831_PP2B'],
                     ['GluR1_memb', 'GluR1_memb_S845', 'GluR1_memb_S831', 'GluR1_memb_S845_S831', 'GluR1_memb_PKAc', 'GluR1_memb_CKCam', 'GluR1_memb_CKpCam', 'GluR1_memb_CKp', 'GluR1_memb_PKCt', 'GluR1_memb_PKCp', 'GluR1_memb_S845_CKCam', 'GluR1_memb_S845_CKpCam', 'GluR1_memb_S845_CKp', 'GluR1_memb_S845_PKCt', 'GluR1_memb_S845_PKCp', 'GluR1_memb_S831_PKAc', 'GluR1_memb_S845_PP1', 'GluR1_memb_S845_S831_PP1', 'GluR1_memb_S831_PP1', 'GluR1_memb_S845_PP2B', 'GluR1_memb_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'],
                     ['GluR2_S880', 'GluR2_S880_PP2A', 'GluR2_memb_S880', 'GluR2_memb_S880_PP2A'],
                     ['GluR2_memb', 'GluR2_memb_PKCt', 'GluR2_memb_PKCp', 'GluR2_memb_S880', 'GluR2_memb_S880_PP2A'],
                     'syncond' ]

Quantification_types = ['abs(target-max val)', 'abs(target-last val)', 'abs(target-(last val/baseline))', 'abs(target-(val(t)/baseline))']

# Experiments: [ [STIMULUS PROTOCOL INDEX], [CAFLUX COEFF], [LFLUX COEFF], [GLUFLUX COEFF], [ACHFLUX COEFF], [BLOCKED], [ALTERED] ]
Experiments = [ [0, 1.0,  0.0, 1.0, 0.0, 'None', []],                  #0; Ma 2008 differential
                [0, 1.0,  0.0, 1.0, 0.0, 'None', [[125,126,127],0.0]], #1; (CK phosphorylation blocked)
                [0, 0.01, 0.0, 1.0, 0.0, 'None', []],                  #2; (post-syn Ca blocked)
                [0, 1.0,  0.0, 1.0, 0.0, 'None', [[317],0]],           #3; (PKAc separation from PKAcAMP4 blocked)
                [1, 1.0,  1.0, 1.0, 0.0, 'None', []],                  #4; Saez-Briones 2015 b2-Adrenoceptor and Flores 2011 hidden
                [1, 1.0,  0.0, 1.0, 0.0, 'None', []],                  #5;
                [2, 1.0,  0.0, 1.0, 0.0, 'None', []],                  #6; Hardingham 2003 neocortical 
                [2, 1.0,  0.0, 1.0, 0.0, 'None', [[125,126,127],0.0]], #7; (CK phosphorylation blocked)
                [3, 1.0,  0.0, 1.0, 0.0, 'None', []],                  #8; Song 2017 selective 
                [3, 1.0,  0.0, 1.0, 0.0, 'None', [[154,187,206,239],0.0]], #9 (s845 phosphorylation by PKA blocked)
                [3, 1.0,  0.0, 1.0, 0.0, 'None', [[157,160,163,166,169,172,175,178,181,184,209,212,215,218,221,224,227,230,233,236],0.0]], #10 (s831 phosphorylation by PKC and CK blocked)
                [4, 1.0,  1.0, 1.0, 0.0, 'None', []],                  #11; Zhou 2013 activation
                [4, 1.0,  0.0, 1.0, 0.0, 'None', []],                  #12;
                [5, 1.0,  0.0, 1.0, 0.0, 'None', []],                  #13; Kirkwood 1997 age-dependent
                [5, 1.0,  0.0, 1.0, 0.0, 'CKx0.0', []],                #14; (CK knockout)
                [6, 1.0,  0.0, 1.0, 0.0, 'None', []],                  #15;
                [6, 1.0,  0.0, 1.0, 0.0, 'CKx0.0', []],                #16; (CK knockout)
                [7, 1.0,  0.0, 1.0, 0.0, 'None', []] ]                 #17; Kotak 2007 developmental 

#Measurement: [ [EXPERIMENT_INDEX], [TARGET_T], [TARGET_VAL] ]
Measurements = [ [ [0,1,2],       [4640000, 4940000, 5240000], [[1.3,1.4,1.3],[1.05,1.02,0.95],[1.05,1.05,1.1]] ],                              #Ma 2008 differential, horizontal
                 [ [0,3,2,1],     [4640000, 4940000, 5240000], [[1.6,1.6,1.6],[1.4,1.4,1.4],[1.3,1.4,1.4],[1.6,1.7,1.6]] ],                     #Ma 2008 differential, ascending
                 [ [4,5],         [4640000, 4940000, 5240000], [[2.0,1.98,1.9],[1.34,1.4,1.36]] ],                                              #Saez-Briones 2015 b2-Adrenoceptor
                 [ [4,5],         [4640000, 4940000, 5240000], [[1.7,1.6,1.64],[1.43,1.45,1.43]] ],                                             #Flores 2011 hidden
                 [ [6,7],         [4640000, 4940000, 5240000], [[1.35,1.4,1.3],[1.25,1.2,1.1]] ],                                               #Hardingham 2003 neocortical
                 [ [8,9,10],      [4640000, 4940000, 5240000], [[1.55,1.4,1.4],[1.1,1.05,1.05],[1.35,1.4,1.3]] ],                               #Song 2017 selective
                 [ [11,12],       [4640000, 4940000, 5240000], [[1.3,1.4,1.4],[1.1,1.2,1.2]] ],                                                 #Zhou 2013 activation
                 [ [13,15],       [4640000, 4940000, 5240000], [[1.3,1.26,1.26],[numpy.nan,0.95,0.95]] ],                                       #Kirkwood 1997 age-dependent, adult neurons
                 [ [13,15],       [4640000, 4940000, 5240000], [[1.2,1.18,1.18],[numpy.nan,0.79,0.82]] ],                                       #Kirkwood 1997 age-dependent, 4-5 week old neurons
                 [ [17],          [4640000, 4940000, 5240000], [[1.98,1.58,1.93]] ],                                                            #Kotak 2007 developmental, LTP-expressing neurons
                 [ [17],          [4640000, 4940000, 5240000], [[0.77,0.68,0.67]] ] ]                                                           #Kotak 2007 developmental, LTD-expressing neurons
Measurements_txt = [['Ma 2008 differential, horizontal', 'CONTROL', 'CK BLOCKED', 'POST-SYN CA BLOCKED'],
                    ['Ma 2008 differential, ascending', 'CONTROL', 'PKA BLOCKED', 'POST-SYN CA BLOCKED', 'CK BLOCKED'],
                    ['Saez-Briones 2015 b2-Adrenoceptor', 'WITH L', 'WITHOUT L'],
                    ['Flores 2011 hidden', 'WITH L', 'WITHOUT L'],
                    ['Hardingham 2003 neocortical', 'CONTROL', 'CK BLOCKED'],
                    ['Song 2017 selective', 'CONTROL', 'S845 BLOCKED', 'S831 BLOCKED'],
                    ['Zhou 2013 activation', 'WITH L', 'WITHOUT L'],
                    ['Kirkwood 1997 age-dependent, adult', 'TBS, CONTROL', 'LFS, CONTROL'],
                    ['Kirkwood 1997 age-dependent, 4-5 week', 'TBS, CONTROL', 'LFS, CONTROL'],
                    ['Kotak 2007 developmental', 'LTP'],
                    ['Kotak 2007 developmental', 'LTD'] ]
Measurements_stds = [[[0.1, 0.1, 0.1], [0.07, 0.08, 0.05], [0.08, 0.1, 0.08]],
                     [[0.12, 0.1, 0.12], [0.11, 0.13, 0.15], [0.15, 0.12, 0.13], [0.15,0.16,0.18]],
                     [[0.08, 0.09, 0.08], [0.08, 0.09, 0.1]],
                     [[0.13, 0.13, 0.1], [0.11, 0.1, 0.09]],
                     [[0.07, 0.1, 0.09], [0.09, 0.12, 0.07]],
                     [[0.05, 0.05, 0.05], [0.07, 0.07, 0.07], [0.1, 0.1, 0.1]], #Figs. 1J, 1E, 4E
                     [[0.15, 0.17, 0.1], [0.1, 0.1, 0.18]],                     #Figs. 2D and 2C
                     [[0.08, 0.07, 0.07], [0.015, 0.02, 0.015], [numpy.nan, 0.05, 0.04], [numpy.nan, 0.03, 0.03]], #Figs. 1A, 1B, 3A, 3C, white
                     [[0.05, 0.05, 0.05], [0.02, 0.03, 0.03], [numpy.nan, 0.03, 0.02], [numpy.nan, 0.035, 0.03]],  #Figs. 1A, 1B, 3A, 3C, black
                     [[0.25, 0.11, 0.21]],                                                                         #Figs. 4A, 4B
                     [[0.09, 0.1, 0.08]]]                                                                          #Figs. 4A, 4B

def get_measurement_protocol():
  return [Measurements, Experiments, protoparams_fixed, protoparams_var, Measured_species, Quantification_types, Measurements_txt, Measurements_stds]

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