Location: BG_TCC @ bc202e51bde6 / parameter_finder / kinetic_parameters_TCC.py

Author:
Shelley Fong <sfon036@UoA.auckland.ac.nz>
Date:
2022-07-06 16:26:28+12:00
Desc:
Updating to LRd 34.4 pL cell parameters
Permanent Source URI:
https://models.fieldml.org/workspace/831/rawfile/bc202e51bde6be8ddf88d35d908ef1465dcf067d/parameter_finder/kinetic_parameters_TCC.py

# fast Na module

# Return kinetic parameters, constraints, and vector of volumes in each
# compartment (pL) (1 if gating variable, or in element corresponding to
# kappa)

# Translated from Pan 2018 cardiac AP 

import numpy as np

def kinetic_parameters(M, include_type2_reactions, dims, V):
    # Set the kinetic rate constants

    num_cols = dims['num_cols']
    num_rows = dims['num_rows']
    # constants are stored in V
    F = V['F']
    R = V['R']
    T = V['T']
    N_A = V['N_A']

    G_GHK = 4.606372687363260e-07 #1.289597323559330e-06     # Unit mA/mM
    P_TCC = G_GHK/F * 1e12 # Unit pL/s . G_GHK [=] Amp/(mol/s)
    x_TCC = 228000/N_A*1e15 # unit fmol. From Droogmans 1989
    x_TCC = V['numChannels']/N_A*1e15 # unit fmol

    # load gate transition parameters
    params_d = [2.66722621712886,	0.876568910405296,	0.483332201416662,	-0.872117216889333]
    params_f = [0.00284309660183583,	-0.576510778245838,	0.830414906761407,	1.73582739545622]

    z_df = params_d[1]
    z_dr = params_d[3]
    z_ff = params_f[1]
    z_fr = params_f[3]
    zf = [z_df, z_ff]
    zr = [z_dr, z_fr]

    # unit    s ^ -1
    alpha_d = params_d[0]*1e3 # unit    s ^ -1
    beta_d = params_d[2]*1e3 # unit    s ^ -1

    alpha_f = params_f[0]*1e3 # unit    s ^ -1
    beta_f = params_f[2]*1e3 # unit    s ^ -1

    kf_Ca = [P_TCC / x_TCC,     # R_GHK
    alpha_d,     # Rb_10
    alpha_d,     # Rb_11
    alpha_f,     # Rg_00
    alpha_f] # Rg_20

    kr_Ca = [P_TCC / x_TCC,     # R_GHK
    beta_d,     # Rb_00
    beta_d,     # Rb_01
    beta_f,     # Rg_00
    beta_f] # Rg_20

    k_kinetic = kf_Ca + kr_Ca

    # CONSTRAINTS
    N_cT = []
    K_C = []

    # volume vector
    # W = list(np.append([1] * num_cols, [V['V_myo']] * num_rows))
    W = [1] * num_cols + [V['V_myo'], V['V_o']] + [1] * (num_rows-2)

    return (k_kinetic, N_cT, K_C, W)