cm_conditions = {'membrane.Na_o': 140000,
'membrane.Na_i': 11200,
'membrane.T': 310}
# Activation protocol (Sakakibara 1992)
protocols.append(
myokit.pacing.steptrain_linear(-100, 30, 10, -140, 9900, 100)
)
sakakibara_conditions = {'membrane.Na_o': 5000,
'membrane.Na_i': 5000,
'membrane.T': 290.15}
conditions.append(cm_conditions)
# Inactivation protocol voltage-shifted +20mV (Sakakibara 1992)
protocols.append(
availability_linear(-120, -10, 10, -140, -20, 10000, 1000, 0, 30)
)
conditions.append(cm_conditions)
'''
# Inactivation protocols (Schneider 1994, pp 32ms)
protocols.append(
availability(vsteps_inact, -135, -20, 10000, 32, 0, 30)
)
conditions.append(schneider_conditions)
# Inactivation protocols (Schneider 1994, pp 64ms)
protocols.append(
availability(vsteps_inact, -135, -20, 10000, 64, 0, 30)
)
conditions.append(schneider_conditions)
variances_inact_300 = [(sd_)**2 for sd_ in sd_inact_300]
li_inact_300_dataset = np.asarray([vsteps_inact, inact_300, variances_inact_300])
vsteps_inact, inact_150, sd_inact_150 = data.inact_Li_all(150)
variances_inact_150 = [(sd_)**2 for sd_ in sd_inact_150]
li_inact_150_dataset = np.asarray([vsteps_inact, inact_150, variances_inact_150])
tpre = 10000
twait = 0
ttest = 300
vhold = -80 # mV
vtest = 10
vlower = -80
dv = 10
vupper = 50+dv
li_inact_1000_protocol = availability_linear(
vlower, vupper, dv, vhold, vtest, tpre, 1000, twait, ttest)
li_inact_300_protocol = availability_linear(
vlower, vupper, dv, vhold, vtest, tpre, 300, twait, ttest)
li_inact_150_protocol = availability_linear(
vlower, vupper, dv, vhold, vtest, tpre, 150, twait, ttest)
def li_inact_sum_stats(data, tstep):
output = []
for d in data.split_periodic(10300+tstep, adjust=True, closed_intervals=False):
d = d.trim_left(10000+tstep, adjust=True)
inact_gate = d['ical.g']
output = output + [max(inact_gate, key=abs)]
norm = output[0] # absence of prepulse
for i in range(len(output)):
output[i] /= norm
# account for incomplete voltage-dependent inactivation
sakakibara_act = Experiment(dataset=[vsteps_act, act, variances_act],
protocol=sakakibara_act_protocol,
conditions=cm_conditions,
sum_stats=sakakibara_act_sum_stats,
description=sakakibara_act_desc)
### Inactivation Sakakibara 1992 (voltage shifted)
sakakibara_inact_desc = """Inactivation curve from Sakakibara 1992.
Voltage shifted +20mV as in Courtemanche 1998."""
vsteps_inact, inact, sd_inact = data.Inact_Sakakibara()
vsteps_inact = [v_ + 20 for v_ in vsteps_inact]
variances_inact = [sd**2 for sd in sd_inact]
sakakibara_inact_protocol = availability_linear(-120, -10, 10, -140, -20,
10000, 1000, 0, 30)
def sakakibara_inact_sum_stats(data):
output = []
for d in data.split_periodic(11030, adjust=True):
d = d.trim(11000, 11030, adjust=True).npview()
gate = d['ina.g']
output = output + [max(gate)]
for i in range(1, len(output)):
output[i] = output[i] / output[0]
output[0] = 1.
return output
sakakibara_inact = Experiment(dataset=[vsteps_inact, inact, variances_inact],
description=dias_iv_desc,
Q10=Q10_cond, # TODO: fix to use different Q10 values?
Q10_factor=[1, -1])
#
# Steady-state inactivation [Rao2009]
#
rao_inact_desc = """Inactivation curve for iCaL in HL-1 from Rao 2009.
Measurements taken at room temperature.
"""
vsteps_inact, inact, sd_inact = data.Inact_Rao()
variances_inact = [sd**2 for sd in sd_inact]
rao_inact_dataset = np.asarray([vsteps_inact, inact, variances_inact])
rao_inact_protocol = availability_linear(-100, 20, 10, -80, -20, 5000, 1000, 0,
400)
rao_conditions = {
'extra.Ca_o': 5e3,
'calcium.Ca_i': 7.6e-3, # assume same as Dias2014
'phys.T': room_temp
}
def rao_inact_sum_stats(data):
output = []
for d in data.split_periodic(6400, adjust=True):
d = d.trim(6000, 6400, adjust=True)
current = d['ical.i_CaL']
output = output + [max(current, key=abs)]
for i in range(1, len(output)):
vsteps_inact, inact, sd_inact = data.Inact_Sakakibara()
variances_inact = [(sd_)**2 for sd_ in sd_inact]
sakakibara_inact_dataset = np.asarray([vsteps_inact, inact, variances_inact])
tpre = 10000 # ms
tstep = 1000
twait = 0
ttest = 30
Vhold = -140 # mV
Vtest = -20
Vlower = -140
dV = 10
Vupper = -30
sakakibara_inact_protocol = availability_linear(
Vlower, Vupper, dV, Vhold, Vtest, tpre, tstep, twait, ttest)
def sakakibara_inact_sum_stats(data):
output = []
for d in data.split_periodic(11030, adjust=True):
d = d.trim_left(11000, adjust = True)
inact_gate = d['ina.g']
output = output+[max(inact_gate, key=abs)]
norm = max(output)
try:
for i in range(len(output)):
output[i] /= norm
except:
for i in range(len(output)):
output[i] = float('inf')
return output
vhold = -135 # mV
vtest = -20
tpre = 10000 # ms
tsteps_inact = prepulses
twait = 0
ttest = 12
vstart = -135
vend = 5
dv = 5
vsteps_inact = np.arange(-135, 10, 5).tolist()
tmp_protocols = []
for tstep in tsteps_inact:
tmp_protocols.append(
availability_linear(vstart, vend + dv, dv, vhold, vtest, tpre, tstep,
twait, ttest))
tsplits_inact = [p.characteristic_time() for p in tmp_protocols]
for i in range(len(tsplits_inact) - 1):
tsplits_inact[i + 1] += tsplits_inact[i]
schneider_inact_protocol = tmp_protocols[0]
for p in tmp_protocols[1:]:
for e in p.events():
schneider_inact_protocol.add_step(e.level(), e.duration())
def schneider_inact_sum_stats(data):
def boltzmann_fn(V, Vh, k):
return 1 / (1 + np.exp((V - Vh) / k))
output_Vh = []
nygren_inact_shift = 32.2 # mV
vsteps_inact_shifted = [v+nygren_inact_shift for v in vsteps_inact]
sakakibara_inact_shifted_dataset = np.asarray([vsteps_inact_shifted, inact, variances_inact])
tpre = 10000 # ms
tstep = 1000
twait = 0
ttest = 30
Vhold = -140 # mV
Vtest = -20
Vlower = -140
dV = 10
Vupper = -30
sakakibara_inact_protocol = availability_linear(
Vlower, Vupper, dV, Vhold, Vtest, tpre, tstep, twait, ttest)
sakakibara_inact_shifted_protocol = availability_linear(
Vlower+nygren_inact_shift,
Vupper+nygren_inact_shift, dV,
Vhold+nygren_inact_shift,
Vtest+nygren_inact_shift, tpre, tstep, twait, ttest)
sakakibara_conditions = {'na_conc.Na_o': 5, # mM
'na_conc.Na_i': 5, # mM
'phys.T': 290.15} # K
def sakakibara_inact_sum_stats(data):
output = []
for d in data.split_periodic(11030, adjust=True):
d = d.trim_left(11000, adjust = True)
inact_gate = d['ina.g']
sum_stats=dias_iv_tau_sum_stats,
description=dias_iv_desc,
Q10=[Q10_cond, Q10_tau, Q10_tau],
Q10_factor=[1, -1, -1])
#
# Inactivation [Nakajima2010]
#
nakajima_desc = """Inactivation curve from Nakajima 2010.
Measurements taken at room temperature.
"""
vsteps_inact, inact, sd_inact = data.Inact_Nakajima()
variances_inact = [sd**2 for sd in sd_inact]
nakajima_inact_dataset = np.asarray([vsteps_inact, inact, variances_inact])
nakajima_inact_protocol = availability_linear(-130, -20, 10, -120, -20, 5000,
500, 0, 100)
nakajima_conditions = {
'extra.Na_o': 145e3,
'sodium.Na_i': 10e3,
'extra.K_o': 0., # Cs used to avoid contamination
'potassium.K_i': 0.,
'phys.T': room_temp
}
def nakajima_inact_sum_stats(data):
output = []
for d in data.split_periodic(5600, adjust=True):
d = d.trim(5500, 5600, adjust=True)
current = d['ina.i_Na']
output = output + [max(current, key=abs)]
description=nguyen_iv_desc,
Q10=Q10_cond,
Q10_factor=1)
#
# Steady-state inactivation [Nguyen2013]
#
nguyen_inact_desc = """Inactivation curve for icat from Nguyen 2013 Fig 5E.
Recordings at room temperature.
"""
vsteps_inact, inact, sd_inact = data.Inact_Nguyen()
variances_inact = [sd**2 for sd in sd_inact]
nguyen_inact_dataset = np.asarray([vsteps_inact, inact, variances_inact])
nguyen_inact_protocol = availability_linear(-80, -20, 5, -90, -10, 5000, 1000,
0, 150)
def nguyen_inact_sum_stats(data):
output = []
for d in data.split_periodic(6150, adjust=True):
d = d.trim(6000, 6150, adjust=True)
current = d['icat.i_CaT']
output = output + [max(current, key=abs)]
for i in range(1, len(output)):
output[i] = output[i] / output[0]
output[0] = 1.
return output
nguyen_inact = Experiment(dataset=nguyen_inact_dataset,
sakakibara_inact_cou_adjust_dataset = np.asarray(
[vsteps_inact_cou_adjust, inact, variances_inact])
tpre = 10000 # ms
tstep = 1000
twait = 0
ttest = 30
Vhold = -140 # mV
Vtest = -20
Vlower = -140
dV = 10
Vupper = -30
sakakibara_inact_protocol = availability_linear(Vlower, Vupper, dV, Vhold,
Vtest, tpre, tstep, twait,
ttest)
sakakibara_inact_nyg_adjust_protocol = availability_linear(
Vlower + nyg_adjust_inact, Vupper + nyg_adjust_inact, dV,
Vhold + nyg_adjust_inact, Vtest + nyg_adjust_inact, tpre, tstep, twait,
ttest)
sakakibara_inact_cou_adjust_protocol = availability_linear(
Vlower + cou_adjust, Vupper + cou_adjust, dV, Vhold + cou_adjust,
Vtest + cou_adjust, tpre, tstep, twait, ttest)
def sakakibara_inact_sum_stats(data):
output = []
for d in data.split_periodic(11030, adjust=True):
d = d.trim_left(11000, adjust=True)
inact_gate = d['ina.g']
Li_inact_1000_dataset = np.asarray([vsteps_inact, inact, variances_inact])
# PROTOCOL
tperiod_inact_Li_1000 = 10000 # ms
twait = 0
ttest = 300
tpre = tperiod_inact_Li_1000 - tstep - twait - ttest
tpreMeasuring_inact_Li_1000 = tperiod_inact_Li_1000 - ttest # tperiod - ttest
Vhold = -80 # mV
Vtest = 10
Vlower = -80
dV = 10
Vupper = 50 + dV # check why the +dv is required
Li_inact_1000_protocol = availability_linear(Vlower, Vupper, dV, Vhold, Vtest,
tpre, tstep, twait, ttest)
# CONDITIONS
Li_conditions = {'membrane.T': room_temp}
# SUMMARY STATISTICS
def Li_inact_1000_sum_stats(data):
output = []
for d in data.split_periodic(tperiod_inact_Li_1000, adjust=True):
d = d.trim_left(tpreMeasuring_inact_Li_1000, adjust=True)
inact_gate = d['i_caL.G_Na_norm']
index = np.argmax(np.abs(inact_gate))
output = output + [np.abs(inact_gate[index])]
Norm = output[0]
for i in range(len(output)):