IonChannelModel)
import data.iha.data_iha as data
import numpy as np
modelfile = 'models/Korhonen2009_iha.mmt'
iha = IonChannelModel('iha',
modelfile,
vvar='membrane.V',
logvars=['environment.time', 'iha.i_ha', 'iha.G_ha'])
### Exp 1 - IV curve.
iv_vsteps, iv_curr, iv_errs, iv_N = data.IV_Sartiani()
iv_data = ExperimentData(x=iv_vsteps,
y=iv_curr,
N=iv_N,
errs=iv_errs,
err_type='SEM')
stim_times = [500, 1500, 500]
stim_levels = [-40, -120, iv_vsteps]
def max_curr(data):
return max(data[0]['iha.i_ha'], key=abs)
iv_prot = ExperimentStimProtocol(stim_times,
stim_levels,
measure_index=2,
measure_fn=max_curr)
sartiani_conditions = dict(T=293, Ko=25000, Ki=120000, Nao=140000, Nai=10800)
import data.ical.data_ical as data
import numpy as np
modelfile = 'models/Generic_iCaL.mmt'
ical = IonChannelModel('ical',
modelfile,
vvar='membrane.V',
logvars=['ical.i_CaL',
'ical.G_CaL',
'environment.time'])
### Exp 1 - IV curve
iv_vsteps, iv_curr, iv_errs, iv_N = data.IV_Rao()
iv_data = ExperimentData(x=iv_vsteps, y=iv_curr, N=iv_N,
errs=iv_errs, err_type='SEM')
stim_times = [1000, 100, 500]
stim_levels = [-80, iv_vsteps, -80]
def peak_curr(data):
return max(data[0]['ical.i_CaL'], key=abs)
iv_prot = ExperimentStimProtocol(stim_times, stim_levels,
measure_index=1,
measure_fn=peak_curr)
rao_conditions = dict(T=298,
Ca_o=5000,
Ca_subSL=0.2)
iv_exp = Experiment(iv_prot, iv_data, rao_conditions)
### Exp 2 - activation
act_vsteps, act_cond, act_errs, act_N = data.Act_Rao()
act_data = ExperimentData(x=act_vsteps, y=act_cond, N=act_N,
IonChannelModel)
import data.ik1.data_ik1 as data
import numpy as np
modelfile = 'models/Generic_iK1.mmt'
ik1 = IonChannelModel('ik1',
modelfile,
vvar='membrane.V',
logvars=['ik1.i_K1'])
### Exp 1 - IV Curve
iv_vsteps, iv_curr, iv_errs, iv_N = data.IV_Goldoni()
iv_data = ExperimentData(x=iv_vsteps,
y=iv_curr,
N=iv_N,
errs=iv_errs,
err_type='STD')
stim_times = [500, 100, 500]
stim_levels = [-50, iv_vsteps, -50]
def ss_curr(data):
return data[0]['ik1.i_K1']
iv_prot = ExperimentStimProtocol(stim_times,
stim_levels,
measure_index=1,
measure_fn=ss_curr,
time_independent=True)
from ionchannelABC import (Experiment, ExperimentData, ExperimentStimProtocol,
IonChannelModel)
import data.ikur.data_ikur as data
import numpy as np
modelfile = 'models/Generic_iKur.mmt'
ikur = IonChannelModel(
'ikur',
modelfile,
vvar='membrane.V',
logvars=['environment.time', 'ikur.i_Kur', 'ikur.G_Kur'])
### Exp 1 - IV curve
iv_vsteps, iv_curr, iv_errs, _ = data.IV_Maharani()
iv_data = ExperimentData(x=iv_vsteps, y=iv_curr, errs=iv_errs, err_type='STD')
stim_times = [5000, 300]
stim_levels = [-60, iv_vsteps]
def max_ikur(data):
return max(data[0]['ikur.i_Kur'])
iv_prot = ExperimentStimProtocol(stim_times,
stim_levels,
measure_index=1,
measure_fn=max_ikur)
maharani_conditions = dict(T=310, Ko=4000, Ki=120000)
iv_exp = Experiment(iv_prot, iv_data, maharani_conditions)
# 'p6': (0, 0.001),
# 'p7': (0, 100),
# 'p8': (0, 1.0),
# 'q1': (0, 100),
# 'q2': (0, 100)}
ikr = IonChannelModel('ikr',
modelfile,
vvar='membrane.V',
logvars=['environment.time',
'ikr.i_Kr',
'ikr.G_Kr'])
### Exp 1 - IV curve.
iv_vsteps, iv_curr, iv_errs, iv_N = data.IV_Toyoda()
iv_data = ExperimentData(x=iv_vsteps, y=iv_curr,
errs=iv_errs)
stim_times = [1000, 1000, 100]
stim_levels = [-50, iv_vsteps, -50]
def tail_curr(data):
return data[0]['ikr.i_Kr'][-1]
iv_prot = ExperimentStimProtocol(stim_times, stim_levels,
measure_index=1,
measure_fn=tail_curr)
toyoda_conditions1 = dict(K_o=5400,
K_i=130000,
Na_o=140330,
Na_i=6000,
T=308)
iv_exp = Experiment(iv_prot, iv_data, toyoda_conditions1)
### Exp 2 - Activation curve.
# r10=(0, 1),
# r11=(0, 10),
# r12=(-0.1, 0),
# r13=(0, 1),
# r14=(-1, 0),
# r15=(-1, 0),
# r16=(0, 100))
ina = IonChannelModel('ina',
modelfile,
vvar='membrane.V',
logvars=['environment.time', 'ina.i_Na', 'ina.G_Na'])
### Exp 1 - IV curve
iv_vsteps, iv_curr, _, _ = data.IV_Lee()
iv_data = ExperimentData(x=iv_vsteps, y=iv_curr)
stim_times = [5000, 20]
stim_levels = [-80, iv_vsteps]
def peak_curr(data):
return max(data[0]['ina.i_Na'], key=abs)
#def normalise_by_peak(sim_results):
# peak = 445.1308 # peak current determined from unmodified sim
# sim_results = [r / peak for r in sim_results]
# return sim_results
def normalise(sim_results):
m = abs(max(sim_results, key=abs))
sim_results = [r / m for r in sim_results]
import data.icat.data_icat as data
import numpy as np
modelfile = 'models/Generic_iCaT.mmt'
icat = IonChannelModel(
'icat',
modelfile,
vvar='membrane.V',
logvars=['environment.time', 'icat.i_CaT', 'icat.G_CaT'])
### Exp 1 - IV curve
iv_vsteps, iv_curr, iv_errs, iv_N = data.IV_Nguyen()
iv_data = ExperimentData(x=iv_vsteps,
y=iv_curr,
N=iv_N,
errs=iv_errs,
err_type='SEM')
stim_times = [5000, 300, 500]
stim_levels = [-75, iv_vsteps, -75]
def max_icat(data):
return max(data[0]['icat.i_CaT'], key=lambda x: abs(x))
iv_prot = ExperimentStimProtocol(stim_times,
stim_levels,
measure_index=1,
measure_fn=max_icat)
nguyen_conditions = dict(Ca_o=5000, Ca_subSL=0.2, T=295)
post_fn=unwrap)
pulse_train_data = ExperimentData(
x=[
'vrp', #'ca_i', #'k_i', 'na_i',
'apa',
'apd90',
#'ca_amp',
't_ca_rise',
't_ca50',
't_ca90'
],
y=[
-67.0, #0.1, #1.5e5, 2.0e4,
105,
42,
#0.7,
52,
157,
397
],
errs=[
-69, #0.05, #0.75e5, 1.0e4,
103,
33,
#0.35,
50,
151,
383
])
pulse_train_exp = Experiment(pulse_train_prot, pulse_train_data,
dias_conditions)