- Author:
- Shelley Fong <s.fong@auckland.ac.nz>
- Date:
- 2021-11-26 14:26:35+13:00
- Desc:
- Updating Knames
- Permanent Source URI:
- https://models.cellml.org/workspace/707/rawfile/88369d2f746a0234b3ff860cfe8cca2efe643f28/parameter_finder/kinetic_parameters_LRGbinding_M2.py
# LRGbinding module - for muscarinic receptor as GPCR
# Based on Iancu 2007
# 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_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
KRc = 30 # uM Kc
KRh = 0.16 # uM Kh
KRL = 11 # uM Kl
KRr = KRL/(KRc*KRh)
kRcp = fastKineticConstant
kRcm = kRcp*KRc
# kRhp = fastKineticConstant
# kRhm = kRhp*KRh
kRLp = fastKineticConstant
kRLm = kRLp*KRL
kRrp = fastKineticConstant
# find kRLm using detailed balance
# kRrm = kRcm*kRhm*kRrp*kRLp/(kRcp*kRhp*kRLm)
kRrm = kRrp * KRr
k_kinetic = [
kRcp, kRrp, kRLp,
kRcm, kRrm, kRLm
]
# CONSTRAINTS
N_cT = []
K_C = []
# volume vector
W = list(np.append([1] * num_cols, [V['V_myo']] * num_rows))
return (k_kinetic, [N_cT], K_C, W)
