- Author:
- Shelley Fong <s.fong@auckland.ac.nz>
- Date:
- 2021-10-28 16:50:30+13:00
- Desc:
- Renaming kinetic_parameters.py
- Permanent Source URI:
- https://models.physiomeproject.org/workspace/707/rawfile/9536ca244adda40276a65de96e97a1e8c1097363/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
Ksig1 = 30 # uM Kc
Ksig2 = 0.16 # uM Kh
Ksig4 = 11 # uM Kl
ksig1p = fastKineticConstant
ksig1m = ksig1p*Ksig1
ksig2p = fastKineticConstant
ksig2m = ksig2p*Ksig2
ksig4p = fastKineticConstant
ksig4m = ksig4p*Ksig4
ksig3p = fastKineticConstant
# find ksig4m using detailed balance
ksig3m = ksig1m*ksig2m*ksig3p*ksig4p/(ksig1p*ksig2p*ksig4m)
k_kinetic = [
ksig1p, ksig2p, ksig3p, ksig4p,
ksig1m, ksig2m, ksig3m, ksig4m
]
# 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)
