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]