def setUpClass(cls):
mass_con_crnt_obj = crnt4sbml.CRNT("./sbml_files/Fig1Ci.xml")
cls.mass_con_c_graph_list = cls.generate_c_graph_list(
mass_con_crnt_obj, True)
cls.mass_con_low_def_list = cls.generate_low_def_list(
mass_con_crnt_obj)
cls.mass_con_list = cls.generate_mass_con_list(mass_con_crnt_obj)
semi_diff_crnt_obj = crnt4sbml.CRNT("./sbml_files/Fig1Cii.xml")
cls.semi_diff_c_graph_list = cls.generate_c_graph_list(
semi_diff_crnt_obj, False)
cls.semi_diff_low_def_list = cls.generate_low_def_list(
semi_diff_crnt_obj)
cls.semi_diff_list = cls.generate_semi_diff_list(semi_diff_crnt_obj)
low_def_crnt_obj = crnt4sbml.CRNT("./sbml_files/feinberg_ex3_13.xml")
cls.low_def_c_graph_list = cls.generate_c_graph_list(
low_def_crnt_obj, False)
cls.low_def_low_def_list = cls.generate_low_def_list(low_def_crnt_obj)
import crnt4sbml
network = crnt4sbml.CRNT("../sbml_files/open_fig5B.xml")
network.basic_report()
network.print_c_graph()
ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
approach = network.get_semi_diffusive_approach()
print("")
approach.print_decision_vector()
print("Key species:")
print(approach.get_key_species())
print("")
print("Non key species:")
print(approach.get_non_key_species())
print("")
print("Boundary species:")
print(approach.get_boundary_species())
bounds = [(1e-3, 1e2)]*17
params_for_global_min, obj_fun_val_for_params = approach.run_optimization(bounds=bounds, iterations=50)
import sys
sys.path.insert(0, "..")
import crnt4sbml
network = crnt4sbml.CRNT("../sbml_files/conradi_paper.xml")
#network = crnt4sbml.CRNT("../sbml_files/Song2.xml")
#network = crnt4sbml.CRNT("../sbml_files/GuanyuWang.xml")
network.basic_report()
ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
opt = network.get_mass_conservation_approach()
#sys.exit()
bounds, concentration_bounds = opt.get_optimization_bounds()
print(bounds)
print(concentration_bounds)
print(opt.get_conservation_laws())
params_for_global_min, obj_fun_val_for_params = opt.run_optimization(
bounds=bounds, iterations=1000, concentration_bounds=concentration_bounds)
# multistable_param_ind, plot_specifications = opt.run_greedy_continuity_analysis(species="s1", parameters=params_for_global_min,
# auto_parameters={'PrincipalContinuationParameter': 'C2'})
import crnt4sbml
network = crnt4sbml.CRNT("../sbml_files/p85-p110-PTEN.xml")
approach = network.get_mass_conservation_approach()
bounds, concentration_bounds = approach.get_optimization_bounds()
params_for_global_min, obj_fun_val_for_params = approach.run_optimization(
bounds=bounds,
iterations=5000,
concentration_bounds=concentration_bounds,
parallel_flag=True)
if approach.get_my_rank() == 0:
network.basic_report()
network.print_c_graph()
ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
print("")
print("Decision Vector:")
print(approach.get_decision_vector())
print("")
print("Species for concentration bounds:")
import crnt4sbml
network = crnt4sbml.CRNT("../sbml_files/conradi2007.xml")
network.basic_report()
network.print_c_graph()
ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
print("")
opt = network.get_mass_conservation_approach()
print("Decision Vector:")
print(opt.get_decision_vector())
print("")
print("Species for concentration bounds:")
print(opt.get_concentration_bounds_species())
bounds = [(1e-2, 1e2)] * 20
concentration_bounds = [(1e-2, 1e2)] * 7
params_for_global_min, obj_fun_val_for_params = opt.run_optimization(
bounds=bounds, concentration_bounds=concentration_bounds, iterations=100)
multistable_param_ind, plot_specifications = opt.run_continuity_analysis(
species='s1',
import numpy
import pandas
import sympy
import scipy.integrate as itg
#from plotnine import ggplot, aes, geom_line, ylim, scale_color_distiller, facet_wrap, theme_bw, geom_path, geom_point
import time
import sys
import sys
sys.path.insert(0, "..")
import crnt4sbml
# 1.
network = crnt4sbml.CRNT("../sbml_files/DoublePhos.xml")
signal = 'C2'
response = 's4'
# 2.
# network = crnt4sbml.CRNT("../sbml_files/Fig4C_closed.xml")
# 3.
#network = crnt4sbml.CRNT("../sbml_files/closed_fig5A.xml")
# 4.
# network = crnt4sbml.CRNT("../sbml_files/irene2014.xml")
# 5.
# network = crnt4sbml.CRNT("../sbml_files/irene2009.xml")
import crnt4sbml
network = crnt4sbml.CRNT("../sbml_files/closed_fig5A.xml")
network.basic_report()
network.print_c_graph()
ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
print("")
approach = network.get_mass_conservation_approach()
print("Decision Vector:")
print(approach.get_decision_vector())
print("")
print("Species for concentration bounds:")
print(approach.get_concentration_bounds_species())
bounds = [(1e-2, 1e2)] * 12
concentration_bounds = [(1e-2, 1e2)] * 6
params_for_global_min, obj_fun_val_for_params = approach.run_optimization(
bounds=bounds, concentration_bounds=concentration_bounds, iterations=100)
multistable_param_ind, plot_specifications = approach.run_continuity_analysis(
species="s9",
import crnt4sbml
network = crnt4sbml.CRNT("../sbml_files/feinberg_ex3_13.xml")
network.basic_report()
network.print_c_graph()
ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
opt = network.get_mass_conservation_approach()
import crnt4sbml
network = crnt4sbml.CRNT("../sbml_files/hervagault_canu.xml")
network.basic_report()
network.print_c_graph()
ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
print("")
approach = network.get_mass_conservation_approach()
print("Decision Vector:")
print(approach.get_decision_vector())
print("")
print("Species for concentration bounds:")
print(approach.get_concentration_bounds_species())
bounds = [(1e-2, 1e2)] * 11
concentration_bounds = [(1e-2, 1e2)] * 4
params_for_global_min, obj_fun_val_for_params = approach.run_optimization(
bounds=bounds, concentration_bounds=concentration_bounds, iterations=100)
multistable_param_ind, plot_specifications = approach.run_continuity_analysis(
species="s1",
y_r_matrix = approach.get_y_r_matrix()
f = io.StringIO()
with contextlib.redirect_stdout(f):
approach.print_decision_vector()
print_decision_vector = f.getvalue()
f.close()
return [
boundary_species, decision_vector, key_species, non_key_species,
mu_vector, s_to_matrix, symbolic_objective_fun,
symbolic_polynomial_fun, y_r_matrix, print_decision_vector
]
mass_con_crnt_obj = crnt4sbml.CRNT("../sbml_files/Fig1Ci.xml")
mass_con_c_graph_list = generate_c_graph_list(mass_con_crnt_obj, True)
mass_con_low_def_list = generate_low_def_list(mass_con_crnt_obj)
mass_con_list = generate_mass_con_list(mass_con_crnt_obj)
# semi_diff_crnt_obj = crnt4sbml.CRNT("../sbml_files/Fig1Cii.xml")
# semi_diff_c_graph_list = generate_c_graph_list(semi_diff_crnt_obj, False)
# semi_diff_low_def_list = generate_low_def_list(semi_diff_crnt_obj)
# semi_diff_list = generate_semi_diff_list(semi_diff_crnt_obj)
#print(semi_diff_list)
# low_def_crnt_obj = crnt4sbml.CRNT("../sbml_files/feinberg_ex3_13.xml")
# low_def_c_graph_list = generate_c_graph_list(low_def_crnt_obj, False)
# low_def_low_def_list = generate_low_def_list(low_def_crnt_obj)
with open('mass_con_c_graph_list.pickle', 'wb') as outf:
import sys
sys.path.insert(0, "..")
import crnt4sbml
import numpy
import sympy
import pandas
import scipy.integrate as itg
import dill
from plotnine import ggplot, aes, geom_line, ylim, scale_color_distiller, facet_wrap, theme_bw, geom_path, geom_point, labs, annotate
from matplotlib import rc
rc('text', usetex=True)
# network = crnt4sbml.CRNT("../sbml_files/insulin_signaling_motifs/Nuts_submodel_4c.xml")
# network = crnt4sbml.CRNT("../sbml_files/insulin_signaling_motifs/Nuts_submodel_1.xml")
network = crnt4sbml.CRNT("../sbml_files/insulin_signaling_motifs/Nuts.xml")
signal = "C2" # "C1"
response = "s11"
# network.basic_report()
# network.print_c_graph()
GA = network.get_general_approach(signal=signal,
response=response,
fix_reactions=False)
# GA = network.get_general_approach()
# print(GA.get_conservation_laws())
# GA.initialize_general_approach(signal=signal, response=response)
print(GA.get_conservation_laws())
import crnt4sbml
import numpy
import pandas
import sympy
import scipy.integrate as itg
from plotnine import ggplot, aes, geom_line, ylim, scale_color_distiller, facet_wrap, theme_bw, geom_path, geom_point
network = crnt4sbml.CRNT("./basic_example_1.xml")
network.print_biological_reaction_types()
ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
# optimization approach
opt = network.get_mass_conservation_approach()
opt.generate_report()
# the decision vector
opt.get_decision_vector()
# this function suggests physiological bounds
bounds, concentration_bounds = opt.get_optimization_bounds()
# overwriting with a narrower or wider range. In this case we are setting narrow range for re1c.
bounds[2] = (0.001, 0.01)
# overwriting specie concentration bounds for s4. Concentrations are in pM.
opt.get_concentration_bounds_species()
concentration_bounds[2] = (0.5, 5e2)
import sys
sys.path.insert(0, "..")
import crnt4sbml
network = crnt4sbml.CRNT(
"../sbml_files/insulin_signaling_motifs/IIS_double_binding.xml")
#network.get_network_graphml()
# network = crnt4sbml.CRNT("../sbml_files/insulin_signaling_motifs/a_b.xml")
#network = crnt4sbml.CRNT("../sbml_files/insulin_signaling_motifs/p85-p110-PTEN_v3.xml")
#network = crnt4sbml.CRNT("../sbml_files/closed_fig5A.xml")
print(network.get_c_graph().get_network_dimensionality_classification())
#sys.exit()
network.basic_report()
network.print_biological_reaction_types()
ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
# optimization approach
opt = network.get_mass_conservation_approach()
plt.switch_backend('agg')
# import matplotlib
# matplotlib.use('Agg')
import sys
sys.path.insert(0, "..")
import crnt4sbml
import numpy
import pandas
import sympy
import scipy.integrate as itg
from plotnine import ggplot, aes, geom_line, ylim, scale_color_distiller, facet_wrap, theme_bw, geom_path, geom_point
import time
import dill
network = crnt4sbml.CRNT("../sbml_files/GuanyuWang.xml")
# network.print_biological_reaction_types()
#
# ldt = network.get_low_deficiency_approach()
# ldt.report_deficiency_zero_theorem()
# ldt.report_deficiency_one_theorem()
#optimization approach
opt = network.get_mass_conservation_approach()
#opt.generate_report()
# the decision vector
opt.get_decision_vector()
# this function suggests physiological bounds
bounds, concentration_bounds = opt.get_optimization_bounds()
import crnt4sbml
network = crnt4sbml.CRNT("../sbml_files/irene2009.xml")
network.basic_report()
network.print_c_graph()
ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
print("")
opt = network.get_mass_conservation_approach()
print("Decision Vector:")
print(opt.get_decision_vector())
print("")
print("Species for concentration bounds:")
print(opt.get_concentration_bounds_species())
bounds = [(1e-2, 1e2)] * 10
concentration_bounds = [(1e-2, 1e2)] * 4
params_for_global_min, obj_fun_val_for_params = opt.run_optimization(
bounds=bounds, concentration_bounds=concentration_bounds, iterations=100)
multistable_param_ind, plot_specifications = opt.run_continuity_analysis(
species="s3",
import crnt4sbml
network = crnt4sbml.CRNT("../sbml_files/Song.xml")
network.basic_report()
network.print_c_graph()
network.get_network_graphml()
approach = network.get_general_approach()
approach.initialize_general_approach(signal="C1",
response="s2",
fix_reactions=True)
print(approach.get_input_vector())
bnds = [(1e-3, 6.0)] * len(network.get_c_graph().get_reactions()) + [
(1e-3, 1000.0)
] * len(network.get_c_graph().get_species())
params, obj_fun_vals = approach.run_optimization(bounds=bnds, iterations=100)
multistable_param_ind, plot_specifications = approach.run_greedy_continuity_analysis(
species="s2",
parameters=params,
auto_parameters={'PrincipalContinuationParameter': "C1"})
approach.generate_report()
import crnt4sbml
network = crnt4sbml.CRNT("../sbml_files/Fig4C_open.xml")
network.basic_report()
network.print_c_graph()
ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
opt = network.get_semi_diffusive_approach()
print("")
opt.print_decision_vector()
print("Key species:")
print(opt.get_key_species())
print("")
print("Non key species:")
print(opt.get_non_key_species())
print("")
print("Boundary species:")
print(opt.get_boundary_species())
bounds = [(1e-2, 1e2)] * 10
params_for_global_min, obj_fun_val_for_params = opt.run_optimization(
import crnt4sbml
c = crnt4sbml.CRNT("../sbml_files/Fig1Ci.xml")
opt = c.get_mass_conservation_approach()
print(opt.get_decision_vector())
bnds = [(1e-2, 1e2)] * 12
conc_bnds = [(1e-2, 1e2)] * 7
num_itr = 100
import numpy
sys_min = numpy.finfo(float).eps
sd = 0
prnt_flg = False
num_dtype = numpy.float64
params_for_global_min, obj_fun_val_for_params = opt.run_optimization(
bounds=bnds,
concentration_bounds=conc_bnds,
iterations=num_itr,
seed=sd,
print_flag=prnt_flg,
numpy_dtype=num_dtype,
sys_min_val=sys_min)
numpy.save('params.npy', params_for_global_min)
########################################################################
########################################################################
# Please review the documentation provided at crnt4sbml.readthedocs.io #
# before running the code below. #
########################################################################
########################################################################
import crnt4sbml
import numpy
network = crnt4sbml.CRNT("./sbml_files/PrionDoublePhos.xml")
network.print_c_graph()
approach = network.get_general_approach()
signal = "C2"
response = "s11"
approach.initialize_general_approach(signal=signal,
response=response,
fix_reactions=False)
bnds = [(2.4, 2.42), (27.5, 28.1), (2.0, 2.15), (48.25, 48.4), (0.5, 1.1),
(1.8, 2.1), (17.0, 17.5), (92.4, 92.6), (0.01, 0.025), (0.2, 0.25),
(0.78, 0.79), (3.6, 3.7), (0.15, 0.25), (0.06, 0.065)] + [(0.0, 100.0),
(18.0, 18.5),
(0.0, 100.0),
(0.0, 100.0),
(27.0, 27.1),
(8.2, 8.3),
import sys
sys.path.insert(0, "..")
import crnt4sbml
network = crnt4sbml.CRNT("../sbml_files/simple_biterminal.xml")
network.print_c_graph()
approach = network.get_general_approach()
signal = "C2"
response = "s11"
approach.initialize_general_approach(signal=signal, response=response, fix_reactions=False)
bnds = [(2.4, 2.42), (27.5, 28.1), (2.0, 2.15), (48.25, 48.4), (0.5, 1.1), (1.8, 2.1), (17.0, 17.5), (92.4, 92.6),
(0.01, 0.025), (0.2, 0.25), (0.78, 0.79), (3.6, 3.7), (0.15, 0.25), (0.06, 0.065)] + [(0.0, 100.0),
(18.0, 18.5), (0.0, 100.0), (0.0, 100.0), (27.0, 27.1), (8.2, 8.3), (90.0, 90.1), (97.5, 97.9), (30.0, 30.1)]
# print(network.get_c_graph().get_species())
# print(approach.get_independent_species())
print(network.get_c_graph().get_ode_system())
print("")
print(approach.get_independent_odes())
# print(approach.get_independent_odes_subs())
# import sympy
# print("hi")
print(approach.get_conservation_laws())
#
#
# sys.exit()
import crnt4sbml
network = crnt4sbml.CRNT("../sbml_files/double_insulin_binding.xml")
network.basic_report()
network.print_c_graph()
ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
approach = network.get_mass_conservation_approach()
print("Decision Vector:")
print(approach.get_decision_vector())
print("")
print("Species for concentration bounds:")
print(approach.get_concentration_bounds_species())
bounds, concentration_bounds = approach.get_optimization_bounds()
params_for_global_min, obj_fun_val_for_params = approach.run_optimization(
bounds=bounds, concentration_bounds=concentration_bounds, iterations=100)
multistable_param_ind, plot_specifications = approach.run_greedy_continuity_analysis(
species="s5",
parameters=params_for_global_min,
auto_parameters={'PrincipalContinuationParameter': 'C2'})
import sys
sys.path.insert(0, "..")
import crnt4sbml
network = crnt4sbml.CRNT("../sbml_files/irreversible_switch_2.xml")
network.basic_report()
ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
opt = network.get_mass_conservation_approach()
import crnt4sbml
network = crnt4sbml.CRNT("../sbml_files/Fig1Cii.xml")
opt = network.get_semi_diffusive_approach()
bounds = [(1e-3, 1e2)]*12
params_for_global_min, obj_fun_val_for_params = opt.run_optimization(bounds=bounds)
multistable_param_ind, plot_specifications = opt.run_greedy_continuity_analysis(species="s7", parameters=params_for_global_min,
auto_parameters={'PrincipalContinuationParameter': 're17'})
opt.generate_report()
########################################################################
########################################################################
# Please review the documentation provided at crnt4sbml.readthedocs.io #
# before running the code below. #
########################################################################
########################################################################
import crnt4sbml
import numpy
network = crnt4sbml.CRNT("./sbml_files/FutileCycle.xml")
approach = network.get_general_approach()
approach.initialize_general_approach(signal="C1",
response="s2",
fix_reactions=True)
bnds = [(1e-3, 6.0)]*len(network.get_c_graph().get_reactions()) + \
[(1e-3, 1000.0)]*len(network.get_c_graph().get_species())
#######################################################################################################################
#######################################################################################################################
# The code below produces the values for bistability (in parallel): ####
########################################################################
# params, obj_fun_vals = approach.run_optimization(bounds=bnds, iterations=100, parallel_flag=True)
#
# if approach.get_my_rank() == 0:
#
# import numpy
# numpy.save('./optimization_parameters/FutileCycle.npy', params)
#######################################################################################################################
########################################################################
########################################################################
# Please review the documentation provided at crnt4sbml.readthedocs.io #
# before running the code below. #
########################################################################
########################################################################
import crnt4sbml
import numpy
network = crnt4sbml.CRNT("./sbml_files/Edelstein.xml")
signal = "C1"
response = "s5"
iters = 50
GA = network.get_general_approach()
GA.initialize_general_approach(signal=signal, response=response, fix_reactions=True)
bnds = [(1e-2, 1e2)]*len(GA.get_input_vector())
#######################################################################################################################
#######################################################################################################################
# The code below produces the values for bistability (in parallel): ####
########################################################################
# params_for_global_min, obj_fun_vals = GA.run_optimization(bounds=bnds, iterations=iters, seed=0, print_flag=False,
# dual_annealing_iters=1000, confidence_level_flag=True,
# parallel_flag=True)
# if GA.get_my_rank() == 0:
# numpy.save('./optimization_parameters/edelstein_params.npy', params_for_global_min)
#######################################################################################################################
import crnt4sbml
network = crnt4sbml.CRNT("../sbml_files/Fig4B_closed.xml")
network.basic_report()
network.print_c_graph()
ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
print("")
opt = network.get_mass_conservation_approach()
print("Decision Vector:")
print(opt.get_decision_vector())
print("")
print("Species for concentration bounds:")
print(opt.get_concentration_bounds_species())
bounds = [(1e-2, 1e2)]*11
concentration_bounds = [(1e-2, 1e2)]*3
params_for_global_min, obj_fun_val_for_params = opt.run_optimization(bounds=bounds,
concentration_bounds=concentration_bounds,
iterations=10000)
opt.generate_report()
import pandas
import scipy.integrate as itg
import dill
from plotnine import ggplot, aes, geom_line, ylim, scale_color_distiller, facet_wrap, theme_bw, geom_path, geom_point, labs, annotate
from matplotlib import rc
rc('text', usetex=True)
# network = crnt4sbml.CRNT("../sbml_files/insulin_signaling_motifs/simple_biterminal.xml")
# network = crnt4sbml.CRNT("../sbml_files/insulin_signaling_motifs/simple_biterminal_v2.xml")
# network = crnt4sbml.CRNT("../sbml_files/insulin_signaling_motifs/Nuts.xml") # No, but zero value found
# network = crnt4sbml.CRNT("../sbml_files/insulin_signaling_motifs/Nuts_submodel_1.xml") # Yes
# network = crnt4sbml.CRNT("../sbml_files/insulin_signaling_motifs/Nuts_submodel_2.xml") # No, bifurcation and limit points found and zero value found
# network = crnt4sbml.CRNT("../sbml_files/insulin_signaling_motifs/Nuts_submodel_3.xml")
# network = crnt4sbml.CRNT("../sbml_files/insulin_signaling_motifs/Nuts_submodel_4c.xml")
network = crnt4sbml.CRNT(
"../sbml_files/insulin_signaling_motifs/Nuts_submodel_1.xml")
# network = crnt4sbml.CRNT("../sbml_files/insulin_signaling_motifs/Nuts_submodel_4d.xml")
#network = crnt4sbml.CRNT("../sbml_files/insulin_signaling_motifs/subspace_strange.xml")
signal = "C1"
response = "s11"
# network = crnt4sbml.CRNT("../sbml_files/two_dim_tk.xml")
# signal = "C1"
# response = "s1"
network.basic_report()
network.print_c_graph()
GA = network.get_general_approach(signal=signal,
# import sys
# sys.path.insert(0, "..")
import crnt4sbml
import numpy
network = crnt4sbml.CRNT(
"../sbml_files/insulin_signaling_motifs/a_b.xml") # yes 10
signal = "C1"
response = "s5"
iters = 50
# network = crnt4sbml.CRNT("../sbml_files/Fig1Ci.xml")
# signal = "C3"
# response = "s15"
network.basic_report()
network.print_c_graph()
GA = network.get_general_approach()
GA.initialize_general_approach(signal=signal,
response=response,
fix_reactions=True)
import sympy
from sympy import *
# species = GA.get_independent_species()
# print(GA.get_independent_species())
# re5, re5r, re6, re6r, re7, re7r = sympy.symbols('re5, re5r, re6, re6r, re7, re7r', positive=True)
#