- Author:
- Shelley Fong <s.fong@auckland.ac.nz>
- Date:
- 2021-11-17 11:19:21+13:00
- Desc:
- Exposure rst file
- Permanent Source URI:
- https://models.cellml.org/workspace/6ba/rawfile/6fba2601af3873977fab9edfabb94fff57d52607/parameter_finder/kinetic_parameters_B1AR.py
# B1AR module
# 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)
# 6 sep 21: removing signalling rewactions (separate module, LRGbinding)
# 5 Oct removing B1tot as the product of PKACI reaction with R. Instead,
# using R as the substrate, which gets phosphorylated to B1AR_p by PKACI.
# This means reactions p1 and p2 are switched around with directions
# reversed.
import numpy as np
def kinetic_parameters(M, include_type2_reactions, dims, V):
# Set the kinetic rate constants.
# original model had reactions that omitted enzymes as substrates e.g. BARK
# convert unit from 1/s to 1/uM.s by dividing by conc of enzyme
# all reactions were irreversible, made reversible by letting kr ~= 0
num_cols = dims['num_cols']
num_rows = dims['num_rows']
# concentration of BARK = 0.6uM (crude approx from litsearch, for GRK)
bigNum = 1e6
fastKineticConstant = bigNum
smallReverse = fastKineticConstant/(pow(bigNum,2))
cBARK = 0.6 #uM
cPKACI = 0.0587898478276673 #uM
Kl = 0.285 # uM
Kr = 0.062 # uM
Kc = 33 # uM
kB11p = 1.1e-3 / cBARK # 1/uM.s
kB11m = 2.2e-3 # 1/s
kB12p = fastKineticConstant#1/s
kB12m = smallReverse # 1/uM.s
kB21p = kB11p # 1/uM.s
kB21m = kB11m # 1/s
kB22p = fastKineticConstant#1/s
kB22m = smallReverse # 1/uM.s
kP1p = 3.6e-3 # 1/uM.s
kP1m = 2.2e-3 # 1/s
kP2p = fastKineticConstant # 1/s
kP2m = smallReverse # 1/uM.s
# detailed balance for kP2p
# kP2p = kP2m*kP1m/kP1p
k_kinetic = [
kB11p, kB12p, kB21p, kB22p, kP1p, kP2p,
kB11m, kB12m, kB21m, kB22m, kP1m, kP2m
]
nid = [1,10]
# CONSTRAINTS
N_cT = np.zeros(len(M[0]))
if True:
# PKACI: substrate R is in eqlm with product B1ARp
N_cT[num_cols + nid[0]] = 1
N_cT[num_cols + nid[1]] = -1
K_C = [1]
# volume vector
W = list(np.append([1] * num_cols, [V['V_myo']] * num_rows))
return (k_kinetic, [N_cT], K_C, W)
