for d in data.split_periodic(11000, adjust=True, closed_intervals=False):
d = d.trim_left(10000, adjust=True)
act_gate = d['ito.g']
output = output + [max(act_gate, key=abs)]
norm = max(output)
try:
for i in range(len(output)):
output[i] /= norm
except:
output = [float('inf'),]*len(output)
return output
wang_act = Experiment(
dataset=wang_act_dataset,
protocol=wang_act_protocol,
conditions=wang_conditions,
sum_stats=wang_act_sum_stats,
description=wang_act_desc,
Q10=None,
Q10_factor=0.)
#
# Inactivation [Wang1993]
#
wang_inact_desc = """
Steady-state inactivation curve for ito in human
atrial myocytes [Wang1993] cf Fig 2c.
Voltage-dependent inactivation was assessed with the use of a
two-pulse protocol with a 1000 ms prepulse to voltages between -80
and +40 mV, followed by a 1000 ms test pulse to +60 mV.
variances_act = [sd_**2 for sd_ in sd_act]
mewes_act_dataset = np.asarray([vsteps_act, act, variances_act])
mewes_act_protocol = myokit.pacing.steptrain(vsteps_act, -40, 10000, 450)
mewes_conditions = {
'phys.T': 295.15, # K
'ca_conc.Ca_o': 1.8 # mM
}
def mewes_act_sum_stats(data):
output = []
for d in data.split_periodic(10450, adjust=True, closed_intervals=False):
d = d.trim_left(10000, adjust=True)
act_gate = d['ical.g']
output = output + [max(act_gate, key=abs)]
norm = max(output)
for i in range(len(output)):
output[i] /= norm
return output
mewes_act = Experiment(dataset=mewes_act_dataset,
protocol=mewes_act_protocol,
conditions=mewes_conditions,
sum_stats=mewes_act_sum_stats,
description=mewes_act_desc,
Q10=None,
Q10_factor=0.)
def dias_iv_sum_stats(data):
return dias_iv_tau_sum_stats(data, ss=True, tau=False)
def dias_taua_sum_stats(data):
return dias_iv_tau_sum_stats(data, ss=False, taua=True, taui=False)
def dias_taui_sum_stats(data):
return dias_iv_tau_sum_stats(data, ss=False, taua=False, taui=True)
dias_iv = Experiment(dataset=dias_iv_dataset,
protocol=dias_iv_protocol,
conditions=dias_conditions,
sum_stats=dias_iv_sum_stats,
description=dias_iv_desc,
Q10=Q10_cond,
Q10_factor=1)
dias_tau_act = Experiment(dataset=dias_taua_dataset,
protocol=dias_iv_protocol,
conditions=dias_conditions,
sum_stats=dias_taui_sum_stats,
description=dias_iv_desc,
Q10=Q10_tau,
Q10_factor=-1)
dias_tau_inact = Experiment(dataset=dias_taui_dataset,
protocol=dias_iv_protocol,
conditions=dias_conditions,
sum_stats=dias_taui_sum_stats,
description=dias_iv_desc,
}
def dias_iv_sum_stats(data):
output = []
for d in data.split_periodic(5100, adjust=True):
d = d.trim(5000, 5100, adjust=True)
current = d['ina.i_Na']
index = np.argmax(np.abs(current))
output = output + [current[index] / max_observed_peak]
return output
dias2014_iv = Experiment(dataset=dias_iv_dataset,
protocol=dias_iv_protocol,
conditions=dias_conditions,
sum_stats=dias_iv_sum_stats,
description=dias_iv_description)
### Inactivation
nakajima_desc = """Inactivation curve from Nakajima 2009.
Measurements taken at room temperature so no adjustment.
"""
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 = {
'membrane.Na_o': 145e3,
def nguyen_iv_sum_stats(data):
output = []
for d in data.split_periodic(5300, adjust=True):
d = d.trim(5000, 5300, adjust=True)
current = d['icat.i_CaT']
index = np.argmax(np.abs(current))
output = output + [current[index] / max_iv_peak]
return output
nguyen_iv = Experiment(dataset=nguyen_iv_dataset,
protocol=nguyen_iv_protocol,
conditions=nguyen_conditions,
sum_stats=nguyen_iv_sum_stats,
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])
} # K
def sakakibara_iv_sum_stats(data):
output = []
for d in data.split_periodic(11000, adjust=True):
d = d.trim_left(10000, adjust=True)
current = d['ina.i_Na']
output = output + [max(current, key=abs)]
return output
sakakibara_iv = Experiment(dataset=sakakibara_iv_dataset,
protocol=sakakibara_iv_protocol,
conditions=sakakibara_conditions,
sum_stats=sakakibara_iv_sum_stats,
description=sakakibara_iv_desc,
Q10=Q10_cond,
Q10_factor=1)
#
# IV curves for varying extracellular Sodium [Sakakibara1992]
#
sakakibara_iv_Nao2_desc = """
describes the protocol used to measure the differents IV-curves in the Sakakibara Paper (figure 3A)
this protocol is for measuring the curve with Nao = 2mM
page 5 of the paper :
Test pulses were applied at 0.1 Hz
"""
Na_o = 2
d = d.trim_left(10000, adjust=True)
current = d['ical.i_CaL']
peak = max(current, key=abs)
ss = current[-1]
try:
output = output + [1 - ss / peak]
except:
output = output + [float('inf')]
return output
sun_inact_kin = Experiment(
dataset=[sun_inact_kin_tf_dataset, sun_inact_kin_ts_dataset],
protocol=sun_inact_kin_protocol,
conditions=sun_conditions,
sum_stats=sun_inact_kin_sum_stats,
description=sun_inact_kin_desc,
Q10=Q10_tau_inact,
Q10_factor=-1)
sun_inact_kin_fast = Experiment(dataset=sun_inact_kin_tf_dataset,
protocol=sun_inact_kin_protocol,
conditions=sun_conditions,
sum_stats=sun_inact_kin_sum_stats_tf,
description=sun_inact_kin_desc,
Q10=Q10_tau_inact,
Q10_factor=-1)
sun_inact_kin_slow = Experiment(dataset=sun_inact_kin_ts_dataset,
protocol=sun_inact_kin_protocol,
conditions=sun_conditions,
'phys.T': 306.15, # K
'k_conc.K_i': 140, # mM
'k_conc.K_o': 5.4
}
def firek_inact_sum_stats(data):
output = []
for d in data.split_periodic(20800, adjust=True, closed_intervals=False):
d = d.trim_left(20400, adjust=True)
inact_gate = d['ito.g']
output = output + [max(inact_gate, key=abs)]
norm = max(output)
try:
for i in range(len(output)):
output[i] /= norm
except:
output = [
float('inf'),
] * len(output)
return output
firek_inact = Experiment(dataset=firek_inact_dataset,
protocol=firek_inact_protocol,
conditions=firek_conditions,
sum_stats=firek_inact_sum_stats,
description=firek_inact_desc,
Q10=None,
Q10_factor=0.)
toyoda_conditions = {'extra.K_o': 5.4e3, 'potassium.K_i': 130e3, 'phys.T': 308}
def toyoda_iv_sum_stats(data):
output = []
for d in data.split_periodic(11000, adjust=True):
d = d.trim(5000, 6000, adjust=True)
output = output + [d['ikr.i_Kr'][-1]] # / max_observed_peak_iv]
return output
toyoda_iv = Experiment(dataset=toyoda_iv_dataset,
protocol=toyoda_iv_protocol,
conditions=toyoda_conditions,
sum_stats=toyoda_iv_sum_stats,
description=toyoda_iv_desc,
Q10=Q10_cond,
Q10_factor=1)
#
# Activation kinetics [Toyoda2010]
#
toyoda_taua_desc = """Activation kinetics Toyoda 2010 (Fig 3C).
Assessed by envelope-of-tails protocol. Series of depolarising
steps of varying duration with single exponential fit to peak
tail current elicited upon repolarisation to holding potential.
Data recorded at 308K.
"""
# SUMMARY STATISTICS
def Li_iv_80_sum_stats(data):
output = []
for d in data.split_periodic(tperiod_iv_Li, adjust=True):
d = d.trim_left(tpreMeasuring_iv_Li, adjust=True)
current = d['i_caL.i_Ca_L'][:-1] # the last value is sometimes a nan
# (because V =0 at the end of the simulation and that I in nygren model is not defined for V = 0)
output = output + [max(current, key=abs) - current[-1]]
return output
# Experiment
Li_iv_80 = Experiment(name=Li_iv_80_name,
dataset=Li_iv_80_dataset,
protocol=Li_iv_80_protocol,
conditions=Li_conditions,
sum_stats=Li_iv_80_sum_stats,
description=Li_iv_80_desc,
Q10=Q10_cond,
Q10_factor=1)
#######################################################################################################################
### IV curve - Li 1997
Li_iv_60_name = "IV HP = -60mV"
Li_iv_60_desc = """
describes the protocol used to measure the IV peak-current curve in the Li Paper figure 1C
page 2 of the paper : I-V relations of Ica were determined using 300-ms depolarizing steps every 10s from HP of -80,-60, and -40 mV
The magnitude was measured as the difference between the peak inward current and the steady state current at the end of the depolarizing step
protocol used in figure 1C: single test pulse at a frequency of 0.1Hz : every 10s, the voltage step occurs.
"""
output_inact = output_inact + [float('inf')]
return output_act + output_inact
def courtemanche_act_kin_sum_stats(data):
return courtemanche_kin_sum_stats(data, act=True, inact=False)
def courtemanche_inact_kin_sum_stats(data):
return courtemanche_kin_sum_stats(data, act=False, inact=True)
courtemanche_kin = Experiment(
dataset=[courtemanche_act_kin_dataset, courtemanche_inact_kin_dataset],
protocol=courtemanche_kin_protocol,
conditions=wang_conditions,
sum_stats=courtemanche_kin_sum_stats,
description=courtemanche_kin_desc,
Q10=Q10_tau,
Q10_factor=-1)
courtemanche_act_kin = Experiment(dataset=courtemanche_act_kin_dataset,
protocol=courtemanche_kin_protocol,
conditions=wang_conditions,
sum_stats=courtemanche_act_kin_sum_stats,
description=courtemanche_kin_desc,
Q10=Q10_tau,
Q10_factor=-1)
courtemanche_inact_kin = Experiment(dataset=courtemanche_inact_kin_dataset,
protocol=courtemanche_kin_protocol,
conditions=wang_conditions,
sum_stats=courtemanche_inact_kin_sum_stats,
description=courtemanche_kin_desc,
}
def lu_iv_sum_stats(data):
output = []
for d in data.split_periodic(5330, adjust=True):
d = d.trim(5030, 5330, adjust=True)
peak_curr = max(d['ikss.i_Kss'], key=abs)
output = output + [peak_curr]
return output
lu_iv = Experiment(dataset=lu_iv_dataset,
protocol=lu_iv_protocol,
conditions=lu_conditions,
sum_stats=lu_iv_sum_stats,
description=lu_iv_desc,
Q10=Q10_cond,
Q10_factor=1)
#
# IV curve [Yang2005]
#
yang_iv_desc = """Peak, steady-state current density and inactivation
time constants at voltage steps for 4-AP sensitive currents in
HL-1 from Yang 2005 Figure 8B.
Assuming steadystate current is ikss.
Measurements taken at room temperature.
"""
