Functions and plotting scripts for each model, sensitivity analysis in each model and KCNA1 mutations in each model
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Code/Functions/Cb_stellate_fxns.py
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960
Code/Functions/Cb_stellate_fxns.py
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import h5py
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import gc
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import scipy
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import json
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import copy
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import os
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import numpy as np
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from numba import types, njit
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from numba.typed import Dict
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from scipy import signal
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from Code.Functions.Utility_fxns import stimulus_init, NumpyEncoder
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@njit
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def Cb_stellate(V, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
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gating, current, ind_dict):
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""" Simulate current and Vm for Cb stellate cell model from Alexander et al 2019 (https://dx.doi.org/10.1523%2FENEURO.0126-19.2019)
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for I_in
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Parameters
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----------
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V : float
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Initial membrane potentia
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g : dict
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maximal conductance for currents
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E : dict
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Reversal potentials for currents
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I_in : array
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Current stimulus input to model
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dt : float
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fixed time step
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currents_included : dict
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Boolean to include current in simulation or not
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stim_time :
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length of stimulus ()
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stim_num : int
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number of different stimulus currents
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C : float
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membrane capacitance
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shift : dict
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for each gating parameter
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scale : dict
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for each gating parameter
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b_param : dict
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Boltzmann parameter values for each gating parameter
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slope_shift : dict
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for each gating parameter
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gating : array
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array of gating values over time
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current : array
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array of current values over time
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ind_dict: dict
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Dictionary of indices in arrays for gating variables
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Returns
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-------
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V_m : array
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Simulated membrane potential in response to I_in
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"""
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V_m = np.zeros((stim_num, stim_time))
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# initialize gating
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# Na activation
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m_inf = (1 / (1 + np.exp((V - shift["m"] - slope_shift["m"] - b_param["m"][0]) / b_param["m"][1]))) ** \
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b_param["m"][2]
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gating[ind_dict['m'], :] = np.ones((stim_num)) * m_inf
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# Na inactivation
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h_inf = ((1 - b_param["h"][3]) / (1 + np.exp((V - shift["h"] - slope_shift["h"] - b_param["h"][0])
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/ b_param["h"][1])) + b_param["h"][3]) ** b_param["h"][2]
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gating[ind_dict['h'], :] = np.ones((stim_num)) * h_inf
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# K activation
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n_inf = (1 / (1 + np.exp((V - shift["n"] - slope_shift["n"] - b_param["n"][0]) / b_param["n"][1]))) ** \
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b_param["n"][2]
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gating[ind_dict['n'], :] = np.ones((stim_num)) * n_inf
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# A-type K activation
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n_A_inf = (1 / (1 + np.exp((V - shift["n_A"] - slope_shift["n_A"] - b_param["n_A"][0]) / b_param["n_A"][1]))) ** \
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b_param["n_A"][2]
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gating[ind_dict['n_A'], :] = np.ones((stim_num)) * n_A_inf
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# A-type K inactivation
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h_A_inf = (1 / (1 + np.exp((V - shift["h_A"] - slope_shift["h_A"] - b_param["h_A"][0]) / b_param["h_A"][1]))) ** \
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b_param["h_A"][2]
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gating[ind_dict['h_A'], :] = np.ones((stim_num)) * h_A_inf
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# mutant A-type K activation
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n_A_mut_inf = (1 / (1 + np.exp((V - shift["n_A_mut"] - slope_shift["n_A_mut"] - b_param["n_A_mut"][0]) / b_param["n_A_mut"][1]))) ** \
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b_param["n_A_mut"][2]
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gating[ind_dict['n_A_mut'], :] = np.ones((stim_num)) * n_A_mut_inf
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# mutant A-type K inactivation
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h_A_mut_inf = (1 / (1 + np.exp((V - shift["h_A_mut"] - slope_shift["h_A_mut"] - b_param["h_A_mut"][0]) / b_param["h_A_mut"][1]))) ** \
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b_param["h_A_mut"][2]
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gating[ind_dict['h_A_mut'], :] = np.ones((stim_num)) * h_A_mut_inf
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# T-type Ca activation
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m_T_inf = (1 / (1 + np.exp((V - shift["m_T"] - slope_shift["m_T"] - b_param["m_T"][0]) / b_param["m_T"][1]))) ** \
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b_param["m_T"][2]
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gating[ind_dict['m_T'], :] = np.ones((stim_num)) * m_T_inf
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# T-type Ca inactivation
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h_T_inf = (1 / (1 + np.exp((V - shift["h_T"] - slope_shift["h_T"] - b_param["h_T"][0]) / b_param["h_T"][1]))) ** \
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b_param["h_T"][2]
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gating[ind_dict['h_T'], :] = np.ones((stim_num)) * h_T_inf
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# initialize currents and Vm
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current[ind_dict['Na'], :] = np.zeros((stim_num))
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current[ind_dict['Kd'], :] = np.zeros((stim_num))
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current[ind_dict['Leak'], :] = np.zeros((stim_num))
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current[ind_dict['A'], :] = np.zeros((stim_num))
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current[ind_dict['A_mut'], :] = np.zeros((stim_num))
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current[ind_dict['T'], :] = np.zeros((stim_num))
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V_m[:, 0] = V
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t = 1
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while t < stim_time:
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# Na activation
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m_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["m"] - slope_shift["m"] - b_param["m"][0]) / b_param["m"][1]))) ** \
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b_param["m"][2]
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gating[ind_dict['m'], :] = m_inf
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gating[ind_dict['m'], :][gating[ind_dict['m'], :] > 1.] = 1.
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gating[ind_dict['m'], :][gating[ind_dict['m'], :] < 0.] = 0.
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# Na inactivation
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h_inf = ((1 - b_param["h"][3]) / (1 + np.exp((V_m[:, t - 1] - shift["h"] - slope_shift["h"] - b_param["h"][0])
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/ b_param["h"][1])) + b_param["h"][3]) ** b_param["h"][2]
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tau_h = 0.1 + (2 * 322 * 46) / (4 * np.pi * (V_m[:, t - 1] - shift["h"] - -74) ** 2 + (46 ** 2))
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h_dot = (h_inf - gating[ind_dict['h'], :]) * (1 / (tau_h * scale["h"]))
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gating[ind_dict['h'], :] = gating[ind_dict['h'], :] + h_dot * dt
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gating[ind_dict['h'], :][gating[ind_dict['h'], :] > 1.] = 1.
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gating[ind_dict['h'], :][gating[ind_dict['h'], :] < 0.] = 0.
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# K activation
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n_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["n"] - slope_shift["n"] - b_param["n"][0]) / b_param["n"][1]))) ** \
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b_param["n"][2]
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tau_n = 6 / (1 + np.exp((V_m[:, t - 1] - shift["n"] + 23) / 15))
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n_dot = (n_inf - gating[ind_dict['n'], :]) * (1 / (tau_n * scale["n"]))
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gating[ind_dict['n'], :] = gating[ind_dict['n'], :] + n_dot * dt
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gating[ind_dict['n'], :][gating[ind_dict['n'], :] > 1.] = 1.
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gating[ind_dict['n'], :][gating[ind_dict['n'], :] < 0.] = 0.
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# A-type K activation
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n_A_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["n_A"] - slope_shift["n_A"] - b_param["n_A"][0]) / b_param["n_A"][1]))) ** \
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b_param["n_A"][2]
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tau_n_A = 5
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n_A_dot = (n_A_inf - gating[ind_dict['n_A'], :]) * (1 / (tau_n_A * scale["n_A"]))
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gating[ind_dict['n_A'], :] = gating[ind_dict['n_A'], :] + n_A_dot * dt
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gating[ind_dict['n_A'], :][gating[ind_dict['n_A'], :] > 1.] = 1.
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gating[ind_dict['n_A'], :][gating[ind_dict['n_A'], :] < 0.] = 0.
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# A-type K inactivation
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h_A_inf = (1 / (1 + np.exp(
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(V_m[:, t - 1] - shift["h_A"] - slope_shift["h_A"] - b_param["h_A"][0]) / b_param["h_A"][1]))) ** \
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b_param["h_A"][2]
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tau_h_A = 10
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h_A_dot = (h_A_inf - gating[ind_dict['h_A'], :]) * (1 / (tau_h_A * scale["h_A"]))
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gating[ind_dict['h_A'], :] = gating[ind_dict['h_A'], :] + h_A_dot * dt
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gating[ind_dict['h_A'], :][gating[ind_dict['h_A'], :] > 1.] = 1.
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gating[ind_dict['h_A'], :][gating[ind_dict['h_A'], :] < 0.] = 0.
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# mutant A-type K activation
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n_A_mut_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["n_A_mut"] - slope_shift["n_A_mut"] - b_param["n_A_mut"][0]) / b_param["n_A_mut"][1]))) ** \
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b_param["n_A_mut"][2]
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tau_n_A_mut = 5
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n_A_mut_dot = (n_A_mut_inf - gating[ind_dict['n_A_mut'], :]) * (1 / (tau_n_A_mut * scale["n_A_mut"]))
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gating[ind_dict['n_A_mut'], :] = gating[ind_dict['n_A_mut'], :] + n_A_mut_dot * dt
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gating[ind_dict['n_A_mut'], :][gating[ind_dict['n_A_mut'], :] > 1.] = 1.
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gating[ind_dict['n_A_mut'], :][gating[ind_dict['n_A_mut'], :] < 0.] = 0.
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# mutant A-type K inactivation
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h_A_mut_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["h_A_mut"] - slope_shift["h_A_mut"] - b_param["h_A_mut"][0]) / b_param["h_A_mut"][1]))) ** \
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b_param["h_A_mut"][2]
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tau_h_A_mut = 10
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h_A_mut_dot = (h_A_mut_inf - gating[ind_dict['h_A_mut'], :]) * (1 / (tau_h_A_mut * scale["h_A_mut"]))
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gating[ind_dict['h_A_mut'], :] = gating[ind_dict['h_A_mut'], :] + h_A_mut_dot * dt
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gating[ind_dict['h_A_mut'], :][gating[ind_dict['h_A_mut'], :] > 1.] = 1.
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gating[ind_dict['h_A_mut'], :][gating[ind_dict['h_A_mut'], :] < 0.] = 0.
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# T-type Ca activation
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m_T_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["m_T"] - slope_shift["m_T"] - b_param["m_T"][0]) / b_param["m_T"][1]))) ** \
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b_param["m_T"][2]
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gating[ind_dict['m_T'], :] = m_T_inf
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gating[ind_dict['m_T'], :][gating[ind_dict['m_T'], :] > 1.] = 1.
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gating[ind_dict['m_T'], :][gating[ind_dict['m_T'], :] < 0.] = 0.
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# T-type Ca inactivation
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h_T_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["h_T"] - slope_shift["h_T"] - b_param["h_T"][0]) / b_param["h_T"][1]))) ** \
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b_param["h_T"][2]
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tau_h_T = 15
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h_T_dot = (h_T_inf - gating[ind_dict['h_T'], :]) * (1 / (tau_h_T * scale["h_T"]))
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gating[ind_dict['h_T'], :] = gating[ind_dict['h_T'], :] + h_T_dot * dt
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gating[ind_dict['h_T'], :][gating[ind_dict['h_T'], :] > 1.] = 1.
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gating[ind_dict['h_T'], :][gating[ind_dict['h_T'], :] < 0.] = 0.
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# update currents
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current[ind_dict['Leak'], :] = g["Leak"] * (V_m[:, t - 1] - E["Leak"]) * currents_included["Leak"]
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current[ind_dict['Na'], :] = g["Na"] * (gating[ind_dict['m'], :] ** 3) * gating[ind_dict['h'], :] * \
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(V_m[:, t - 1] - E["Na"]) * currents_included["Na"]
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current[ind_dict['Kd'], :] = g["Kd"] * (gating[ind_dict['n'], :] ** 4) * (V_m[:, t - 1] - E["K"]) * \
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currents_included["Kd"]
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current[ind_dict['A'], :] = g["A"] * gating[ind_dict['n_A'], :] * gating[ind_dict['h_A'], :] * (
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V_m[:, t - 1] - E["K"]) * currents_included["A"]
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current[ind_dict['A_mut'], :] = g["A_mut"] * gating[ind_dict['n_A_mut'], :] * gating[ind_dict['h_A_mut'], :] * (
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V_m[:, t - 1] - E["K"]) * currents_included["A_mut"]
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current[ind_dict['T'], :] = g["T"] * gating[ind_dict['m_T'], :] * gating[ind_dict['h_T'], :] * (
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V_m[:, t - 1] - E["Ca"]) * currents_included["T"]
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# update dV/dt
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V_dot = (1 / C) * (I_in[t, :] - (current[ind_dict['Leak'], :] + current[ind_dict['Na'], :] +
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current[ind_dict['Kd'], :] + current[ind_dict['A'], :] +
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current[ind_dict['A_mut'], :] + current[ind_dict['T'], :]))
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# update V_m
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V_m[:, t] = V_m[:, t - 1] + V_dot * dt
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t += 1
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return V_m
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def Cb_stellate_mut(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
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gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
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initial_period, sec):
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""" Simulate mutations for Cb Stellate model
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Parameters
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----------
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V_init : float
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Initial membrane potential
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g : dict
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maximal conductance for currents
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E : dict
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Reversal potentials for currents
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I_in : array
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Current stimulus input to model
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dt : float
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fixed time step
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currents_included : dict
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Boolean to include current in simulation or not
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stim_time :
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length of stimulus ()
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stim_num : int
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number of different stimulus currents
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C : float
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membrane capacitance
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shift : dict
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for each gating parameter
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scale : dict
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for each gating parameter
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b_param : dict
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Boltzmann parameter values for each gating parameter
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slope_shift : dict
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for each gating parameter
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gating : array
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array of gating values over time
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current : array
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array of current values over time
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prominence : float
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required spike prominence
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desired_AUC_width : float
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width of AUC from rheobase to rheobase+desired_AUC_width
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mutations : dict
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dictionary of mutation effects
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mut : str
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mutation
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folder : str
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folder to save hdf5 file in
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high : float
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upper current step magnitude
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low : float
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lowest current step magnitude
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number_steps : int
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number of current steps between low and high
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initial_period : float
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length of I = 0 before current step
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sec:
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length of current step in seconds
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Returns
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-------
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saves mut.hdf5 file to folder
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"""
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# setup for simulation
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g_multi = copy.deepcopy(g)
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E_multi = copy.deepcopy(E)
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b_param_multi = copy.deepcopy(b_param)
|
||||||
|
shift_multi = copy.deepcopy(shift)
|
||||||
|
scale_multi = copy.deepcopy(scale)
|
||||||
|
slope_shift_multi = copy.deepcopy(slope_shift)
|
||||||
|
currents_included_multi = copy.deepcopy(currents_included)
|
||||||
|
gating2 = copy.deepcopy(gating)
|
||||||
|
current2 = copy.deepcopy(current)
|
||||||
|
|
||||||
|
g_effect = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in g_multi.items():
|
||||||
|
g_effect[k] = v
|
||||||
|
b_param_effect = Dict.empty(key_type=types.unicode_type, value_type=types.float64[:], )
|
||||||
|
for k, v in b_param_multi.items():
|
||||||
|
b_param_effect[k] = np.array(v)
|
||||||
|
shift2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in shift_multi.items():
|
||||||
|
shift2[k] = v
|
||||||
|
scale2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in scale_multi.items():
|
||||||
|
scale2[k] = v
|
||||||
|
slope_shift2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in slope_shift_multi.items():
|
||||||
|
slope_shift2[k] = v
|
||||||
|
E2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in E_multi.items():
|
||||||
|
E2[k] = v
|
||||||
|
currents_included2 = Dict.empty(key_type=types.unicode_type, value_type=types.boolean, )
|
||||||
|
for k, v in currents_included_multi.items():
|
||||||
|
currents_included2[k] = v
|
||||||
|
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['m', 'h', 'n', 'n_A', 'h_A', 'n_A_mut', 'h_A_mut', 'm_T', 'h_T']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['Na', 'Kd', 'A', 'A_mut', 'T', 'Leak']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
|
||||||
|
# effect of mutation
|
||||||
|
g_effect['A_mut'] = g['A_mut'] * mutations[mut]['g_ratio']
|
||||||
|
b_param_effect['n_A_mut'][0] = b_param_effect['n_A_mut'][0] + mutations[mut]['activation_Vhalf_diff']
|
||||||
|
b_param_effect['n_A_mut'][1] = b_param_effect['n_A_mut'][1] * mutations[mut]['activation_k_ratio']
|
||||||
|
|
||||||
|
# initial simulation for current range from low to high
|
||||||
|
V_m = Cb_stellate(V_init * np.ones((stim_num)), g_effect, E2, I_in, dt, currents_included2, stim_time, stim_num, C,
|
||||||
|
shift2, scale2, b_param_effect, slope_shift2, gating2, current2, ind_dict)
|
||||||
|
stim_start = np.int(initial_period * 1 / dt)
|
||||||
|
min_spike_height = V_m[0, stim_start] + prominence ##########################################################
|
||||||
|
|
||||||
|
# create hdf5 file for the mutation and save data and metadata
|
||||||
|
fname = os.path.join(folder, "{}.hdf5".format(mut.replace(" ", "_")))
|
||||||
|
with h5py.File(fname, "a") as f:
|
||||||
|
data = f.create_group("data")
|
||||||
|
data.create_dataset('V_m', data=V_m, dtype='float64', compression="gzip", compression_opts=9)
|
||||||
|
metadata = {'I_high': high, 'I_low': low, 'stim_num': number_steps, 'stim_time': stim_time, 'sec': sec, 'dt': dt,
|
||||||
|
'initial_period': initial_period, 'g': json.dumps(dict(g_effect)), 'E': json.dumps(dict(E2)), 'C': C,
|
||||||
|
'V_init': V_init, 'ind_dict': json.dumps(dict(ind_dict)),
|
||||||
|
'b_param': json.dumps(dict(b_param_effect), cls=NumpyEncoder), 'currents': json.dumps(dict(currents_included2)),
|
||||||
|
'shift': json.dumps(dict(shift2)), 'scale': json.dumps(dict(scale2)), 'slope_shift': json.dumps(dict(slope_shift2)),
|
||||||
|
'prominence': prominence, 'desired_AUC_width': desired_AUC_width, 'mut': str(mut),
|
||||||
|
'min_spike_height': min_spike_height, }
|
||||||
|
data.attrs.update(metadata)
|
||||||
|
|
||||||
|
# firing frequency analysis and rheobase
|
||||||
|
with h5py.File(fname, "r+") as f:
|
||||||
|
I_mag = np.arange(low, high, (high - low) / stim_num)
|
||||||
|
analysis = f.create_group("analysis")
|
||||||
|
dtyp = h5py.special_dtype(vlen=np.dtype('float64'))
|
||||||
|
for i in np.array(['spike_times', 'spike_times_rheo', 'ISI', 'Freq', 'amplitude']):
|
||||||
|
analysis.create_dataset(i, (I_mag.shape[0],), dtype=dtyp, compression="gzip", compression_opts=9)
|
||||||
|
analysis.create_dataset('F_inf', (I_mag.shape[0],), dtype='float64')
|
||||||
|
analysis.create_dataset('F_null', (I_mag.shape[0],), dtype='float64')
|
||||||
|
analysis.create_dataset('AP', (I_mag.shape[0],), dtype='bool', compression="gzip", compression_opts=9)
|
||||||
|
analysis.create_dataset('AP_rheo', (I_mag.shape[0],), dtype='bool', compression="gzip", compression_opts=9)
|
||||||
|
analysis.create_dataset('rheobase', (1,), dtype='float64', compression="gzip", compression_opts=9)
|
||||||
|
for stim in range(I_mag.shape[0]):
|
||||||
|
# spike detection
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m[stim, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
analysis['spike_times'][stim] = ap_all * dt
|
||||||
|
if analysis['spike_times'][stim].size == 0:
|
||||||
|
analysis['AP'][stim] = False
|
||||||
|
else:
|
||||||
|
analysis['AP'][stim] = True
|
||||||
|
analysis['amplitude'][stim] = np.array(ap_prop["peak_heights"])
|
||||||
|
analysis['ISI'][stim] = np.array([x - analysis['spike_times'][stim][i - 1] if i else None for i, x in
|
||||||
|
enumerate(analysis['spike_times'][stim])][1:]) # msec
|
||||||
|
analysis['Freq'][stim] = 1 / (analysis['ISI'][stim] / 1000)
|
||||||
|
|
||||||
|
if analysis['Freq'][stim].size != 0:
|
||||||
|
analysis['F_null'][stim] = analysis['Freq'][stim][0]
|
||||||
|
last_second_spikes = analysis['spike_times'][stim] > np.int(
|
||||||
|
f['data'].attrs['stim_time'] * dt - (500 + initial_period))
|
||||||
|
if np.any(last_second_spikes == True):
|
||||||
|
last_second_spikes_1 = np.delete(last_second_spikes,0) # remove first spike because it has no ISI/freq
|
||||||
|
analysis['F_inf'][stim] = np.mean(analysis['Freq'][stim][last_second_spikes_1])
|
||||||
|
else: # if no ISI detected
|
||||||
|
analysis['F_null'][stim] = 0
|
||||||
|
analysis['F_inf'][stim] = 0
|
||||||
|
|
||||||
|
|
||||||
|
# rheobase
|
||||||
|
# find where the first AP occurs
|
||||||
|
if np.any(analysis['AP'][:] == True):
|
||||||
|
# setup current range to be between the current step before and with first AP
|
||||||
|
I_first_spike = I_mag[np.argwhere(analysis['AP'][:] == True)[0][0]]
|
||||||
|
I_before_spike = I_mag[np.argwhere(analysis['AP'][:] == True)[0][0] - 1]
|
||||||
|
analysis.create_dataset('I_first_spike', data=I_first_spike, dtype='float64')
|
||||||
|
analysis.create_dataset('I_before_spike', data=I_before_spike, dtype='float64')
|
||||||
|
number_steps_rheo = 100
|
||||||
|
stim_time, I_in_rheo, stim_num_rheo, V_m_rheo = stimulus_init(I_before_spike, I_first_spike,
|
||||||
|
number_steps_rheo,
|
||||||
|
initial_period, dt, sec)
|
||||||
|
# simulate response to this current range and save
|
||||||
|
V_m_rheo = Cb_stellate(V_init * np.ones((stim_num_rheo)), g_effect, E2, I_in_rheo, dt, currents_included2,
|
||||||
|
stim_time, stim_num_rheo, C, shift2, scale2, b_param_effect, slope_shift2,
|
||||||
|
np.zeros((len(b_param), stim_num_rheo)),
|
||||||
|
np.zeros((len(currents_included), stim_num_rheo)), ind_dict)
|
||||||
|
f['data'].create_dataset('V_m_rheo', data=V_m_rheo, dtype='float64', compression="gzip", compression_opts=9)
|
||||||
|
|
||||||
|
# run AP detection
|
||||||
|
stim_start = np.int(initial_period * 1 / dt)
|
||||||
|
for stim in range(I_in_rheo.shape[1]):
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_rheo[stim, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
analysis['spike_times_rheo'][stim] = ap_all * dt
|
||||||
|
if analysis['spike_times_rheo'][stim].size == 0:
|
||||||
|
analysis['AP_rheo'][stim] = False
|
||||||
|
else:
|
||||||
|
analysis['AP_rheo'][stim] = True
|
||||||
|
# find rheobase as smallest current step to elicit AP and save
|
||||||
|
analysis['rheobase'][:] = I_in_rheo[-1, np.argwhere(analysis['AP_rheo'][:] == True)[0][0]] * 1000
|
||||||
|
|
||||||
|
# AUC
|
||||||
|
# find rheobase of steady state firing (F_inf)
|
||||||
|
with h5py.File(fname, "a") as f:
|
||||||
|
F_inf = f['analysis']['F_inf'][:]
|
||||||
|
try:
|
||||||
|
# setup current range to be in current step between no and start of steady state firing
|
||||||
|
F_inf_start_ind = np.argwhere(F_inf != 0)[0][0]
|
||||||
|
dI = (high - low) / stim_num
|
||||||
|
I_start = I_mag[F_inf_start_ind - 1]
|
||||||
|
I_end = I_mag[F_inf_start_ind]
|
||||||
|
I_rel = np.arange(I_start, I_end + (I_end - I_start) / 100, (I_end - I_start) / 100)
|
||||||
|
I_rel = np.reshape(I_rel, (1, I_rel.shape[0]))
|
||||||
|
I_in = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_rel
|
||||||
|
I_in[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_rel
|
||||||
|
stim_num = I_in.shape[1]
|
||||||
|
|
||||||
|
# simulate response to this current step range
|
||||||
|
V_m_inf_rheo = Cb_stellate(V_init * np.ones(stim_num), g_effect, E2, I_in, dt, currents_included2,
|
||||||
|
stim_time, stim_num, C, shift2, scale2, b_param_effect, slope_shift2,
|
||||||
|
np.zeros((len(b_param), stim_num)), np.zeros((len(currents_included), stim_num)),
|
||||||
|
ind_dict)
|
||||||
|
|
||||||
|
# save response, analyse and save
|
||||||
|
for i in np.array(['spike_times_Finf', 'ISI_Finf', 'Freq_Finf']):
|
||||||
|
f['analysis'].require_dataset(i, shape=(I_rel.shape[1],), dtype=dtyp, compression="gzip",
|
||||||
|
compression_opts=9)
|
||||||
|
f['analysis'].require_dataset('F_inf_Finf', shape=(I_rel.shape[1],), dtype='float64')
|
||||||
|
f['analysis'].require_dataset('F_null_Finf', shape=(I_rel.shape[1],), dtype='float64')
|
||||||
|
for stim in range(I_rel.shape[1]):
|
||||||
|
# spike peak detection
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_inf_rheo[stim, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
f['analysis']['spike_times_Finf'][stim] = ap_all * dt
|
||||||
|
f['analysis']['ISI_Finf'][stim] = np.array(
|
||||||
|
[x - f['analysis']['spike_times_Finf'][stim][i - 1] if i else None for i, x in
|
||||||
|
enumerate(f['analysis']['spike_times_Finf'][stim])][1:]) # msec
|
||||||
|
f['analysis']['Freq_Finf'][stim] = 1 / (f['analysis']['ISI_Finf'][stim] / 1000)
|
||||||
|
if f['analysis']['Freq_Finf'][stim].size != 0:
|
||||||
|
last_second_spikes = f['analysis']['spike_times_Finf'][stim] > np.int(
|
||||||
|
f['data'].attrs['stim_time'] * dt - (500 + f['data'].attrs['initial_period']))
|
||||||
|
if np.any(last_second_spikes == True):
|
||||||
|
last_second_spikes_1 = np.delete(last_second_spikes, 0) # remove first spike because it has no ISI/freq
|
||||||
|
f['analysis']['F_inf_Finf'][stim] = np.mean(
|
||||||
|
f['analysis']['Freq_Finf'][stim][last_second_spikes_1])
|
||||||
|
else:
|
||||||
|
f['analysis']['F_null_Finf'][stim] = 0
|
||||||
|
f['analysis']['F_inf_Finf'][stim] = 0
|
||||||
|
|
||||||
|
# find AUC relative to F_inf rheobase
|
||||||
|
# setup current inputs with current range around F_inf rheobase
|
||||||
|
I_first_inf = I_rel[0, np.argwhere(f['analysis']['F_inf_Finf'][:] != 0)[0][0]]
|
||||||
|
I_start = I_first_inf - 0.00005
|
||||||
|
I_end = I_first_inf + desired_AUC_width * high
|
||||||
|
I_rel2 = np.arange(I_start, I_end + (I_end - I_start) / 200, (I_end - I_start) / 200)
|
||||||
|
I_rel2 = np.reshape(I_rel2, (1, I_rel2.shape[0]))
|
||||||
|
I_in2 = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_rel2
|
||||||
|
I_in2[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_rel2
|
||||||
|
stim_num = I_in2.shape[1]
|
||||||
|
|
||||||
|
# simulate response to this current step range
|
||||||
|
V_m_AUC = Cb_stellate(V_init * np.ones(stim_num), g_effect, E2, I_in2, dt, currents_included2, stim_time,
|
||||||
|
stim_num, C, shift2, scale2, b_param_effect, slope_shift2,
|
||||||
|
np.zeros((len(b_param), stim_num)), np.zeros((len(currents_included), stim_num)),
|
||||||
|
ind_dict)
|
||||||
|
|
||||||
|
# save data and analysis for this current step range including AUC
|
||||||
|
f['data'].require_dataset('V_m_AUC', shape=V_m_AUC.shape, dtype='float64')
|
||||||
|
f['data']['V_m_AUC'][:] = V_m_AUC
|
||||||
|
dtyp = h5py.special_dtype(vlen=np.dtype('float64'))
|
||||||
|
for i in np.array(['spike_times_Finf_AUC', 'ISI_Finf_AUC', 'Freq_Finf_AUC']): # analysis_names:
|
||||||
|
f['analysis'].require_dataset(i, shape=(I_rel2.shape[1],), dtype=dtyp, compression="gzip",
|
||||||
|
compression_opts=9)
|
||||||
|
f['analysis'].require_dataset('F_inf_Finf_AUC', shape=(I_rel2.shape[1],), dtype='float64')
|
||||||
|
f['analysis'].require_dataset('F_null_Finf_AUC', shape=(I_rel2.shape[1],), dtype='float64')
|
||||||
|
for stim in range(I_rel2.shape[1]):
|
||||||
|
# spike peak detection
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_AUC[stim, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
f['analysis']['spike_times_Finf_AUC'][stim] = ap_all * dt
|
||||||
|
f['analysis']['ISI_Finf_AUC'][stim] = np.array(
|
||||||
|
[x - f['analysis']['spike_times_Finf_AUC'][stim][i - 1] if i else None for i, x in
|
||||||
|
enumerate(f['analysis']['spike_times_Finf_AUC'][stim])][1:]) # msec
|
||||||
|
f['analysis']['Freq_Finf_AUC'][stim] = 1 / (f['analysis']['ISI_Finf_AUC'][stim] / 1000)
|
||||||
|
if f['analysis']['Freq_Finf_AUC'][stim].size != 0:
|
||||||
|
last_second_spikes = f['analysis']['spike_times_Finf_AUC'][stim] > np.int(
|
||||||
|
f['data'].attrs['stim_time'] * dt - (500 + f['data'].attrs['initial_period']))
|
||||||
|
if np.any(last_second_spikes == True):
|
||||||
|
last_second_spikes_1 = np.delete(last_second_spikes, 0) # remove first spike because it has no ISI/freq
|
||||||
|
f['analysis']['F_inf_Finf_AUC'][stim] = np.mean(
|
||||||
|
f['analysis']['Freq_Finf_AUC'][stim][last_second_spikes_1])
|
||||||
|
else:
|
||||||
|
f['analysis']['F_null_Finf_AUC'][stim] = 0
|
||||||
|
f['analysis']['F_inf_Finf_AUC'][stim] = 0
|
||||||
|
f['analysis'].require_dataset('AUC_rel_acc', shape=(1,), dtype='float64', compression="gzip",
|
||||||
|
compression_opts=9)
|
||||||
|
f['analysis']['AUC_rel_acc'][:] = np.trapz(f['analysis']['F_inf_Finf_AUC'][:],
|
||||||
|
I_rel2[:] * 1000, dI * 1000) # AUC
|
||||||
|
except:
|
||||||
|
f['analysis'].require_dataset('AUC_rel_acc', shape=(1,), dtype='float64', compression="gzip",
|
||||||
|
compression_opts=9)
|
||||||
|
f['analysis']['AUC_rel_acc'][:] = 0 # AUC
|
||||||
|
print('exception', f['analysis']['AUC_rel_acc'][()])
|
||||||
|
|
||||||
|
# ramp protocol #####################
|
||||||
|
# setup ramp current
|
||||||
|
sec = 4
|
||||||
|
ramp_len = np.int(sec *1000 *1/dt)
|
||||||
|
stim_time = ramp_len *2
|
||||||
|
I_amp = np.array([0, -0.000001])
|
||||||
|
I_amp = np.reshape(I_amp, (1,I_amp.shape[0]))
|
||||||
|
I_step = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1))@ I_amp
|
||||||
|
I_step[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_amp
|
||||||
|
stim_num_step = I_step.shape[1]
|
||||||
|
start = np.int(initial_period * 1 / dt)
|
||||||
|
I_step[start:np.int(start+ramp_len),0] = np.linspace(0, high, ramp_len)
|
||||||
|
I_step[np.int(start + ramp_len):np.int(start + ramp_len*2),0] = np.linspace(high, 0, ramp_len)
|
||||||
|
stim_time_step = I_step.shape[0]
|
||||||
|
|
||||||
|
# simulate response to ramp
|
||||||
|
V_m_step = Cb_stellate(-70 * np.ones(stim_num_step), g_effect, E2, I_step, dt, currents_included2, stim_time_step,
|
||||||
|
stim_num_step, C, shift2, scale2, b_param_effect, slope_shift2,
|
||||||
|
np.zeros((len(b_param), stim_num_step)), np.zeros((len(currents_included), stim_num_step)),
|
||||||
|
ind_dict)
|
||||||
|
|
||||||
|
|
||||||
|
# spike peak detection
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_step[0, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
# save ramp firing properties
|
||||||
|
with h5py.File(fname, "a") as f:
|
||||||
|
f['data'].create_dataset('V_m_ramp', data=V_m_step, dtype='float64', compression="gzip", compression_opts=9)
|
||||||
|
if ap_all.shape[0] != 0:
|
||||||
|
if ap_all[0] < stim_start + ramp_len:
|
||||||
|
f['analysis'].create_dataset('ramp_I_up', data =I_step[stim_start+ap_all[0], 0] *1000, dtype='float64')
|
||||||
|
else:
|
||||||
|
f['analysis'].create_dataset('ramp_I_up', data =np.NaN, dtype='float64')
|
||||||
|
if ap_all[-1] > stim_start + ramp_len:
|
||||||
|
f['analysis'].create_dataset('ramp_I_down', data =I_step[stim_start+ap_all[-1], 0]*1000, dtype='float64')
|
||||||
|
else:
|
||||||
|
f['analysis'].create_dataset('ramp_I_down', data =np.NaN, dtype='float64')
|
||||||
|
f['analysis'].create_dataset('hysteresis', data =f['analysis']['ramp_I_down'][()] - f['analysis']['ramp_I_up'][()], dtype='float64')
|
||||||
|
else: # if no spikes in response to ramp
|
||||||
|
f['analysis'].create_dataset('ramp_I_up', data =np.NaN, dtype='float64')
|
||||||
|
f['analysis'].create_dataset('ramp_I_down', data =np.NaN, dtype='float64')
|
||||||
|
f['analysis'].create_dataset('hysteresis', data =np.NaN, dtype='float64')
|
||||||
|
|
||||||
|
#########################################
|
||||||
|
|
||||||
|
if f.__bool__():
|
||||||
|
f.close()
|
||||||
|
gc.collect()
|
||||||
|
|
||||||
|
|
||||||
|
def SA_Cb_stellate(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, folder, high, low, number_steps, initial_period,
|
||||||
|
sec, lin_array, log_array, alt_types, alt_ind, alt):
|
||||||
|
""" Sensitivity analysis for Cb Stellate model
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
V_init : float
|
||||||
|
Initial membrane potential
|
||||||
|
g : dict
|
||||||
|
maximal conductance for currents
|
||||||
|
E : dict
|
||||||
|
Reversal potentials for currents
|
||||||
|
I_in : array
|
||||||
|
Current stimulus input to model
|
||||||
|
dt : float
|
||||||
|
fixed time step
|
||||||
|
currents_included : dict
|
||||||
|
Boolean to include current in simulation or not
|
||||||
|
stim_time :
|
||||||
|
length of stimulus ()
|
||||||
|
stim_num : int
|
||||||
|
number of different stimulus currents
|
||||||
|
C : float
|
||||||
|
membrane capacitance
|
||||||
|
shift : dict
|
||||||
|
for each gating parameter
|
||||||
|
scale : dict
|
||||||
|
for each gating parameter
|
||||||
|
b_param : dict
|
||||||
|
Boltzmann parameter values for each gating parameter
|
||||||
|
slope_shift : dict
|
||||||
|
for each gating parameter
|
||||||
|
gating : array
|
||||||
|
array of gating values over time
|
||||||
|
current : array
|
||||||
|
array of current values over time
|
||||||
|
prominence: float
|
||||||
|
required spike prominence
|
||||||
|
desired_AUC_width: float
|
||||||
|
width of AUC from rheobase to rheobase + desired_AUC_width
|
||||||
|
folder : str
|
||||||
|
folder to save hdf5 in
|
||||||
|
high : float
|
||||||
|
upper current step magnitude
|
||||||
|
low : float
|
||||||
|
lowest current step magnitude
|
||||||
|
number_steps : int
|
||||||
|
number of current steps between low and high
|
||||||
|
initial_period: float
|
||||||
|
length of I = 0 before current step
|
||||||
|
sec : float
|
||||||
|
length of current step in seconds
|
||||||
|
lin_array : array
|
||||||
|
array of linear shifts from -10 tp 10
|
||||||
|
log_array : array
|
||||||
|
array of log2 values from -1 to 1
|
||||||
|
alt_types : array
|
||||||
|
array of arrays of strings of [variable, alteration type]
|
||||||
|
alt_ind : int
|
||||||
|
index for alt_type
|
||||||
|
alt :
|
||||||
|
index for lin_array of log_array
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
saves hdf5 file to folder
|
||||||
|
"""
|
||||||
|
|
||||||
|
# setup for simulation
|
||||||
|
g_multi = copy.deepcopy(g)
|
||||||
|
E_multi = copy.deepcopy(E)
|
||||||
|
b_param_multi = copy.deepcopy(b_param)
|
||||||
|
shift_multi = copy.deepcopy(shift)
|
||||||
|
scale_multi = copy.deepcopy(scale)
|
||||||
|
slope_shift_multi = copy.deepcopy(slope_shift)
|
||||||
|
currents_included_multi = copy.deepcopy(currents_included)
|
||||||
|
gating2 = copy.deepcopy(gating)
|
||||||
|
current2 = copy.deepcopy(current)
|
||||||
|
|
||||||
|
g2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in g_multi.items():
|
||||||
|
g2[k] = v
|
||||||
|
b_param2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64[:], )
|
||||||
|
for k, v in b_param_multi.items():
|
||||||
|
b_param2[k] = np.array(v)
|
||||||
|
shift2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in shift_multi.items():
|
||||||
|
shift2[k] = v
|
||||||
|
scale2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in scale_multi.items():
|
||||||
|
scale2[k] = v
|
||||||
|
slope_shift2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in slope_shift_multi.items():
|
||||||
|
slope_shift2[k] = v
|
||||||
|
E2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in E_multi.items():
|
||||||
|
E2[k] = v
|
||||||
|
currents_included2 = Dict.empty(key_type=types.unicode_type, value_type=types.boolean, )
|
||||||
|
for k, v in currents_included_multi.items():
|
||||||
|
currents_included2[k] = v
|
||||||
|
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['m', 'h', 'n', 'n_A', 'h_A', 'n_A_mut', 'h_A_mut', 'm_T', 'h_T']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['Na', 'Kd', 'A', 'A_mut', 'T', 'Leak']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
|
||||||
|
# alteration to gating ##########################################################################
|
||||||
|
if alt_types[alt_ind, 1] == 'shift':
|
||||||
|
shift2[alt_types[alt_ind, 0]] = lin_array[alt]
|
||||||
|
fname = os.path.join(folder,
|
||||||
|
"{}_{}_{}.hdf5".format(alt_types[alt_ind, 1], alt_types[alt_ind, 0], lin_array[alt]))
|
||||||
|
|
||||||
|
elif alt_types[alt_ind, 1] == 'slope':
|
||||||
|
b_param2[alt_types[alt_ind, 0]][1] = b_param2[alt_types[alt_ind, 0]][1] * log_array[alt]
|
||||||
|
fname = os.path.join(folder,
|
||||||
|
"{}_{}_{}.hdf5".format(alt_types[alt_ind, 1], alt_types[alt_ind, 0],
|
||||||
|
np.int(np.around(log_array[alt], 2) * 100)))
|
||||||
|
if b_param2[alt_types[alt_ind, 0]][2] != 1:
|
||||||
|
# adjust slope_shift to account for slope not changing (rotating) around 0.5
|
||||||
|
V_test = np.arange(-150, 100, 0.001)
|
||||||
|
if b_param2[alt_types[alt_ind, 0]].shape[0] == 4:
|
||||||
|
steady_state = ((1 - b_param2[alt_types[alt_ind, 0]][3]) / (
|
||||||
|
1 + np.exp(
|
||||||
|
(V_test - b_param2[alt_types[alt_ind, 0]][0]) / b_param2[alt_types[alt_ind, 0]][1])) +
|
||||||
|
b_param2[alt_types[alt_ind, 0]][3]) ** b_param2[alt_types[alt_ind, 0]][2]
|
||||||
|
orig = ((1 - b_param[alt_types[alt_ind, 0]][3]) / (
|
||||||
|
1 + np.exp((V_test - b_param[alt_types[alt_ind, 0]][0]) / b_param[alt_types[alt_ind, 0]][1])) +
|
||||||
|
b_param[alt_types[alt_ind, 0]][3]) ** b_param[alt_types[alt_ind, 0]][2]
|
||||||
|
else:
|
||||||
|
steady_state = (1 / (1 + np.exp(
|
||||||
|
(V_test - b_param2[alt_types[alt_ind, 0]][0]) / b_param2[alt_types[alt_ind, 0]][1]))) ** \
|
||||||
|
b_param2[alt_types[alt_ind, 0]][2]
|
||||||
|
orig = (1 / (1 + np.exp(
|
||||||
|
(V_test - b_param[alt_types[alt_ind, 0]][0]) / b_param[alt_types[alt_ind, 0]][1]))) ** \
|
||||||
|
b_param[alt_types[alt_ind, 0]][2]
|
||||||
|
orig_V_half = V_test[(np.abs(orig - 0.5)).argmin()]
|
||||||
|
V_half_new = V_test[(np.abs(steady_state - 0.5)).argmin()]
|
||||||
|
slope_shift2[alt_types[alt_ind, 0]] = orig_V_half - V_half_new
|
||||||
|
|
||||||
|
elif alt_types[alt_ind, 1] == 'g':
|
||||||
|
g2[alt_types[alt_ind, 0]] = g2[alt_types[alt_ind, 0]] * log_array[alt]
|
||||||
|
fname = os.path.join(folder, "{}_{}_{}.hdf5".format(alt_types[alt_ind, 1], alt_types[alt_ind, 0],
|
||||||
|
np.int(np.around(log_array[alt], 2) * 100)))
|
||||||
|
###########################################################################
|
||||||
|
|
||||||
|
# initial simulation for current range from low to high
|
||||||
|
V_m = Cb_stellate(V_init * np.ones((stim_num)), g2, E2, I_in, dt, currents_included2, stim_time, stim_num, C,
|
||||||
|
shift2, scale2, b_param2, slope_shift2, gating2, current2, ind_dict)
|
||||||
|
stim_start = np.int(initial_period * 1 /dt)
|
||||||
|
min_spike_height = V_m[0, stim_start] + prominence
|
||||||
|
|
||||||
|
# create hdf5 file and save data and metadata
|
||||||
|
with h5py.File(fname, "a") as f:
|
||||||
|
data = f.create_group("data")
|
||||||
|
data.create_dataset('V_m', data=V_m, dtype='float64', compression="gzip", compression_opts=9)
|
||||||
|
metadata = {'I_high': high, 'I_low': low, 'stim_num': number_steps, 'stim_time': stim_time, 'sec': sec, 'dt': dt,
|
||||||
|
'initial_period': initial_period, 'g': json.dumps(dict(g2)), 'E': json.dumps(dict(E2)), 'C': C,
|
||||||
|
'V_init': V_init, 'ind_dict': json.dumps(dict(ind_dict)),
|
||||||
|
'b_param': json.dumps(dict(b_param2), cls=NumpyEncoder),
|
||||||
|
'currents': json.dumps(dict(currents_included2)), 'shift': json.dumps(dict(shift2)),
|
||||||
|
'var_change': str(alt_types[alt_ind, 1]), 'var': str(alt_types[alt_ind, 0]),
|
||||||
|
'scale': json.dumps(dict(scale2)), 'slope_shift': json.dumps(dict(slope_shift2)),
|
||||||
|
'prominence': prominence, 'desired_AUC_width': desired_AUC_width,
|
||||||
|
'alteration_info': str(alt_types[alt_ind, :]), 'alt_index': str(alt),
|
||||||
|
'min_spike_height': min_spike_height, }
|
||||||
|
data.attrs.update(metadata)
|
||||||
|
if alt_types[alt_ind, 1] == 'shift':
|
||||||
|
meta2 = {'alteration': lin_array[alt]}
|
||||||
|
else:
|
||||||
|
meta2 = {'alteration': log_array[alt]}
|
||||||
|
data.attrs.update(meta2)
|
||||||
|
|
||||||
|
# firing frequency analysis and rheobase
|
||||||
|
with h5py.File(fname, "r+") as f:
|
||||||
|
I_mag = np.arange(low, high, (high - low) / stim_num)
|
||||||
|
analysis = f.create_group("analysis")
|
||||||
|
dtyp = h5py.special_dtype(vlen=np.dtype('float64'))
|
||||||
|
for i in np.array(['spike_times', 'spike_times_rheo', 'ISI', 'Freq']):
|
||||||
|
analysis.create_dataset(i, (I_mag.shape[0],), dtype=dtyp, compression="gzip", compression_opts=9)
|
||||||
|
analysis.create_dataset('F_inf', (I_mag.shape[0],), dtype='float64')
|
||||||
|
analysis.create_dataset('F_null', (I_mag.shape[0],), dtype='float64')
|
||||||
|
analysis.create_dataset('AP', (I_mag.shape[0],), dtype='bool', compression="gzip", compression_opts=9)
|
||||||
|
analysis.create_dataset('AP_rheo', (I_mag.shape[0],), dtype='bool', compression="gzip", compression_opts=9)
|
||||||
|
for i in np.array(['rheobase']):
|
||||||
|
analysis.create_dataset(i, (1,), dtype='float64', compression="gzip", compression_opts=9)
|
||||||
|
try:
|
||||||
|
for stim in range(I_mag.shape[0]):
|
||||||
|
# spike detection
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m[stim, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
analysis['spike_times'][stim] = ap_all * dt
|
||||||
|
if analysis['spike_times'][stim].size == 0:
|
||||||
|
analysis['AP'][stim] = False
|
||||||
|
else:
|
||||||
|
analysis['AP'][stim] = True
|
||||||
|
analysis['ISI'][stim] = np.array([x - analysis['spike_times'][stim][i - 1] if i else None for i, x in
|
||||||
|
enumerate(analysis['spike_times'][stim])][1:]) # msec
|
||||||
|
analysis['Freq'][stim] = 1 / (analysis['ISI'][stim] / 1000)
|
||||||
|
|
||||||
|
if analysis['Freq'][stim].size != 0:
|
||||||
|
#####################################################################################################################################################
|
||||||
|
analysis['F_null'][stim] = analysis['Freq'][stim][0]
|
||||||
|
if np.argwhere(ap_all >= 1000 * 1 / dt).shape[0] != 0: # if first spike is after 1 ms
|
||||||
|
half_second_spikes = np.logical_and(analysis['spike_times'][stim] < np.int(
|
||||||
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt + 500),
|
||||||
|
analysis['spike_times'][stim] > np.int(
|
||||||
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][
|
||||||
|
0]] * dt)) # spikes in half second window
|
||||||
|
if np.sum(half_second_spikes == True) > 1:
|
||||||
|
half_second_spikes_1 = np.delete(half_second_spikes,
|
||||||
|
np.argwhere(half_second_spikes == True)[0][0])
|
||||||
|
analysis['F_inf'][stim] = np.mean(analysis['Freq'][stim][half_second_spikes_1])
|
||||||
|
else:
|
||||||
|
analysis['F_inf'][stim] = 0
|
||||||
|
else:
|
||||||
|
analysis['F_inf'][stim] = 0
|
||||||
|
else: # if no ISI detected
|
||||||
|
analysis['F_null'][stim] = 0
|
||||||
|
analysis['F_inf'][stim] = 0
|
||||||
|
except:
|
||||||
|
print(alt_types[alt_ind, 0], alt_types[alt_ind, 1], alt, 'Freq analysis unsuccessful')
|
||||||
|
|
||||||
|
# rheobase
|
||||||
|
# find where the first AP occurs
|
||||||
|
if np.any(analysis['AP'][:] == True):
|
||||||
|
# setup current range to be between the current step before and with first AP
|
||||||
|
|
||||||
|
I_first_spike = I_mag[np.argwhere(analysis['AP'][:] == True)[0][0]]
|
||||||
|
I_before_spike = I_mag[np.argwhere(analysis['AP'][:] == True)[0][0] - 1]
|
||||||
|
analysis.create_dataset('I_first_spike', data=I_first_spike, dtype='float64')
|
||||||
|
analysis.create_dataset('I_before_spike', data=I_before_spike, dtype='float64')
|
||||||
|
number_steps_rheo = 100
|
||||||
|
stim_time, I_in_rheo, stim_num_rheo, V_m_rheo = stimulus_init(I_before_spike, I_first_spike,
|
||||||
|
number_steps_rheo, initial_period,
|
||||||
|
dt, sec)
|
||||||
|
|
||||||
|
# simulate response to this current range and save
|
||||||
|
V_m_rheo = Cb_stellate(V_init * np.ones((stim_num_rheo)), g2, E2, I_in_rheo, dt, currents_included2,
|
||||||
|
stim_time, stim_num_rheo, C, shift2, scale2, b_param2, slope_shift2,
|
||||||
|
np.zeros((len(b_param), stim_num_rheo)),
|
||||||
|
np.zeros((len(currents_included), stim_num_rheo)), ind_dict)
|
||||||
|
# run AP detection
|
||||||
|
try:
|
||||||
|
# stim_start = np.int(initial_period * 1 / dt)
|
||||||
|
for stim in range(I_in_rheo.shape[1]): # range(stim_num):
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_rheo[stim, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
analysis['spike_times_rheo'][stim] = ap_all * dt
|
||||||
|
if analysis['spike_times_rheo'][stim].size == 0:
|
||||||
|
analysis['AP_rheo'][stim] = False
|
||||||
|
else:
|
||||||
|
analysis['AP_rheo'][stim] = True
|
||||||
|
|
||||||
|
except:
|
||||||
|
print(alt_types[alt_ind, 0], alt_types[alt_ind, 1], alt, 'RHEOBASE NO AP')
|
||||||
|
# find rheobase as smallest current step to elicit AP and save
|
||||||
|
analysis['rheobase'][:] = I_in_rheo[-1, np.argwhere(analysis['AP_rheo'][:] == True)[0][0]] * 1000
|
||||||
|
|
||||||
|
# AUC
|
||||||
|
# find rheobase of steady state firing (F_inf)
|
||||||
|
with h5py.File(fname, "a") as f:
|
||||||
|
F_inf = f['analysis']['F_inf'][:]
|
||||||
|
try:
|
||||||
|
# setup current range to be in current step between no and start of steady state firing
|
||||||
|
F_inf_start_ind = np.argwhere(F_inf != 0)[0][0]
|
||||||
|
dI = (high - low) / stim_num
|
||||||
|
I_start = I_mag[F_inf_start_ind - 1]
|
||||||
|
I_end = I_mag[F_inf_start_ind]
|
||||||
|
I_rel = np.arange(I_start, I_end + (I_end - I_start) / 100, (I_end - I_start) / 100)
|
||||||
|
I_rel = np.reshape(I_rel, (1, I_rel.shape[0]))
|
||||||
|
I_in = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_rel
|
||||||
|
I_in[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_rel
|
||||||
|
stim_num = I_in.shape[1]
|
||||||
|
|
||||||
|
# simulate response to this current step range
|
||||||
|
V_m_inf_rheo = Cb_stellate(V_init * np.ones(stim_num), g2, E2, I_in, dt, currents_included2, stim_time,
|
||||||
|
stim_num, C, shift2, scale2, b_param2, slope_shift2,
|
||||||
|
np.zeros((len(b_param), stim_num)), np.zeros((len(currents_included), stim_num)),
|
||||||
|
ind_dict)
|
||||||
|
|
||||||
|
# save response, analyse and save
|
||||||
|
for i in np.array(['spike_times_Finf', 'ISI_Finf', 'Freq_Finf']):
|
||||||
|
f['analysis'].require_dataset(i, shape=(I_rel.shape[1],), dtype=dtyp, compression="gzip",
|
||||||
|
compression_opts=9)
|
||||||
|
f['analysis'].require_dataset('F_inf_Finf', shape=(I_rel.shape[1],), dtype='float64')
|
||||||
|
f['analysis'].require_dataset('F_null_Finf', shape=(I_rel.shape[1],), dtype='float64')
|
||||||
|
for stim in range(I_rel.shape[1]):
|
||||||
|
# spike peak detection
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_inf_rheo[stim, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
f['analysis']['spike_times_Finf'][stim] = ap_all * dt
|
||||||
|
f['analysis']['ISI_Finf'][stim] = np.array(
|
||||||
|
[x - f['analysis']['spike_times_Finf'][stim][i - 1] if i else None for i, x in
|
||||||
|
enumerate(f['analysis']['spike_times_Finf'][stim])][1:]) # msec
|
||||||
|
f['analysis']['Freq_Finf'][stim] = 1 / (f['analysis']['ISI_Finf'][stim] / 1000)
|
||||||
|
if f['analysis']['Freq_Finf'][stim].size != 0:
|
||||||
|
if np.argwhere(ap_all >= 1000 * 1 / dt).shape[0] != 0: # if first spike is after 1 ms
|
||||||
|
half_second_spikes = np.logical_and(f['analysis']['spike_times_Finf'][stim] < np.int(
|
||||||
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt + 500),
|
||||||
|
f['analysis']['spike_times_Finf'][stim] > np.int(
|
||||||
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt)) # spikes in half second window
|
||||||
|
if np.sum(half_second_spikes == True) > 1:
|
||||||
|
half_second_spikes_1 = np.delete(half_second_spikes,
|
||||||
|
np.argwhere(half_second_spikes == True)[0][0]) # remove first spike because it has no ISI/freq
|
||||||
|
f['analysis']['F_inf_Finf'][stim] = np.mean(
|
||||||
|
f['analysis']['Freq_Finf'][stim][half_second_spikes_1])
|
||||||
|
else:
|
||||||
|
f['analysis']['F_inf_Finf'][stim] = 0
|
||||||
|
else:
|
||||||
|
f['analysis']['F_null_Finf'][stim] = 0
|
||||||
|
f['analysis']['F_inf_Finf'][stim] = 0
|
||||||
|
else:
|
||||||
|
f['analysis']['F_null_Finf'][stim] = 0
|
||||||
|
f['analysis']['F_inf_Finf'][stim] = 0
|
||||||
|
|
||||||
|
# find AUC relative to F_inf rheobase
|
||||||
|
# setup current inputs with current range around F_inf rheobase
|
||||||
|
I_first_inf = I_rel[0, np.argwhere(f['analysis']['F_inf_Finf'][:] != 0)[0][0]]
|
||||||
|
I_start = I_first_inf - 0.00005
|
||||||
|
I_end = I_first_inf + 0.2 * high
|
||||||
|
I_rel2 = np.arange(I_start, I_end + (I_end - I_start) / 100, (I_end - I_start) / 100)
|
||||||
|
I_rel2 = np.reshape(I_rel2, (1, I_rel2.shape[0]))
|
||||||
|
I_in2 = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_rel2
|
||||||
|
I_in2[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_rel2
|
||||||
|
stim_num = I_in2.shape[1]
|
||||||
|
|
||||||
|
# simulate response to this current step range
|
||||||
|
V_m_AUC = Cb_stellate(V_init * np.ones(stim_num), g2, E2, I_in2, dt, currents_included2, stim_time,
|
||||||
|
stim_num, C, shift2, scale2, b_param2, slope_shift2,
|
||||||
|
np.zeros((len(b_param), stim_num)), np.zeros((len(currents_included), stim_num)),
|
||||||
|
ind_dict)
|
||||||
|
|
||||||
|
# save data and analysis for this current step range including AUC
|
||||||
|
f['data'].require_dataset('V_m_AUC', shape=V_m_AUC.shape, dtype='float64')
|
||||||
|
f['data']['V_m_AUC'][:] = V_m_AUC
|
||||||
|
dtyp = h5py.special_dtype(vlen=np.dtype('float64'))
|
||||||
|
for i in np.array(['spike_times_Finf_AUC', 'ISI_Finf_AUC', 'Freq_Finf_AUC']): # analysis_names:
|
||||||
|
f['analysis'].require_dataset(i, shape=(I_rel2.shape[1],), dtype=dtyp, compression="gzip",
|
||||||
|
compression_opts=9)
|
||||||
|
f['analysis'].require_dataset('F_inf_Finf_AUC', shape=(I_rel2.shape[1],), dtype='float64')
|
||||||
|
f['analysis'].require_dataset('F_null_Finf_AUC', shape=(I_rel2.shape[1],), dtype='float64')
|
||||||
|
for stim in range(I_rel2.shape[1]):
|
||||||
|
# spike peak detection
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_AUC[stim, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
f['analysis']['spike_times_Finf_AUC'][stim] = ap_all * dt
|
||||||
|
f['analysis']['ISI_Finf_AUC'][stim] = np.array(
|
||||||
|
[x - f['analysis']['spike_times_Finf_AUC'][stim][i - 1] if i else None for i, x in
|
||||||
|
enumerate(f['analysis']['spike_times_Finf_AUC'][stim])][1:]) # msec
|
||||||
|
f['analysis']['Freq_Finf_AUC'][stim] = 1 / (f['analysis']['ISI_Finf_AUC'][stim] / 1000)
|
||||||
|
if f['analysis']['Freq_Finf_AUC'][stim].size != 0:
|
||||||
|
if np.argwhere(ap_all >= 1000 * 1 / dt).shape[0] != 0: # if first spike is after 1 ms
|
||||||
|
half_second_spikes = np.logical_and(f['analysis']['spike_times_Finf_AUC'][stim] < np.int(
|
||||||
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt + 500),
|
||||||
|
f['analysis']['spike_times_Finf_AUC'][stim] > np.int(
|
||||||
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][
|
||||||
|
0]] * dt)) # spikes in half second window
|
||||||
|
if np.sum(half_second_spikes == True) > 1:
|
||||||
|
half_second_spikes_1 = np.delete(half_second_spikes,
|
||||||
|
np.argwhere(half_second_spikes == True)[0][0]) # remove first spike because it has no ISI/freq
|
||||||
|
f['analysis']['F_inf_Finf_AUC'][stim] = np.mean(
|
||||||
|
f['analysis']['Freq_Finf_AUC'][stim][half_second_spikes_1])
|
||||||
|
else:
|
||||||
|
f['analysis']['F_inf_Finf_AUC'][stim] = 0
|
||||||
|
else:
|
||||||
|
f['analysis']['F_null_Finf_AUC'][stim] = 0
|
||||||
|
f['analysis']['F_inf_Finf_AUC'][stim] = 0
|
||||||
|
else:
|
||||||
|
f['analysis']['F_null_Finf_AUC'][stim] = 0
|
||||||
|
f['analysis']['F_inf_Finf_AUC'][stim] = 0
|
||||||
|
f['analysis'].require_dataset('AUC', shape=(1,), dtype='float64', compression="gzip",
|
||||||
|
compression_opts=9)
|
||||||
|
f['analysis']['AUCc'][:] = np.trapz(f['analysis']['F_inf_Finf_AUC'][:],
|
||||||
|
I_rel2[:] * 1000, dI * 1000)
|
||||||
|
except:
|
||||||
|
f['analysis'].require_dataset('AUC', shape=(1,), dtype='float64', compression="gzip",
|
||||||
|
compression_opts=9)
|
||||||
|
f['analysis']['AUC'][:] = 0
|
||||||
|
if f.__bool__():
|
||||||
|
f.close()
|
||||||
|
gc.collect()
|
||||||
|
|
||||||
993
Code/Functions/Cb_stellate_fxns_Kv.py
Normal file
993
Code/Functions/Cb_stellate_fxns_Kv.py
Normal file
@ -0,0 +1,993 @@
|
|||||||
|
import h5py
|
||||||
|
import scipy
|
||||||
|
import json
|
||||||
|
import copy
|
||||||
|
import os
|
||||||
|
import gc
|
||||||
|
import numpy as np
|
||||||
|
from numba import types, njit
|
||||||
|
from numba.typed import Dict
|
||||||
|
from scipy import signal
|
||||||
|
from Code.Functions.Utility_fxns import stimulus_init, NumpyEncoder
|
||||||
|
|
||||||
|
|
||||||
|
@njit
|
||||||
|
def Cb_stellate_Kv(V, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param,
|
||||||
|
slope_shift, gating, current, ind_dict):
|
||||||
|
""" Simulate current and Vm for Cb stellate cell model from Alexander et al 2019 (https://dx.doi.org/10.1523%2FENEURO.0126-19.2019)
|
||||||
|
with Kv1.1 from Ranjan et al. 2019 (https://dx.doi.org/10.3389/fncel.2019.00358) added for I_in
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
V : float
|
||||||
|
Initial membrane potentia
|
||||||
|
g : dict
|
||||||
|
maximal conductance for currents
|
||||||
|
E : dict
|
||||||
|
Reversal potentials for currents
|
||||||
|
I_in : array
|
||||||
|
Current stimulus input to model
|
||||||
|
dt : float
|
||||||
|
fixed time step
|
||||||
|
currents_included : dict
|
||||||
|
Boolean to include current in simulation or not
|
||||||
|
stim_time :
|
||||||
|
length of stimulus ()
|
||||||
|
stim_num : int
|
||||||
|
number of different stimulus currents
|
||||||
|
C : float
|
||||||
|
membrane capacitance
|
||||||
|
shift : dict
|
||||||
|
for each gating parameter
|
||||||
|
scale : dict
|
||||||
|
for each gating parameter
|
||||||
|
b_param : dict
|
||||||
|
Boltzmann parameter values for each gating parameter
|
||||||
|
slope_shift : dict
|
||||||
|
for each gating parameter
|
||||||
|
gating : array
|
||||||
|
array of gating values over time
|
||||||
|
current : array
|
||||||
|
array of current values over time
|
||||||
|
ind_dict: dict
|
||||||
|
Dictionary of indices in arrays for gating variables
|
||||||
|
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
V_m : array
|
||||||
|
Simulated membrane potential in response to I_in
|
||||||
|
"""
|
||||||
|
|
||||||
|
V_m = np.zeros((stim_num, stim_time))
|
||||||
|
# initialize gating
|
||||||
|
|
||||||
|
# Na activation
|
||||||
|
m_inf = (1 / (1 + np.exp((V - shift["m"] - slope_shift["m"] - b_param["m"][0]) / b_param["m"][1]))) ** \
|
||||||
|
b_param["m"][2]
|
||||||
|
gating[ind_dict['m'], :] = np.ones((stim_num)) * m_inf
|
||||||
|
|
||||||
|
# Na inactivation
|
||||||
|
h_inf = ((1 - b_param["h"][3]) / (1 + np.exp((V - shift["h"] - slope_shift["h"] - b_param["h"][0])
|
||||||
|
/ b_param["h"][1])) + b_param["h"][3]) ** b_param["h"][2]
|
||||||
|
gating[ind_dict['h'], :] = np.ones((stim_num)) * h_inf
|
||||||
|
|
||||||
|
# K activation
|
||||||
|
n_inf = (1 / (1 + np.exp((V - shift["n"] - slope_shift["n"] - b_param["n"][0]) / b_param["n"][1]))) ** \
|
||||||
|
b_param["n"][2]
|
||||||
|
gating[ind_dict['n'], :] = np.ones((stim_num)) * n_inf
|
||||||
|
|
||||||
|
# A-type K activation
|
||||||
|
n_A_inf = (1 / (1 + np.exp((V - shift["n_A"] - slope_shift["n_A"] - b_param["n_A"][0]) / b_param["n_A"][1]))) ** \
|
||||||
|
b_param["n_A"][2]
|
||||||
|
gating[ind_dict['n_A'], :] = np.ones((stim_num)) * n_A_inf
|
||||||
|
|
||||||
|
# A-type K inactivation
|
||||||
|
h_A_inf = (1 / (1 + np.exp((V - shift["h_A"] - slope_shift["h_A"] - b_param["h_A"][0]) / b_param["h_A"][1]))) ** \
|
||||||
|
b_param["h_A"][2]
|
||||||
|
gating[ind_dict['h_A'], :] = np.ones((stim_num)) * h_A_inf
|
||||||
|
|
||||||
|
# T-type Ca activation
|
||||||
|
m_T_inf = (1 / (1 + np.exp((V - shift["m_T"] - slope_shift["m_T"] - b_param["m_T"][0]) / b_param["m_T"][1]))) ** \
|
||||||
|
b_param["m_T"][2]
|
||||||
|
gating[ind_dict['m_T'], :] = np.ones((stim_num)) * m_T_inf
|
||||||
|
|
||||||
|
# T-type Ca inactivation
|
||||||
|
h_T_inf = (1 / (1 + np.exp((V - shift["h_T"] - slope_shift["h_T"] - b_param["h_T"][0]) / b_param["h_T"][1]))) ** \
|
||||||
|
b_param["h_T"][2]
|
||||||
|
gating[ind_dict['h_T'], :] = np.ones((stim_num)) * h_T_inf
|
||||||
|
|
||||||
|
# Kv1.1 activation
|
||||||
|
s_inf = (1 / (1 + np.exp((V - shift["s"] - slope_shift["s"] - b_param["s"][0]) / b_param["s"][1]))) ** \
|
||||||
|
b_param["s"][2]
|
||||||
|
gating[ind_dict['s'], :] = np.ones((stim_num)) * s_inf
|
||||||
|
|
||||||
|
# Kv1.1 inactivation
|
||||||
|
u_inf = ((1 - b_param["u"][3]) / (1 + np.exp((V - shift["u"] - slope_shift["u"] - b_param["u"][0])
|
||||||
|
/ b_param["u"][1])) + b_param["u"][3]) ** b_param["u"][2]
|
||||||
|
gating[ind_dict['u'], :] = np.ones((stim_num)) * u_inf
|
||||||
|
|
||||||
|
# mutant Kv1.1 activation
|
||||||
|
s_mut_inf = (1 / (1 + np.exp((V - shift["s_mut"] - slope_shift["s_mut"] - b_param["s_mut"][0]) / b_param["s_mut"][1]))) ** \
|
||||||
|
b_param["s_mut"][2]
|
||||||
|
gating[ind_dict['s_mut'], :] = np.ones((stim_num)) * s_mut_inf
|
||||||
|
|
||||||
|
# mutant Kv1.1 inactivation
|
||||||
|
u_mut_inf = ((1 - b_param["u_mut"][3]) / (1 + np.exp((V - shift["u"] - slope_shift["u_mut"] - b_param["u_mut"][0])
|
||||||
|
/ b_param["u_mut"][1])) + b_param["u_mut"][3]) ** b_param["u_mut"][2]
|
||||||
|
gating[ind_dict['u_mut'], :] = np.ones((stim_num)) * u_mut_inf
|
||||||
|
|
||||||
|
# initialize currents and Vm
|
||||||
|
current[ind_dict['Na'], :] = np.zeros((stim_num))
|
||||||
|
current[ind_dict['Kd'], :] = np.zeros((stim_num))
|
||||||
|
current[ind_dict['Kv'], :] = np.zeros((stim_num))
|
||||||
|
current[ind_dict['Kv_mut'], :] = np.zeros((stim_num))
|
||||||
|
current[ind_dict['Leak'], :] = np.zeros((stim_num))
|
||||||
|
current[ind_dict['A'], :] = np.zeros((stim_num))
|
||||||
|
current[ind_dict['T'], :] = np.zeros((stim_num))
|
||||||
|
V_m[:, 0] = V
|
||||||
|
t = 1
|
||||||
|
while t < stim_time:
|
||||||
|
# Na activation
|
||||||
|
m_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["m"] - slope_shift["m"] - b_param["m"][0]) / b_param["m"][1]))) ** \
|
||||||
|
b_param["m"][2]
|
||||||
|
gating[ind_dict['m'], :] = m_inf
|
||||||
|
gating[ind_dict['m'], :][gating[ind_dict['m'], :] > 1.] = 1.
|
||||||
|
gating[ind_dict['m'], :][gating[ind_dict['m'], :] < 0.] = 0.
|
||||||
|
|
||||||
|
# Na inactivation
|
||||||
|
h_inf = ((1 - b_param["h"][3]) / (1 + np.exp((V_m[:, t - 1] - shift["h"] - slope_shift["h"] - b_param["h"][0])
|
||||||
|
/ b_param["h"][1])) + b_param["h"][3]) ** b_param["h"][2]
|
||||||
|
tau_h = 0.1 + (2 * 322 * 46) / (4 * np.pi * (V_m[:, t - 1] - shift["h"] - -74) ** 2 + (46 ** 2))
|
||||||
|
h_dot = (h_inf - gating[ind_dict['h'], :]) * (1 / (tau_h * scale["h"]))
|
||||||
|
gating[ind_dict['h'], :] = gating[ind_dict['h'], :] + h_dot * dt
|
||||||
|
gating[ind_dict['h'], :][gating[ind_dict['h'], :] > 1.] = 1.
|
||||||
|
gating[ind_dict['h'], :][gating[ind_dict['h'], :] < 0.] = 0.
|
||||||
|
|
||||||
|
# K activation
|
||||||
|
n_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["n"] - slope_shift["n"] - b_param["n"][0]) / b_param["n"][1]))) ** \
|
||||||
|
b_param["n"][2]
|
||||||
|
tau_n = 6 / (1 + np.exp((V_m[:, t - 1] - shift["n"] + 23) / 15))
|
||||||
|
n_dot = (n_inf - gating[ind_dict['n'], :]) * (1 / (tau_n * scale["n"]))
|
||||||
|
gating[ind_dict['n'], :] = gating[ind_dict['n'], :] + n_dot * dt
|
||||||
|
gating[ind_dict['n'], :][gating[ind_dict['n'], :] > 1.] = 1.
|
||||||
|
gating[ind_dict['n'], :][gating[ind_dict['n'], :] < 0.] = 0.
|
||||||
|
|
||||||
|
# A-type K activation
|
||||||
|
n_A_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["n_A"] - slope_shift["n_A"] - b_param["n_A"][0]) / b_param["n_A"][1]))) ** \
|
||||||
|
b_param["n_A"][2]
|
||||||
|
tau_n_A = 5
|
||||||
|
n_A_dot = (n_A_inf - gating[ind_dict['n_A'], :]) * (1 / (tau_n_A * scale["n_A"]))
|
||||||
|
gating[ind_dict['n_A'], :] = gating[ind_dict['n_A'], :] + n_A_dot * dt
|
||||||
|
gating[ind_dict['n_A'], :][gating[ind_dict['n_A'], :] > 1.] = 1.
|
||||||
|
gating[ind_dict['n_A'], :][gating[ind_dict['n_A'], :] < 0.] = 0.
|
||||||
|
|
||||||
|
# A-type K inactivation
|
||||||
|
h_A_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["h_A"] - slope_shift["h_A"] - b_param["h_A"][0]) / b_param["h_A"][1]))) ** \
|
||||||
|
b_param["h_A"][2]
|
||||||
|
tau_h_A = 10
|
||||||
|
h_A_dot = (h_A_inf - gating[ind_dict['h_A'], :]) * (1 / (tau_h_A * scale["h_A"]))
|
||||||
|
gating[ind_dict['h_A'], :] = gating[ind_dict['h_A'], :] + h_A_dot * dt
|
||||||
|
gating[ind_dict['h_A'], :][gating[ind_dict['h_A'], :] > 1.] = 1.
|
||||||
|
gating[ind_dict['h_A'], :][gating[ind_dict['h_A'], :] < 0.] = 0.
|
||||||
|
|
||||||
|
# T-type Ca activation
|
||||||
|
m_T_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["m_T"] - slope_shift["m_T"] - b_param["m_T"][0]) / b_param["m_T"][1]))) ** \
|
||||||
|
b_param["m_T"][2]
|
||||||
|
gating[ind_dict['m_T'], :] = m_T_inf
|
||||||
|
gating[ind_dict['m_T'], :][gating[ind_dict['m_T'], :] > 1.] = 1.
|
||||||
|
gating[ind_dict['m_T'], :][gating[ind_dict['m_T'], :] < 0.] = 0.
|
||||||
|
|
||||||
|
# T-type Ca inactivation
|
||||||
|
h_T_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["h_T"] - slope_shift["h_T"] - b_param["h_T"][0]) / b_param["h_T"][1]))) ** \
|
||||||
|
b_param["h_T"][2]
|
||||||
|
tau_h_T = 15
|
||||||
|
h_T_dot = (h_T_inf - gating[ind_dict['h_T'], :]) * (1 / (tau_h_T * scale["h_T"]))
|
||||||
|
gating[ind_dict['h_T'], :] = gating[ind_dict['h_T'], :] + h_T_dot * dt
|
||||||
|
gating[ind_dict['h_T'], :][gating[ind_dict['h_T'], :] > 1.] = 1.
|
||||||
|
gating[ind_dict['h_T'], :][gating[ind_dict['h_T'], :] < 0.] = 0.
|
||||||
|
|
||||||
|
# Kv1.1 activation
|
||||||
|
s_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["s"] - slope_shift["s"] - b_param["s"][0]) / b_param["s"][1]))) ** \
|
||||||
|
b_param["s"][2]
|
||||||
|
temp = 36
|
||||||
|
vBreak = -46.7
|
||||||
|
amp1 = 52.7
|
||||||
|
amp2 = 15.98
|
||||||
|
vh1 = -49.87
|
||||||
|
vh2 = -41.64
|
||||||
|
offset = 0.9
|
||||||
|
slope1 = 5
|
||||||
|
slope2 = 24.99
|
||||||
|
sigswitch = 1 / (1 + np.exp((V_m[:, t - 1] - shift["s"] - vBreak) / 3))
|
||||||
|
sig1 = sigswitch * amp1 / (1 + np.exp((V_m[:, t - 1] - shift["s"] - vh1) / slope1))
|
||||||
|
sig2 = (1 - sigswitch) * offset + (amp2 - offset) / (1 + np.exp((V_m[:, t - 1] - shift["s"] - vh2) / slope2))
|
||||||
|
sTauFunc = sig1 + sig2
|
||||||
|
s_tau_Q10 = (7.54 * np.exp(-(V_m[:, t - 1] - shift["s"]) / 379.7) * np.exp(-temp / 35.66)) ** ((temp - 25) / 10)
|
||||||
|
tau_s = sTauFunc / s_tau_Q10
|
||||||
|
s_dot = (s_inf - gating[ind_dict['s'], :]) * (1 / (tau_s * scale["s"]))
|
||||||
|
gating[ind_dict['s'], :] = gating[ind_dict['s'], :] + s_dot * dt
|
||||||
|
gating[ind_dict['s'], :][gating[ind_dict['s'], :] > 1.] = 1.
|
||||||
|
gating[ind_dict['s'], :][gating[ind_dict['s'], :] < 0.] = 0.
|
||||||
|
|
||||||
|
# Kv1.1 inactivation
|
||||||
|
u_inf = ((1 - b_param["u"][3]) / (1 + np.exp((V_m[:, t - 1] - shift["u"] - slope_shift["u"] - b_param["u"][0])
|
||||||
|
/ b_param["u"][1])) + b_param["u"][3]) ** b_param["u"][2]
|
||||||
|
u_tau_Q10 = 2.7 ** ((temp - 25) / 10)
|
||||||
|
tau_u = (86.86 + (408.78 / (1 + np.exp((V_m[:, t - 1] - shift["u"] + 13.6) / 7.46)))) / u_tau_Q10
|
||||||
|
u_dot = (u_inf - gating[ind_dict['u'], :]) * (1 / (tau_u * scale["u"]))
|
||||||
|
gating[ind_dict['u'], :] = gating[ind_dict['u'], :] + u_dot * dt
|
||||||
|
gating[ind_dict['u'], :][gating[ind_dict['u'], :] > 1.] = 1.
|
||||||
|
gating[ind_dict['u'], :][gating[ind_dict['u'], :] < 0.] = 0.
|
||||||
|
|
||||||
|
# mutant Kv1.1 activation
|
||||||
|
s_mut_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["s_mut"] - slope_shift["s_mut"] - b_param["s_mut"][0]) / b_param["s_mut"][1]))) ** \
|
||||||
|
b_param["s_mut"][2]
|
||||||
|
|
||||||
|
s_mut_dot = (s_mut_inf - gating[ind_dict['s_mut'], :]) * (1 / (tau_s * scale["s_mut"]))
|
||||||
|
gating[ind_dict['s_mut'], :] = gating[ind_dict['s_mut'], :] + s_mut_dot * dt
|
||||||
|
gating[ind_dict['s_mut'], :][gating[ind_dict['s_mut'], :] > 1.] = 1.
|
||||||
|
gating[ind_dict['s_mut'], :][gating[ind_dict['s_mut'], :] < 0.] = 0.
|
||||||
|
|
||||||
|
# mutant Kv1.1 inactivation
|
||||||
|
u_mut_inf = ((1 - b_param["u_mut"][3]) / (1 + np.exp((V_m[:, t - 1] - shift["u_mut"] - slope_shift["u_mut"] - b_param["u_mut"][0])
|
||||||
|
/ b_param["u_mut"][1])) + b_param["u_mut"][3]) ** b_param["u_mut"][2]
|
||||||
|
u_mut_dot = (u_mut_inf - gating[ind_dict['u_mut'], :]) * (1 / (tau_u * scale["u_mut"]))
|
||||||
|
gating[ind_dict['u_mut'], :] = gating[ind_dict['u_mut'], :] + u_mut_dot * dt
|
||||||
|
gating[ind_dict['u_mut'], :][gating[ind_dict['u_mut'], :] > 1.] = 1.
|
||||||
|
gating[ind_dict['u_mut'], :][gating[ind_dict['u_mut'], :] < 0.] = 0.
|
||||||
|
|
||||||
|
# update currents
|
||||||
|
current[ind_dict['Leak'], :] = g["Leak"] * (V_m[:, t - 1] - E["Leak"]) * currents_included["Leak"]
|
||||||
|
current[ind_dict['Na'], :] = g["Na"] * (gating[ind_dict['m'], :] ** 3) * gating[ind_dict['h'], :] * (V_m[:, t - 1] - E["Na"]) * \
|
||||||
|
currents_included["Na"]
|
||||||
|
current[ind_dict['Kd'], :] = g["Kd"] * (gating[ind_dict['n'], :] ** 4) * (V_m[:, t - 1] - E["K"]) * \
|
||||||
|
currents_included["Kd"]
|
||||||
|
current[ind_dict['Kv'], :] = g["Kv"] * gating[ind_dict['s'], :] * gating[ind_dict['u'], :] * (
|
||||||
|
V_m[:, t - 1] - E["K"]) * currents_included["Kv"]
|
||||||
|
current[ind_dict['Kv_mut'], :] = g["Kv_mut"] * gating[ind_dict['s_mut'], :] * gating[ind_dict['u_mut'], :] * \
|
||||||
|
(V_m[:, t - 1] - E["K"]) * currents_included["Kv_mut"]
|
||||||
|
current[ind_dict['A'], :] = g["A"] * gating[ind_dict['n_A'], :] * gating[ind_dict['h_A'], :] * (
|
||||||
|
V_m[:, t - 1] - E["K"]) * currents_included["A"]
|
||||||
|
current[ind_dict['T'], :] = g["T"] * gating[ind_dict['m_T'], :] * gating[ind_dict['h_T'], :] * (
|
||||||
|
V_m[:, t - 1] - E["Ca"]) * currents_included["T"]
|
||||||
|
|
||||||
|
# update dV/dt
|
||||||
|
V_dot = (1 / C) * (I_in[t, :] - (current[ind_dict['Leak'], :] + current[ind_dict['Na'], :] +
|
||||||
|
current[ind_dict['Kd'], :] + current[ind_dict['A'], :] +
|
||||||
|
current[ind_dict['Kv'], :] + current[ind_dict['Kv_mut'], :] +
|
||||||
|
current[ind_dict['T'], :]))
|
||||||
|
# update V_m
|
||||||
|
V_m[:, t] = V_m[:, t - 1] + V_dot * dt
|
||||||
|
t += 1
|
||||||
|
|
||||||
|
return V_m
|
||||||
|
|
||||||
|
|
||||||
|
def 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):
|
||||||
|
""" Simulate mutations for Cb Stellate Kv model
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
V_init : float
|
||||||
|
Initial membrane potential
|
||||||
|
g : dict
|
||||||
|
maximal conductance for currents
|
||||||
|
E : dict
|
||||||
|
Reversal potentials for currents
|
||||||
|
I_in : array
|
||||||
|
Current stimulus input to model
|
||||||
|
dt : float
|
||||||
|
fixed time step
|
||||||
|
currents_included : dict
|
||||||
|
Boolean to include current in simulation or not
|
||||||
|
stim_time :
|
||||||
|
length of stimulus ()
|
||||||
|
stim_num : int
|
||||||
|
number of different stimulus currents
|
||||||
|
C : float
|
||||||
|
membrane capacitance
|
||||||
|
shift : dict
|
||||||
|
for each gating parameter
|
||||||
|
scale : dict
|
||||||
|
for each gating parameter
|
||||||
|
b_param : dict
|
||||||
|
Boltzmann parameter values for each gating parameter
|
||||||
|
slope_shift : dict
|
||||||
|
for each gating parameter
|
||||||
|
gating : array
|
||||||
|
array of gating values over time
|
||||||
|
current : array
|
||||||
|
array of current values over time
|
||||||
|
prominence : float
|
||||||
|
required spike prominence
|
||||||
|
desired_AUC_width : float
|
||||||
|
width of AUC from rheobase to rheobase+desired_AUC_width
|
||||||
|
mutations : dict
|
||||||
|
dictionary of mutation effects
|
||||||
|
mut : str
|
||||||
|
mutation
|
||||||
|
folder : str
|
||||||
|
folder to save hdf5 file in
|
||||||
|
high : float
|
||||||
|
upper current step magnitude
|
||||||
|
low : float
|
||||||
|
lowest current step magnitude
|
||||||
|
number_steps : int
|
||||||
|
number of current steps between low and high
|
||||||
|
initial_period : float
|
||||||
|
length of I = 0 before current step
|
||||||
|
sec:
|
||||||
|
length of current step in seconds
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
saves mut.hdf5 file to folder
|
||||||
|
"""
|
||||||
|
# setup for simulation
|
||||||
|
g_multi = copy.deepcopy(g)
|
||||||
|
E_multi = copy.deepcopy(E)
|
||||||
|
b_param_multi = copy.deepcopy(b_param)
|
||||||
|
shift_multi = copy.deepcopy(shift)
|
||||||
|
scale_multi = copy.deepcopy(scale)
|
||||||
|
slope_shift_multi = copy.deepcopy(slope_shift)
|
||||||
|
currents_included_multi = copy.deepcopy(currents_included)
|
||||||
|
gating2 = copy.deepcopy(gating)
|
||||||
|
current2 = copy.deepcopy(current)
|
||||||
|
|
||||||
|
g_effect = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in g_multi.items():
|
||||||
|
g_effect[k] = v
|
||||||
|
b_param_effect = Dict.empty(key_type=types.unicode_type, value_type=types.float64[:], )
|
||||||
|
for k, v in b_param_multi.items():
|
||||||
|
b_param_effect[k] = np.array(v)
|
||||||
|
shift2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in shift_multi.items():
|
||||||
|
shift2[k] = v
|
||||||
|
scale2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in scale_multi.items():
|
||||||
|
scale2[k] = v
|
||||||
|
slope_shift2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in slope_shift_multi.items():
|
||||||
|
slope_shift2[k] = v
|
||||||
|
E2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in E_multi.items():
|
||||||
|
E2[k] = v
|
||||||
|
currents_included2 = Dict.empty(key_type=types.unicode_type, value_type=types.boolean, )
|
||||||
|
for k, v in currents_included_multi.items():
|
||||||
|
currents_included2[k] = v
|
||||||
|
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['Na', 'Kd', 'A', 'Kv', 'Kv_mut', 'T', 'Leak']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
|
||||||
|
# effect of mutation
|
||||||
|
g_effect['Kv_mut'] = g['Kv_mut'] * mutations[mut]['g_ratio'] # mutation_properties[mut, 2] # g ratio A
|
||||||
|
b_param_effect['s_mut'][0] = b_param_effect['s_mut'][0] + mutations[mut]['activation_Vhalf_diff']
|
||||||
|
b_param_effect['s_mut'][1] = b_param_effect['s_mut'][1] * mutations[mut]['activation_k_ratio']
|
||||||
|
|
||||||
|
# initial simulation for current range from low to high
|
||||||
|
V_m = Cb_stellate_Kv(V_init * np.ones((stim_num)), g_effect, E2, I_in, dt, currents_included2, stim_time, stim_num,
|
||||||
|
C, shift2, scale2, b_param_effect, slope_shift2, gating2, current2, ind_dict)
|
||||||
|
stim_start = np.int(initial_period * 1 / dt)
|
||||||
|
min_spike_height = V_m[0, stim_start] + prominence
|
||||||
|
|
||||||
|
# create hdf5 file for the mutation and save data and metadata
|
||||||
|
fname = os.path.join(folder, "{}.hdf5".format(mut.replace(" ", "_")))
|
||||||
|
with h5py.File(fname, "a") as f:
|
||||||
|
data = f.create_group("data")
|
||||||
|
data.create_dataset('V_m', data=V_m, dtype='float64', compression="gzip", compression_opts=9)
|
||||||
|
metadata = {'I_high': high, 'I_low': low, 'stim_num': number_steps, 'stim_time': stim_time, 'sec': sec, 'dt': dt,
|
||||||
|
'initial_period': initial_period, 'g': json.dumps(dict(g_effect)), 'E': json.dumps(dict(E2)), 'C': C,
|
||||||
|
'V_init': V_init, 'ind_dict': json.dumps(dict(ind_dict)),
|
||||||
|
'b_param': json.dumps(dict(b_param_effect), cls=NumpyEncoder),
|
||||||
|
'currents': json.dumps(dict(currents_included2)), 'shift': json.dumps(dict(shift2)),
|
||||||
|
'scale': json.dumps(dict(scale2)), 'slope_shift': json.dumps(dict(slope_shift2)),
|
||||||
|
'prominence': prominence, 'desired_AUC_width': desired_AUC_width, 'mut': str(mut),
|
||||||
|
'min_spike_height': min_spike_height,}
|
||||||
|
data.attrs.update(metadata)
|
||||||
|
|
||||||
|
# firing frequency analysis and rheobase
|
||||||
|
with h5py.File(fname, "r+") as f:
|
||||||
|
I_mag = np.arange(low, high, (high - low) / stim_num)
|
||||||
|
analysis = f.create_group("analysis")
|
||||||
|
dtyp = h5py.special_dtype(vlen=np.dtype('float64'))
|
||||||
|
for i in np.array(['spike_times', 'spike_times_rheo', 'ISI', 'Freq', 'amplitude',]):
|
||||||
|
analysis.create_dataset(i, (I_mag.shape[0],), dtype=dtyp, compression="gzip", compression_opts=9)
|
||||||
|
analysis.create_dataset('F_inf', (I_mag.shape[0],), dtype='float64')
|
||||||
|
analysis.create_dataset('F_null', (I_mag.shape[0],), dtype='float64')
|
||||||
|
analysis.create_dataset('AP', (I_mag.shape[0],), dtype='bool', compression="gzip", compression_opts=9)
|
||||||
|
analysis.create_dataset('AP_rheo', (I_mag.shape[0],), dtype='bool', compression="gzip", compression_opts=9)
|
||||||
|
analysis.create_dataset('rheobase', (1,), dtype='float64', compression="gzip", compression_opts=9)
|
||||||
|
|
||||||
|
# Firing analysis
|
||||||
|
for stim in range(I_mag.shape[0]):
|
||||||
|
# spike detection
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m[stim, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
analysis['spike_times'][stim] = ap_all * dt
|
||||||
|
if analysis['spike_times'][stim].size == 0:
|
||||||
|
analysis['AP'][stim] = False
|
||||||
|
else:
|
||||||
|
analysis['AP'][stim] = True
|
||||||
|
analysis['amplitude'][stim] = np.array(ap_prop["peak_heights"])
|
||||||
|
analysis['ISI'][stim] = np.array([x - analysis['spike_times'][stim][i - 1] if i else None for i, x in
|
||||||
|
enumerate(analysis['spike_times'][stim])][1:]) # msec
|
||||||
|
analysis['Freq'][stim] = 1 / (analysis['ISI'][stim] / 1000)
|
||||||
|
|
||||||
|
if analysis['Freq'][stim].size != 0:
|
||||||
|
analysis['F_null'][stim] = analysis['Freq'][stim][0]
|
||||||
|
last_second_spikes = analysis['spike_times'][stim] > np.int(
|
||||||
|
f['data'].attrs['stim_time'] * dt - (500 + initial_period))
|
||||||
|
if np.any(last_second_spikes == True):
|
||||||
|
last_second_spikes_1 = np.delete(last_second_spikes,
|
||||||
|
0) # remove first spike because it has no ISI/freq
|
||||||
|
analysis['F_inf'][stim] = np.mean(analysis['Freq'][stim][last_second_spikes_1])
|
||||||
|
else: # if no ISI detected
|
||||||
|
analysis['F_null'][stim] = 0
|
||||||
|
analysis['F_inf'][stim] = 0
|
||||||
|
|
||||||
|
# rheobase
|
||||||
|
# find where the first AP occurs
|
||||||
|
if np.any(analysis['AP'][:] == True):
|
||||||
|
# setup current range to be between the current step before and with first AP
|
||||||
|
I_first_spike = I_mag[np.argwhere(analysis['AP'][:] == True)[0][0]]
|
||||||
|
I_before_spike = I_mag[np.argwhere(analysis['AP'][:] == True)[0][0] - 1]
|
||||||
|
analysis.create_dataset('I_first_spike', data=I_first_spike, dtype='float64')
|
||||||
|
analysis.create_dataset('I_before_spike', data=I_before_spike, dtype='float64')
|
||||||
|
number_steps_rheo = 100
|
||||||
|
stim_time, I_in_rheo, stim_num_rheo, V_m_rheo = stimulus_init(I_before_spike, I_first_spike,
|
||||||
|
number_steps_rheo, initial_period,
|
||||||
|
dt, sec)
|
||||||
|
|
||||||
|
# simulate response to this current range and save
|
||||||
|
V_m_rheo = Cb_stellate_Kv(V_init * np.ones((stim_num_rheo)), g_effect, E2, I_in_rheo, f['data'].attrs['dt'],
|
||||||
|
currents_included2, stim_time, stim_num_rheo, f['data'].attrs['C'], shift2,
|
||||||
|
scale2, b_param_effect, slope_shift2, np.zeros((len(b_param), stim_num_rheo)),
|
||||||
|
np.zeros((len(currents_included), stim_num_rheo)), ind_dict)
|
||||||
|
f['data'].create_dataset('V_m_rheo', data=V_m_rheo, dtype='float64', compression="gzip", compression_opts=9)
|
||||||
|
|
||||||
|
# run AP detection
|
||||||
|
stim_start = np.int(initial_period * 1 / dt)
|
||||||
|
for stim in range(I_in_rheo.shape[1]):
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_rheo[stim, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
analysis['spike_times_rheo'][stim] = ap_all * dt
|
||||||
|
if analysis['spike_times_rheo'][stim].size == 0:
|
||||||
|
analysis['AP_rheo'][stim] = False
|
||||||
|
else:
|
||||||
|
analysis['AP_rheo'][stim] = True
|
||||||
|
# find rheobase as smallest current step to elicit AP and save
|
||||||
|
analysis['rheobase'][:] = I_in_rheo[-1, np.argwhere(analysis['AP_rheo'][:] == True)[0][0]] * 1000
|
||||||
|
|
||||||
|
# AUC
|
||||||
|
with h5py.File(fname, "a") as f:
|
||||||
|
# find rheobase of steady state firing (F_inf)
|
||||||
|
F_inf = f['analysis']['F_inf'][:]
|
||||||
|
try:
|
||||||
|
# setup current range to be in current step between no and start of steady state firing
|
||||||
|
F_inf_start_ind = np.argwhere(F_inf != 0)[0][0]
|
||||||
|
dI = (high - low) / stim_num
|
||||||
|
I_start = I_mag[F_inf_start_ind - 1]
|
||||||
|
I_end = I_mag[F_inf_start_ind]
|
||||||
|
I_rel = np.arange(I_start, I_end + (I_end - I_start) / 100, (I_end - I_start) / 100)
|
||||||
|
I_rel = np.reshape(I_rel, (1, I_rel.shape[0]))
|
||||||
|
I_in = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_rel
|
||||||
|
I_in[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_rel
|
||||||
|
stim_num = I_in.shape[1]
|
||||||
|
|
||||||
|
# simulate response to this current step range
|
||||||
|
V_m_inf_rheo = Cb_stellate_Kv(V_init * np.ones(stim_num), g_effect, E2, I_in, dt, currents_included2,
|
||||||
|
stim_time, stim_num, C, shift2, scale2, b_param_effect, slope_shift2,
|
||||||
|
np.zeros((len(b_param), stim_num)),
|
||||||
|
np.zeros((len(currents_included), stim_num)), ind_dict)
|
||||||
|
|
||||||
|
# save response, analyse and save
|
||||||
|
for i in np.array(['spike_times_Finf', 'ISI_Finf', 'Freq_Finf']): # analysis_names:
|
||||||
|
f['analysis'].require_dataset(i, shape=(I_rel.shape[1],), dtype=dtyp, compression="gzip", compression_opts=9)
|
||||||
|
f['analysis'].require_dataset('F_inf_Finf', shape=(I_rel.shape[1],), dtype='float64')
|
||||||
|
f['analysis'].require_dataset('F_null_Finf', shape=(I_rel.shape[1],), dtype='float64')
|
||||||
|
for stim in range(I_rel.shape[1]):
|
||||||
|
# spike peak detection
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_inf_rheo[stim, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
f['analysis']['spike_times_Finf'][stim] = ap_all * dt
|
||||||
|
f['analysis']['ISI_Finf'][stim] = np.array(
|
||||||
|
[x - f['analysis']['spike_times_Finf'][stim][i - 1] if i else None for i, x in
|
||||||
|
enumerate(f['analysis']['spike_times_Finf'][stim])][1:]) # msec
|
||||||
|
f['analysis']['Freq_Finf'][stim] = 1 / (f['analysis']['ISI_Finf'][stim] / 1000)
|
||||||
|
if f['analysis']['Freq_Finf'][stim].size != 0:
|
||||||
|
last_second_spikes = f['analysis']['spike_times_Finf'][stim] > np.int(
|
||||||
|
f['data'].attrs['stim_time'] * dt - (500 + f['data'].attrs['initial_period']))
|
||||||
|
if np.any(last_second_spikes == True):
|
||||||
|
last_second_spikes_1 = np.delete(last_second_spikes,
|
||||||
|
0) # remove first spike because it has no ISI/freq
|
||||||
|
f['analysis']['F_inf_Finf'][stim] = np.mean(
|
||||||
|
f['analysis']['Freq_Finf'][stim][last_second_spikes_1])
|
||||||
|
else:
|
||||||
|
f['analysis']['F_null_Finf'][stim] = 0
|
||||||
|
f['analysis']['F_inf_Finf'][stim] = 0
|
||||||
|
|
||||||
|
# find AUC relative to F_inf rheobase
|
||||||
|
# setup current inputs with current range around F_inf rheobase
|
||||||
|
I_first_inf = I_rel[0, np.argwhere(f['analysis']['F_inf_Finf'][:] != 0)[0][0]]
|
||||||
|
I_start = I_first_inf - 0.00005
|
||||||
|
I_end = I_first_inf + desired_AUC_width * high
|
||||||
|
I_rel2 = np.arange(I_start, I_end + (I_end - I_start) / 200, (I_end - I_start) / 200)
|
||||||
|
I_rel2 = np.reshape(I_rel2, (1, I_rel2.shape[0]))
|
||||||
|
I_in2 = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_rel2
|
||||||
|
I_in2[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_rel2
|
||||||
|
stim_num = I_in2.shape[1]
|
||||||
|
|
||||||
|
# simulate response to this current step range
|
||||||
|
V_m_AUC = Cb_stellate_Kv(V_init * np.ones(stim_num), g_effect, E2, I_in2, dt, currents_included2, stim_time,
|
||||||
|
stim_num, C, shift2, scale2, b_param_effect, slope_shift2,
|
||||||
|
np.zeros((len(b_param), stim_num)), np.zeros((len(currents_included), stim_num)),
|
||||||
|
ind_dict)
|
||||||
|
|
||||||
|
# save data and analysis for this current step range including AUC
|
||||||
|
f['data'].require_dataset('V_m_AUC', shape=V_m_AUC.shape, dtype='float64')
|
||||||
|
f['data']['V_m_AUC'][:] = V_m_AUC
|
||||||
|
dtyp = h5py.special_dtype(vlen=np.dtype('float64'))
|
||||||
|
for i in np.array(['spike_times_Finf_AUC', 'ISI_Finf_AUC', 'Freq_Finf_AUC']):
|
||||||
|
f['analysis'].require_dataset(i, shape=(I_rel2.shape[1],), dtype=dtyp, compression="gzip",
|
||||||
|
compression_opts=9)
|
||||||
|
f['analysis'].require_dataset('F_inf_Finf_AUC', shape=(I_rel2.shape[1],), dtype='float64')
|
||||||
|
f['analysis'].require_dataset('F_null_Finf_AUC', shape=(I_rel2.shape[1],), dtype='float64')
|
||||||
|
for stim in range(I_rel2.shape[1]):
|
||||||
|
# spike peak detection
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_AUC[stim, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
f['analysis']['spike_times_Finf_AUC'][stim] = ap_all * dt
|
||||||
|
f['analysis']['ISI_Finf_AUC'][stim] = np.array(
|
||||||
|
[x - f['analysis']['spike_times_Finf_AUC'][stim][i - 1] if i else None for i, x in
|
||||||
|
enumerate(f['analysis']['spike_times_Finf_AUC'][stim])][1:]) # msec
|
||||||
|
f['analysis']['Freq_Finf_AUC'][stim] = 1 / (f['analysis']['ISI_Finf_AUC'][stim] / 1000)
|
||||||
|
if f['analysis']['Freq_Finf_AUC'][stim].size != 0:
|
||||||
|
last_second_spikes = f['analysis']['spike_times_Finf_AUC'][stim] > np.int(
|
||||||
|
f['data'].attrs['stim_time'] * dt - (500 + f['data'].attrs['initial_period']))
|
||||||
|
if np.any(last_second_spikes == True):
|
||||||
|
last_second_spikes_1 = np.delete(last_second_spikes, 0) # remove first spike because it has no ISI/freq
|
||||||
|
f['analysis']['F_inf_Finf_AUC'][stim] = np.mean(
|
||||||
|
f['analysis']['Freq_Finf_AUC'][stim][last_second_spikes_1])
|
||||||
|
else:
|
||||||
|
f['analysis']['F_null_Finf_AUC'][stim] = 0
|
||||||
|
f['analysis']['F_inf_Finf_AUC'][stim] = 0
|
||||||
|
f['analysis'].require_dataset('AUC_rel_acc', shape=(1,), dtype='float64', compression="gzip",
|
||||||
|
compression_opts=9)
|
||||||
|
f['analysis']['AUC_rel_acc'][:] = np.trapz(f['analysis']['F_inf_Finf_AUC'][:],
|
||||||
|
I_rel2[:] * 1000, dI * 1000) #
|
||||||
|
except:
|
||||||
|
f['analysis'].require_dataset('AUC', shape=(1,), dtype='float64', compression="gzip",
|
||||||
|
compression_opts=9)
|
||||||
|
f['analysis']['AUC'][:] = 0
|
||||||
|
|
||||||
|
# ramp protocol #####################
|
||||||
|
# setup ramp current
|
||||||
|
ramp_len = np.int(sec *1000 *1/dt)
|
||||||
|
stim_time = ramp_len *2
|
||||||
|
I_amp = np.array([0, -0.000001])
|
||||||
|
I_amp = np.reshape(I_amp, (1,I_amp.shape[0]))
|
||||||
|
I_step = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1))@ I_amp
|
||||||
|
I_step[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_amp
|
||||||
|
stim_num_step = I_step.shape[1]
|
||||||
|
start = np.int(initial_period * 1 / dt)
|
||||||
|
I_step[start:np.int(start+ramp_len),0] = np.linspace(0, high, ramp_len)
|
||||||
|
I_step[np.int(start + ramp_len):np.int(start + ramp_len*2),0] = np.linspace(high, 0, ramp_len)
|
||||||
|
stim_time_step = I_step.shape[0]
|
||||||
|
|
||||||
|
# simulate response to ramp
|
||||||
|
V_m_step = Cb_stellate_Kv(-70 * np.ones(stim_num_step), g_effect, E2, I_step, dt, currents_included2,
|
||||||
|
stim_time_step, stim_num_step, C, shift2, scale2, b_param_effect, slope_shift2,
|
||||||
|
np.zeros((len(b_param), stim_num_step)),
|
||||||
|
np.zeros((len(currents_included), stim_num_step)), ind_dict)
|
||||||
|
|
||||||
|
|
||||||
|
# spike peak detection
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_step[0, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
# save ramp firing properties
|
||||||
|
with h5py.File(fname, "a") as f:
|
||||||
|
f['data'].create_dataset('V_m_ramp', data=V_m_step, dtype='float64', compression="gzip", compression_opts=9)
|
||||||
|
if ap_all.shape[0] != 0:
|
||||||
|
if ap_all[0] < stim_start + ramp_len:
|
||||||
|
f['analysis'].create_dataset('ramp_I_up', data =I_step[stim_start+ap_all[0], 0] *1000, dtype='float64')
|
||||||
|
else:
|
||||||
|
f['analysis'].create_dataset('ramp_I_up', data =np.NaN, dtype='float64')
|
||||||
|
if ap_all[-1] > stim_start + ramp_len:
|
||||||
|
f['analysis'].create_dataset('ramp_I_down', data =I_step[stim_start+ap_all[-1], 0]*1000, dtype='float64')
|
||||||
|
else:
|
||||||
|
f['analysis'].create_dataset('ramp_I_down', data =np.NaN, dtype='float64')
|
||||||
|
f['analysis'].create_dataset('hysteresis', data =f['analysis']['ramp_I_down'][()] - f['analysis']['ramp_I_up'][()], dtype='float64')
|
||||||
|
else:# if no spikes in response to ramp
|
||||||
|
f['analysis'].create_dataset('ramp_I_up', data =np.NaN, dtype='float64')
|
||||||
|
f['analysis'].create_dataset('ramp_I_down', data =np.NaN, dtype='float64')
|
||||||
|
f['analysis'].create_dataset('hysteresis', data =np.NaN, dtype='float64')
|
||||||
|
if f.__bool__():
|
||||||
|
f.close()
|
||||||
|
gc.collect()
|
||||||
|
|
||||||
|
|
||||||
|
def SA_Cb_stellate_Kv(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, folder, high, low, number_steps,
|
||||||
|
initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt):
|
||||||
|
""" Sensitivity analysis for Cb Stellate Kv model
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
V_init : float
|
||||||
|
Initial membrane potential
|
||||||
|
g : dict
|
||||||
|
maximal conductance for currents
|
||||||
|
E : dict
|
||||||
|
Reversal potentials for currents
|
||||||
|
I_in : array
|
||||||
|
Current stimulus input to model
|
||||||
|
dt : float
|
||||||
|
fixed time step
|
||||||
|
currents_included : dict
|
||||||
|
Boolean to include current in simulation or not
|
||||||
|
stim_time :
|
||||||
|
length of stimulus ()
|
||||||
|
stim_num : int
|
||||||
|
number of different stimulus currents
|
||||||
|
C : float
|
||||||
|
membrane capacitance
|
||||||
|
shift : dict
|
||||||
|
for each gating parameter
|
||||||
|
scale : dict
|
||||||
|
for each gating parameter
|
||||||
|
b_param : dict
|
||||||
|
Boltzmann parameter values for each gating parameter
|
||||||
|
slope_shift : dict
|
||||||
|
for each gating parameter
|
||||||
|
gating : array
|
||||||
|
array of gating values over time
|
||||||
|
current : array
|
||||||
|
array of current values over time
|
||||||
|
prominence: float
|
||||||
|
required spike prominence
|
||||||
|
desired_AUC_width: float
|
||||||
|
width of AUC from rheobase to rheobase + desired_AUC_width
|
||||||
|
folder : str
|
||||||
|
folder to save hdf5 in
|
||||||
|
high : float
|
||||||
|
upper current step magnitude
|
||||||
|
low : float
|
||||||
|
lowest current step magnitude
|
||||||
|
number_steps : int
|
||||||
|
number of current steps between low and high
|
||||||
|
initial_period: float
|
||||||
|
length of I = 0 before current step
|
||||||
|
sec : float
|
||||||
|
length of current step in seconds
|
||||||
|
lin_array : array
|
||||||
|
array of linear shifts from -10 tp 10
|
||||||
|
log_array : array
|
||||||
|
array of log2 values from -1 to 1
|
||||||
|
alt_types : array
|
||||||
|
array of arrays of strings of [variable, alteration type]
|
||||||
|
alt_ind : int
|
||||||
|
index for alt_type
|
||||||
|
alt :
|
||||||
|
index for lin_array of log_array
|
||||||
|
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
saves hdf5 file to folder
|
||||||
|
"""
|
||||||
|
# setup for simulation
|
||||||
|
g_multi = copy.deepcopy(g)
|
||||||
|
E_multi = copy.deepcopy(E)
|
||||||
|
b_param_multi = copy.deepcopy(b_param)
|
||||||
|
shift_multi = copy.deepcopy(shift)
|
||||||
|
scale_multi = copy.deepcopy(scale)
|
||||||
|
slope_shift_multi = copy.deepcopy(slope_shift)
|
||||||
|
currents_included_multi = copy.deepcopy(currents_included)
|
||||||
|
gating2 = copy.deepcopy(gating)
|
||||||
|
current2 = copy.deepcopy(current)
|
||||||
|
|
||||||
|
g2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in g_multi.items():
|
||||||
|
g2[k] = v
|
||||||
|
b_param2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64[:], )
|
||||||
|
for k, v in b_param_multi.items():
|
||||||
|
b_param2[k] = np.array(v)
|
||||||
|
shift2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in shift_multi.items():
|
||||||
|
shift2[k] = v
|
||||||
|
scale2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in scale_multi.items():
|
||||||
|
scale2[k] = v
|
||||||
|
slope_shift2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in slope_shift_multi.items():
|
||||||
|
slope_shift2[k] = v
|
||||||
|
E2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
||||||
|
for k, v in E_multi.items():
|
||||||
|
E2[k] = v
|
||||||
|
currents_included2 = Dict.empty(key_type=types.unicode_type, value_type=types.boolean, )
|
||||||
|
for k, v in currents_included_multi.items():
|
||||||
|
currents_included2[k] = v
|
||||||
|
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['m', 'h', 'n', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['Na', 'Kd', 'A', 'Kv', 'Kv_mut', 'T', 'Leak']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
|
||||||
|
# gating alteration ##########################################################################
|
||||||
|
if alt_types[alt_ind, 1] == 'shift':
|
||||||
|
shift2[alt_types[alt_ind, 0]] = lin_array[alt]
|
||||||
|
fname = os.path.join(folder,
|
||||||
|
"{}_{}_{}.hdf5".format(alt_types[alt_ind, 1], alt_types[alt_ind, 0], lin_array[alt]))
|
||||||
|
|
||||||
|
elif alt_types[alt_ind, 1] == 'slope':
|
||||||
|
b_param2[alt_types[alt_ind, 0]][1] = b_param2[alt_types[alt_ind, 0]][1] * log_array[alt]
|
||||||
|
fname = os.path.join(folder, "{}_{}_{}.hdf5".format(alt_types[alt_ind, 1], alt_types[alt_ind, 0],
|
||||||
|
np.int(np.around(log_array[alt], 2) * 100)))
|
||||||
|
if b_param2[alt_types[alt_ind, 0]][2] != 1:
|
||||||
|
# adjust slope_shift to account for slope not changing (rotating) around 0.5
|
||||||
|
V_test = np.arange(-150, 100, 0.001)
|
||||||
|
if b_param2[alt_types[alt_ind, 0]].shape[0] == 4:
|
||||||
|
steady_state = ((1 - b_param2[alt_types[alt_ind, 0]][3]) / (
|
||||||
|
1 + np.exp(
|
||||||
|
(V_test - b_param2[alt_types[alt_ind, 0]][0]) / b_param2[alt_types[alt_ind, 0]][1])) +
|
||||||
|
b_param2[alt_types[alt_ind, 0]][3]) ** b_param2[alt_types[alt_ind, 0]][2]
|
||||||
|
orig = ((1 - b_param[alt_types[alt_ind, 0]][3]) / (
|
||||||
|
1 + np.exp((V_test - b_param[alt_types[alt_ind, 0]][0]) / b_param[alt_types[alt_ind, 0]][1])) +
|
||||||
|
b_param[alt_types[alt_ind, 0]][3]) ** b_param[alt_types[alt_ind, 0]][2]
|
||||||
|
else:
|
||||||
|
steady_state = (1 / (1 + np.exp(
|
||||||
|
(V_test - b_param2[alt_types[alt_ind, 0]][0]) / b_param2[alt_types[alt_ind, 0]][1]))) ** \
|
||||||
|
b_param2[alt_types[alt_ind, 0]][2]
|
||||||
|
orig = (1 / (1 + np.exp(
|
||||||
|
(V_test - b_param[alt_types[alt_ind, 0]][0]) / b_param[alt_types[alt_ind, 0]][1]))) ** \
|
||||||
|
b_param[alt_types[alt_ind, 0]][2]
|
||||||
|
orig_V_half = V_test[(np.abs(orig - 0.5)).argmin()]
|
||||||
|
V_half_new = V_test[(np.abs(steady_state - 0.5)).argmin()]
|
||||||
|
slope_shift2[alt_types[alt_ind, 0]] = orig_V_half - V_half_new
|
||||||
|
|
||||||
|
elif alt_types[alt_ind, 1] == 'g':
|
||||||
|
g2[alt_types[alt_ind, 0]] = g2[alt_types[alt_ind, 0]] * log_array[alt]
|
||||||
|
fname = os.path.join(folder, "{}_{}_{}.hdf5".format(alt_types[alt_ind, 1], alt_types[alt_ind, 0],
|
||||||
|
np.int(np.around(log_array[alt], 2) * 100)))
|
||||||
|
###########################################################################
|
||||||
|
|
||||||
|
# initial simulation for current range from low to high
|
||||||
|
V_m = Cb_stellate_Kv(V_init * np.ones((stim_num)), g2, E2, I_in, dt, currents_included2, stim_time, stim_num, C,
|
||||||
|
shift2, scale2, b_param2, slope_shift2, gating2, current2, ind_dict)
|
||||||
|
stim_start = np.int(initial_period * 1 /dt)
|
||||||
|
min_spike_height = V_m[0, stim_start] + prominence
|
||||||
|
|
||||||
|
# create hdf5 file and save data and metadata
|
||||||
|
with h5py.File(fname, "a") as f:
|
||||||
|
data = f.create_group("data")
|
||||||
|
data.create_dataset('V_m', data=V_m, dtype='float64', compression="gzip", compression_opts=9)
|
||||||
|
metadata = {'I_high': high, 'I_low': low, 'stim_num': number_steps, 'stim_time': stim_time, 'sec': sec,
|
||||||
|
'dt': dt, 'initial_period': initial_period, 'g': json.dumps(dict(g2)), 'E': json.dumps(dict(E2)),
|
||||||
|
'C': C, 'V_init': V_init, 'ind_dict': json.dumps(dict(ind_dict)),
|
||||||
|
'b_param': json.dumps(dict(b_param2), cls=NumpyEncoder),
|
||||||
|
'currents': json.dumps(dict(currents_included2)), 'shift': json.dumps(dict(shift2)),
|
||||||
|
'var_change': str(alt_types[alt_ind, 1]), 'var': str(alt_types[alt_ind, 0]),
|
||||||
|
'scale': json.dumps(dict(scale2)), 'slope_shift': json.dumps(dict(slope_shift2)),
|
||||||
|
'prominence': prominence, 'desired_AUC_width': desired_AUC_width,
|
||||||
|
'alteration_info': str(alt_types[alt_ind, :]), 'alt_index': str(alt),
|
||||||
|
'min_spike_height': min_spike_height,}
|
||||||
|
data.attrs.update(metadata)
|
||||||
|
if alt_types[alt_ind, 1] == 'shift':
|
||||||
|
meta2 = {'alteration': lin_array[alt]}
|
||||||
|
else:
|
||||||
|
meta2 = {'alteration': log_array[alt]}
|
||||||
|
data.attrs.update(meta2)
|
||||||
|
|
||||||
|
# firing frequency analysis and rheobase
|
||||||
|
with h5py.File(fname, "r+") as f:
|
||||||
|
I_mag = np.arange(low, high, (high - low) / stim_num)
|
||||||
|
analysis = f.create_group("analysis")
|
||||||
|
dtyp = h5py.special_dtype(vlen=np.dtype('float64'))
|
||||||
|
for i in np.array(['spike_times', 'spike_times_rheo', 'ISI', 'Freq']): # analysis_names:
|
||||||
|
analysis.create_dataset(i, (I_mag.shape[0],), dtype=dtyp, compression="gzip", compression_opts=9)
|
||||||
|
analysis.create_dataset('F_inf', (I_mag.shape[0],), dtype='float64')
|
||||||
|
analysis.create_dataset('F_null', (I_mag.shape[0],), dtype='float64')
|
||||||
|
analysis.create_dataset('AP', (I_mag.shape[0],), dtype='bool', compression="gzip", compression_opts=9)
|
||||||
|
analysis.create_dataset('AP_rheo', (I_mag.shape[0],), dtype='bool', compression="gzip", compression_opts=9)
|
||||||
|
analysis.create_dataset('rheobase', (1,), dtype='float64', compression="gzip", compression_opts=9)
|
||||||
|
try:
|
||||||
|
for stim in range(I_mag.shape[0]):
|
||||||
|
# spike detection
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m[stim, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
analysis['spike_times'][stim] = ap_all * dt
|
||||||
|
if analysis['spike_times'][stim].size == 0:
|
||||||
|
analysis['AP'][stim] = False
|
||||||
|
else:
|
||||||
|
analysis['AP'][stim] = True
|
||||||
|
analysis['ISI'][stim] = np.array([x - analysis['spike_times'][stim][i - 1] if i else None for i, x in
|
||||||
|
enumerate(analysis['spike_times'][stim])][1:]) # msec
|
||||||
|
analysis['Freq'][stim] = 1 / (analysis['ISI'][stim] / 1000)
|
||||||
|
|
||||||
|
if analysis['Freq'][stim].size != 0:
|
||||||
|
analysis['F_null'][stim] = analysis['Freq'][stim][0]
|
||||||
|
if np.argwhere(ap_all >= 1000 * 1 / dt).shape[0] != 0: # if first spike is after 1 ms
|
||||||
|
half_second_spikes = np.logical_and(analysis['spike_times'][stim] < np.int(
|
||||||
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt + 500),
|
||||||
|
analysis['spike_times'][stim] > np.int(
|
||||||
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt)) # spikes in half second window
|
||||||
|
if np.sum(half_second_spikes == True) > 1:
|
||||||
|
half_second_spikes_1 = np.delete(half_second_spikes,
|
||||||
|
np.argwhere(half_second_spikes == True)[0][0])
|
||||||
|
analysis['F_inf'][stim] = np.mean(analysis['Freq'][stim][half_second_spikes_1])
|
||||||
|
else:
|
||||||
|
analysis['F_inf'][stim] = 0
|
||||||
|
else:
|
||||||
|
analysis['F_inf'][stim] = 0
|
||||||
|
else: # if no ISI detected
|
||||||
|
analysis['F_null'][stim] = 0
|
||||||
|
analysis['F_inf'][stim] = 0
|
||||||
|
except:
|
||||||
|
print(alt_types[alt_ind, 0], alt_types[alt_ind, 1], alt, 'Freq analysis unsuccessful')
|
||||||
|
|
||||||
|
# rheobase
|
||||||
|
# find where the first AP occurs
|
||||||
|
if np.any(analysis['AP'][:] == True):
|
||||||
|
# setup current range to be between the current step before and with first AP
|
||||||
|
I_first_spike = I_mag[np.argwhere(analysis['AP'][:] == True)[0][0]]
|
||||||
|
I_before_spike = I_mag[np.argwhere(analysis['AP'][:] == True)[0][0] - 1]
|
||||||
|
analysis.create_dataset('I_first_spike', data=I_first_spike, dtype='float64')
|
||||||
|
analysis.create_dataset('I_before_spike', data=I_before_spike, dtype='float64')
|
||||||
|
number_steps_rheo = 100
|
||||||
|
stim_time, I_in_rheo, stim_num_rheo, V_m_rheo = stimulus_init(I_before_spike, I_first_spike,
|
||||||
|
number_steps_rheo, initial_period, dt,
|
||||||
|
sec)
|
||||||
|
|
||||||
|
# simulate response to this current range and save
|
||||||
|
V_m_rheo = Cb_stellate_Kv(V_init * np.ones((stim_num_rheo)), g2, E2, I_in_rheo, dt, currents_included2,
|
||||||
|
stim_time, stim_num_rheo, C, shift2, scale2, b_param2, slope_shift2,
|
||||||
|
np.zeros((len(b_param), stim_num_rheo)),
|
||||||
|
np.zeros((len(currents_included), stim_num_rheo)), ind_dict)
|
||||||
|
|
||||||
|
# run AP detection
|
||||||
|
try:
|
||||||
|
for stim in range(I_in_rheo.shape[1]):
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_rheo[stim, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
analysis['spike_times_rheo'][stim] = ap_all * dt
|
||||||
|
if analysis['spike_times_rheo'][stim].size == 0:
|
||||||
|
analysis['AP_rheo'][stim] = False
|
||||||
|
else:
|
||||||
|
analysis['AP_rheo'][stim] = True
|
||||||
|
# find rheobase as smallest current step to elicit AP and save
|
||||||
|
analysis['rheobase'][:] = I_in_rheo[-1, np.argwhere(analysis['AP_rheo'][:] == True)[0][0]] * 1000
|
||||||
|
except:
|
||||||
|
print(alt_types[alt_ind, 0], alt_types[alt_ind, 1], alt, 'RHEOBASE NO AP')
|
||||||
|
|
||||||
|
# AUC
|
||||||
|
# find rheobase of steady state firing (F_inf)
|
||||||
|
with h5py.File(fname, "a") as f:
|
||||||
|
try:
|
||||||
|
# setup current range to be in current step between no and start of steady state firing
|
||||||
|
F_inf_start_ind = np.argwhere(f['analysis']['F_inf'][:] != 0)[0][0]
|
||||||
|
dI = (high - low) / stim_num
|
||||||
|
I_start = I_mag[F_inf_start_ind - 1]
|
||||||
|
I_end = I_mag[F_inf_start_ind]
|
||||||
|
I_rel = np.arange(I_start, I_end + (I_end - I_start) / 100, (I_end - I_start) / 100)
|
||||||
|
I_rel = np.reshape(I_rel, (1, I_rel.shape[0]))
|
||||||
|
I_in = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_rel
|
||||||
|
I_in[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_rel
|
||||||
|
|
||||||
|
# simulate response to this current step range
|
||||||
|
V_m_inf_rheo = Cb_stellate_Kv(V_init * np.ones(stim_num), g2, E2, I_in, dt, currents_included2, stim_time,
|
||||||
|
stim_num, C, shift2, scale2, b_param2, slope_shift2,
|
||||||
|
np.zeros((len(b_param), stim_num)),
|
||||||
|
np.zeros((len(currents_included), stim_num)), ind_dict)
|
||||||
|
|
||||||
|
# save response, analyse and save
|
||||||
|
for i in np.array(['spike_times_Finf', 'ISI_Finf', 'Freq_Finf']):
|
||||||
|
f['analysis'].require_dataset(i, shape=(I_rel.shape[1],), dtype=dtyp, compression="gzip",
|
||||||
|
compression_opts=9)
|
||||||
|
f['analysis'].require_dataset('F_inf_Finf', shape=(I_rel.shape[1],), dtype='float64')
|
||||||
|
f['analysis'].require_dataset('F_null_Finf', shape=(I_rel.shape[1],), dtype='float64')
|
||||||
|
for stim in range(I_rel.shape[1]):
|
||||||
|
# spike peak detection
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_inf_rheo[stim, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
f['analysis']['spike_times_Finf'][stim] = ap_all * dt
|
||||||
|
f['analysis']['ISI_Finf'][stim] = np.array(
|
||||||
|
[x - f['analysis']['spike_times_Finf'][stim][i - 1] if i else None for i, x in
|
||||||
|
enumerate(f['analysis']['spike_times_Finf'][stim])][1:]) # msec
|
||||||
|
f['analysis']['Freq_Finf'][stim] = 1 / (f['analysis']['ISI_Finf'][stim] / 1000)
|
||||||
|
if f['analysis']['Freq_Finf'][stim].size != 0:
|
||||||
|
if np.argwhere(ap_all >= 1000 * 1 / dt).shape[0] != 0: # if first spike is after 1 ms
|
||||||
|
half_second_spikes = np.logical_and(f['analysis']['spike_times_Finf'][stim] < np.int(
|
||||||
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt + 500),
|
||||||
|
f['analysis']['spike_times_Finf'][stim] > np.int(
|
||||||
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt)) # spikes in half second window
|
||||||
|
if np.sum(half_second_spikes == True) > 1:
|
||||||
|
half_second_spikes_1 = np.delete(half_second_spikes,
|
||||||
|
np.argwhere(half_second_spikes == True)[0][0]) # remove first spike because it has no ISI/freq
|
||||||
|
f['analysis']['F_inf_Finf'][stim] = np.mean( f['analysis']['Freq_Finf'][stim][half_second_spikes_1])
|
||||||
|
else:
|
||||||
|
f['analysis']['F_inf_Finf'][stim] = 0
|
||||||
|
else:
|
||||||
|
f['analysis']['F_null_Finf'][stim] = 0
|
||||||
|
f['analysis']['F_inf_Finf'][stim] = 0
|
||||||
|
|
||||||
|
else:
|
||||||
|
f['analysis']['F_null_Finf'][stim] = 0
|
||||||
|
f['analysis']['F_inf_Finf'][stim] = 0
|
||||||
|
|
||||||
|
# find AUC relative to F_inf rheobase
|
||||||
|
# setup current inputs with current range around F_inf rheobase
|
||||||
|
I_first_inf = I_rel[0, np.argwhere(f['analysis']['F_inf_Finf'][:] != 0)[0][0]]
|
||||||
|
I_start = I_first_inf - 0.00005
|
||||||
|
I_end = I_first_inf + 0.2 * f['data'].attrs['I_high'][()]
|
||||||
|
I_rel2 = np.arange(I_start, I_end + (I_end - I_start) / 100, (I_end - I_start) / 100)
|
||||||
|
I_rel2 = np.reshape(I_rel2, (1, I_rel2.shape[0]))
|
||||||
|
I_in2 = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_rel2
|
||||||
|
I_in2[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_rel2
|
||||||
|
stim_num = I_in2.shape[1]
|
||||||
|
|
||||||
|
# simulate response to this current step range
|
||||||
|
V_m_AUC = Cb_stellate_Kv(V_init * np.ones(stim_num), g2, E2, I_in2, dt, currents_included2, stim_time,
|
||||||
|
stim_num, C, shift2, scale2, b_param2, slope_shift2,
|
||||||
|
np.zeros((len(b_param), stim_num)), np.zeros((len(currents_included), stim_num)),
|
||||||
|
ind_dict)
|
||||||
|
|
||||||
|
# save data and analysis for this current step range including AUC
|
||||||
|
f['data'].require_dataset('V_m_AUC', shape=V_m_AUC.shape, dtype='float64')
|
||||||
|
f['data']['V_m_AUC'][:] = V_m_AUC
|
||||||
|
dtyp = h5py.special_dtype(vlen=np.dtype('float64'))
|
||||||
|
for i in np.array(['spike_times_Finf_AUC', 'ISI_Finf_AUC', 'Freq_Finf_AUC']): # analysis_names:
|
||||||
|
f['analysis'].require_dataset(i, shape=(I_rel2.shape[1],), dtype=dtyp, compression="gzip", compression_opts=9)
|
||||||
|
f['analysis'].require_dataset('F_inf_Finf_AUC', shape=(I_rel2.shape[1],), dtype='float64')
|
||||||
|
f['analysis'].require_dataset('F_null_Finf_AUC', shape=(I_rel2.shape[1],), dtype='float64')
|
||||||
|
for stim in range(I_rel2.shape[1]):
|
||||||
|
# spike peak detection
|
||||||
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_AUC[stim, stim_start:], min_spike_height,
|
||||||
|
prominence=prominence, distance=1 * 1 / dt)
|
||||||
|
f['analysis']['spike_times_Finf_AUC'][stim] = ap_all * dt
|
||||||
|
f['analysis']['ISI_Finf_AUC'][stim] = np.array(
|
||||||
|
[x - f['analysis']['spike_times_Finf_AUC'][stim][i - 1] if i else None for i, x in
|
||||||
|
enumerate(f['analysis']['spike_times_Finf_AUC'][stim])][1:]) # msec
|
||||||
|
f['analysis']['Freq_Finf_AUC'][stim] = 1 / (f['analysis']['ISI_Finf_AUC'][stim] / 1000)
|
||||||
|
if f['analysis']['Freq_Finf_AUC'][stim].size != 0:
|
||||||
|
if np.argwhere(ap_all >= 1000 * 1 / dt).shape[0] != 0: # if first spike is after 1 ms
|
||||||
|
half_second_spikes = np.logical_and(f['analysis']['spike_times_Finf_AUC'][stim] < np.int(
|
||||||
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt + 500),
|
||||||
|
f['analysis']['spike_times_Finf_AUC'][stim] > np.int(
|
||||||
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt)) # spikes in half second window
|
||||||
|
if np.sum(half_second_spikes == True) > 1:
|
||||||
|
half_second_spikes_1 = np.delete(half_second_spikes,
|
||||||
|
np.argwhere(half_second_spikes == True)[0][0]) # remove first spike because it has no ISI/freq
|
||||||
|
f['analysis']['F_inf_Finf_AUC'][stim] = np.mean(
|
||||||
|
f['analysis']['Freq_Finf_AUC'][stim][half_second_spikes_1])
|
||||||
|
else:
|
||||||
|
f['analysis']['F_inf_Finf_AUC'][stim] = 0
|
||||||
|
else:
|
||||||
|
f['analysis']['F_null_Finf_AUC'][stim] = 0
|
||||||
|
f['analysis']['F_inf_Finf_AUC'][stim] = 0
|
||||||
|
else:
|
||||||
|
f['analysis']['F_null_Finf_AUC'][stim] = 0
|
||||||
|
f['analysis']['F_inf_Finf_AUC'][stim] = 0
|
||||||
|
f['analysis'].require_dataset('AUC', shape=(1,), dtype='float64', compression="gzip", compression_opts=9)
|
||||||
|
f['analysis']['AUC'][:] = np.trapz(f['analysis']['F_inf_Finf_AUC'][:], I_rel2[:] * 1000, dI * 1000)
|
||||||
|
except:
|
||||||
|
f['analysis'].require_dataset('AUC', shape=(1,), dtype='float64', compression="gzip", compression_opts=9)
|
||||||
|
f['analysis']['AUC'][:] = 0
|
||||||
|
|
||||||
|
|
||||||
|
if f.__bool__():
|
||||||
|
f.close()
|
||||||
|
gc.collect()
|
||||||
|
|
||||||
1009
Code/Functions/Pospischil_fxns.py
Normal file
1009
Code/Functions/Pospischil_fxns.py
Normal file
File diff suppressed because it is too large
Load Diff
1005
Code/Functions/STN_fxns.py
Normal file
1005
Code/Functions/STN_fxns.py
Normal file
File diff suppressed because it is too large
Load Diff
1047
Code/Functions/STN_fxns_Kv.py
Normal file
1047
Code/Functions/STN_fxns_Kv.py
Normal file
File diff suppressed because it is too large
Load Diff
135
Code/Functions/Utility_fxns.py
Normal file
135
Code/Functions/Utility_fxns.py
Normal file
@ -0,0 +1,135 @@
|
|||||||
|
import json
|
||||||
|
import numpy as np
|
||||||
|
from numba import njit
|
||||||
|
|
||||||
|
@njit
|
||||||
|
def sqrt_log(x, *p):
|
||||||
|
""" Function of sum of square root and logarithmic function to fit to fI curves
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
x : array
|
||||||
|
input x value
|
||||||
|
*p : array
|
||||||
|
parameters for function
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
p[0] * np.sqrt(x + p[1]) + p[2] * np.log(x + (p[1] + 1)) + p[3]
|
||||||
|
"""
|
||||||
|
return p[0] * np.sqrt(x + p[1]) + p[2] * np.log(x + (p[1] + 1)) + p[3]
|
||||||
|
|
||||||
|
|
||||||
|
@njit
|
||||||
|
def capacitance(diam, C_m):
|
||||||
|
""" calculate the capacitance of a cell's membrane give the diameter
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
diam: float
|
||||||
|
diameter of spherical cell body
|
||||||
|
C_m: float
|
||||||
|
specific membrane capacitance of cell
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
capacitance: float
|
||||||
|
capacitance of cell
|
||||||
|
surf_area: flaot
|
||||||
|
surface area of cell
|
||||||
|
"""
|
||||||
|
r = diam * 1E-4 / 2 # cm
|
||||||
|
l = r
|
||||||
|
surf_area = 2 * np.pi * r ** 2 + 2 * np.pi * r * l # cm^2
|
||||||
|
C = C_m * surf_area # uF
|
||||||
|
return (C, surf_area)
|
||||||
|
|
||||||
|
|
||||||
|
@njit
|
||||||
|
def stimulus_init(low, high, number_steps, initial_period, dt, sec):
|
||||||
|
""" initiation of stimulus pulse from stimulus magnitudes I_in_mag
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
low : float
|
||||||
|
lowest current step magnitude
|
||||||
|
high : float
|
||||||
|
upper current step magnitude
|
||||||
|
number_steps : int
|
||||||
|
number of current steps between low and high
|
||||||
|
initial_period : float
|
||||||
|
length of I=0 before current step
|
||||||
|
dt : float
|
||||||
|
time step
|
||||||
|
sec:
|
||||||
|
length of current step in seconds
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
stim_time: float
|
||||||
|
length of I_in in samples
|
||||||
|
I_in: array
|
||||||
|
I input array
|
||||||
|
stim_num: int
|
||||||
|
number of current steps
|
||||||
|
V_m: array
|
||||||
|
membrane potential array initialization of shape:(stim_num, stim_time)
|
||||||
|
"""
|
||||||
|
stim_time = np.int(1000 * 1 / dt * sec) # 1000 msec/sec * 1/dt
|
||||||
|
I_in_mag = np.arange(low, high, (high - low) / number_steps)
|
||||||
|
I_in_mag = np.reshape(I_in_mag, (1, I_in_mag.shape[0]))
|
||||||
|
I_in = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_in_mag
|
||||||
|
I_in[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_in_mag
|
||||||
|
stim_num = I_in.shape[1] # number of different currents injected
|
||||||
|
stim_time = stim_time + np.int(initial_period * 1 / dt)
|
||||||
|
V_m = np.zeros((stim_num, stim_time))
|
||||||
|
|
||||||
|
return stim_time, I_in, stim_num, V_m
|
||||||
|
|
||||||
|
class NumpyEncoder(json.JSONEncoder):
|
||||||
|
""" Special json encoder for numpy types """
|
||||||
|
def default(self, obj):
|
||||||
|
if isinstance(obj, np.integer):
|
||||||
|
return int(obj)
|
||||||
|
elif isinstance(obj, np.floating):
|
||||||
|
return float(obj)
|
||||||
|
elif isinstance(obj, np.ndarray):
|
||||||
|
return obj.tolist()
|
||||||
|
return json.JSONEncoder.default(self, obj)
|
||||||
|
|
||||||
|
|
||||||
|
def init_dict(variable):
|
||||||
|
""" Initialize dictionaries for simulations
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
variable : array
|
||||||
|
array of variable name strings to put in dicts
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
shift : dict
|
||||||
|
with every variable name in variable = 0.
|
||||||
|
scale : dict
|
||||||
|
with every variable name in variable = 1.
|
||||||
|
slope_shift : dict
|
||||||
|
with every variable name in variable = 0.
|
||||||
|
E : dict
|
||||||
|
empty dictionary
|
||||||
|
currents_included : dict
|
||||||
|
empty dictionary
|
||||||
|
b_param : dict
|
||||||
|
with every variable name in variable = np.array of 2 zeros
|
||||||
|
g : dict
|
||||||
|
empty dictionary
|
||||||
|
"""
|
||||||
|
shift = {}
|
||||||
|
scale = {}
|
||||||
|
slope_shift = {}
|
||||||
|
b_param ={}
|
||||||
|
g = {}
|
||||||
|
E ={}
|
||||||
|
currents_included = {}
|
||||||
|
for var in variable:
|
||||||
|
shift[var] = 0.
|
||||||
|
scale[var] = 1.0
|
||||||
|
slope_shift[var] = 0.
|
||||||
|
b_param[var] = np.zeros(3, dtype=np.dtype('float64'))
|
||||||
|
return shift, scale, slope_shift, E, currents_included, b_param, g
|
||||||
|
|
||||||
112
Code/KCNA1_mutations/mut_Cb_stellate.py
Normal file
112
Code/KCNA1_mutations/mut_Cb_stellate.py
Normal file
@ -0,0 +1,112 @@
|
|||||||
|
# -*- 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()))
|
||||||
|
|
||||||
121
Code/KCNA1_mutations/mut_Cb_stellate_Delta_Kv.py
Normal file
121
Code/KCNA1_mutations/mut_Cb_stellate_Delta_Kv.py
Normal file
@ -0,0 +1,121 @@
|
|||||||
|
# -*- 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()))
|
||||||
119
Code/KCNA1_mutations/mut_Cb_stellate_Kv.py
Normal file
119
Code/KCNA1_mutations/mut_Cb_stellate_Kv.py
Normal file
@ -0,0 +1,119 @@
|
|||||||
|
# -*- 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()))
|
||||||
119
Code/KCNA1_mutations/mut_FS_Kv.py
Normal file
119
Code/KCNA1_mutations/mut_FS_Kv.py
Normal file
@ -0,0 +1,119 @@
|
|||||||
|
# -*- 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()))
|
||||||
126
Code/KCNA1_mutations/mut_RS_inhib_Kv.py
Normal file
126
Code/KCNA1_mutations/mut_RS_inhib_Kv.py
Normal file
@ -0,0 +1,126 @@
|
|||||||
|
# -*- 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()))
|
||||||
120
Code/KCNA1_mutations/mut_RS_pyramidal_Kv.py
Normal file
120
Code/KCNA1_mutations/mut_RS_pyramidal_Kv.py
Normal file
@ -0,0 +1,120 @@
|
|||||||
|
# -*- 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()))
|
||||||
107
Code/KCNA1_mutations/mut_STN.py
Normal file
107
Code/KCNA1_mutations/mut_STN.py
Normal file
@ -0,0 +1,107 @@
|
|||||||
|
# -*- 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()))
|
||||||
129
Code/KCNA1_mutations/mut_STN_Delta_Kv.py
Normal file
129
Code/KCNA1_mutations/mut_STN_Delta_Kv.py
Normal file
@ -0,0 +1,129 @@
|
|||||||
|
# -*- 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()))
|
||||||
|
|
||||||
|
|
||||||
129
Code/KCNA1_mutations/mut_STN_Kv.py
Normal file
129
Code/KCNA1_mutations/mut_STN_Kv.py
Normal file
@ -0,0 +1,129 @@
|
|||||||
|
# -*- 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()))
|
||||||
|
|
||||||
88
Code/Neuron_models/Cb_stellate_Delta_Kv_model.py
Normal file
88
Code/Neuron_models/Cb_stellate_Delta_Kv_model.py
Normal file
@ -0,0 +1,88 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Fri Jun 18 10:47:40 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
import os
|
||||||
|
from 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))
|
||||||
|
|
||||||
|
# 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])
|
||||||
|
|
||||||
|
# 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
|
||||||
|
|
||||||
|
prominence = 50
|
||||||
|
min_spike_height = 0
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
folder = '../Neuron_models'
|
||||||
|
mut = 'Cb_stellate_Delta_Kv'
|
||||||
|
mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
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)
|
||||||
87
Code/Neuron_models/Cb_stellate_Kv_model.py
Normal file
87
Code/Neuron_models/Cb_stellate_Kv_model.py
Normal file
@ -0,0 +1,87 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Sat May 8 16:20:11 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
import os
|
||||||
|
from 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))
|
||||||
|
|
||||||
|
# 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])
|
||||||
|
|
||||||
|
|
||||||
|
# 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
|
||||||
|
|
||||||
|
|
||||||
|
prominence = 50
|
||||||
|
min_spike_height = 0
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
folder = '../Neuron_models'
|
||||||
|
mut = 'Cb_stellate_Kv'
|
||||||
|
mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
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)
|
||||||
73
Code/Neuron_models/Cb_stellate_model.py
Normal file
73
Code/Neuron_models/Cb_stellate_model.py
Normal file
@ -0,0 +1,73 @@
|
|||||||
|
import numpy as np
|
||||||
|
import os
|
||||||
|
from Code.Functions.Utility 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))
|
||||||
|
|
||||||
|
# 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.])
|
||||||
|
|
||||||
|
|
||||||
|
# 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
|
||||||
|
|
||||||
|
prominence = 50
|
||||||
|
min_spike_height = 0
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
folder = '../Neuron_models'
|
||||||
|
mut = 'Cb_stellate'
|
||||||
|
mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
|
||||||
|
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)
|
||||||
100
Code/Neuron_models/FS_Kv_model.py
Normal file
100
Code/Neuron_models/FS_Kv_model.py
Normal file
@ -0,0 +1,100 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Fri May 7 23:37:22 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
import os
|
||||||
|
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(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']))
|
||||||
|
|
||||||
|
# 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([-34.33054521, -8.21450277, 1.42295686])
|
||||||
|
b_param['h'] = np.zeros(4)
|
||||||
|
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.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])
|
||||||
|
|
||||||
|
|
||||||
|
# reversal potentials
|
||||||
|
E["Na"] = 50.
|
||||||
|
E["K"] = -90.
|
||||||
|
E["Ca"] = 120.
|
||||||
|
E["Leak"] = -70.4
|
||||||
|
|
||||||
|
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
|
||||||
|
|
||||||
|
Kv_ratio = 0.10
|
||||||
|
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
|
||||||
|
tau_max_p = 502
|
||||||
|
V_init = -70
|
||||||
|
V_T = -57.9
|
||||||
|
|
||||||
|
prominence = 50
|
||||||
|
min_spike_height = 0
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
folder = '../Neuron_models'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
mut = 'FS_Kv'
|
||||||
|
mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
|
||||||
|
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)
|
||||||
101
Code/Neuron_models/FS_model.py
Normal file
101
Code/Neuron_models/FS_model.py
Normal file
@ -0,0 +1,101 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Fri May 7 23:37:22 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
|
||||||
|
Original Pospischil FS (no Kv1.1)
|
||||||
|
"""
|
||||||
|
|
||||||
|
# Todo: error when run
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
import os
|
||||||
|
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(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']))
|
||||||
|
|
||||||
|
# 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([-34.33054521, -8.21450277, 1.42295686])
|
||||||
|
b_param['h'] = np.zeros(4)
|
||||||
|
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.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])
|
||||||
|
|
||||||
|
# reversal potentials
|
||||||
|
E["Na"] = 50.
|
||||||
|
E["K"] = -90.
|
||||||
|
E["Ca"] = 120.
|
||||||
|
E["Leak"] = -70.4
|
||||||
|
|
||||||
|
# currents in model
|
||||||
|
currents_included["Na"] = True
|
||||||
|
currents_included["Kd"] = True
|
||||||
|
currents_included["Kv"] = False
|
||||||
|
currents_included["Kv_mut"] = False
|
||||||
|
currents_included["L"] = False
|
||||||
|
currents_included["M"] = True
|
||||||
|
currents_included["Leak"] = True
|
||||||
|
|
||||||
|
# model conductances
|
||||||
|
g["Na"] = 58. * surf_area
|
||||||
|
g["Kd"] = 3.9 * surf_area
|
||||||
|
g["M"] = 0.075 * surf_area
|
||||||
|
g["Kv"] = 0.
|
||||||
|
g["Kv_mut"] = 0.
|
||||||
|
g["L"] = 0.
|
||||||
|
g["Leak"] = 0.038 * surf_area
|
||||||
|
tau_max_p = 502
|
||||||
|
V_init = -70
|
||||||
|
V_T = -57.9
|
||||||
|
prominence = 50
|
||||||
|
min_spike_height = 0
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
folder = '../Neuron_models'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
mut = 'FS'
|
||||||
|
mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
|
||||||
|
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)
|
||||||
114
Code/Neuron_models/RS_inhib_Kv_model.py
Normal file
114
Code/Neuron_models/RS_inhib_Kv_model.py
Normal file
@ -0,0 +1,114 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Fri May 7 23:37:22 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
import os
|
||||||
|
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 = 0.00035
|
||||||
|
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['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
|
||||||
|
b_param['h'] = np.zeros(4)
|
||||||
|
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.])
|
||||||
|
b_param['p'][:] = np.array([-45., -9.9998807337, 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"] = 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
|
||||||
|
|
||||||
|
prominence = 50
|
||||||
|
min_spike_height = 0
|
||||||
|
desired_AUC_width = high/5 #0.0005
|
||||||
|
|
||||||
|
folder = '../Neuron_models'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
mut = 'RS_inhib_Kv'
|
||||||
|
mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
|
||||||
|
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)
|
||||||
102
Code/Neuron_models/RS_inhib_model.py
Normal file
102
Code/Neuron_models/RS_inhib_model.py
Normal file
@ -0,0 +1,102 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Fri May 7 23:37:22 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
|
||||||
|
Original Pospischil RS inhibitory (no Kv1.1)
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
import os
|
||||||
|
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 = 0.00035
|
||||||
|
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['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
|
||||||
|
b_param['h'] = np.zeros(4)
|
||||||
|
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.])
|
||||||
|
b_param['p'][:] = np.array([-45., -9.9998807337, 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])
|
||||||
|
|
||||||
|
# reversal potential
|
||||||
|
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"] = False
|
||||||
|
currents_included["Kv_mut"] = False
|
||||||
|
currents_included["L"] = False
|
||||||
|
currents_included["M"] = True
|
||||||
|
currents_included["Leak"] = True
|
||||||
|
|
||||||
|
g["Na"] = 10. * surf_area
|
||||||
|
g["Kd"] = 2.1 * surf_area
|
||||||
|
g["M"] = 0.0098 * surf_area
|
||||||
|
g["Kv"] = 0.
|
||||||
|
g["Kv_mut"] = 0.
|
||||||
|
g["L"] = 0.
|
||||||
|
g["Leak"] = 0.0205 * surf_area
|
||||||
|
|
||||||
|
prominence = 50
|
||||||
|
min_spike_height = 0
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
folder = '../Neuron_models'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
mut = 'RS_inhib'
|
||||||
|
mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
|
||||||
|
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)
|
||||||
|
|
||||||
117
Code/Neuron_models/RS_pyramidal_Kv_model.py
Normal file
117
Code/Neuron_models/RS_pyramidal_Kv_model.py
Normal file
@ -0,0 +1,117 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Fri May 7 23:37:22 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
import os
|
||||||
|
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(61.4, 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']))
|
||||||
|
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['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
|
||||||
|
b_param['h'] = np.zeros(4)
|
||||||
|
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.])
|
||||||
|
b_param['p'][:] = np.array([-45., -9.9998807337, 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"] = 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"] = False
|
||||||
|
currents_included["L"] = False
|
||||||
|
currents_included["M"] = False
|
||||||
|
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
|
||||||
|
|
||||||
|
prominence = 50
|
||||||
|
min_spike_height = 0
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
folder = '../Neuron_models'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
mut = 'RS_pyramidal_Kv'
|
||||||
|
mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
|
||||||
|
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)
|
||||||
|
|
||||||
99
Code/Neuron_models/RS_pyramidal_model.py
Normal file
99
Code/Neuron_models/RS_pyramidal_model.py
Normal file
@ -0,0 +1,99 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Fri May 7 23:37:22 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
import os
|
||||||
|
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(61.4, 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']))
|
||||||
|
V_init = -70
|
||||||
|
V_T = -56.2
|
||||||
|
|
||||||
|
# initialize parameters
|
||||||
|
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([-34.33054521, -8.21450277, 1.42295686])
|
||||||
|
b_param['h'] = np.zeros(4)
|
||||||
|
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.])
|
||||||
|
b_param['p'][:] = np.array(
|
||||||
|
[-45., -9.9998807337, 1.]) # -45 with 10 mV shift so that it contributes to resting potential
|
||||||
|
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])
|
||||||
|
|
||||||
|
# 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"] = False
|
||||||
|
currents_included["Kv_mut"] = False
|
||||||
|
currents_included["L"] = False
|
||||||
|
currents_included["M"] = True
|
||||||
|
currents_included["Leak"] = True
|
||||||
|
|
||||||
|
# model conductances
|
||||||
|
g["Na"] = 56. * surf_area
|
||||||
|
g["Kd"] = 6. * surf_area
|
||||||
|
g["M"] = 0.075 * surf_area
|
||||||
|
g["Kv"] = 0.
|
||||||
|
g["Kv_mut"] = 0.
|
||||||
|
g["L"] = 0.
|
||||||
|
g["Leak"] = 0.0205 * surf_area
|
||||||
|
|
||||||
|
|
||||||
|
prominence = 50
|
||||||
|
min_spike_height = 0
|
||||||
|
desired_AUC_width = high / 5
|
||||||
|
|
||||||
|
folder = '../Neuron_models'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
mut = 'RS_pyramidal'
|
||||||
|
mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
|
||||||
|
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)
|
||||||
111
Code/Neuron_models/STN_Delta_Kv_model.py
Normal file
111
Code/Neuron_models/STN_Delta_Kv_model.py
Normal file
@ -0,0 +1,111 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Fri Jun 18 10:53:45 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
import numpy as np
|
||||||
|
import os
|
||||||
|
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 = {}
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut', 'Ca_conc','E_Ca']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['Na', 'Kd', 'A', 'L', 'Kv', 'Kv_mut', 'T', 'Leak', 'Ca_K']):
|
||||||
|
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])
|
||||||
|
|
||||||
|
|
||||||
|
# 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
|
||||||
|
|
||||||
|
prominence = 50
|
||||||
|
min_spike_height = 0
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
folder = '../Neuron_models'
|
||||||
|
mut = 'STN_Delta_Kv'
|
||||||
|
mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
105
Code/Neuron_models/STN_Kv_model.py
Normal file
105
Code/Neuron_models/STN_Kv_model.py
Normal file
@ -0,0 +1,105 @@
|
|||||||
|
import numpy as np
|
||||||
|
import os
|
||||||
|
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 = {}
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut', 'Ca_conc','E_Ca']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['Na', 'Kd', 'A', 'L', 'Kv', 'Kv_mut', 'T', 'Leak', 'Ca_K']):
|
||||||
|
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])
|
||||||
|
|
||||||
|
# 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
|
||||||
|
|
||||||
|
prominence = 50
|
||||||
|
min_spike_height = 0
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
folder = '../Neuron_models'
|
||||||
|
mut = 'STN_Kv'
|
||||||
|
mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
94
Code/Neuron_models/STN_model.py
Normal file
94
Code/Neuron_models/STN_model.py
Normal file
@ -0,0 +1,94 @@
|
|||||||
|
import numpy as np
|
||||||
|
import os
|
||||||
|
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 = {}
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['m', 'h', 'n', 'a', 'b', 'a_mut', 'b_mut', 'c', 'd1', 'd2', 'p', 'q', 'r', 'Ca_conc','E_Ca']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['Na', 'Kd', 'A', 'L', 'A_mut', 'T', 'Leak', 'Ca_K']):
|
||||||
|
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
|
||||||
|
|
||||||
|
|
||||||
|
prominence = 50
|
||||||
|
min_spike_height = 0
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
folder = '../Neuron_models'
|
||||||
|
mut = 'STN'
|
||||||
|
mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
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)
|
||||||
20
Code/README.md
Normal file
20
Code/README.md
Normal file
@ -0,0 +1,20 @@
|
|||||||
|
### Python code repository for Koch et al. 2022
|
||||||
|
Modelling on the effects of ion current property changes on firing behaviour in diverse neuronal models and in with Kv1.1 mutations that cause episodic ataxia type 1.
|
||||||
|
|
||||||
|
|
||||||
|
## ./Functions
|
||||||
|
- Python function files for models
|
||||||
|
- from Pospischil et al. ()
|
||||||
|
- from Alexander et al. () with and without added Kv1.1
|
||||||
|
- from Otsuka et al () with and without added Kv1.1
|
||||||
|
- Utility_fxns for common useful functions
|
||||||
|
|
||||||
|
## ./Neuron_models
|
||||||
|
- Scripts for simulation of each model to current steps and ramp currents
|
||||||
|
|
||||||
|
## ./Sensitivity_Analysis
|
||||||
|
- Scripts for sensitivity analysis in each model
|
||||||
|
|
||||||
|
|
||||||
|
## ./KCNA1_mutations
|
||||||
|
- Scripts for simulation of KCNA1 mutations described in Lauxmann et al. (2021)
|
||||||
110
Code/Sensitivity_Analysis/SA_Cb_stellate.py
Normal file
110
Code/Sensitivity_Analysis/SA_Cb_stellate.py
Normal file
@ -0,0 +1,110 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Fri Jun 4 08:24:33 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
from joblib import Parallel, delayed
|
||||||
|
import os
|
||||||
|
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
|
||||||
|
from Code.Functions.Cb_stellate_fxns import SA_Cb_stellate
|
||||||
|
|
||||||
|
# model parameters
|
||||||
|
dt = 0.01
|
||||||
|
sec = 2
|
||||||
|
low = 0
|
||||||
|
high = 0.001
|
||||||
|
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', 'n_A', 'h_A', 'n_A_mut', 'h_A_mut', 'm_T', 'h_T']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['Na', 'Kd', 'A', 'A_mut', 'T', '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.])
|
||||||
|
|
||||||
|
|
||||||
|
# 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"] = False
|
||||||
|
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"] = 0.
|
||||||
|
g["A"] = 15.0159 * surf_area
|
||||||
|
g["T"] = 0.45045 * surf_area
|
||||||
|
g["Leak"] = 0.07407 * surf_area
|
||||||
|
|
||||||
|
|
||||||
|
# save folder
|
||||||
|
folder = './SA/Cb_stellate'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
#%% setup for one-factor-at-a-time SA
|
||||||
|
var = np.array(['m', 'h', 'n', 'n_A', 'h_A'])
|
||||||
|
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
|
||||||
|
np.array(['slope' for i in range(var.shape[0])]))
|
||||||
|
cur = np.array(['Na', 'Kd', 'A', 'Leak'])
|
||||||
|
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
|
||||||
|
var = np.append(var, var)
|
||||||
|
var = np.append(var, cur)
|
||||||
|
alt_types = np.c_[var, type_names]
|
||||||
|
lin_array = np.arange(-10, 11, 1)
|
||||||
|
log_array = np.logspace(-1,1,21, base=2)
|
||||||
|
|
||||||
|
# %% multiprocessing
|
||||||
|
prominence = 50
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
Parallel(n_jobs=8, verbose=9)(
|
||||||
|
delayed(SA_Cb_stellate)(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, folder, high, low, number_steps,
|
||||||
|
initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
|
||||||
|
for alt_ind in range(alt_types.shape[0]) for alt in range(21))
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
116
Code/Sensitivity_Analysis/SA_Cb_stellate_Delta_Kv.py
Normal file
116
Code/Sensitivity_Analysis/SA_Cb_stellate_Delta_Kv.py
Normal file
@ -0,0 +1,116 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Fri Jun 4 08:24:33 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
from joblib import Parallel, delayed
|
||||||
|
import os
|
||||||
|
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
|
||||||
|
from Code.Functions.Cb_stellate_fxns_Kv import SA_Cb_stellate_Kv
|
||||||
|
|
||||||
|
# model parameters
|
||||||
|
dt = 0.01
|
||||||
|
sec = 2
|
||||||
|
low = 0
|
||||||
|
high = 0.001
|
||||||
|
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', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['Na', 'Kd', 'A', 'Kv', 'Kv_mut', 'T', '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])
|
||||||
|
|
||||||
|
|
||||||
|
# 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"] = False
|
||||||
|
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 = './SA/Cb_stellate_Kv_only'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
#%% setup for one-factor-at-a-time SA
|
||||||
|
var = np.array(['m', 'h', 'n', 's', 'u'])
|
||||||
|
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
|
||||||
|
np.array(['slope' for i in range(var.shape[0])]))
|
||||||
|
cur = np.array(['Na', 'Kd', 'Kv', 'Leak'])
|
||||||
|
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
|
||||||
|
var = np.append(var, var)
|
||||||
|
var = np.append(var, cur)
|
||||||
|
alt_types = np.c_[var, type_names]
|
||||||
|
lin_array = np.arange(-10, 11, 1)
|
||||||
|
log_array = np.logspace(-1,1,21, base=2)
|
||||||
|
|
||||||
|
# %% multiprocessing
|
||||||
|
prominence = 50
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
Parallel(n_jobs=8, verbose=9)(
|
||||||
|
delayed(SA_Cb_stellate_Kv)(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, folder, high, low, number_steps,
|
||||||
|
initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
|
||||||
|
for alt_ind in range(alt_types.shape[0]) for alt in range(21))
|
||||||
|
|
||||||
139
Code/Sensitivity_Analysis/SA_Cb_stellate_Kv.py
Normal file
139
Code/Sensitivity_Analysis/SA_Cb_stellate_Kv.py
Normal file
@ -0,0 +1,139 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Fri Jun 4 08:24:33 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
from joblib import Parallel, delayed
|
||||||
|
import os
|
||||||
|
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
|
||||||
|
from Code.Functions.Cb_stellate_fxns_Kv import SA_Cb_stellate_Kv
|
||||||
|
|
||||||
|
# model parameters
|
||||||
|
dt = 0.01
|
||||||
|
sec = 2
|
||||||
|
low = 0
|
||||||
|
high = 0.001
|
||||||
|
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', 'n_A', 'h_A', 'm_T', 'h_T', 's', 'u', 's_mut', 'u_mut']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['Na', 'Kd', 'A', 'Kv', 'Kv_mut', 'T', '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])
|
||||||
|
|
||||||
|
|
||||||
|
# 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"] = False
|
||||||
|
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"] = 9.0556 * Kv_ratio * surf_area
|
||||||
|
g["Kv_mut"] = 0.
|
||||||
|
g["A"] = 15.0159 * surf_area
|
||||||
|
g["T"] = 0.45045 * surf_area
|
||||||
|
g["Leak"] = 0.07407 * surf_area
|
||||||
|
|
||||||
|
|
||||||
|
# save folder
|
||||||
|
folder = './Sensitivity_Analysis/Cb_stellate_Kv'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
#%% setup for one-factor-at-a-time SA
|
||||||
|
var = np.array(['m', 'h', 'n', 'n_A', 'h_A', 's', 'u'])
|
||||||
|
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
|
||||||
|
np.array(['slope' for i in range(var.shape[0])]))
|
||||||
|
cur = np.array(['Na', 'Kd', 'A', 'Kv', 'Leak'])
|
||||||
|
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
|
||||||
|
var = np.append(var, var)
|
||||||
|
var = np.append(var, cur)
|
||||||
|
alt_types = np.c_[var, type_names]
|
||||||
|
lin_array = np.arange(-10, 11, 1)
|
||||||
|
log_array = np.logspace(-1,1,21, base=2)
|
||||||
|
|
||||||
|
# %% multiprocessing
|
||||||
|
prominence = 50
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
Parallel(n_jobs=8, verbose=9)(
|
||||||
|
delayed(SA_Cb_stellate_Kv)(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, folder, high, low, number_steps,
|
||||||
|
initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
|
||||||
|
for alt_ind in range(alt_types.shape[0]) for alt in range(21))
|
||||||
|
|
||||||
|
|
||||||
|
# #%% Get pd Dataframes for certain variables
|
||||||
|
# import pandas as pd
|
||||||
|
# AUC = pd.DataFrame(columns=mutations.keys())
|
||||||
|
# AUC_rel = pd.DataFrame(columns=mutations.keys())
|
||||||
|
# rheobase = pd.DataFrame(columns=mutations.keys())
|
||||||
|
# rheobase_fit = pd.DataFrame(columns=mutations.keys())
|
||||||
|
# rheobase_null_fit = pd.DataFrame(columns=mutations.keys())
|
||||||
|
|
||||||
|
# for mut in list(mutations.keys()):
|
||||||
|
# fname = os.path.join(folder, "{}.hdf5".format(mut))
|
||||||
|
# f = h5py.File(fname, "r")
|
||||||
|
# AUC['{}'.format(mut)] = f['analysis']['AUC']
|
||||||
|
# AUC_rel['{}'.format(mut)] = f['analysis']['AUC_rel']
|
||||||
|
# rheobase['{}'.format(mut)] = f['analysis']['rheobase']
|
||||||
|
# rheobase_fit['{}'.format(mut)] = f['analysis']['rheobase_fit']
|
||||||
|
# rheobase_null_fit['{}'.format(mut)] = f['analysis']['rheobase_null_fit']
|
||||||
|
|
||||||
|
# AUC.to_json(os.path.join(folder, 'AUC.json'))
|
||||||
|
# AUC_rel.to_json(os.path.join(folder, 'AUC_rel.json'))
|
||||||
|
# rheobase.to_json(os.path.join(folder, 'rheobase.json'))
|
||||||
|
# rheobase_fit.to_json(os.path.join(folder, 'rheobase_fit.json'))
|
||||||
|
# rheobase_null_fit.to_json(os.path.join(folder, 'rheobase_null_fit.json'))
|
||||||
|
|
||||||
120
Code/Sensitivity_Analysis/SA_FS.py
Normal file
120
Code/Sensitivity_Analysis/SA_FS.py
Normal file
@ -0,0 +1,120 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Sat Jun 5 18:14:31 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
|
||||||
|
Original Pospischil FS Sensitivity analysis (no Kv1.1)
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
from joblib import Parallel, delayed
|
||||||
|
import os
|
||||||
|
from Code.Functions.Utility import capacitance, stimulus_init, init_dict, NumpyEncoder
|
||||||
|
from Code.Functions.Pospischil_fxns import SA_Pospischil
|
||||||
|
|
||||||
|
|
||||||
|
# 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 # Threshold
|
||||||
|
|
||||||
|
# initialize arrays
|
||||||
|
current = np.zeros((num_current, stim_num))
|
||||||
|
gating = np.zeros((num_gating, stim_num))
|
||||||
|
|
||||||
|
# create 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([-34.33054521, -8.21450277, 1.42295686])
|
||||||
|
b_param['h'] = np.zeros(4)
|
||||||
|
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.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])
|
||||||
|
|
||||||
|
# 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"] = False
|
||||||
|
currents_included["Kv_mut"] = False
|
||||||
|
currents_included["L"] = False
|
||||||
|
currents_included["M"] = True
|
||||||
|
currents_included["Leak"] = True
|
||||||
|
|
||||||
|
# model conductances
|
||||||
|
g["Na"] = 58. * surf_area
|
||||||
|
g["Kd"] = 3.9 * surf_area
|
||||||
|
g["M"] = 0.075 * surf_area
|
||||||
|
g["Kv"] = 0.
|
||||||
|
g["Kv_mut"] = 0.
|
||||||
|
g["L"] = 0.
|
||||||
|
g["Leak"] = 0.038 * surf_area
|
||||||
|
|
||||||
|
|
||||||
|
# folder to save to
|
||||||
|
folder = './Sensitivity_Analysis/FS'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
#%% setup for one-factor-at-a-time SA
|
||||||
|
var = np.array(['m', 'h', 'n'])
|
||||||
|
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
|
||||||
|
np.array(['slope' for i in range(var.shape[0])]))
|
||||||
|
cur = np.array(['Na', 'Kd', 'Leak'])
|
||||||
|
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
|
||||||
|
var = np.append(var, var)
|
||||||
|
var = np.append(var, cur)
|
||||||
|
alt_types = np.c_[var, type_names]
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
lin_array = np.arange(-10, 11, 1)
|
||||||
|
log_array = np.logspace(-1,1,21, base=2)
|
||||||
|
|
||||||
|
# %% multiprocessing
|
||||||
|
prominence = 50
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
Parallel(n_jobs=8, verbose=9)(
|
||||||
|
delayed(SA_Pospischil)(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, folder, high, low,
|
||||||
|
number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
|
||||||
|
for alt_ind in range(alt_types.shape[0]) for alt in range(21))
|
||||||
|
|
||||||
153
Code/Sensitivity_Analysis/SA_FS_Kv.py
Normal file
153
Code/Sensitivity_Analysis/SA_FS_Kv.py
Normal file
@ -0,0 +1,153 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Sat Jun 5 18:14:31 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
from joblib import Parallel, delayed
|
||||||
|
import os
|
||||||
|
from Utility import capacitance, stimulus_init, init_dict, NumpyEncoder
|
||||||
|
from Code.Functions.Pospischil_fxns import SA_Pospischil
|
||||||
|
|
||||||
|
|
||||||
|
# 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['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
|
||||||
|
b_param['h'] = np.zeros(4)
|
||||||
|
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.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"] = 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"] = False
|
||||||
|
currents_included["L"] = False
|
||||||
|
currents_included["M"] = True
|
||||||
|
currents_included["Leak"] = True
|
||||||
|
|
||||||
|
# model conductances
|
||||||
|
Kv_ratio = 0.10
|
||||||
|
g["Na"] = 58. * surf_area
|
||||||
|
g["Kd"] = 3.9 * (1 - Kv_ratio) * surf_area
|
||||||
|
g["M"] = 0.075 * surf_area
|
||||||
|
g["Kv"] = 3.9 * Kv_ratio * surf_area
|
||||||
|
g["Kv_mut"] = 0.
|
||||||
|
g["L"] = 0. * surf_area
|
||||||
|
g["Leak"] = 0.038 * surf_area
|
||||||
|
|
||||||
|
# save folder
|
||||||
|
folder = './Sensitivity_Analysis/FS_Kv'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
#%% setup for one-factor-at-a-time SA
|
||||||
|
var = np.array(['m', 'h', 'n', 's', 'u'])
|
||||||
|
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
|
||||||
|
np.array(['slope' for i in range(var.shape[0])]))
|
||||||
|
cur = np.array(['Na', 'Kd', 'Kv', 'Leak'])
|
||||||
|
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
|
||||||
|
var = np.append(var, var)
|
||||||
|
var = np.append(var, cur)
|
||||||
|
alt_types = np.c_[var, type_names]
|
||||||
|
lin_array = np.arange(-10, 11, 1)
|
||||||
|
log_array = np.logspace(-1,1,20, base=2)
|
||||||
|
log_array = np.append(log_array,1)
|
||||||
|
|
||||||
|
# %% multiprocessing
|
||||||
|
prominence = 50
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
Parallel(n_jobs=8, verbose=9)(
|
||||||
|
delayed(SA_Pospischil)(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, folder, high,
|
||||||
|
low, number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
|
||||||
|
for alt_ind in range(alt_types.shape[0]) for alt in range(21))
|
||||||
|
|
||||||
|
|
||||||
|
# #%% Get pd Dataframes for certain variables
|
||||||
|
# import pandas as pd
|
||||||
|
# AUC = pd.DataFrame(columns=mutations.keys())
|
||||||
|
# AUC_rel = pd.DataFrame(columns=mutations.keys())
|
||||||
|
# rheobase = pd.DataFrame(columns=mutations.keys())
|
||||||
|
# rheobase_fit = pd.DataFrame(columns=mutations.keys())
|
||||||
|
# rheobase_null_fit = pd.DataFrame(columns=mutations.keys())
|
||||||
|
|
||||||
|
# for mut in list(mutations.keys()):
|
||||||
|
# fname = os.path.join(folder, "{}.hdf5".format(mut))
|
||||||
|
# f = h5py.File(fname, "r")
|
||||||
|
# AUC['{}'.format(mut)] = f['analysis']['AUC']
|
||||||
|
# AUC_rel['{}'.format(mut)] = f['analysis']['AUC_rel']
|
||||||
|
# rheobase['{}'.format(mut)] = f['analysis']['rheobase']
|
||||||
|
# rheobase_fit['{}'.format(mut)] = f['analysis']['rheobase_fit']
|
||||||
|
# rheobase_null_fit['{}'.format(mut)] = f['analysis']['rheobase_null_fit']
|
||||||
|
|
||||||
|
# AUC.to_json(os.path.join(folder, 'AUC.json'))
|
||||||
|
# AUC_rel.to_json(os.path.join(folder, 'AUC_rel.json'))
|
||||||
|
# rheobase.to_json(os.path.join(folder, 'rheobase.json'))
|
||||||
|
# rheobase_fit.to_json(os.path.join(folder, 'rheobase_fit.json'))
|
||||||
|
# rheobase_null_fit.to_json(os.path.join(folder, 'rheobase_null_fit.json'))
|
||||||
118
Code/Sensitivity_Analysis/SA_RS_inhib.py
Normal file
118
Code/Sensitivity_Analysis/SA_RS_inhib.py
Normal file
@ -0,0 +1,118 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Sat Jun 5 18:14:31 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
from joblib import Parallel, delayed
|
||||||
|
import os
|
||||||
|
from Code.Functions.Utility import capacitance, stimulus_init, init_dict, NumpyEncoder
|
||||||
|
from Code.Functions.Pospischil_fxns import SA_Pospischil
|
||||||
|
|
||||||
|
# model parameters
|
||||||
|
dt = 0.01
|
||||||
|
sec = 2
|
||||||
|
low = 0
|
||||||
|
high = 0.00035
|
||||||
|
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['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
|
||||||
|
b_param['h'] = np.zeros(4)
|
||||||
|
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.])
|
||||||
|
b_param['p'][:] = np.array([-45., -9.9998807337, 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])
|
||||||
|
|
||||||
|
|
||||||
|
# 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"] = False
|
||||||
|
currents_included["Kv_mut"] = False
|
||||||
|
currents_included["L"] = False
|
||||||
|
currents_included["M"] = True
|
||||||
|
currents_included["Leak"] = True
|
||||||
|
|
||||||
|
# model conductances
|
||||||
|
g["Na"] = 10. * surf_area
|
||||||
|
g["Kd"] = 2.1 * surf_area
|
||||||
|
g["M"] = 0.0098 * surf_area
|
||||||
|
g["Kv"] = 0.
|
||||||
|
g["Kv_mut"] = 0.
|
||||||
|
g["L"] = 0.
|
||||||
|
g["Leak"] = 0.0205 * surf_area
|
||||||
|
|
||||||
|
# save folder
|
||||||
|
folder = './Sensitivity_Analysis/RS_inhib'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
#%% setup for one-factor-at-a-time SA
|
||||||
|
var = np.array(['m', 'h', 'n'])
|
||||||
|
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
|
||||||
|
np.array(['slope' for i in range(var.shape[0])]))
|
||||||
|
cur = np.array(['Na', 'Kd', 'Leak'])
|
||||||
|
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
|
||||||
|
var = np.append(var, var)
|
||||||
|
var = np.append(var, cur)
|
||||||
|
alt_types = np.c_[var, type_names]
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
lin_array = np.arange(-10, 11, 1)
|
||||||
|
log_array = np.logspace(-1,1,21, base=2)
|
||||||
|
|
||||||
|
# %% multiprocessing
|
||||||
|
prominence = 50
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
Parallel(n_jobs=8, verbose=9)(
|
||||||
|
delayed(SA_Pospischil)(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, folder, high, low,
|
||||||
|
number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
|
||||||
|
for alt_ind in range(alt_types.shape[0]) for alt in range(21))
|
||||||
|
|
||||||
116
Code/Sensitivity_Analysis/SA_RS_inhib_Kv.py
Normal file
116
Code/Sensitivity_Analysis/SA_RS_inhib_Kv.py
Normal file
@ -0,0 +1,116 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Sat Jun 5 18:14:31 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
from joblib import Parallel, delayed
|
||||||
|
import os
|
||||||
|
from Code.Functions.Utility import capacitance, stimulus_init, init_dict, NumpyEncoder
|
||||||
|
from Code.Functions.Pospischil_fxns import SA_Pospischil
|
||||||
|
|
||||||
|
|
||||||
|
# model parameters
|
||||||
|
dt = 0.01
|
||||||
|
sec = 2
|
||||||
|
low = 0
|
||||||
|
high = 0.00035
|
||||||
|
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['m'][:] = np.array([-34.33054521, -8.21450277, 1.42295686])
|
||||||
|
b_param['h'] = np.zeros(4)
|
||||||
|
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.])
|
||||||
|
b_param['p'][:] = np.array([-45., -9.9998807337, 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])
|
||||||
|
|
||||||
|
# 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"] = False
|
||||||
|
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
|
||||||
|
g["Kv"] = 2.1 * Kv_ratio * surf_area
|
||||||
|
g["Kv_mut"] = 0.
|
||||||
|
g["L"] = 0. * surf_area
|
||||||
|
g["Leak"] = 0.0205 * surf_area
|
||||||
|
|
||||||
|
# save folder
|
||||||
|
folder = './Sensitivity_Analysis/RS_inhib_Kv'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
#%% setup for one-factor-at-a-time SA
|
||||||
|
var = np.array(['m', 'h', 'n', 's', 'u'])
|
||||||
|
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
|
||||||
|
np.array(['slope' for i in range(var.shape[0])]))
|
||||||
|
cur = np.array(['Na', 'Kd', 'Kv', 'Leak'])
|
||||||
|
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
|
||||||
|
var = np.append(var, var)
|
||||||
|
var = np.append(var, cur)
|
||||||
|
alt_types = np.c_[var, type_names]
|
||||||
|
lin_array = np.arange(-10, 11, 1)
|
||||||
|
log_array = np.logspace(-1,1,21, base=2)
|
||||||
|
|
||||||
|
# %% multiprocessing
|
||||||
|
prominence = 50
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
Parallel(n_jobs=8, verbose=9)(
|
||||||
|
delayed(SA_Pospischil)(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, folder, high,
|
||||||
|
low, number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
|
||||||
|
for alt_ind in range(alt_types.shape[0]) for alt in range(21))
|
||||||
|
|
||||||
|
|
||||||
100
Code/Sensitivity_Analysis/SA_RS_pyramidal.py
Normal file
100
Code/Sensitivity_Analysis/SA_RS_pyramidal.py
Normal file
@ -0,0 +1,100 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Fri Jun 4 08:24:33 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from joblib import Parallel, delayed
|
||||||
|
import os
|
||||||
|
from Code.Functions.Utility import capacitance, stimulus_init, init_dict, NumpyEncoder
|
||||||
|
from Code.Functions.Pospischil_fxns import SA_Pospischil
|
||||||
|
|
||||||
|
|
||||||
|
# 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))
|
||||||
|
|
||||||
|
# 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])
|
||||||
|
|
||||||
|
|
||||||
|
# reversal potential
|
||||||
|
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"] = False
|
||||||
|
currents_included["Kv_mut"] = False
|
||||||
|
currents_included["L"] = False
|
||||||
|
currents_included["M"] = True
|
||||||
|
currents_included["Leak"] = True
|
||||||
|
|
||||||
|
# model conductances
|
||||||
|
g["Na"] = 56. * surf_area
|
||||||
|
g["Kd"] = 6. * surf_area
|
||||||
|
g["M"] = 0.075 * surf_area
|
||||||
|
g["Kv"] = 0.
|
||||||
|
g["Kv_mut"] = 0.
|
||||||
|
g["L"] = 0.
|
||||||
|
g["Leak"] = 0.0205 * surf_area
|
||||||
|
|
||||||
|
folder = './Sensitivity_Analysis/RS_pyramidal'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
#%% setup for one-factor-at-a-time SA
|
||||||
|
var = np.array(['m', 'h', 'n'])
|
||||||
|
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
|
||||||
|
np.array(['slope' for i in range(var.shape[0])]))
|
||||||
|
cur = np.array(['Na', 'Kd', 'Leak'])
|
||||||
|
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
|
||||||
|
var = np.append(var, var)
|
||||||
|
var = np.append(var, cur)
|
||||||
|
alt_types = np.c_[var, type_names]
|
||||||
|
lin_array = np.arange(-10, 11, 1)
|
||||||
|
log_array = np.logspace(-1,1,21, base=2)
|
||||||
|
|
||||||
|
# %% multiprocessing
|
||||||
|
prominence = 50
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
Parallel(n_jobs=8, verbose=9)(
|
||||||
|
delayed(SA_Pospischil)(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, folder, high, low,
|
||||||
|
number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
|
||||||
|
for alt_ind in range(alt_types.shape[0]) for alt in range(21))
|
||||||
|
|
||||||
115
Code/Sensitivity_Analysis/SA_RS_pyramidal_Kv.py
Normal file
115
Code/Sensitivity_Analysis/SA_RS_pyramidal_Kv.py
Normal file
@ -0,0 +1,115 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Fri Jun 4 08:24:33 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
from joblib import Parallel, delayed
|
||||||
|
import os
|
||||||
|
from Code.Functions.Utility import capacitance, stimulus_init, init_dict, NumpyEncoder
|
||||||
|
from Code.Functions.Pospischil_fxns import SA_Pospischil
|
||||||
|
|
||||||
|
|
||||||
|
# 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))
|
||||||
|
|
||||||
|
# create 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])
|
||||||
|
|
||||||
|
|
||||||
|
# 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"] = False
|
||||||
|
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
|
||||||
|
g["Kv"] = 6. * Kv_ratio * surf_area
|
||||||
|
g["Kv_mut"] = 0.
|
||||||
|
g["L"] = 0. * surf_area
|
||||||
|
g["Leak"] = 0.0205 * surf_area
|
||||||
|
|
||||||
|
# save folder
|
||||||
|
folder = './Sensitivity_Analysis/RS_pyramidal_Kv'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
#%% setup for one-factor-at-a-time SA
|
||||||
|
var = np.array(['m', 'h', 'n', 's', 'u'])
|
||||||
|
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
|
||||||
|
np.array(['slope' for i in range(var.shape[0])]))
|
||||||
|
cur = np.array(['Na', 'Kd', 'Kv', 'Leak'])
|
||||||
|
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
|
||||||
|
var = np.append(var, var)
|
||||||
|
var = np.append(var, cur)
|
||||||
|
alt_types = np.c_[var, type_names]
|
||||||
|
lin_array = np.arange(-10, 11, 1)
|
||||||
|
log_array = np.logspace(-1,1,21, base=2)
|
||||||
|
|
||||||
|
# %% multiprocessing
|
||||||
|
prominence = 50
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
Parallel(n_jobs=8, verbose=9)(
|
||||||
|
delayed(SA_Pospischil)(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, folder, high,
|
||||||
|
low, number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt)
|
||||||
|
for alt_ind in range(alt_types.shape[0]) for alt in range(21))
|
||||||
|
|
||||||
|
|
||||||
116
Code/Sensitivity_Analysis/SA_STN.py
Normal file
116
Code/Sensitivity_Analysis/SA_STN.py
Normal file
@ -0,0 +1,116 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Sat Jun 5 19:44:04 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
|
||||||
|
original STN model SA
|
||||||
|
"""
|
||||||
|
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
from joblib import Parallel, delayed
|
||||||
|
import os
|
||||||
|
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
|
||||||
|
from Code.Functions.STN_fxns import SA_STN
|
||||||
|
|
||||||
|
# model parameters
|
||||||
|
dt = 0.01
|
||||||
|
sec = 2
|
||||||
|
low = 0
|
||||||
|
high = 0.001
|
||||||
|
number_steps = 200
|
||||||
|
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']))
|
||||||
|
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', 'a', 'b', 'a_mut', 'b_mut', 'c', 'd1', 'd2', 'p', 'q', 'r','Ca_conc','E_Ca']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['Na', 'Kd', 'A', 'L', 'A_mut', 'T', 'Leak', 'Ca_K']):
|
||||||
|
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"] =False
|
||||||
|
currents_included["Leak"] = True
|
||||||
|
|
||||||
|
# model conductances
|
||||||
|
g["Na"] = 49. * surf_area
|
||||||
|
g["Kd"] = 57. * surf_area
|
||||||
|
g["A"] = 5. * surf_area
|
||||||
|
g["A_mut"] = 0.
|
||||||
|
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 = '../Sensitivity_Analysis/STN'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
#%% setup for one-factor-at-a-time SA
|
||||||
|
var = np.array(['m', 'h', 'n', 'a', 'b'])
|
||||||
|
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
|
||||||
|
np.array(['slope' for i in range(var.shape[0])]))
|
||||||
|
cur = np.array(['Na', 'Kd', 'A', 'Leak'])
|
||||||
|
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
|
||||||
|
var = np.append(var, var)
|
||||||
|
var = np.append(var, cur)
|
||||||
|
alt_types = np.c_[var, type_names]
|
||||||
|
lin_array = np.arange(-10, 11, 1)
|
||||||
|
log_array = np.logspace(-1,1,21, base=2)
|
||||||
|
|
||||||
|
# %% run SA with multiprocessing
|
||||||
|
prominence = 50
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
Parallel(n_jobs=8, verbose=9)(
|
||||||
|
delayed(SA_STN)(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, folder, high, low, number_steps, initial_period, sec, vol,
|
||||||
|
lin_array, log_array, alt_types, alt_ind, alt)
|
||||||
|
for alt_ind in range(alt_types.shape[0]) for alt in range(21))
|
||||||
129
Code/Sensitivity_Analysis/SA_STN_Delta_Kv.py
Normal file
129
Code/Sensitivity_Analysis/SA_STN_Delta_Kv.py
Normal file
@ -0,0 +1,129 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Sat Jun 5 19:44:04 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
from joblib import Parallel, delayed
|
||||||
|
import os
|
||||||
|
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
|
||||||
|
from Code.Functions.STN_fxns_Kv import SA_Kv_STN
|
||||||
|
|
||||||
|
|
||||||
|
# model parameters
|
||||||
|
dt = 0.01
|
||||||
|
sec = 2
|
||||||
|
low = 0
|
||||||
|
high = 0.001
|
||||||
|
number_steps = 200
|
||||||
|
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', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut', 'Ca_conc','E_Ca']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['Na', 'Kd', 'A', 'L', 'Kv', 'Kv_mut', 'T', 'Leak', 'Ca_K']):
|
||||||
|
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])
|
||||||
|
|
||||||
|
|
||||||
|
# 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"] = False
|
||||||
|
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 = 0.01
|
||||||
|
g["Na"] = 49. * surf_area
|
||||||
|
g["Kd"] = 57. * surf_area
|
||||||
|
g["Kv"] = 0.5 * surf_area
|
||||||
|
g["Kv_mut"] = 0
|
||||||
|
g["A"] = 0. * 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 = './Sensitivity_Analysis/STN_Kv_only'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
#%% setup for one-factor-at-a-time SA
|
||||||
|
var = np.array(['m', 'h', 'n', 's','u'])
|
||||||
|
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
|
||||||
|
np.array(['slope' for i in range(var.shape[0])]))
|
||||||
|
cur = np.array(['Na', 'Kd', 'Kv', 'Leak'])
|
||||||
|
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
|
||||||
|
var = np.append(var, var)
|
||||||
|
var = np.append(var, cur)
|
||||||
|
alt_types = np.c_[var, type_names]
|
||||||
|
lin_array = np.arange(-10, 11, 1)
|
||||||
|
log_array = np.logspace(-1,1,21, base=2)
|
||||||
|
|
||||||
|
# %% run SA with multiprocessing
|
||||||
|
prominence = 50
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
Parallel(n_jobs=8, verbose=9)(
|
||||||
|
delayed(SA_Kv_STN)(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, folder, high, low, number_steps, initial_period,
|
||||||
|
sec, vol, lin_array, log_array, alt_types, alt_ind, alt)
|
||||||
|
for alt_ind in range(alt_types.shape[0]) for alt in range(21))
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
126
Code/Sensitivity_Analysis/SA_STN_Kv.py
Normal file
126
Code/Sensitivity_Analysis/SA_STN_Kv.py
Normal file
@ -0,0 +1,126 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Sat Jun 5 19:44:04 2021
|
||||||
|
|
||||||
|
@author: nils
|
||||||
|
"""
|
||||||
|
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numba import types
|
||||||
|
from numba.typed import Dict
|
||||||
|
from joblib import Parallel, delayed
|
||||||
|
import os
|
||||||
|
from Code.Functions.Utility import capacitance, stimulus_init, init_dict
|
||||||
|
from Code.Functions.STN_fxns_Kv import SA_Kv_STN
|
||||||
|
|
||||||
|
# model parameters
|
||||||
|
dt = 0.01
|
||||||
|
sec = 2
|
||||||
|
low = 0
|
||||||
|
high = 0.001
|
||||||
|
number_steps = 200
|
||||||
|
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', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut', 'Ca_conc','E_Ca']):
|
||||||
|
ind_dict[var] = i
|
||||||
|
i += 1
|
||||||
|
i = 0
|
||||||
|
for var in np.array(['Na', 'Kd', 'A', 'L', 'Kv', 'Kv_mut', 'T', 'Leak', 'Ca_K']):
|
||||||
|
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])
|
||||||
|
|
||||||
|
|
||||||
|
# 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"] = False
|
||||||
|
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 = 0.01
|
||||||
|
g["Na"] = 49. * surf_area
|
||||||
|
g["Kd"] = 57. * (1-Kv_ratio)* surf_area
|
||||||
|
g["Kv"] = 57. * Kv_ratio * surf_area
|
||||||
|
g["Kv_mut"] = 0
|
||||||
|
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 = '../Sensitivity_Analysis/STN_Kv'
|
||||||
|
if not os.path.isdir(folder):
|
||||||
|
os.makedirs(folder)
|
||||||
|
|
||||||
|
#%% setup for one-factor-at-a-time SA
|
||||||
|
var = np.array(['m', 'h', 'n', 'a', 'b', 's','u'])
|
||||||
|
type_names = np.append(np.array(['shift' for i in range(var.shape[0])]),
|
||||||
|
np.array(['slope' for i in range(var.shape[0])]))
|
||||||
|
cur = np.array(['Na', 'Kd', 'A','Kv', 'Leak'])
|
||||||
|
type_names = np.append(type_names, np.array(['g' for i in range(cur.shape[0])]))
|
||||||
|
var = np.append(var, var)
|
||||||
|
var = np.append(var, cur)
|
||||||
|
alt_types = np.c_[var, type_names]
|
||||||
|
lin_array = np.arange(-10, 11, 1)
|
||||||
|
log_array = np.logspace(-1,1,21, base=2)
|
||||||
|
|
||||||
|
# %% run SA with multiprocessing
|
||||||
|
prominence = 50
|
||||||
|
desired_AUC_width = high/5
|
||||||
|
|
||||||
|
Parallel(n_jobs=8, verbose=9)(
|
||||||
|
delayed(SA_Kv_STN)(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, folder, high, low, number_steps, initial_period, sec,
|
||||||
|
vol, lin_array, log_array, alt_types, alt_ind, alt)
|
||||||
|
for alt_ind in range(alt_types.shape[0]) for alt in range(21))
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
1
Code/mutations_effects_dict.json
Normal file
1
Code/mutations_effects_dict.json
Normal file
@ -0,0 +1 @@
|
|||||||
|
{"I407M": {"activation_Vhalf_diff": 44.2, "activation_k_ratio": 1.0, "g_ratio": 0.43523316100000004}, "G311D": {"activation_Vhalf_diff": 0.0, "activation_k_ratio": 1.0, "g_ratio": 0.333333333}, "V408A 1": {"activation_Vhalf_diff": 1.0, "activation_k_ratio": 1.0, "g_ratio": 0.658536585}, "V404I": {"activation_Vhalf_diff": 11.3, "activation_k_ratio": 1.25, "g_ratio": 1.0}, "V408A 3": {"activation_Vhalf_diff": 0.9, "activation_k_ratio": 1.228915663, "g_ratio": 1.0}, "F249C": {"activation_Vhalf_diff": 0.0, "activation_k_ratio": 1.0, "g_ratio": 0.001}, "F414S": {"activation_Vhalf_diff": 14.9, "activation_k_ratio": 1.0, "g_ratio": 0.079493088}, "F184C 2": {"activation_Vhalf_diff": 25.9, "activation_k_ratio": 0.87654321, "g_ratio": 0.151}, "E325D 1": {"activation_Vhalf_diff": 60.4, "activation_k_ratio": 2.160493827, "g_ratio": 0.038}, "G311S": {"activation_Vhalf_diff": 31.7, "activation_k_ratio": 1.654320988, "g_ratio": 0.22899999999999998}, "V408A 2": {"activation_Vhalf_diff": 0.4, "activation_k_ratio": 0.9259259259999999, "g_ratio": 0.498}, "E325D 2": {"activation_Vhalf_diff": 21.8, "activation_k_ratio": 1.518072289, "g_ratio": 1.0}, "C185W 1": {"activation_Vhalf_diff": 4.4, "activation_k_ratio": 1.0, "g_ratio": 0.298791019}, "A242P": {"activation_Vhalf_diff": -4.8, "activation_k_ratio": 0.947368421, "g_ratio": 0.1}, "T226R": {"activation_Vhalf_diff": 0.0, "activation_k_ratio": 1.0, "g_ratio": 1.0}, "R307C": {"activation_Vhalf_diff": 11.4, "activation_k_ratio": 1.0, "g_ratio": 0.254608295}, "T226M": {"activation_Vhalf_diff": 14.8, "activation_k_ratio": 1.0, "g_ratio": 0.039}, "V408L": {"activation_Vhalf_diff": 0.0, "activation_k_ratio": 1.0, "g_ratio": 1.0}, "R167M": {"activation_Vhalf_diff": 10.0, "activation_k_ratio": 1.0, "g_ratio": 0.283246978}, "wt": {"activation_Vhalf_diff": 0.0, "activation_k_ratio": 1.0, "g_ratio": 1.0}, "F249I": {"activation_Vhalf_diff": -0.6, "activation_k_ratio": 0.7654320990000001, "g_ratio": 0.01}, "N255D": {"activation_Vhalf_diff": 31.0, "activation_k_ratio": 1.0, "g_ratio": 0.180904523}, "E283K": {"activation_Vhalf_diff": 9.3, "activation_k_ratio": 1.054054054, "g_ratio": 0.666666667}, "F414C": {"activation_Vhalf_diff": 2.2, "activation_k_ratio": 1.216666667, "g_ratio": 1.0}, "R324T": {"activation_Vhalf_diff": 0.0, "activation_k_ratio": 1.0, "g_ratio": 1.0}, "R239S": {"activation_Vhalf_diff": 0.0, "activation_k_ratio": 1.0, "g_ratio": 1.0}, "F303V": {"activation_Vhalf_diff": 36.0, "activation_k_ratio": 1.333333333, "g_ratio": 0.035714286}, "C185W 2": {"activation_Vhalf_diff": 0.8, "activation_k_ratio": 0.739130435, "g_ratio": 1.0}, "F184C 1": {"activation_Vhalf_diff": 28.0, "activation_k_ratio": 1.0, "g_ratio": 0.39024390200000003}, "I262M": {"activation_Vhalf_diff": 0.0, "activation_k_ratio": 1.0, "g_ratio": 0.075268817}, "R417Stop": {"activation_Vhalf_diff": 8.5, "activation_k_ratio": 1.684210526, "g_ratio": 0.02}, "V174F": {"activation_Vhalf_diff": 35.1, "activation_k_ratio": 0.75308642, "g_ratio": 0.076}, "I262T": {"activation_Vhalf_diff": 33.6, "activation_k_ratio": 2.136363636, "g_ratio": 0.3}, "I177N": {"activation_Vhalf_diff": -2.8, "activation_k_ratio": 1.226666667, "g_ratio": 1.0}, "T226A": {"activation_Vhalf_diff": 14.3, "activation_k_ratio": 0.740740741, "g_ratio": 0.024}}
|
||||||
@ -112,17 +112,17 @@ Nils A. Koch\textsuperscript{1,2}, Lukas Sonnenberg\textsuperscript{1,2}, Ulrike
|
|||||||
|
|
||||||
\subsection{Author Contributions - Each author must be identified with at least one of the following: Designed research, Performed research, Contributed unpublished reagents/ analytic tools, Analyzed data, Wrote the paper.}
|
\subsection{Author Contributions - Each author must be identified with at least one of the following: Designed research, Performed research, Contributed unpublished reagents/ analytic tools, Analyzed data, Wrote the paper.}
|
||||||
\notenk{Adjust as you deem appropriate}\\
|
\notenk{Adjust as you deem appropriate}\\
|
||||||
NK, LS, JB Designed Research;
|
NK, LS, UBSH, SL, JB Designed Research;
|
||||||
NK Performed research;
|
NK Performed research;
|
||||||
NK, LS Analyzed data;
|
NK, LS Analyzed data;
|
||||||
NK, LS, JB Wrote the paper
|
NK, LS,UBSH, SL, JB Wrote the paper
|
||||||
|
|
||||||
\subsection{Correspondence should be addressed to (include email address)}
|
\subsection{Correspondence should be addressed to (include email address)}
|
||||||
\ \notenk{Nils oder Jan?}
|
\ \notenk{Nils oder Jan?}
|
||||||
\subsection{Number of Figures}
|
\subsection{Number of Figures}
|
||||||
5
|
5
|
||||||
\subsection{Number of Tables}
|
\subsection{Number of Tables}
|
||||||
2
|
3
|
||||||
\subsection{Number of Multimedia}
|
\subsection{Number of Multimedia}
|
||||||
0
|
0
|
||||||
\subsection{Number of words for Abstract}
|
\subsection{Number of words for Abstract}
|
||||||
@ -389,9 +389,9 @@ The effects of altered ion channel properties on firing is generally influenced
|
|||||||
\bibliographystyle{eneuro} %dcu
|
\bibliographystyle{eneuro} %dcu
|
||||||
|
|
||||||
|
|
||||||
\newpage
|
%\newpage
|
||||||
|
|
||||||
\section*{Figure/Table/Extended Data Legends}
|
\section*{Figures}
|
||||||
% \textit{Figures must be numbered independently of tables and multimedia and cited in the manuscript. Do not duplicate data by presenting it both in the text and in a figure.
|
% \textit{Figures must be numbered independently of tables and multimedia and cited in the manuscript. Do not duplicate data by presenting it both in the text and in a figure.
|
||||||
% A title should be part of the legend and not lettered onto the figure. A legend must be included in the manuscript document after the reference list, and should include enough detail to be intelligible without reference to the text. Specific individuals’ contributions to data acquisition, analysis, or other responsibility resulting in a figure may be included at the end of each legend. Please use the heading “Figure Contributions” and state each contribution with the author’s full name.
|
% A title should be part of the legend and not lettered onto the figure. A legend must be included in the manuscript document after the reference list, and should include enough detail to be intelligible without reference to the text. Specific individuals’ contributions to data acquisition, analysis, or other responsibility resulting in a figure may be included at the end of each legend. Please use the heading “Figure Contributions” and state each contribution with the author’s full name.
|
||||||
% Figure Contributions: John Smith performed the experiments; Jane Jones analyzed the data.
|
% Figure Contributions: John Smith performed the experiments; Jane Jones analyzed the data.
|
||||||
@ -401,8 +401,53 @@ The effects of altered ion channel properties on firing is generally influenced
|
|||||||
% Remove top and right borderlines that to not contain measuring metrics from all graph/histogram figure panels (i.e., do not box the panels in). Do not include any two-bar graphs/histograms; instead state those values in the text.
|
% Remove top and right borderlines that to not contain measuring metrics from all graph/histogram figure panels (i.e., do not box the panels in). Do not include any two-bar graphs/histograms; instead state those values in the text.
|
||||||
% All illustrations documenting results must include a bar to indicate the scale. All labels used in a figure should be explained in the legend. The migration of protein molecular weight size markers or nucleic acid size markers must be indicated and labeled appropriately (e.g., “kD”, “nt”, “bp”) on all figure panels showing gel electrophoresis.}
|
% All illustrations documenting results must include a bar to indicate the scale. All labels used in a figure should be explained in the legend. The migration of protein molecular weight size markers or nucleic acid size markers must be indicated and labeled appropriately (e.g., “kD”, “nt”, “bp”) on all figure panels showing gel electrophoresis.}
|
||||||
|
|
||||||
add from manuscript text before submission
|
\setcounter{figure}{0}
|
||||||
%\setcounter{figure}{0}
|
|
||||||
|
\begin{figure}[tp]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=\linewidth]{Figures/diversity_in_firing.pdf}
|
||||||
|
\linespread{1.}\selectfont
|
||||||
|
\caption[]{Diversity in Neuronal Model Firing. Spike trains (left), frequency-current (fI) curves (right) for Cb stellate (A), RS inhibitory (B), FS (C), RS pyramidal (D), RS inhibitory +\Kv (E), Cb stellate +\Kv (F), FS +\Kv (G), RS pyramidal +\Kv (H), STN +\Kv (I), Cb stellate \(\Delta\)\Kv (J), STN \(\Delta\)\Kv (K), and STN (L) neuron models. Black marker on the fI curves indicate the current step at which the spike train occurs. The green marker indicates the current at which firing begins in response to an ascending current ramp, whereas the red marker indicates the current at which firing ceases in response to a descending current ramp (see \Cref{fig:ramp_firing}).}
|
||||||
|
\label{fig:diversity_in_firing}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\begin{figure}[tp]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=0.5\linewidth]{Figures/firing_characterization_arrows.pdf}
|
||||||
|
\linespread{1.}\selectfont
|
||||||
|
\caption[]{Characterization of firing with AUC and rheobase. (A) The area under the curve (AUC) of the repetitive firing frequency-current (fI) curve. (B)
|
||||||
|
Changes in firing as characterized by \(\Delta\)AUC and \(\Delta\)rheobase occupy 4 quadrants separated by no changes in AUC and rheobase. Representative schematic fI curves in red with respect to a reference fI curve (blue) depict the general changes associated with each quadrant.}
|
||||||
|
\label{fig:firing_characterization}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\begin{figure}[tp]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=\linewidth]{Figures/AUC_correlation.pdf}
|
||||||
|
\linespread{1.}\selectfont
|
||||||
|
\caption[]{Effects of altered channel kinetics on AUC in various neuron models. The fI curves corresponding to shifts in FS \(+\)\Kv model delayed rectifier K half activation \(V_{1/2}\) (A), changes \Kv activation slope factor \(k\) in the FS \(+\)\Kv model (B), and changes in maximal conductance of delayed rectifier K current in the STN \(+\)\Kv model (C) are shown. The \ndAUC of fI curves is plotted against delayed rectifier K half activation potential (\(\Delta V_{1/2}\); B), \Kv activation slope factor \(k\) (k/\(\textrm{k}_{WT}\); E) and maximal conductance \(g\) of the delayed rectifier K current (g/\(\textrm{g}_{WT}\); H) for all models (thin lines) with relationships from the fI curve examples (A, D, G respectively) highlighted by thick lines with colors corresponding to the box highlighting each set of fI curves. The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in half maximal potential \(V_{1/2}\) and \ndAUC (C), slope factor k and \ndAUC (F) as well as maximal current conductances and \ndAUC (I) for each model and current property is computed. The relationships between \(\Delta V_{1/2}\), k/\(\textrm{k}_{WT}\), and g/\(\textrm{g}_{WT}\) and \ndAUC for the Kendall rank correlations highlighted in the black boxes are depicted in (B), (E) and (H) respectively.}
|
||||||
|
\label{fig:AUC_correlation}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
|
\begin{figure}[tp]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=\linewidth]{Figures/rheobase_correlation.pdf}
|
||||||
|
\linespread{1.}\selectfont
|
||||||
|
\caption[]{Effects of altered channel kinetics on rheobase. The fI curves corresponding to shifts in FS \(+\)\Kv model \Kv activation \(V_{1/2}\) (A), changes \Kv inactivation slope factor \(k\) in the Cb stellate \(+\)\Kv model (B), and changes in maximal conductance of the leak current in the Cb stellate model (C) are shown. The \drheo of fI curves is plotted against \Kv half activation potential (\(\Delta V_{1/2}\); B), \Kv inactivation slope factor \(k\) (k/\(\textrm{k}_{WT}\); E) and maximal conductance \(g\) of the leak current (g/\(\textrm{g}_{WT}\); H) for all models (thin lines) with relationships from the fI curve examples (A, D, G respectively) highlighted by thick lines with colors corresponding to the box highlighting each set of fI curves. The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in half maximal potential \(V_{1/2}\) and \drheo (C), slope factor k and \drheo (F) as well as maximal current conductances and \drheo (I) for each model and current property is computed. The relationships between \(\Delta V_{1/2}\), k/\(\textrm{k}_{WT}\), and g/\(\textrm{g}_{WT}\) and \drheo for the Kendall rank correlations highlighted in the black boxes are depicted in (B), (E) and (H) respectively..}
|
||||||
|
\label{fig:rheobase_correlation}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
\begin{figure}[tp]
|
||||||
|
\centering
|
||||||
|
\includegraphics[width=\linewidth]{Figures/simulation_model_comparison.pdf}
|
||||||
|
\linespread{1.}\selectfont
|
||||||
|
\caption[]{Effects of episodic ataxia type~1 associated \Kv mutations on firing. Effects of \Kv mutations on AUC (percent change in normalized \(\Delta\)AUC) and rheobase (\(\Delta\)Rheobase) compared to wild type for RS pyramidal +\Kv (A), RS inhibitory +\Kv (B), FS +\Kv (C), Cb stellate (D), Cb stellate +\Kv (E), Cb stellate \(\Delta\)\Kv (F), STN (G), STN +\Kv (H) and STN \(\Delta\)\Kv (I) models. V174F, F414C, E283K, and V404I mutations are highlighted in color for each model. Pairwise Kendall rank correlation coefficients (Kendall \(\tau\)) between the effects of \Kv mutations on rheobase and on AUC are shown in J and K respectively. Marker shape is indicative of model/firing type, and grey dashed lines denote the quadrants of firing characterization (see \Cref{fig:firing_characterization}).}
|
||||||
|
\label{fig:simulation_model_comparision}
|
||||||
|
\end{figure}
|
||||||
|
|
||||||
|
|
||||||
%\captionof{figure}{Characterization of firing with AUC and rheobase. (A) The area under the curve (AUC) of the repetitive firing frequency-current (fI) curve. (B) Changes in firing as characterized by \(\Delta\)AUC and \(\Delta\)rheobase occupy 4 quadrants separated by no changes in AUC and rheobase. Representative schematic fI curves in blue with respect to a reference fI curve (black) depict the general changes associated with each quadrant.}
|
%\captionof{figure}{Characterization of firing with AUC and rheobase. (A) The area under the curve (AUC) of the repetitive firing frequency-current (fI) curve. (B) Changes in firing as characterized by \(\Delta\)AUC and \(\Delta\)rheobase occupy 4 quadrants separated by no changes in AUC and rheobase. Representative schematic fI curves in blue with respect to a reference fI curve (black) depict the general changes associated with each quadrant.}
|
||||||
%
|
%
|
||||||
%\captionof{figure}{Diversity in Neuronal Model Firing. Spike trains (left), frequency-current (fI) curves (right) for Cb stellate (A), RS inhibitory (B), FS (C), RS pyramidal (D), RS inhibitory +\Kv (E), Cb stellate +\Kv (F), FS +\Kv (G), RS pyramidal +\Kv (H), STN +\Kv (I), Cb stellate \(\Delta\)\Kv (J), STN \(\Delta\)\Kv (K), and STN (L) neuron models. Black marker on the fI curves indicate the current step at which the spike train occurs. The green marker indicates the current at which firing begins in response to an ascending current ramp, whereas the red marker indicates the current at which firing ceases in response to a descending current ramp.}
|
%\captionof{figure}{Diversity in Neuronal Model Firing. Spike trains (left), frequency-current (fI) curves (right) for Cb stellate (A), RS inhibitory (B), FS (C), RS pyramidal (D), RS inhibitory +\Kv (E), Cb stellate +\Kv (F), FS +\Kv (G), RS pyramidal +\Kv (H), STN +\Kv (I), Cb stellate \(\Delta\)\Kv (J), STN \(\Delta\)\Kv (K), and STN (L) neuron models. Black marker on the fI curves indicate the current step at which the spike train occurs. The green marker indicates the current at which firing begins in response to an ascending current ramp, whereas the red marker indicates the current at which firing ceases in response to a descending current ramp.}
|
||||||
@ -414,17 +459,23 @@ add from manuscript text before submission
|
|||||||
%\captionof{figure}{Effects of episodic ataxia type~1 associated \Kv mutations on firing. Effects of \Kv mutations on AUC (\(AUC_{contrast}\)) and rheobase (\(\Delta\)rheobase) compared to wild type for RS pyramidal +\Kv (A), RS inhibitory +\Kv (B), FS +\Kv (C), Cb stellate (D), Cb stellate +\Kv (E), Cb stellate \(\Delta\)\Kv (F), STN (G), STN +\Kv (H) and STN \(\Delta\)\Kv (I) models V174F, F414C, E283K, and V404I mutations are highlighted in color for each model. Pairwise Kendall rank correlation coefficients (Kendall \(\tau\)) between the effects of \Kv mutations on rheobase and on AUC are shown in J and K respectively.}
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%\captionof{figure}{Effects of episodic ataxia type~1 associated \Kv mutations on firing. Effects of \Kv mutations on AUC (\(AUC_{contrast}\)) and rheobase (\(\Delta\)rheobase) compared to wild type for RS pyramidal +\Kv (A), RS inhibitory +\Kv (B), FS +\Kv (C), Cb stellate (D), Cb stellate +\Kv (E), Cb stellate \(\Delta\)\Kv (F), STN (G), STN +\Kv (H) and STN \(\Delta\)\Kv (I) models V174F, F414C, E283K, and V404I mutations are highlighted in color for each model. Pairwise Kendall rank correlation coefficients (Kendall \(\tau\)) between the effects of \Kv mutations on rheobase and on AUC are shown in J and K respectively.}
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%\newpage
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%\newpage
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\subsection*{Tables}
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\section*{Tables}
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% \textit{All tables must be numbered independently of figures, multimedia, and 3D models and cited in the manuscript. Do not duplicate data by presenting it both in the text and in a table.
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% \textit{All tables must be numbered independently of figures, multimedia, and 3D models and cited in the manuscript. Do not duplicate data by presenting it both in the text and in a table.
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% Each table should include a title and legend; legends should be included in the manuscript file after the reference list. Legends should include sufficient detail to be intelligible without reference to the text and define all symbols and include essential information.
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% Each table should include a title and legend; legends should be included in the manuscript file after the reference list. Legends should include sufficient detail to be intelligible without reference to the text and define all symbols and include essential information.
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% Each table should be double-spaced. Multiple-part tables (A and B sections with separate subtitles) should be avoided, especially when there are two [different] sets [or types] of column headings.
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% Each table should be double-spaced. Multiple-part tables (A and B sections with separate subtitles) should be avoided, especially when there are two [different] sets [or types] of column headings.
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% Do not use color or shading, bold or italic fonts, or lines to highlight information. Indention of text and sometimes, additional space between lines is preferred. Tables with color or shading in the table body will need to be processed as a figure.}
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% Do not use color or shading, bold or italic fonts, or lines to highlight information. Indention of text and sometimes, additional space between lines is preferred. Tables with color or shading in the table body will need to be processed as a figure.}
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\setcounter{table}{0}
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\setcounter{table}{0}
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%\input{g_table}
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%add from manuscript text before submission
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add from manuscript text before submission
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\input{g_table}
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\input{gating_table}
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\input{statistical_table}
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\FloatBarrier
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%\newpage
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%\newpage
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\subsection*{Extended Data}
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\section*{Extended Data}
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\beginsupplement
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\beginsupplement
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\begin{figure}[tp]%described
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\begin{figure}[tp]%described
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\centering
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\centering
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Loading…
Reference in New Issue
Block a user