- Author:
- Shelley Fong <s.fong@auckland.ac.nz>
- Date:
- 2022-04-01 15:08:17+13:00
- Desc:
- With new densities found from fitting Pan - K + Ks to Clancy model
- Permanent Source URI:
- https://models.physiomeproject.org/workspace/82d/rawfile/5bbfc4d00cc6a4f7e910d47b7fbae5b14c56ab15/parameter_finder/kinetic_parameters_Ks.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 = 8.633865763157799e-09 # G_GHK [=] mA/mM
P_ks = G_GHK/F * 1e12 # Unit pL/s .
x_Ks_channel = (30833/1)/N_A*1e15 # fmol. From inferring whole cell conductance (Clancy) against single cell (3 Ps, Chinn)
x_Ks_channel = (10e5*30833/70)/N_A*1e15 # fmol. From inferring whole cell conductance (Clancy) against single cell (3 Ps, Chinn)
x_Ks_channel = 1.63E+6/N_A*1e15
# load gate transition parameters
params_xs1 = [1.4553735686818794, 0.7139137911117777, 1.4406316908249004, -0.48710409409640176]
params_xs2 = [0.3348069123572211, 0.7798495649896808, 0.3176442832555819, -0.518794842790217]
alpha_xs1 = params_xs1[0] # unit s ^ -1
beta_xs1 = params_xs1[2] # unit s ^ -1
alpha_xs2 = params_xs2[0] # unit s ^ -1
beta_xs2 = params_xs2[2] # unit s ^ -1
# Calculate bond graph constants from kinetic parameters
# Note: units of kappa are fmol/s, units of K are fmol^-1
kf_Ks = [P_ks / x_Ks_channel, # R_GHK
alpha_xs1, # Rx1_0
alpha_xs1, # Rx1_1
alpha_xs2, # Rx2_0
alpha_xs2] # Rx2_1
kr_Ks = [P_ks / x_Ks_channel, # R_GHK
beta_xs1, # Rx1_0
beta_xs1, # Rx1_1
beta_xs2, # Rx2_0
beta_xs2] # Rx2_1
k_kinetic = kf_Ks + kr_Ks
# 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)
