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Vandenberg-2006-open-probability-mean.py
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#!/usr/bin/env python2
#
# Try to reproduce similar figures in Vandenberg et al. 2006
# In particular its Figure 3 and 5.
#
from __future__ import print_function
import sys
sys.path.append('../lib')
import os
import numpy as np
import matplotlib
if '--show' not in sys.argv:
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import myokit
import pickle
import model_ikr as m
from protocols import Vandenberg2006_isochronal_tail_current as prt_act
from protocols import Vandenberg2006_double_pulse as prt_inact
# Set parameter transformation
import parametertransform
transform_to_model_param = parametertransform.log_transform_to_model_param
transform_from_model_param = parametertransform.log_transform_from_model_param
plot_kylie = False
debug = False
common_conductance = 1.0
fit_seed = 542811797
temperatures = ['25.0', '33.0'][::-1]
color_mid = {'25.0':'#6baed6',
'33.0':'#fd8d3c',
'37.0':'C2',}
file_list = {
'25.0': 'herg25oc',
'33.0': 'herg33oc',
'37.0': 'herg37oc',
}
# Fan chart
fan_red = [
'#ec9999',
# '#e77c7c',
# '#e25f60',
'#dd4243',
# '#d62728', #
'#b92223',
'#9d1d1d',
# '#801718',
]
fan_blue = ['#b5c7d5',
# '#adc1d0',
# '#91abbc',
'#85a0b1',
# '#6b8fa9',
# '#62869f',
'#587c96',
# '#477390',
'#3f6c88',
]
fan_green = [
'#94e294',
# '#7ada7a',
# '#5fd35f',
'#52cf52', # 4c4?
# '#3b3',
# '#2ca02c', #
'#289328',
'#1d6b1d',
]
color_fan = {
'25.0':fan_blue,
'33.0':fan_red,
'37.0':fan_green,
}
# Load pseudo2hbm
mean_chains = []
cov_chains = []
for temperature in temperatures:
file_name = file_list[temperature]
load_file = './out-mcmc/%s-pseudo2hbm-lognorm-mean.txt' % (file_name)
mean_chain = np.loadtxt(load_file) # transformed
load_file = './out-mcmc/%s-pseudo2hbm-lognorm-cov.pkl' % (file_name)
cov_chain = pickle.load(open(load_file, "rb")) # transformed
mean_chains.append(mean_chain)
cov_chains.append(cov_chain)
mean_chains = np.asarray(mean_chains)
cov_chains = np.asarray(cov_chains)
'''
# Eyring and Q10
from temperature_models import eyringA, eyringB, eyringG, eyringT
from temperature_models import q10A, q10B, q10G, q10T
from temperature_models import eyring_transform_to_model_param
eyring_mean = np.loadtxt('%s/eyring-mean.txt' % file_dir)
q10_mean = np.loadtxt('%s/q10-mean.txt' % file_dir)
'''
#
# Functions to get I-V curves
#
def get_IV(folded_current, n_steps, t_start, t_end):
# Simple method to find minimum or maximum
times = folded_current['time']
time_window = np.where(np.logical_and(times > t_start, times <= t_end))[0]
time_window_90 = time_window[int(len(time_window) * 0.05):
int(len(time_window) * 0.25)]
I = []
for i in range(n_steps):
if (folded_current[str(i) + '.current'][time_window_90] <= 0).all():
peak_I = np.min(folded_current[str(i) + '.current'][time_window])
else:
peak_I = np.max(folded_current[str(i) + '.current'][time_window])
I.append(peak_I)
return I
def get_corrected_IV(folded_current, n_steps, t_start, t_end, debug=False):
# use 2-parameters exponential fit to the tail
import scipy
def exp_func(t, a, b):
# do a "proper exponential" decay fit
# i.e. shift the t to t' where t' has zero at the start of the
# voltage step
return - a * np.exp( -b * (t - x[0]))
times = folded_current['time']
time_window = np.where(np.logical_and(times > t_start, times <= t_end))[0]
I = np.zeros(n_steps)
i_trim = 200 # assuming DT=1e-4 s -> 20 ms
i_fit_until = 1000 # assuming DT=1e-4 s -> 100 ms
if debug:
fig = plt.figure()
for i in range(n_steps):
# trim off the first i_trim (20ms) in case it is still shooting down...
x = times[time_window[0] + i_trim:time_window[0] + i_fit_until]
y = folded_current[str(i) + '.current'][time_window[0] + i_trim:
time_window[0] + i_fit_until]
# if np.mean(y) < 0:
try:
popt, pcov = scipy.optimize.curve_fit(exp_func, x, y)
fitted = exp_func(times[time_window[0]:
time_window[0] + i_fit_until], *popt)
I[i] = np.max(fitted[0])
except:
raise Exception('CANNOT FIT TO voltage step %d' % i)
# else:
# I[i] = np.max(y)
if debug:
plt.plot(times[time_window[0] - 500:time_window[-1] + 500],
folded_current[str(i) + '.current'][time_window[0] -
500:time_window[-1] + 500],
c='#d62728' if i != 0 else 'C1',
zorder=0 if i != 0 else 10)
plt.plot(times[time_window[0]:time_window[0] + i_fit_until],
fitted, '--', c='#1f77b4', zorder=0 if i != 0 else 10)
plt.plot(times[time_window][0], I[i], 'kx')
if debug:
plt.axvline(x=times[time_window[0] + i_trim])
plt.axvline(x=times[time_window[0] + i_fit_until])
if '--show' in sys.argv:
plt.show()
else:
plt.savefig('figs/Vandenberg2006/fig5a2/'
'Vandenberg2006-fig5a2-%sC-%s-%s.png'
% (temperature, file_name, cell))
plt.close()
# Plot Figure 5B2 for this cell too
plt.plot(I)
if '--show' in sys.argv:
plt.show()
else:
plt.savefig('figs/Vandenberg2006/fig5a2/'
'Vandenberg2006-fig5a2-%sC-%s-%s-2.png'
% (temperature, file_name, cell))
plt.close()
return I
# Simulate
times_act, ttotal_act, tmeasure_act = prt_act(None, return_times=True)
I_activations = {}
I_activations_cov = {}
av_steps = prt_act(None, return_voltage=True)
times_inact, ttotal_inact, tmeasure_inact = prt_inact(None, return_times=True)
I_inactivations = {}
g_inactivations = {}
g_inactivations_cov = {}
iv_steps = prt_inact(None, return_voltage=True)
p_cov = {}
for i_T, temperature in enumerate(temperatures):
I_activations[temperature] = []
I_inactivations[temperature] = []
g_inactivations[temperature] = []
# Model
model_act = m.Model('../mmt-model-files/kylie-2017-IKr.mmt',
protocol_def=prt_act,
temperature=273.15 + float(temperature), # K
transform=None,
useFilterCap=False, # ignore capacitive spike
effEK=False, # OK to switch this off here
concK=[4.8 + 0.3, 120 + 20])
model_inact = m.Model('../mmt-model-files/kylie-2017-IKr.mmt',
protocol_def=prt_inact,
temperature=273.15 + float(temperature), # K
transform=None,
useFilterCap=False, # ignore capacitive spike
effEK=False, # OK to switch this off here
concK=[4.8 + 0.3, 120 + 20])
T = 273.15 + float(temperature)
# HBM mean parameters
hbm_T_mean = transform_to_model_param(
np.mean(mean_chains[i_T], axis=0))
'''
# Eyring parameters
eyring_T_mean = eyringT(eyring_mean, T)
eyring_param = eyring_transform_to_model_param(eyring_T_mean, T)
# Q10 parameters
q10_T_mean = q10T(q10_mean, T)
q10_param = eyring_transform_to_model_param(q10_T_mean, T)
'''
p = hbm_T_mean
p[0] = common_conductance
a = myokit.DataLog()
a['time'] = times_act
a['current'] = model_act.simulate(p, times_act)
a['voltage'] = model_act.voltage(times_act)
a.set_time_key('time')
a = a.fold(ttotal_act)
i = myokit.DataLog()
i['time'] = times_inact
i['current'] = model_inact.simulate(p, times_inact)
i['voltage'] = model_inact.voltage(times_inact)
i.set_time_key('time')
i = i.fold(ttotal_inact)
if debug and False:
# Figure 3A
for ii in range(len(av_steps)):
plt.plot(a['time'], a[str(ii)+'.current'])
if '--show' in sys.argv:
plt.show()
else:
plt.savefig('figs/Vandenberg2006/' +
'Vandenberg2006-fig3a-%sC.png'%temperature)
plt.close()
if debug and False:
# Figure 5A
for ii in range(len(iv_steps)):
plt.plot(i['time'], i[str(ii)+'.current'])
if '--show' in sys.argv:
plt.show()
else:
plt.savefig('figs/Vandenberg2006/'
+ 'Vandenberg2006-fig5a-%sC.png'%temperature)
plt.close()
I_activation = get_IV(a, len(av_steps),
tmeasure_act, tmeasure_act + 0.5)
I_activations[temperature].append(I_activation)
I_inactivation = get_corrected_IV(i, len(iv_steps),
tmeasure_inact,
tmeasure_inact + 0.5,
debug=False)
I_inactivations[temperature].append(I_inactivation)
conductance_inactivation = \
I_inactivation / (iv_steps - model_inact.EK())
g_inactivations[temperature].append(conductance_inactivation)
del(a, i)
np.random.seed(101)
i_act_cov_all = []
g_inact_cov_all = []
p_cov_all = []
for s in range(50):
p = np.random.multivariate_normal(mean_chains[i_T][s, :],
cov_chains[i_T][s, :, :])
p = transform_to_model_param(p)
p[0] = common_conductance
a = myokit.DataLog()
a['time'] = times_act
a['current'] = model_act.simulate(p, times_act)
a['voltage'] = model_act.voltage(times_act)
a.set_time_key('time')
a = a.fold(ttotal_act)
i = myokit.DataLog()
i['time'] = times_inact
i['current'] = model_inact.simulate(p, times_inact)
i['voltage'] = model_inact.voltage(times_inact)
i.set_time_key('time')
i = i.fold(ttotal_inact)
I_activation = get_IV(a, len(av_steps),
tmeasure_act, tmeasure_act + 0.5)
I_activation = I_activation / np.min(I_activation)
i_act_cov_all.append(I_activation)
I_inactivation = get_corrected_IV(i, len(iv_steps),
tmeasure_inact,
tmeasure_inact + 0.5,
debug=False)
conductance_inactivation = \
I_inactivation / (iv_steps - model_inact.EK())
conductance_inactivation = conductance_inactivation \
/ np.max(conductance_inactivation)
g_inact_cov_all.append(conductance_inactivation)
av_steps_tmp = np.around(np.array(av_steps) * 1e3, 1)
iv_steps_tmp = np.around(np.array(iv_steps) * 1e3, 1)
v_steps = iv_steps_tmp[:]
po = []
for v in v_steps:
# act
if v in av_steps_tmp:
i = np.where(np.abs(av_steps_tmp - v) < 1e-5)[0][0]
act = I_activation[i]
elif v > 30.0: # mV
act = 1.0 # approx. as fully open
elif v < -70.0: # mV
act = 0.0 # approx. as fully closed
else:
print('...')
# inact
if v in iv_steps_tmp:
i = np.where(np.abs(iv_steps_tmp - v) < 1e-5)[0][0]
inact = conductance_inactivation[i]
else:
print('...')
po.append(act * inact)
p_cov_all.append(po)
del(a, i)
I_activations_cov[temperature] = []
g_inactivations_cov[temperature] = []
p_cov[temperature] = []
percentiles = [90, 60, 30]
fan_chart_data_top = []
fan_chart_data_bot = []
for i_p, p in enumerate(percentiles):
top = np.nanpercentile(i_act_cov_all, 50 + p / 2., axis=0)
bot = np.nanpercentile(i_act_cov_all, 50 - p / 2., axis=0)
I_activations_cov[temperature].append([top, bot])
top = np.nanpercentile(g_inact_cov_all, 50 + p / 2., axis=0)
bot = np.nanpercentile(g_inact_cov_all, 50 - p / 2., axis=0)
g_inactivations_cov[temperature].append([top, bot])
top = np.nanpercentile(p_cov_all, 50 + p / 2., axis=0)
bot = np.nanpercentile(p_cov_all, 50 - p / 2., axis=0)
p_cov[temperature].append([top, bot])
# Kylie
if plot_kylie:
# Model
model_act = m.Model('../mmt-model-files/kylie-2017-IKr.mmt',
protocol_def=prt_act,
temperature=273.15 + 22, # K
transform=None,
useFilterCap=False, # ignore capacitive spike
effEK=False, # OK to switch this off here
concK=[4.8 + 0.3, 120 + 20])
model_inact = m.Model('../mmt-model-files/kylie-2017-IKr.mmt',
protocol_def=prt_inact,
temperature=273.15 + 22, # K
transform=None,
useFilterCap=False, # ignore capacitive spike
effEK=False, # OK to switch this off here
concK=[4.8 + 0.3, 120 + 20])
path_to_solutions = '../room-temperature-only/kylie-room-temperature'
last_solution = path_to_solutions + '/last-solution_log-mean.txt'
obtained_parameters = np.loadtxt(last_solution)
obtained_parameters[0] = common_conductance
a = myokit.DataLog()
a['time'] = times_act
a['current'] = model_act.simulate(obtained_parameters, times_act)
a['voltage'] = model_act.voltage(times_act)
a.set_time_key('time')
a = a.fold(ttotal_act)
i = myokit.DataLog()
i['time'] = times_inact
i['current'] = model_inact.simulate(obtained_parameters, times_inact)
i['voltage'] = model_inact.voltage(times_inact)
i.set_time_key('time')
i = i.fold(ttotal_inact)
kI_activation = get_IV(a, len(av_steps),
tmeasure_act, tmeasure_act + 0.5)
kI_inactivation = get_corrected_IV(i, len(iv_steps),
tmeasure_inact,
tmeasure_inact + 0.5,
debug=False)
kg_inactivation = \
kI_inactivation / (iv_steps - model_inact.EK())
del(a, i)
# convert units V -> mV
av_steps = np.around(np.array(av_steps) * 1e3, 1)
iv_steps = np.around(np.array(iv_steps) * 1e3, 1)
# Figure 3C
fig, axes = plt.subplots(1, 2, sharey=True, figsize=(9, 4))
for temperature in temperatures:
for i, I_activation in enumerate(I_activations[temperature]):
axes[1].plot(av_steps, I_activation/np.min(I_activation),
c=color_mid[temperature], lw=1.5,
label='__nolegend__' if i else temperature+' $^o$C')
# fan charts
for temperature in temperatures:
for i_p, p in enumerate(percentiles):
alpha = 0.8
color = color_fan[temperature][i_p]
top, bot = I_activations_cov[temperature][i_p]
axes[1].fill_between(av_steps, top, bot, color=color,
alpha=alpha, linewidth=0)
# Kylie
if plot_kylie:
axes[1].plot(av_steps, kI_activation/np.min(kI_activation),
c='r', label='Beattie et al. 2018 22 $^o$C')
# Vandenberg
u1 = np.loadtxt('vandenberg-et-al-2006/vandenberg-et-al-2006-fig3c-22oc',
delimiter=',', skiprows=1)
u2 = np.loadtxt('vandenberg-et-al-2006/vandenberg-et-al-2006-fig3c-32oc',
delimiter=',', skiprows=1)
axes[0].plot(u1[:, 0], u1[:, 1], marker='s', label=r'Vandenberg et al. 2006 22 $^o$C')
axes[0].plot(u2[:, 0], u2[:, 1], marker='s', label=r'Vandenberg et al. 2006 32 $^o$C')
for i in range(2):
axes[i].legend()
axes[i].set_xlabel('Voltage [mV]', fontsize=14)
axes[i].set_xlim([-124, 55])
axes[0].set_ylabel('Normalised tail peak current', fontsize=14)
plt.tight_layout(pad=0.05, w_pad=0.15, h_pad=0.15)
if '--show' in sys.argv:
plt.show()
else:
plt.savefig('figs/paper/re-Vandenberg2006-fig3c.png')
plt.savefig('figs/paper/re-Vandenberg2006-fig3c.pdf', format='pdf',
bbox_inches='tight')
plt.close()
# Figure 5D
fig, axes = plt.subplots(1, 2, sharey=True, figsize=(9, 4))
for temperature in temperatures:
for i, g_inactivation in enumerate(g_inactivations[temperature]):
axes[1].plot(iv_steps, g_inactivation/np.max(g_inactivation),
c=color_mid[temperature], lw=1.5,
label='__nolegend__' if i else temperature+' $^o$C')
# fan charts
for temperature in temperatures:
for i_p, p in enumerate(percentiles):
alpha = 0.8
color = color_fan[temperature][i_p]
top, bot = g_inactivations_cov[temperature][i_p]
axes[1].fill_between(iv_steps, top, bot, color=color,
alpha=alpha, linewidth=0)
# Kylie
if plot_kylie:
axes[1].plot(iv_steps, kg_inactivation/np.max(kg_inactivation),
c='r', label='Beattie et al. 2018 22 $^o$C')
# Vandenberg
w1 = np.loadtxt('vandenberg-et-al-2006/vandenberg-et-al-2006-fig5d-22oc',
delimiter=',', skiprows=1)
w2 = np.loadtxt('vandenberg-et-al-2006/vandenberg-et-al-2006-fig5d-32oc',
delimiter=',', skiprows=1)
axes[0].plot(w1[4:, 0], w1[4:, 1] / np.max(w1[4:, 1]), marker='s',
label=r'Vandenberg et al. 2006 22 $^o$C')
axes[0].plot(w2[4:, 0], w2[4:, 1] / np.max(w2[4:, 1]), marker='s',
label=r'Vandenberg et al. 2006 32 $^o$C')
for i in range(2):
axes[i].legend()
axes[i].set_xlabel('Voltage [mV]', fontsize=14)
axes[i].set_xlim([-124, 55])
axes[0].set_ylabel('Normalised conductance', fontsize=14)
plt.tight_layout(pad=0.05, w_pad=0.15, h_pad=0.15)
if '--show' in sys.argv:
plt.show()
else:
plt.savefig('figs/paper/re-Vandenberg2006-fig5d.png')
plt.savefig('figs/paper/re-Vandenberg2006-fig5d.pdf', format='pdf',
bbox_inches='tight')
plt.close()
# Figure 6
for temperature in temperatures:
v_steps = iv_steps[:]
for ii, (I_activation, g_inactivation) in \
enumerate(zip(I_activations[temperature],
g_inactivations[temperature])):
po = []
for v in v_steps:
# act
if v in av_steps:
i = np.where(np.abs(av_steps - v) < 1e-5)[0][0]
act = I_activation[i] / np.min(I_activation)
elif v > 30.0: # mV
act = 1.0 # approx. as fully open
elif v < -70.0: # mV
act = 0.0 # approx. as fully closed
else:
print('...')
# inact
if v in iv_steps:
i = np.where(np.abs(iv_steps - v) < 1e-5)[0][0]
inact = g_inactivation[i] / np.max(g_inactivation)
else:
print('...')
po.append(act * inact)
plt.plot(v_steps, po, c=color_mid[temperature],
label='_nolegend_' if ii else temperature+' $^o$C')
# fan charts
for temperature in temperatures:
v_steps = iv_steps[:]
for i_p, p in enumerate(percentiles):
alpha = 0.8
color = color_fan[temperature][i_p]
top, bot = p_cov[temperature][i_p]
plt.fill_between(v_steps, top, bot, color=color,
alpha=alpha, linewidth=0)
# Kylie
if plot_kylie:
v_steps = iv_steps[:]
po = []
for v in v_steps:
# act
if v in av_steps:
i = np.where(np.abs(av_steps - v) < 1e-5)[0][0]
act = kI_activation[i] / np.min(kI_activation)
elif v > 30.0: # mV
act = 1.0 # approx. as fully open
elif v < -70.0: # mV
act = 0.0 # approx. as fully closed
else:
print('...')
# inact
if v in iv_steps:
i = np.where(np.abs(iv_steps - v) < 1e-5)[0][0]
inact = kg_inactivation[i] / np.max(kg_inactivation)
else:
print('...')
po.append(act * inact)
plt.plot(v_steps, po, c='r', label='Beattie et al. 2018 22 $^o$C')
# Vandenberg
v1 = np.loadtxt('vandenberg-et-al-2006/vandenberg-et-al-2006-fig6-22oc',
delimiter=',', skiprows=1)
v2 = np.loadtxt('vandenberg-et-al-2006/vandenberg-et-al-2006-fig6-32oc',
delimiter=',', skiprows=1)
plt.scatter(v1[:, 0], v1[:, 1], label=r'Vandenberg et al. 2006 22 $^o$C')
plt.scatter(v2[:, 0], v2[:, 1], label=r'Vandenberg et al. 2006 32 $^o$C')
plt.legend()
plt.xlabel('Voltage [mV]', fontsize=14)
plt.ylabel('Open probability', fontsize=14)
if '--show' in sys.argv:
plt.show()
else:
plt.savefig('figs/paper/re-Vandenberg2006-fig6.png')
plt.savefig('figs/paper/re-Vandenberg2006-fig6.pdf', format='pdf',
bbox_inches='tight')
plt.close()
# Figure 6 v2
fig, axes = plt.subplots(1, 2, sharey=True, figsize=(9, 4))
for temperature in temperatures:
v_steps = iv_steps[:]
for ii, (I_activation, g_inactivation) in \
enumerate(zip(I_activations[temperature],
g_inactivations[temperature])):
po = []
for v in v_steps:
# act
if v in av_steps:
i = np.where(np.abs(av_steps - v) < 1e-5)[0][0]
act = I_activation[i] / np.min(I_activation)
elif v > 30.0: # mV
act = 1.0 # approx. as fully open
elif v < -70.0: # mV
act = 0.0 # approx. as fully closed
else:
print('...')
# inact
if v in iv_steps:
i = np.where(np.abs(iv_steps - v) < 1e-5)[0][0]
inact = g_inactivation[i] / np.max(g_inactivation)
else:
print('...')
po.append(act * inact)
axes[1].plot(v_steps, po, c=color_mid[temperature], lw=1.5,
label='_nolegend_' if ii else temperature+' $^o$C')
# fan charts
for temperature in temperatures:
v_steps = iv_steps[:]
for i_p, p in enumerate(percentiles):
alpha = 0.8
color = color_fan[temperature][i_p]
top, bot = p_cov[temperature][i_p]
axes[1].fill_between(v_steps, top, bot, color=color,
alpha=alpha, linewidth=0)
# Kylie
if plot_kylie:
v_steps = iv_steps[:]
po = []
for v in v_steps:
# act
if v in av_steps:
i = np.where(np.abs(av_steps - v) < 1e-5)[0][0]
act = kI_activation[i] / np.min(kI_activation)
elif v > 30.0: # mV
act = 1.0 # approx. as fully open
elif v < -70.0: # mV
act = 0.0 # approx. as fully closed
else:
print('...')
# inact
if v in iv_steps:
i = np.where(np.abs(iv_steps - v) < 1e-5)[0][0]
inact = kg_inactivation[i] / np.max(kg_inactivation)
else:
print('...')
po.append(act * inact)
axes[1].plot(v_steps, po, c='r', label='Beattie et al. 2018 22 $^o$C')
# Vandenberg
v1 = np.loadtxt('vandenberg-et-al-2006/vandenberg-et-al-2006-fig6-22oc',
delimiter=',', skiprows=1)
v2 = np.loadtxt('vandenberg-et-al-2006/vandenberg-et-al-2006-fig6-32oc',
delimiter=',', skiprows=1)
axes[0].plot(v1[:, 0], v1[:, 1], marker='s', label=r'Vandenberg et al. 2006 22 $^o$C')
axes[0].plot(v2[:, 0], v2[:, 1], marker='s', label=r'Vandenberg et al. 2006 32 $^o$C')
for i in range(2):
axes[i].legend()
axes[i].set_xlabel('Voltage [mV]', fontsize=14)
axes[i].set_xlim([-124, 55])
axes[0].set_ylabel('Open probability', fontsize=14)
plt.tight_layout(pad=0.05, w_pad=0.15, h_pad=0.15)
if '--show' in sys.argv:
plt.show()
else:
plt.savefig('figs/paper/re-Vandenberg2006-fig6-v2.png')
plt.savefig('figs/paper/re-Vandenberg2006-fig6-v2.pdf', format='pdf',
bbox_inches='tight')
plt.close()
## eof