"K3pep": np.array([.1]),
"K2pep": np.array([.3]),
"vemax": np.array([1.1]),
"Kefdp": np.array([.45]),
"ne": np.array([2]),
"d": np.array([.25]),
"V4max": np.array([.2]),
"k1cat": np.array([1]),
"V3max": np.array([1]),
"V2max": np.array([1]),
"ac": np.array([.1])
}
# get experimental system steady state data without noise
exp_xss, exp_fss, exp_ssid, perturbation_details = \
generate_expdata(y0, cvode_options, ode_parameter_values, noise=1, number_of_samples=5, kinetics=1,
dynamic_plot=1, perturbation_plot=0)
# arrange experimental data to form multiple data sets
exp_flux_index = np.array([0, 3, 2, 4, 1, 5])
# get combination of 3 experiments and perform identifiability on all fluxes that require 3 data sets
print(
'Practical Identifiability Analysis of v3 with 3 parameters: V3max, K3fdp and K3pep \n'
)
# choose identifiability functions to test
ident_fun_choice = [0]
# get combinations of experimental datasets
experimental_datasets_3_expts, \
experiment_choice, combination_choice = arrange_experimental_data(exp_xss, exp_fss, perturbation_details,
experiments_per_set=3, flux_id=exp_flux_index,
experiment_choice=[0,
"K3pep": np.array([.1]),
"K2pep": np.array([.3]),
"vemax": np.array([1.1]),
"Kefdp": np.array([.45]),
"ne": np.array([2]),
"d": np.array([.25]),
"V4max": np.array([.2]),
"k1cat": np.array([1]),
"V3max": np.array([1]),
"V2max": np.array([1]),
"ac": np.array([.1])
}
# get experimental system steady state data without noise
exp_xss, exp_fss, exp_ssid, perturbation_details = \
generate_expdata(y0, cvode_options, ode_parameter_values, noise=0, kinetics=2, dynamic_plot=0,
perturbation_plot=0)
# arrange experimental data to form multiple data sets
exp_flux_index = np.array([0, 3, 2, 4, 1, 5])
# get combination of 2 experiments and perform identifiability on all fluxes that require 2 data sets
print('Practical Identifiability Analysis of fluxes with 2 parameters \n')
# choose which identifiability functions to test
ident_fun_choice_2 = [0, 1]
# get combinations of experimental datasets
experimental_datasets_2_expts, \
experiment_choice_2, combination_choice_2 = arrange_experimental_data(exp_xss, exp_fss, perturbation_details,
experiments_per_set=2, flux_id=exp_flux_index,
experiment_choice=[0, 1, 2, 8, 9, 10, 11, 12])
ident_details_2 = flux_ident_2_data_combination(
experimental_datasets_2_expts,
from kotte_model import arrange_experimental_data
from process_ident_data import process_info
# from kotte_model import write_results_2_file
# from plot_ident_results import flux_parameter_plot_data
from process_ident_data import useful_experiments
# generate noisy experimental data for testing identifiability
y0 = np.array([5, 1, 1])
# default parameter values
cvode_options = ('Newton', 'Adams', 1e-10, 1e-10, 200)
ode_parameter_values = np.array(
[.1, .1, 4e6, .1, .3, 1.1, .45, 2, .25, .2, 1, 1, 1, .1])
# get noisy experimental system steady state data
noisy_exp_xss, noisy_exp_fss, noisy_exp_ssid, perturbation_details = \
generate_expdata(y0, cvode_options, ode_parameter_values)
# arrange experimental data to form multiple data sets
exp_flux_index = np.array([0, 3, 2, 4])
# get combinations of experimental datasets
experimental_datasets = arrange_experimental_data(noisy_exp_xss, noisy_exp_fss,
perturbation_details, 3,
exp_flux_index)
# identifiability for all kotte fluxes
ident_details = establish_kotte_flux_identifiability(experimental_datasets,
choose=10)
print('Perturbation analysis for identifiability complete.\n')
# data processing
data_list, original_data_ident, combo_data_ident, max_parameter = process_info(
from kotte_model import flux_ident_3_data_combination
from process_ident_data import process_info_sample
from plot_ident_results import data_utility_plot
from plot_ident_results import parameter_identifibaility_plot
from plot_ident_results import parameter_experiment_info_plot
# generate noisy experimental data for testing identifiability
y0 = np.array([5, 1, 1])
# default parameter values
cvode_options = ('Newton', 'Adams', 1e-10, 1e-10, 200)
ode_parameter_values = np.array(
[.1, .1, 4e6, .1, .3, 1.1, .45, 2, .25, .2, 1, 1, 1, .1])
# get experimental system steady state data without noise
exp_xss, exp_fss, exp_ssid, perturbation_details = \
generate_expdata(y0, cvode_options, ode_parameter_values, noise=1, number_of_samples=50)
# arrange experimental data to form multiple data sets
exp_flux_index = np.array([0, 3, 2, 4])
# get combination of 2 experiments and perform identifiability on all fluxes that require 2 data sets
print('Practical Identifiability Analysis of fluxes with 2 parameters \n')
choose_2 = range(
0, 306) # choose numbr of experimental datasets to use of analysis
# get combinations of experimental datasets
experimental_datasets_2_expts = \
arrange_experimental_data(exp_xss, exp_fss, perturbation_details, 2, exp_flux_index, choose_2)
ident_details_2 = flux_ident_2_data_combination(experimental_datasets_2_expts,
choose=choose_2,
flux_ids=[1, 2],
flux_choice=[1, 0])