Location: BG_Inhib1 @ f9bdcb85bc92 / parameter_finder / kinetic_parameters_Inhib1.py

Author:

Shelley Fong <s.fong@auckland.ac.nz>

Date:

2021-11-17 10:25:01+13:00

Desc:

Using sympy for rational nullspace

Permanent Source URI:

https://models.cellml.org/workspace/6d6/rawfile/f9bdcb85bc92bc1fa231b0b5f12c098624985937/parameter_finder/kinetic_parameters_Inhib1.py

# PLB module
# all rxn directions align with kinetic Saucerman model (Km unchanged,
# aside from dimensional scaling)

# 5 AUG PLB separated into PLB and Inhib1 submodules
# (parameter error was large with both submodules together)

# 25 Aug
#     return (k_kinetic, N_cT, K_C, W) kinetic parameters, constraints, and vector of volumes in each
# compartment (pL) (1 if gating variable, or in element corresponding to
# kappa)


import numpy as np 

def kinetic_parameters(M, include_all_reactions, dims, V):
    # Set the kinetic rate constants.
    # all reactions are reversible. no closed loops.
    
    num_cols = dims['num_cols']
    num_rows = dims['num_rows']

    bigNum = 1e3
    fastKineticConstant = bigNum
    smallReverse = fastKineticConstant/(pow(bigNum,2))
    
    cPP2A = 0.224 # uM (Bhalla Iyengar a on PMR)    but not sure what cell type this is

    # reaction IDs
#         PKAi1 (reverse), PKAi2 (reverse)
#         PP2ai1, PP2ai2

    k_pka_i1 = 60 #: per_sec 60
    Km_pka_i1 = 1 #: uM 1
    Vmax_pp2a_i1 = 14 #: uM_per_sec 14
    Km_pp2a_i1 = 1 #: uM 1

    vKm = [Km_pka_i1, Km_pp2a_i1]
    vkcat = [
        k_pka_i1, 
        Vmax_pp2a_i1/cPP2A
        ]

    N = len(vKm) 
    k1m = [] #np.zeros(N)
    k1p = [] #np.zeros(N)
    k2m = [] #np.zeros(N)
    k2p = [] #np.zeros(N)

    for i in range(N): # forward rxns
            k2p.append(vkcat[i])
            k1m.append(fastKineticConstant) # 1/s
            k1p.append((k1m[i] + k2p[i]) / (vKm[i]))
            k2m.append(k1p[i]*k2p[i]/k1m[i]) # detailed balance
    
        
    if include_all_reactions:
        k_kinetic = k1p + k2p + k1m + k2m
    else:
        irr = range(3)
        k_kinetic = [
            k1p[irr], k2p[irr], k1m[irr], k2m[irr]
            ]
    

    # CONSTRAINTS
    N_cT = [] 
#     N_cT = [] 
# #     # Reaction i: [PKA:PKI] = [C][PKI] at SS   big error. Not isolated reaction
#     # repeat for type 1 and 2
#     if False:
#         N_cT[0][num_cols + 1] = 1
#         N_cT[0][num_cols + 5] = 1
#         N_cT[0][num_cols + 8) = -1
#     
#     # Gibbs free energy of L + R binding                            **MED_ERROR**
#     if False:
#         N_cT[2][num_cols + 3] = 1   # ARC
#         N_cT[2][num_cols) = -1  # cAMP
#         N_cT[2][num_cols + 2] = -1  # RC
#         G_0_bind = -45.1872 # kJ/mol
#         R = 8.314
#         T = 310
#         K_bind = np.exp(G_0_bind/(R*T))*10^6
#     
    
    K_C = []

    # volume vector
    W = list(np.append([1] * num_cols, [V['V_myo']] * num_rows))
    
    return (k_kinetic, [N_cT], K_C, W)