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Functions and plotting scripts for each model, sensitivity analysis in each model and KCNA1 mutations in each model

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nkoch1
2022-08-28 15:57:33 -04:00
parent 64e663d857
commit 47f5c1db02
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Code/KCNA1_mutations/mut_Cb_stellate.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on Sat May 8 16:20:11 2021
@author: nils
"""
import numpy as np
from joblib import Parallel, delayed
import json
import os
from numba import types
from numba.typed import Dict
from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
from Code.Functions.Cb_stellate_fxns import Cb_stellate_mut
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.0015
number_steps = 200
initial_period = 1000
num_gating = 9
num_current = 6
C, surf_area = capacitance(61.4, 1.50148)
variable = np.array(['m', 'h', 'n', 'n_A', 'h_A', 'n_A_mut', 'h_A_mut', 'm_T', 'h_T'])
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'n_A', 'h_A','n_A_mut', 'h_A_mut', 'm_T', 'h_T']))
V_init = -70
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param['m'][:] = np.array([-37., -3, 1])
b_param['h'] = np.zeros(4)
b_param['h'][:] = np.array([-40., 4., 1., 0])
b_param['n'][:] = np.array([-23, -5, 1])
b_param['n_A'][:] = np.array([-27, -13.2, 1.])
b_param['h_A'][:] = np.array([-80., 6.5, 1.])
b_param['n_A_mut'][:] = np.array([-27, -13.2, 1.])
b_param['h_A_mut'][:] = np.array([-80., 6.5, 1.])
b_param['m_T'][:] = np.array([-50., -3, 1.])
b_param['h_T'][:] = np.array([-68., 3.75, 1.])
mut_act_Vhalf_wt = -30.01851851851851
mut_act_k_wt = -7.7333333333333325
s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
s_diff_k = mut_act_k_wt - b_param['s'][1]
b_param['s'][1] = b_param['s'][1] + s_diff_k
b_param['u'][1] = b_param['u'][1] + s_diff_k
b_param['s'][0] = b_param['s'][0] + s_diff_Vhalf
b_param['u'][0] = b_param['u'][0] + s_diff_Vhalf
b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
b_param['s_mut'][0] = b_param['s_mut'][0] + s_diff_Vhalf
b_param['u_mut'][0] = b_param['u_mut'][0] + s_diff_Vhalf
# reversal potentials
E["Na"] = 55.
E["K"] = -80.
E["Ca"] = 22.
E["Leak"] = -70. # as per Molineux et al 2005 and NOT the -38 in Alexander et al 2019
# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["A_mut"] = True
currents_included["A"] = True
currents_included["T"] = True
currents_included["Leak"] = True
# model conductances
g["Na"] = 3.4 * surf_area
g["Kd"] = 9.0556 * surf_area
g["A_mut"] = 15.0159 * surf_area * 0.5
g["A"] = 15.0159 * surf_area* 0.5
g["T"] = 0.45045 * surf_area
g["Leak"] = 0.07407 * surf_area
# save folder
folder = './KCNA1_mutations/Cb_stellate'
if not os.path.isdir(folder):
os.makedirs(folder)
#
# mutation properties
mutations = json.load(open("mutations_effects_dict.json"))
# %%
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(Cb_stellate_mut)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
initial_period, sec) for mut in list(mutations.keys()))

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Code/KCNA1_mutations/mut_Cb_stellate_Delta_Kv.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on Fri Jun 18 10:47:40 2021
@author: nils
"""
import numpy as np
from joblib import Parallel, delayed
import json
import os
from numba import types
from numba.typed import Dict
from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
from Code.Functions.Cb_stellate_fxns_Kv import Cb_stellate_Kv_mut
#%%
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.0015
number_steps = 200
initial_period = 1000
num_gating = 11
num_current = 7
C, surf_area = capacitance(61.4, 1.50148)
variable = np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut'])
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']))
V_init = -70
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param['m'][:] = np.array([-37., -3, 1])
b_param['h'] = np.zeros(4)
b_param['h'][:] = np.array([-40., 4., 1., 0])
b_param['n'][:] = np.array([-23, -5, 1])
b_param['n_A'][:] = np.array([-27, -13.2, 1.])
b_param['h_A'][:] = np.array([-80., 6.5, 1.])
b_param['m_T'][:] = np.array([-50., -3, 1.])
b_param['h_T'][:] = np.array([-68., 3.75, 1.])
b_param['s'][:] = np.array([-14.16, -10.15, 1.])
b_param['u'] = np.zeros(4)
b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.zeros(4)
b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
mut_act_Vhalf_wt = -30.01851851851851
mut_act_k_wt = -7.7333333333333325
s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
s_diff_k = mut_act_k_wt - b_param['s'][1]
b_param['s'][1] = b_param['s'][1] + s_diff_k
b_param['u'][1] = b_param['u'][1] + s_diff_k
b_param['s'][0] = b_param['s'][0] + s_diff_Vhalf
b_param['u'][0] = b_param['u'][0] + s_diff_Vhalf
b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
b_param['s_mut'][0] = b_param['s_mut'][0] + s_diff_Vhalf
b_param['u_mut'][0] = b_param['u_mut'][0] + s_diff_Vhalf
# reversal potentials
E["Na"] = 55.
E["K"] = -80.
E["Ca"] = 22.
E["Leak"] = -70. # as per Molineux et al 2005 and NOT the -38 in Alexander et al 2019
# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["Kv"] = True
currents_included["Kv_mut"] = True
currents_included["A"] = False
currents_included["T"] = True
currents_included["Leak"] = True
# model conductances
g["Na"] = 34 * surf_area
g["Kd"] = 9.0556 * surf_area
if currents_included["Kv_mut"] == True:
g["Kv"] = 1.50159 / 2 * surf_area
else:
g["Kv"] = 1.50159 / 2 * surf_area * 2
g["Kv_mut"] = 1.50159 / 2 * surf_area
g["A"] = 0 * surf_area
g["T"] = 0.45045 * surf_area
g["Leak"] = 0.07407 * surf_area
# save folder
folder = './KCNA1_mutations/Cb_stellate_Kv_only'
if not os.path.isdir(folder):
os.makedirs(folder)
#
# mutation properties
mutations = json.load(open("mutations_effects_dict.json"))
# %%
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(Cb_stellate_Kv_mut)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale,
b_param, slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low,
number_steps, initial_period, sec) for mut in list(mutations.keys()))

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Code/KCNA1_mutations/mut_Cb_stellate_Kv.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on Sat May 8 16:20:11 2021
@author: nils
"""
import numpy as np
from joblib import Parallel, delayed
import json
import os
from numba import types
from numba.typed import Dict
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
from Code.Functions.Cb_stellate_fxns_Kv import Cb_stellate_Kv_mut
#%%
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.0015
number_steps = 200
initial_period = 1000
num_gating = 11
num_current = 7
C, surf_area = capacitance(61.4, 1.50148)
variable = np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut'])
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']))
V_init = -70
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param['m'][:] = np.array([-37., -3, 1])
b_param['h'] = np.zeros(4)
b_param['h'][:] = np.array([-40., 4., 1., 0])
b_param['n'][:] = np.array([-23, -5, 1])
b_param['n_A'][:] = np.array([-27, -13.2, 1.])
b_param['h_A'][:] = np.array([-80., 6.5, 1.])
b_param['m_T'][:] = np.array([-50., -3, 1.])
b_param['h_T'][:] = np.array([-68., 3.75, 1.])
b_param['s'][:] = np.array([-14.16, -10.15, 1.])
b_param['u'] = np.zeros(4)
b_param['u'][:] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'][:] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.zeros(4)
b_param['u_mut'][:] = np.array([-31., 5.256, 1., 0.245])
mut_act_Vhalf_wt = -30.01851851851851
mut_act_k_wt = -7.7333333333333325
s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
s_diff_k = mut_act_k_wt - b_param['s'][1]
b_param['s'][1] = b_param['s'][1] + s_diff_k
b_param['u'][1] = b_param['u'][1] + s_diff_k
b_param['s'][0] = b_param['s'][0] + s_diff_Vhalf
b_param['u'][0] = b_param['u'][0] + s_diff_Vhalf
b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
b_param['s_mut'][0] = b_param['s_mut'][0] + s_diff_Vhalf
b_param['u_mut'][0] = b_param['u_mut'][0] + s_diff_Vhalf
# reversal potentials
E["Na"] = 55.
E["K"] = -80.
E["Ca"] = 22.
E["Leak"] = -70. # as per Molineux et al 2005 and NOT the -38 in Alexander et al 2019
# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["Kv"] = True
currents_included["Kv_mut"] = True
currents_included["A"] = True
currents_included["T"] = True
currents_included["Leak"] = True
# model conductances
Kv_ratio = 0.1
g["Na"] = 3.4 * surf_area
g["Kd"] = 9.0556 * (1-Kv_ratio) * surf_area
g["Kv"] = 6. * Kv_ratio/2 * surf_area * (2 * int(currents_included["Kv_mut"] == False))
g["Kv_mut"] = 6. * Kv_ratio/2 * surf_area
g["A"] = 15.0159 * surf_area
g["T"] = 0.45045 * surf_area
g["Leak"] = 0.07407 * surf_area
# save folder
folder = './KCNA1_mutations/Cb_stellate_Kv'
if not os.path.isdir(folder):
os.makedirs(folder)
#
# mutation properties
mutations = json.load(open("mutations_effects_dict.json"))
# %%
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(Cb_stellate_Kv_mut)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
initial_period, sec) for mut in list(mutations.keys()))

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Code/KCNA1_mutations/mut_FS_Kv.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on Sat May 29 21:10:20 2021
@author: nils
"""
import numpy as np
import os
import json
from numba import types
from numba.typed import Dict
from joblib import Parallel, delayed
from Code.Functions.Utility import capacitance, stimulus_init, init_dict, NumpyEncoder
from Code.Functions.Pospischil_fxns import Pospischil_mut
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.001
number_steps = 200
initial_period = 1000
num_gating = 10
num_current = 7
C, surf_area = capacitance(56.9, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
tau_max_p = 502
V_init = -70
V_T = -57.9
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param = {}
b_param['m'] = np.array([-34.33054521, -8.21450277, 1.42295686])
b_param['h'] = np.array([-34.51951036, 4.04059373, 1., 0.])
b_param['n'] = np.array([-63.76096946, -13.83488194, 7.35347425])
b_param['q'] = np.array([-39.03684525, -5.57756176, 2.25190197])
b_param['r'] = np.array([-57.37, 20.98, 1.])
b_param['p'] = np.array([-45., -9.9998807337, 1.])
b_param['s'] = np.array([-14.16, -10.15, 1.])
b_param['u'] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
mut_act_Vhalf_wt = -30.01851851851851
mut_act_k_wt = -7.7333333333333325
s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
s_diff_k = mut_act_k_wt - b_param['s'][1]
b_param['s'][1] = b_param['s'][1] + s_diff_k
b_param['u'][1] = b_param['u'][1] + s_diff_k
b_param['s'][0] = b_param['s'][0] + s_diff_Vhalf
b_param['u'][0] = b_param['u'][0] + s_diff_Vhalf
b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
b_param['s_mut'][0] = b_param['s_mut'][0] + s_diff_Vhalf
b_param['u_mut'][0] = b_param['u_mut'][0] + s_diff_Vhalf
# reversal potentials
E["Na"] = 50.
E["K"] = -90.
E["Ca"] = 120.
E["Leak"] = -70.4
# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["Kv"] = True
currents_included["Kv_mut"] = True
currents_included["L"] = False
currents_included["M"] = True
currents_included["Leak"] = True
# model conductances
Kv_ratio = 0.1
g["Na"] = 58. * surf_area
g["Kd"] = 3.9 * (1 - Kv_ratio) * surf_area
g["M"] = 0.075 * surf_area
if currents_included["Kv_mut"] == True:
g["Kv"] = 3.9 * Kv_ratio / 2 * surf_area
else:
g["Kv"] = 3.9 * Kv_ratio / 2 * surf_area * 2
g["Kv_mut"] = 3.9 * Kv_ratio / 2 * surf_area
g["L"] = 0. * surf_area
g["Leak"] = 0.038 * surf_area
# save folder
folder = './KCNA1_mutations/FS_Kv'
if not os.path.isdir(folder):
os.makedirs(folder)
# mutation properties
mutations = json.load(open("mutations_effects_dict.json"))
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(Pospischil_mut)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift, scale,
b_param, slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut, folder,
high, low, number_steps, initial_period, sec)
for mut in list(mutations.keys()))

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Code/KCNA1_mutations/mut_RS_inhib_Kv.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on Sat May 29 21:10:48 2021
@author: nils
"""
import numpy as np
from joblib import Parallel, delayed
import os
import h5py
import json
from numba import types
from numba.typed import Dict
from joblib import Parallel, delayed
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
from Code.Functions.Pospischil_fxns import Pospischil_mut
# model parameters
tau_max_p = 608
dt = 0.01
sec = 2
low = 0
high = 0.001
number_steps = 200
initial_period = 1000
num_gating = 10
num_current = 7
C, surf_area = capacitance(61.8, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
tau_max_p = 934
V_init = -70
V_T = -67.9
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param = {}
b_param['m'] = np.array([-34.33054521, -8.21450277, 1.42295686])
b_param['h'] = np.array([-34.51951036, 4.04059373, 1., 0.05])
b_param['n'] = np.array([-63.76096946, -13.83488194, 7.35347425])
b_param['q'] = np.array([-39.03684525, -5.57756176, 2.25190197])
b_param['r'] = np.array([-57.37, 20.98, 1.0])
b_param['p'] = np.array([-45., -9.9998807337, 1.])
b_param['s'] = np.array([-14.16, -10.15, 1.0])
b_param['u'] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
mut_act_Vhalf_wt = -30.01851851851851
mut_act_k_wt = -7.7333333333333325
s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
s_diff_k = mut_act_k_wt - b_param['s'][1]
b_param['s'][1] = b_param['s'][1] + s_diff_k
b_param['u'][1] = b_param['u'][1] + s_diff_k
b_param['s'][0] = b_param['s'][0] + s_diff_Vhalf
b_param['u'][0] = b_param['u'][0] + s_diff_Vhalf
b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
b_param['s_mut'][0] = b_param['s_mut'][0] + s_diff_Vhalf
b_param['u_mut'][0] = b_param['u_mut'][0] + s_diff_Vhalf
# reversal potentials
E["Na"] = 50.
E["K"] = -90.
E["Ca"] = 120.
E["Leak"] = -56.2
# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["Kv"] = True
currents_included["Kv_mut"] = True
currents_included["L"] = False
currents_included["M"] = True
currents_included["Leak"] = True
# model conductances
Kv_ratio = 0.1
g["Na"] = 10. * surf_area
g["Kd"] = 2.1 * (1 - Kv_ratio) * surf_area
g["M"] = 0.0098 * surf_area
if currents_included["Kv_mut"] == True:
g["Kv"] = 2.1 * Kv_ratio / 2 * surf_area
else:
g["Kv"] = 2.1 * Kv_ratio / 2 * surf_area * 2
g["Kv_mut"] = 2.1 * Kv_ratio / 2 * surf_area
g["L"] = 0. * surf_area
g["Leak"] = 0.0205 * surf_area
# save folder
folder = './KCNA1_mutations/RS_inhib_Kv'
if not os.path.isdir(folder):
os.makedirs(folder)
# mutation properties
mutations = json.load(open("mutations_effects_dict.json"))
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(Pospischil_mut)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift,
scale, b_param, slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut,
folder, high, low, number_steps, initial_period, sec)
for mut in list(mutations.keys()))

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Code/KCNA1_mutations/mut_RS_pyramidal_Kv.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on Sat May 8 16:20:11 2021
@author: nils
"""
import numpy as np
import os
import json
from numba import types
from numba.typed import Dict
from joblib import Parallel, delayed
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
from Code.Functions.Pospischil_fxns import Pospischil_mut
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.001
number_steps = 200
initial_period = 1000
num_gating = 10
num_current = 7
C, surf_area = capacitance(61.4, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(
np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']))
tau_max_p = 608
V_init = -70
V_T = -56.2
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param = {}
b_param['m'] = np.array([-34.33054521, -8.21450277, 1.42295686])
b_param['h'] = np.array([-34.51951036, 4.04059373, 1., 0.05])
b_param['n'] = np.array([-63.76096946, -13.83488194, 7.35347425])
b_param['q'] = np.array([-39.03684525, -5.57756176, 2.25190197])
b_param['r'] = np.array([-57.37, 20.98, 1.0])
b_param['p'] = np.array([-45., -9.9998807337, 1.])
b_param['s'] = np.array([-14.16, -10.15, 1.0])
b_param['u'] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
mut_act_Vhalf_wt = -30.01851851851851
mut_act_k_wt = -7.7333333333333325
s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
s_diff_k = mut_act_k_wt - b_param['s'][1]
b_param['s'][1] = b_param['s'][1] + s_diff_k
b_param['u'][1] = b_param['u'][1] + s_diff_k
b_param['s'][0] = b_param['s'][0] + s_diff_Vhalf
b_param['u'][0] = b_param['u'][0] + s_diff_Vhalf
b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
b_param['s_mut'][0] = b_param['s_mut'][0] + s_diff_Vhalf
b_param['u_mut'][0] = b_param['u_mut'][0] + s_diff_Vhalf
# reversal potentials
E["Na"] = 50.
E["K"] = -90.
E["Ca"] = 120.
E["Leak"] = -70.3
# model currents
currents_included["Na"] = True
currents_included["Kd"] = True
currents_included["Kv"] = True
currents_included["Kv_mut"] = True
currents_included["L"] = False
currents_included["M"] = True
currents_included["Leak"] = True
# model conductances
Kv_ratio = 0.1
g["Na"] = 56. * surf_area
g["Kd"] = 6. * (1 - Kv_ratio) * surf_area
g["M"] = 0.075 * surf_area
if currents_included["Kv_mut"] == True:
g["Kv"] = 6. * Kv_ratio / 2 * surf_area
else:
g["Kv"] = 6. * Kv_ratio / 2 * surf_area * 2
g["Kv_mut"] = 6. * Kv_ratio / 2 * surf_area
g["L"] = 0. * surf_area
g["Leak"] = 0.0205 * surf_area
# save folder
folder = './KCNA1_mutations/RS_pyramidal_Kv'
if not os.path.isdir(folder):
os.makedirs(folder)
# mutation properties
mutations = json.load(open("mutations_effects_dict.json"))
# # %%
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(Pospischil_mut)(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift,
scale, b_param, slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut,
folder, high, low, number_steps, initial_period, sec)
for mut in list(mutations.keys()))

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# -*- coding: utf-8 -*-
"""
Created on Sat May 8 16:20:11 2021
@author: nils
"""
import numpy as np
from joblib import Parallel, delayed
import json
import os
from numba import types
from numba.typed import Dict
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
from Code.Functions.STN_fxns import STN_mut
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.005
number_steps = 100
initial_period = 1000
num_gating = 15
num_current = 8
d = 61.4
r = d/2 * 10**-6 # radius in meters
vol = np.pi * r**3 # volume in liters
C, surf_area = capacitance(d, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'a', 'b', 'a_mut', 'b_mut', 'c', 'd1', 'd2', 'p', 'q', 'r', 'Ca_conc', 'E_Ca']))
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param['m'] = np.array([-40., -1. , 1.])
b_param['h'] = np.zeros(4)
b_param['h'] = np.array([-45.5, 1. , 1., 0.05])
b_param['n'] = np.array([-41., -1., 1.])
b_param['p'] = np.array([-56., -1., 1.])
b_param['q'] = np.array([-85., 1., 1.])
b_param['r'] = np.array([0.17, -0.08, 1.])
b_param['a'] = np.array([-45., -14.7, 1.])
b_param['b'] = np.array([-90., 7.5, 1.])
b_param['a_mut'] = np.array([-45., -14.7, 1.])
b_param['b_mut'] = np.array([-90., 7.5, 1.])
b_param['c'] = np.array([-30.6, -5., 1.])
b_param['d1'] = np.array([-60, 7.5, 1.])
b_param['d2'] = np.array([0.1, 0.02, 1.])
# reversal potentials
E["Na"] = 60.
E["K"] = -90.
E["Leak"] = -60.
# model currents
currents_included["Na"] = True
currents_included["Kd"] =True
currents_included["L"] = True
currents_included["T"] = True
currents_included["Ca_K"] = True
currents_included["A"] =True
currents_included["A_mut"] =True
currents_included["Leak"] = True
# model conductances
Kv_ratio = 0.1
g["Na"] = 49. * surf_area
g["Kd"] = 27. * (1-Kv_ratio)* surf_area
g["A"] = 5. * surf_area *0.5
g["A_mut"] = 5. * surf_area *0.5
g["L"] = 5. * surf_area
g["T"] = 5. * surf_area
g["Ca_K"] = 1 * surf_area
g["Leak"] = 0.035 * surf_area
V_init = -70
# save folder
folder = './KCNA1_mutations/STN'
if not os.path.isdir(folder):
os.makedirs(folder)
#
# mutation properties
mutations = json.load(open("mutations_effects_dict.json"))
prominence = 50
desired_AUC_width = high/5
#%%
Parallel(n_jobs=8, verbose=9)(
delayed(STN_mut)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
initial_period, sec, vol) for mut in list(mutations.keys()))

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Code/KCNA1_mutations/mut_STN_Delta_Kv.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on Fri Jun 18 10:53:45 2021
@author: nils
"""
import numpy as np
from joblib import Parallel, delayed
import json
import os
from numba import types
from numba.typed import Dict
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
from Code.Functions.STN_fxns_Kv import STN_Kv_mut
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.005
number_steps = 100
initial_period = 1000
num_gating = 17
num_current = 9
d = 61.4
r = d/2 * 10**-6 # radius in meters
vol = np.pi * r**3 # volume in liters
C, surf_area = capacitance(d, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut','Ca_conc', 'E_Ca']))
V_init = -70
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param['m'] = np.array([-40., -1. , 1.])
b_param['h'] = np.zeros(4)
b_param['h'] = np.array([-45.5, 1. , 1., 0.05])
b_param['n'] = np.array([-41., -1., 1.])
b_param['p'] = np.array([-56., -1., 1.])
b_param['q'] = np.array([-85., 1., 1.])
b_param['r'] = np.array([0.17, -0.08, 1.])
b_param['a'] = np.array([-45., -14.7, 1.])
b_param['b'] = np.array([-90., 7.5, 1.])
b_param['c'] = np.array([-30.6, -5., 1.])
b_param['d1'] = np.array([-60, 7.5, 1.])
b_param['d2'] = np.array([0.1, 0.02, 1.])
b_param['s'] = np.array([-14.16, -10.15, 1.])
b_param['u'] = np.zeros(4)
b_param['u'] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.zeros(4)
b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
mut_act_Vhalf_wt = -30.01851851851851
mut_act_k_wt = -7.7333333333333325
s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
s_diff_k = mut_act_k_wt - b_param['s'][1]
b_param['s'][1] = b_param['s'][1] + s_diff_k
b_param['u'][1] = b_param['u'][1] + s_diff_k
b_param['s'][0] = b_param['s'][0] + s_diff_Vhalf
b_param['u'][0] = b_param['u'][0] + s_diff_Vhalf
b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
b_param['s_mut'][0] = b_param['s_mut'][0] + s_diff_Vhalf
b_param['u_mut'][0] = b_param['u_mut'][0] + s_diff_Vhalf
# reversal potentials
E["Na"] = 60.
E["K"] = -90.
E["Leak"] = -60.
# model currents
currents_included["Na"] = True
currents_included["Kd"] =True
currents_included["Kv"] = True
currents_included["Kv_mut"] = True
currents_included["L"] = True
currents_included["T"] = True
currents_included["Ca_K"] = True
currents_included["A"] =True
currents_included["Leak"] = True
# model conductances
Kv_ratio = 0.1
g["Na"] = 49. * surf_area
g["Kd"] = 27. * (1-Kv_ratio)* surf_area
if currents_included["Kv_mut"]==True:
g["Kv"] = 27. * Kv_ratio/2 * surf_area
else:
g["Kv"] = 27. * Kv_ratio/2 * surf_area * 2
g["Kv_mut"] = 27. * Kv_ratio/2 * surf_area
g["A"] = 5. * surf_area
g["L"] = 5. * surf_area
g["T"] = 5. * surf_area
g["Ca_K"] = 1 * surf_area
g["Leak"] = 0.035 * surf_area
# save folder
folder = './KCNA1_mutations/STN_Kv_only'
if not os.path.isdir(folder):
os.makedirs(folder)
#
# mutation properties
mutations = json.load(open("mutations_effects_dict.json"))
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(STN_Kv_mut)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param,
slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low,
number_steps, initial_period, sec, vol) for mut in list(mutations.keys()))

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Code/KCNA1_mutations/mut_STN_Kv.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on Sat May 8 16:20:11 2021
@author: nils
"""
import numpy as np
from joblib import Parallel, delayed
import json
import os
from numba import types
from numba.typed import Dict
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
from Code.Functions.STN_fxns_Kv import STN_Kv_mut
# model parameters
dt = 0.01
sec = 2
low = 0
high = 0.005
number_steps = 100
initial_period = 1000
num_gating = 17
num_current = 9
d = 61.4
r = d/2 * 10**-6 # radius in meters
vol = np.pi * r**3 # volume in liters
C, surf_area = capacitance(d, 1)
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut','Ca_conc', 'E_Ca']))
# initialize arrays
current = np.zeros((num_current, stim_num))
gating = np.zeros((num_gating, stim_num))
# initialize dictionary
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
i = 0
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
ind_dict[var] = i
i += 1
i = 0
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
ind_dict[var] = i
i += 1
# gating parameters
b_param['m'] = np.array([-40., -1. , 1.])
b_param['h'] = np.zeros(4)
b_param['h'] = np.array([-45.5, 1. , 1., 0.05])
b_param['n'] = np.array([-41., -1., 1.])
b_param['p'] = np.array([-56., -1., 1.])
b_param['q'] = np.array([-85., 1., 1.])
b_param['r'] = np.array([0.17, -0.08, 1.])
b_param['a'] = np.array([-45., -14.7, 1.])
b_param['b'] = np.array([-90., 7.5, 1.])
b_param['c'] = np.array([-30.6, -5., 1.])
b_param['d1'] = np.array([-60, 7.5, 1.])
b_param['d2'] = np.array([0.1, 0.02, 1.])
b_param['s'] = np.array([-14.16, -10.15, 1.])
b_param['u'] = np.zeros(4)
b_param['u'] = np.array([-31., 5.256, 1., 0.245])
b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
b_param['u_mut'] = np.zeros(4)
b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
mut_act_Vhalf_wt = -30.01851851851851
mut_act_k_wt = -7.7333333333333325
s_diff_Vhalf = mut_act_Vhalf_wt - b_param['s'][0]
s_diff_k = mut_act_k_wt - b_param['s'][1]
b_param['s'][1] = b_param['s'][1] + s_diff_k
b_param['u'][1] = b_param['u'][1] + s_diff_k
b_param['s'][0] = b_param['s'][0] + s_diff_Vhalf
b_param['u'][0] = b_param['u'][0] + s_diff_Vhalf
b_param['s_mut'][1] = b_param['s_mut'][1] + s_diff_k
b_param['u_mut'][1] = b_param['u_mut'][1] + s_diff_k
b_param['s_mut'][0] = b_param['s_mut'][0] + s_diff_Vhalf
b_param['u_mut'][0] = b_param['u_mut'][0] + s_diff_Vhalf
# reversal potentials
E["Na"] = 60.
E["K"] = -90.
E["Leak"] = -60.
# model currents
currents_included["Na"] = True
currents_included["Kd"] =True
currents_included["Kv"] = True
currents_included["Kv_mut"] = True
currents_included["L"] = True
currents_included["T"] = True
currents_included["Ca_K"] = True
currents_included["A"] =True
currents_included["Leak"] = True
# model conductances
Kv_ratio = 0.1
g["Na"] = 49. * surf_area
g["Kd"] = 27. * (1-Kv_ratio)* surf_area
if currents_included["Kv_mut"]==True:
g["Kv"] = 27. * Kv_ratio/2 * surf_area
else:
g["Kv"] = 27. * Kv_ratio/2 * surf_area * 2
g["Kv_mut"] = 27. * Kv_ratio/2 * surf_area
g["A"] = 5. * surf_area
g["L"] = 5. * surf_area
g["T"] = 5. * surf_area
g["Ca_K"] = 1 * surf_area
g["Leak"] = 0.035 * surf_area
V_init = -70
# save folder
folder = './KCNA1_mutations/STN_Kv'
if not os.path.isdir(folder):
os.makedirs(folder)
#
# mutation properties
mutations = json.load(open("mutations_effects_dict.json"))
prominence = 50
desired_AUC_width = high/5
Parallel(n_jobs=8, verbose=9)(
delayed(STN_Kv_mut)(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param,
slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
initial_period, sec, vol) for mut in list(mutations.keys()))