def init_metabolomicsData(self):
self.init_model()
data_concentrations = data_dir_tests + '/test_geo01.json'
metabolomics_data = thermodynamics_metabolomicsData()
metabolomics_data.import_metabolomics_data(data_concentrations)
metabolomics_data.format_metabolomics_data()
metabolomics_data.generate_estimated_metabolomics_data(self.cobra_model)
self.metabolomics_data = metabolomics_data
def run_metabolomicsData(self):
# load metabolomics data
data_concentrations = data_dir_tests + '/test_geo01.json'
metabolomics_data = thermodynamics_metabolomicsData()
metabolomics_data.import_metabolomics_data(data_concentrations)
assert(metabolomics_data.measured_concentrations['pep']['concentration'] == 0.000122661)
assert(metabolomics_data.measured_concentrations['pep']['concentration_lb'] == 2.73e-05)
assert(metabolomics_data.measured_concentrations['pep']['concentration_ub'] == 0.000551246)
assert(metabolomics_data.measured_concentrations['pep']['concentration_var'] == 1.32798777)
assert(metabolomics_data.measured_concentrations['pep']['concentration_units'] == 'M')
metabolomics_data.format_metabolomics_data() # add compartment identifiers to metabolite ids
assert(metabolomics_data.measured_concentrations['pep_c']['concentration'] == 0.000122661)
assert(metabolomics_data.measured_concentrations['pep_c']['concentration_lb'] == 2.73e-05)
assert(metabolomics_data.measured_concentrations['pep_c']['concentration_ub'] == 0.000551246)
assert(metabolomics_data.measured_concentrations['pep_c']['concentration_var'] == 1.32798777)
assert(metabolomics_data.measured_concentrations['pep_c']['concentration_units'] == 'M')
metabolomics_data.generate_estimated_metabolomics_data(self.cobra_model)
assert(metabolomics_data.estimated_concentrations['pep_c']['concentration'] == 5.000000000000004e-05)
assert(metabolomics_data.estimated_concentrations['pep_c']['concentration_lb'] == 1.5811388300841896e-06)
assert(metabolomics_data.estimated_concentrations['pep_c']['concentration_ub'] == 0.0015811388300841897)
assert(metabolomics_data.estimated_concentrations['pep_c']['concentration_var'] == 176.21560953198042)
assert(metabolomics_data.estimated_concentrations['pep_c']['concentration_units'] == 'M')
self.metabolomics_data = metabolomics_data
def execute_calculate_dG_r(self,experiment_id_I,models_I,model_ids_I = [],
time_points_I=[],sample_name_abbreviations_I=[],
inconsistent_dG_f_I=[],inconsistent_concentrations_I=[],
measured_concentration_coverage_criteria_I=0.5,
measured_dG_f_coverage_criteria_I=0.99):
'''calculate dG0_r, dG_r, displacements, and perform a thermodynamic consistency check'''
print('calculating dG_r for experiment_id ' + experiment_id_I);
dG0_r_O = [];
dG_r_O = [];
tcc_O = [];
# get the model ids:
if model_ids_I:
model_ids = model_ids_I;
else:
model_ids = [];
model_ids = self.get_modelID_experimentID_dataStage03QuantificationSimulation(experiment_id_I);
for model_id in model_ids:
print('calculating dG_r for model_id ' + model_id);
# get the cobra model
cobra_model = models_I[model_id];
# get simulated data
fva_data,sra_data = {},{};
fva_data,sra_data = self.get_rowsDict_experimentIDAndModelID_dataStage03QuantificationSimulatedData(experiment_id_I,model_id);
# load simulated data
simulated_data = thermodynamics_simulatedData(fva_data_I=fva_data,sra_data_I=sra_data);
simulated_data.check_data();
# get the time-points
if time_points_I:
time_points = time_points_I;
else:
time_points = [];
time_points = self.get_timePoints_experimentIDAndModelID_dataStage03QuantificationSimulation(experiment_id_I,model_id);
for tp in time_points:
# get sample_name_abbreviations
if sample_name_abbreviations_I:
sample_name_abbreviations = sample_name_abbreviations_I;
else:
sample_name_abbreviations = [];
sample_name_abbreviations = self.get_sampleNameAbbreviations_experimentIDAndModelIDAndTimePoint_dataStage03QuantificationSimulation(experiment_id_I,model_id,tp);
for sna in sample_name_abbreviations:
print('calculating dG_r for sample_name_abbreviation ' + sna);
# get otherData
pH,temperature,ionic_strength = {},{},{}
pH,temperature,ionic_strength = self.get_rowsFormatted_experimentIDAndTimePointAndSampleNameAbbreviation_dataStage03QuantificationOtherData(experiment_id_I,tp,sna);
# load pH, ionic_strength, and temperature parameters
other_data = thermodynamics_otherData(pH_I=pH,temperature_I=temperature,ionic_strength_I=ionic_strength);
other_data.check_data();
# get dG_f data:
dG_f = {};
dG_f = self.get_rowsDict_experimentIDAndModelIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationDGf(experiment_id_I,model_id,tp,sna);
dG_f_data = thermodynamics_dG_f_data(dG_f_I=dG_f);
dG_f_data.format_dG_f();
dG_f_data.generate_estimated_dG_f(cobra_model)
dG_f_data.check_data();
# remove an inconsistent dGf values
if inconsistent_dG_f_I: dG_f_data.remove_measured_dG_f(inconsistent_dG_f_I)
# query metabolomicsData
concentrations = [];
concentrations = self.get_rowsDict_experimentIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationMetabolomicsData(experiment_id_I,tp,sna);
# load metabolomicsData
metabolomics_data = thermodynamics_metabolomicsData(measured_concentrations_I=concentrations);
metabolomics_data.generate_estimated_metabolomics_data(cobra_model);
# remove an inconsistent concentration values
if inconsistent_concentrations_I: metabolomics_data.remove_measured_concentrations(inconsistent_concentrations_I);
# calculate dG0r, dGr, displacements, and perform a thermodynamic consistency check based on model simulations
tcc = thermodynamics_dG_r_data();
tcc.calculate_dG0_r_v3(cobra_model, dG_f_data.measured_dG_f, dG_f_data.estimated_dG_f, other_data.temperature); # calculate the change in free energy of reaction without accounting for metabolite concentrations
tcc.calculate_dG_r_v3(cobra_model,metabolomics_data.measured_concentrations, metabolomics_data.estimated_concentrations,
other_data.pH, other_data.ionic_strength, other_data.temperature); # adjust the change in free energy of reaction for intracellular metabolite concentrations
tcc.check_thermodynamicConsistency(cobra_model,simulated_data.fva_data,
metabolomics_data.measured_concentrations,
metabolomics_data.estimated_concentrations,
other_data.pH,other_data.ionic_strength,other_data.temperature,
measured_concentration_coverage_criteria_I,
measured_dG_f_coverage_criteria_I); # check the thermodynamic consistency of the data
tcc.calculate_displacement_v3(cobra_model,metabolomics_data.measured_concentrations, metabolomics_data.estimated_concentrations); # calculate the displacements from equillibrium
#tcc.simulate_infeasibleReactions(cobra_model); # simulate thermodynamically inconsistent data
#tcc.constrain_infeasibleReactions(cobra_model); # remove thermodynamically inconsistent reactions from the model
# upload dG0r, dGr, displacements, and results of tcc
for k,v in tcc.dG_r.items():
dG0_r_tmp = {'experiment_id':experiment_id_I,
'model_id':model_id,
'sample_name_abbreviation':sna,
'time_point':tp,
'rxn_id':k,
'Keq_lb':tcc.dG0_r[k]['Keq_lb'],
'Keq_ub':tcc.dG0_r[k]['Keq_ub'],
'dG0_r':tcc.dG0_r[k]['dG_r'],
'dG0_r_var':tcc.dG0_r[k]['dG_r_var'],
'dG0_r_units':tcc.dG0_r[k]['dG_r_units'],
'dG0_r_lb':tcc.dG0_r[k]['dG_r_lb'],
'dG0_r_ub':tcc.dG0_r[k]['dG_r_ub'],
'used_':True,
'comment_':None};
dG0_r_O.append(dG0_r_tmp);
dG_r_tmp = {'experiment_id':experiment_id_I,
'model_id':model_id,
'sample_name_abbreviation':sna,
'time_point':tp,
'rxn_id':k,
'Keq_lb':tcc.dG_r[k]['Keq_lb'],
'Keq_ub':tcc.dG_r[k]['Keq_ub'],
'dG_r':tcc.dG_r[k]['dG_r'],
'dG_r_var':tcc.dG_r[k]['dG_r_var'],
'dG_r_units':tcc.dG_r[k]['dG_r_units'],
'dG_r_lb':tcc.dG_r[k]['dG_r_lb'],
'dG_r_ub':tcc.dG_r[k]['dG_r_ub'],
'displacement_lb':tcc.displacement[k]['displacement_lb'],
'displacement_ub':tcc.displacement[k]['displacement_ub'],
'Q_lb':tcc.displacement[k]['Q_lb'],
'Q_ub':tcc.displacement[k]['Q_ub'],
'used_':True,
'comment_':None};
dG_r_O.append(dG_r_tmp);
tcc_tmp = {'experiment_id':experiment_id_I,
'model_id':model_id,
'sample_name_abbreviation':sna,
'time_point':tp,
'rxn_id':k,
'feasible':tcc.thermodynamic_consistency_check[k],
'measured_concentration_coverage_criteria':measured_concentration_coverage_criteria_I,
'measured_dG_f_coverage_criteria':measured_dG_f_coverage_criteria_I,
'measured_concentration_coverage':tcc.metabolomics_coverage[k],
'measured_dG_f_coverage':tcc.dG_r_coverage[k],
'used_':True,
'comment_':None};
tcc_O.append(tcc_tmp);
#try:
# row = None;
# row = data_stage03_quantification_dG0_r(experiment_id_I,
# model_id,
# sna,
# tp,
# k,
# tcc.dG0_r[k]['Keq_lb'],
# tcc.dG0_r[k]['Keq_ub'],
# tcc.dG0_r[k]['dG_r'],
# tcc.dG0_r[k]['dG_r_var'],
# tcc.dG0_r[k]['dG_r_units'],
# tcc.dG0_r[k]['dG_r_lb'],
# tcc.dG0_r[k]['dG_r_ub'],
# True,
# None);
# self.session.add(row);
# row = None;
# row = data_stage03_quantification_dG_r(experiment_id_I,
# model_id,
# sna,
# tp,
# k,
# tcc.dG_r[k]['Keq_lb'],
# tcc.dG_r[k]['Keq_ub'],
# tcc.dG_r[k]['dG_r'],
# tcc.dG_r[k]['dG_r_var'],
# tcc.dG_r[k]['dG_r_units'],
# tcc.dG_r[k]['dG_r_lb'],
# tcc.dG_r[k]['dG_r_ub'],
# tcc.displacement[k]['displacement_lb'],
# tcc.displacement[k]['displacement_ub'],
# tcc.displacement[k]['Q_lb'],
# tcc.displacement[k]['Q_ub'],
# True,
# None);
# self.session.add(row);
# row = None;
# row = data_stage03_quantification_tcc(experiment_id_I,
# model_id,
# sna,
# tp,
# k,
# tcc.thermodynamic_consistency_check[k],
# measured_concentration_coverage_criteria_I,
# measured_dG_f_coverage_criteria_I,
# tcc.metabolomics_coverage[k],
# tcc.dG_r_coverage[k],
# True,
# None);
# self.session.add(row);
#except sqlalchemy.exc.IntegrityError as e:
# print(e);
# print("Press any key to continue")
# a=input();
#self.session.commit();
# add datat to the DB
self.add_dataStage03QuantificationDG0r(dG0_r_O);
self.add_dataStage03QuantificationDGr(dG_r_O);
self.add_dataStage03QuantificationTcc(tcc_O);
def execute_thermodynamicSampling(self,simulation_id_I,models_I,
data_dir_I,rxn_ids_I=[],
inconsistent_dG_f_I=[],inconsistent_concentrations_I=[],
inconsistent_tcc_I=[],
measured_concentration_coverage_criteria_I=0.5,
measured_dG_f_coverage_criteria_I=0.99,
solver_I='glpk'):
'''execute a thermodynamic analysis using the thermodynamic
module for cobrapy
Input:
inconsistent_dG_f_I = dG_f measured values to be substituted for estimated values
inconsistent_concentrations_I = concentration measured values to be substituted for estimated values
inconsistent_tcc_I = reactions considered feasible to be changed to infeasible so that dG0_r constraints do not break the model
measured_concentration_coverage_criteria_I = float, minimum concentration coverage to consider for feasibility
measured_dG_f_coveragea_criteria_I = float, minimum dG_f coverage to consider for feasibility
data_dir_I = directory of sampled points
solver_I = string, solver name
'''
modelsCOBRA = models_COBRA_dependencies();
print('execute_thermodynamicSampling...')
# get simulation information
simulation_info_all = [];
simulation_info_all = self.get_rows_simulationIDAndSimulationType_dataStage03QuantificationSimulation(simulation_id_I,'sampling')
if not simulation_info_all:
print('simulation not found!')
return;
simulation_info = simulation_info_all[0]; # unique constraint guarantees only 1 row will be returned
# get simulation parameters
simulation_parameters_all = [];
simulation_parameters_all = self.get_rows_simulationID_dataStage03QuantificationSimulationParameters(simulation_id_I);
if not simulation_parameters_all:
print('simulation not found!')
return;
simulation_parameters = simulation_parameters_all[0]; # unique constraint guarantees only 1 row will be returned
# get the cobra model
cobra_model = models_I[simulation_info['model_id']];
# copy the model
cobra_model_copy = cobra_model.copy();
# get rxn_ids
if rxn_ids_I:
rxn_ids = rxn_ids_I;
else:
rxn_ids = [];
rxn_ids = self.get_rows_experimentIDAndModelIDAndSampleNameAbbreviation_dataStage03QuantificationMeasuredFluxes(simulation_info['experiment_id'],simulation_info['model_id'],simulation_info['sample_name_abbreviation']);
for rxn in rxn_ids:
# constrain the model
cobra_model_copy.reactions.get_by_id(rxn['rxn_id']).lower_bound = rxn['flux_lb'];
cobra_model_copy.reactions.get_by_id(rxn['rxn_id']).upper_bound = rxn['flux_ub'];
# make the model irreversible
convert_to_irreversible(cobra_model_copy);
# get otherData
pH,temperature,ionic_strength = {},{},{}
pH,temperature,ionic_strength = self.get_rowsFormatted_experimentIDAndTimePointAndSampleNameAbbreviation_dataStage03QuantificationOtherData(simulation_info['experiment_id'],simulation_info['time_point'],simulation_info['sample_name_abbreviation']);
# load pH, ionic_strength, and temperature parameters
other_data = thermodynamics_otherData(pH_I=pH,temperature_I=temperature,ionic_strength_I=ionic_strength);
other_data.check_data();
# get dG_f data:
dG_f = {};
dG_f = self.get_rowsDict_experimentIDAndModelIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationDGf(simulation_info['experiment_id'],simulation_info['model_id'],simulation_info['time_point'],simulation_info['sample_name_abbreviation']);
dG_f_data = thermodynamics_dG_f_data(dG_f_I=dG_f);
dG_f_data.format_dG_f();
dG_f_data.generate_estimated_dG_f(cobra_model)
dG_f_data.check_data();
# remove an inconsistent dGf values
if inconsistent_dG_f_I: dG_f_data.remove_measured_dG_f(inconsistent_dG_f_I)
# query metabolomicsData
concentrations = [];
concentrations = self.get_rowsDict_experimentIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationMetabolomicsData(simulation_info['experiment_id'],simulation_info['time_point'],simulation_info['sample_name_abbreviation']);
# load metabolomicsData
metabolomics_data = thermodynamics_metabolomicsData(measured_concentrations_I=concentrations);
metabolomics_data.generate_estimated_metabolomics_data(cobra_model);
# remove an inconsistent concentration values
if inconsistent_concentrations_I: metabolomics_data.remove_measured_concentrations(inconsistent_concentrations_I);
# get dG0r, dGr, and tcc data
dG0_r = {};
dG0_r = self.get_rowsDict_experimentIDAndModelIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationDG0r(simulation_info['experiment_id'],simulation_info['model_id'],simulation_info['time_point'],simulation_info['sample_name_abbreviation'])
measured_concentration_coverage,measured_dG_f_coverage,feasible = {},{},{};
measured_concentration_coverage,measured_dG_f_coverage,feasible = self.get_rowsDict_experimentIDAndModelIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationTCC(simulation_info['experiment_id'],simulation_info['model_id'],simulation_info['time_point'],simulation_info['sample_name_abbreviation'],0,0)
tcc = thermodynamics_dG_r_data(dG0_r_I = dG0_r,
dG_r_coverage_I = measured_dG_f_coverage,
metabolomics_coverage_I = measured_concentration_coverage,
thermodynamic_consistency_check_I = feasible);
if inconsistent_tcc_I: tcc.change_feasibleReactions(inconsistent_tcc_I);
# apply tfba constraints
tfba = thermodynamics_tfba()
tfba._add_conc_ln_constraints_transport(cobra_model_copy, metabolomics_data.measured_concentrations, metabolomics_data.estimated_concentrations,
tcc.dG0_r, other_data.pH,other_data.temperature,tcc.metabolomics_coverage,
tcc.dG_r_coverage, tcc.thermodynamic_consistency_check,
measured_concentration_coverage_criteria_I, measured_dG_f_coverage_criteria_I,
use_measured_concentrations=True,use_measured_dG0_r=True);
# Test model
if modelsCOBRA.test_model(cobra_model_I=cobra_model_copy):
sampling = cobra_sampling(data_dir_I = data_dir_I);
if simulation_parameters['sampler_id']=='gpSampler':
filename_model = simulation_id_I + '.mat';
filename_script = simulation_id_I + '.m';
filename_points = simulation_id_I + '_points' + '.mat';
sampling.export_sampling_matlab(cobra_model=cobra_model_copy,filename_model=filename_model,filename_script=filename_script,filename_points=filename_points,\
solver_id_I = simulation_parameters['solver_id'],\
n_points_I = simulation_parameters['n_points'],\
n_steps_I = simulation_parameters['n_steps'],\
max_time_I = simulation_parameters['max_time']);
elif simulation_parameters['sampler_id']=='optGpSampler':
return;
else:
print('sampler_id not recognized');
else:
print('no solution found!');
def execute_analyzeThermodynamicSamplingPoints(self,simulation_id_I,models_I,
data_dir_I,data_dir_O,rxn_ids_I=[],
inconsistent_dG_f_I=[],inconsistent_concentrations_I=[],
inconsistent_tcc_I=[],
measured_concentration_coverage_criteria_I=0.5,
measured_dG_f_coverage_criteria_I=0.99,
remove_pointsNotInSolutionSpace_I=True,
min_pointsInSolutionSpace_I=1000):
'''Load and analyze sampling points
Input:
inconsistent_dG_f_I = dG_f measured values to be substituted for estimated values
inconsistent_concentrations_I = concentration measured values to be substituted for estimated values
inconsistent_tcc_I = reactions considered feasible to be changed to infeasible so that dG0_r constraints do not break the model
measured_concentration_coverage_criteria_I = float, minimum concentration coverage to consider for feasibility
measured_dG_f_coveragea_criteria_I = float, minimum dG_f coverage to consider for feasibility
remove_pointsNotInSolutionSpace_I = boolean, remove points not in the solution space (i.e., within the lower/upper bounds)
min_pointsInSolutionSpace_I = int, minimum number of points in the solution space.
if the number of points is less that the minimum, the solution space will be increased by
(upper_bounds-lower_bounds)/4 until the minimum number of points is met
data_dir_I = directory of sampled points
data_dir_O = director to write QC'd sampled points
solver_I = string, solver name
'''
print('analyzing sampling points');
modelsCOBRA = models_COBRA_dependencies();
# get simulation information
simulation_info_all = [];
simulation_info_all = self.get_rows_simulationIDAndSimulationType_dataStage03QuantificationSimulation(simulation_id_I,'sampling')
if not simulation_info_all:
print('simulation not found!')
return;
simulation_info = simulation_info_all[0]; # unique constraint guarantees only 1 row will be returned
# get simulation parameters
simulation_parameters_all = [];
simulation_parameters_all = self.get_rows_simulationID_dataStage03QuantificationSimulationParameters(simulation_id_I);
if not simulation_parameters_all:
print('simulation not found!')
return;
simulation_parameters = simulation_parameters_all[0]; # unique constraint guarantees only 1 row will be returned
# get the cobra model
cobra_model = models_I[simulation_info['model_id']];
# copy the model
cobra_model_copy = cobra_model.copy();
# get rxn_ids
if rxn_ids_I:
rxn_ids = rxn_ids_I;
else:
rxn_ids = [];
rxn_ids = self.get_rows_experimentIDAndModelIDAndSampleNameAbbreviation_dataStage03QuantificationMeasuredFluxes(simulation_info['experiment_id'],simulation_info['model_id'],simulation_info['sample_name_abbreviation']);
for rxn in rxn_ids:
# constrain the model
cobra_model_copy.reactions.get_by_id(rxn['rxn_id']).lower_bound = rxn['flux_lb'];
cobra_model_copy.reactions.get_by_id(rxn['rxn_id']).upper_bound = rxn['flux_ub'];
# make the model irreversible
convert_to_irreversible(cobra_model_copy);
# get otherData
pH,temperature,ionic_strength = {},{},{}
pH,temperature,ionic_strength = self.get_rowsFormatted_experimentIDAndTimePointAndSampleNameAbbreviation_dataStage03QuantificationOtherData(simulation_info['experiment_id'],simulation_info['time_point'],simulation_info['sample_name_abbreviation']);
# load pH, ionic_strength, and temperature parameters
other_data = thermodynamics_otherData(pH_I=pH,temperature_I=temperature,ionic_strength_I=ionic_strength);
other_data.check_data();
# get dG_f data:
dG_f = {};
dG_f = self.get_rowsDict_experimentIDAndModelIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationDGf(simulation_info['experiment_id'],simulation_info['model_id'],simulation_info['time_point'],simulation_info['sample_name_abbreviation']);
dG_f_data = thermodynamics_dG_f_data(dG_f_I=dG_f);
dG_f_data.format_dG_f();
dG_f_data.generate_estimated_dG_f(cobra_model)
dG_f_data.check_data();
# remove an inconsistent dGf values
if inconsistent_dG_f_I: dG_f_data.remove_measured_dG_f(inconsistent_dG_f_I)
# query metabolomicsData
concentrations = [];
concentrations = self.get_rowsDict_experimentIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationMetabolomicsData(simulation_info['experiment_id'],simulation_info['time_point'],simulation_info['sample_name_abbreviation']);
# load metabolomicsData
metabolomics_data = thermodynamics_metabolomicsData(measured_concentrations_I=concentrations);
metabolomics_data.generate_estimated_metabolomics_data(cobra_model);
# remove an inconsistent concentration values
if inconsistent_concentrations_I: metabolomics_data.remove_measured_concentrations(inconsistent_concentrations_I);
# get dG0r, dGr, and tcc data
dG0_r = {};
dG0_r = self.get_rowsDict_experimentIDAndModelIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationDG0r(simulation_info['experiment_id'],simulation_info['model_id'],simulation_info['time_point'],simulation_info['sample_name_abbreviation'])
measured_concentration_coverage,measured_dG_f_coverage,feasible = {},{},{};
measured_concentration_coverage,measured_dG_f_coverage,feasible = self.get_rowsDict_experimentIDAndModelIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationTCC(simulation_info['experiment_id'],simulation_info['model_id'],simulation_info['time_point'],simulation_info['sample_name_abbreviation'],0,0)
tcc = thermodynamics_dG_r_data(dG0_r_I = dG0_r,
dG_r_coverage_I = measured_dG_f_coverage,
metabolomics_coverage_I = measured_concentration_coverage,
thermodynamic_consistency_check_I = feasible);
if inconsistent_tcc_I: tcc.change_feasibleReactions(inconsistent_tcc_I);
# apply tfba constraints
tfba = thermodynamics_tfba()
tfba._add_conc_ln_constraints_transport(cobra_model_copy, metabolomics_data.measured_concentrations, metabolomics_data.estimated_concentrations,
tcc.dG0_r, other_data.pH,other_data.temperature,tcc.metabolomics_coverage,
tcc.dG_r_coverage, tcc.thermodynamic_consistency_check,
measured_concentration_coverage_criteria_I, measured_dG_f_coverage_criteria_I,
use_measured_concentrations=True,use_measured_dG0_r=True);
# Test each model
if modelsCOBRA.test_model(cobra_model_I=cobra_model_copy):
sampling = cobra_sampling(data_dir_I = data_dir_I,model_I = cobra_model_copy);
if simulation_parameters['sampler_id']=='gpSampler':
# load the results of sampling
filename_points = simulation_id_I + '_points' + '.mat';
sampling.get_points_matlab(filename_points,'sampler_out');
# check if points were sampled outside the solution space
if remove_pointsNotInSolutionSpace_I:
pruned_reactions = sampling.remove_points_notInSolutionSpace(min_points_I=min_pointsInSolutionSpace_I);
## check if the model contains loops
#sampling.simulate_loops(data_fva=settings.workspace_data + '/loops_fva_tmp.json');
#sampling.find_loops(data_fva=settings.workspace_data + '/loops_fva_tmp.json');
#sampling.remove_loopsFromPoints();
sampling.descriptive_statistics();
elif simulation_parameters['sampler_id']=='optGpSampler':
return;
else:
print('sampler_id not recognized');
# add data to the database
row = {'simulation_id':simulation_id_I,
'simulation_dateAndTime':sampling.simulation_dateAndTime,
'mixed_fraction':sampling.mixed_fraction,
'data_dir':data_dir_I+'/'+filename_points,
'infeasible_loops':sampling.loops,
'used_':True,
'comment_':None
};
self.add_dataStage03QuantificationSampledPoints([row])
#row = None;
#row = data_stage03_quantification_sampledPoints(
# simulation_id_I,
# sampling.simulation_dateAndTime,
# sampling.mixed_fraction,
# data_dir_I+'/'+filename_points,
# sampling.loops,
# True,
# None);
#self.session.add(row);
# write points to json file
# add data to the database
sampledData_O = [];
for k,v in sampling.points_statistics.items():
type,units = tfba.get_variableTypeAndUnits(k);
row = {'simulation_id':simulation_id_I,
'simulation_dateAndTime':sampling.simulation_dateAndTime,
'variable_id':k,
'variable_type':type,
'variable_units':units,
'sampling_points':None, #v['points'],
'sampling_ave':v['ave'],
'sampling_var':v['var'],
'sampling_lb':v['lb'],
'sampling_ub':v['ub'],
'sampling_ci':0.95,
'sampling_min':v['min'],
'sampling_max':v['max'],
'sampling_median':v['median'],
'sampling_iq_1':v['iq_1'],
'sampling_iq_3':v['iq_3'],
'used_':True,
'comment_':None};
sampledData_O.append(row);
#row = None;
#row = data_stage03_quantification_sampledData(
# simulation_id_I,
# sampling.simulation_dateAndTime,
# k,
# type,
# units,
# None, #v['points'],
# v['ave'],
# v['var'],
# v['lb'],
# v['ub'],
# v['min'],
# 0.95,
# v['max'],
# v['median'],
# v['iq_1'],
# v['iq_3'],
# True,
# None);
#self.session.add(row);
self.add_dataStage03QuantificationSampledData(sampledData_O);
else:
print('no solution found!');
def check_thermodynamicConstraints(self,simulation_id_I,models_I,rxn_ids_I=[],
inconsistent_dG_f_I=[],inconsistent_concentrations_I=[],
measured_concentration_coverage_criteria_I=0.5,
measured_dG_f_coverage_criteria_I=0.99,
n_checks_I = 5,
diagnose_solver_I='glpk',diagnose_threshold_I=0.98,diagnose_break_I=0.1):
print('check_thermodynamicConstraints...')
# get simulation information
simulation_info_all = [];
simulation_info_all = self.get_rows_simulationID_dataStage03QuantificationSimulation(simulation_id_I);
if not simulation_info_all:
print('simulation not found!')
return;
simulation_info = simulation_info_all[0]; # unique constraint guarantees only 1 row will be returned
# get simulation parameters
simulation_parameters_all = [];
simulation_parameters_all = self.get_rows_simulationID_dataStage03QuantificationSimulationParameters(simulation_id_I);
if not simulation_parameters_all:
print('simulation not found!')
return;
simulation_parameters = simulation_parameters_all[0]; # unique constraint guarantees only 1 row will be returned
# get the cobra model
cobra_model = models_I[simulation_info['model_id']];
# copy the model
cobra_model_copy = cobra_model.copy();
# get rxn_ids
if rxn_ids_I:
rxn_ids = rxn_ids_I;
else:
rxn_ids = [];
rxn_ids = self.get_rows_experimentIDAndModelIDAndSampleNameAbbreviation_dataStage03QuantificationMeasuredFluxes(simulation_info['experiment_id'],simulation_info['model_id'],simulation_info['sample_name_abbreviation']);
for rxn in rxn_ids:
# constrain the model
cobra_model_copy.reactions.get_by_id(rxn['rxn_id']).lower_bound = rxn['flux_lb'];
cobra_model_copy.reactions.get_by_id(rxn['rxn_id']).upper_bound = rxn['flux_ub'];
# make the model irreversible
convert_to_irreversible(cobra_model_copy);
# get otherData
pH,temperature,ionic_strength = {},{},{}
pH,temperature,ionic_strength = self.get_rowsFormatted_experimentIDAndTimePointAndSampleNameAbbreviation_dataStage03QuantificationOtherData(simulation_info['experiment_id'],simulation_info['time_point'],simulation_info['sample_name_abbreviation']);
# load pH, ionic_strength, and temperature parameters
other_data = thermodynamics_otherData(pH_I=pH,temperature_I=temperature,ionic_strength_I=ionic_strength);
other_data.check_data();
# get dG_f data:
dG_f = {};
dG_f = self.get_rowsDict_experimentIDAndModelIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationDGf(simulation_info['experiment_id'],simulation_info['model_id'],simulation_info['time_point'],simulation_info['sample_name_abbreviation']);
dG_f_data = thermodynamics_dG_f_data(dG_f_I=dG_f);
dG_f_data.format_dG_f();
dG_f_data.generate_estimated_dG_f(cobra_model)
dG_f_data.check_data();
# remove an inconsistent dGf values
if inconsistent_dG_f_I: dG_f_data.remove_measured_dG_f(inconsistent_dG_f_I)
# query metabolomicsData
concentrations = [];
concentrations = self.get_rowsDict_experimentIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationMetabolomicsData(simulation_info['experiment_id'],simulation_info['time_point'],simulation_info['sample_name_abbreviation']);
# load metabolomicsData
metabolomics_data = thermodynamics_metabolomicsData(measured_concentrations_I=concentrations);
metabolomics_data.generate_estimated_metabolomics_data(cobra_model);
# remove an inconsistent concentration values
if inconsistent_concentrations_I: metabolomics_data.remove_measured_concentrations(inconsistent_concentrations_I);
# get dG0r, dGr, and tcc data
dG0_r = {};
dG0_r = self.get_rowsDict_experimentIDAndModelIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationDG0r(simulation_info['experiment_id'],simulation_info['model_id'],simulation_info['time_point'],simulation_info['sample_name_abbreviation'])
measured_concentration_coverage,measured_dG_f_coverage,feasible = {},{},{};
measured_concentration_coverage,measured_dG_f_coverage,feasible = self.get_rowsDict_experimentIDAndModelIDAndTimePointAndSampleNameAbbreviations_dataStage03QuantificationTCC(simulation_info['experiment_id'],simulation_info['model_id'],simulation_info['time_point'],simulation_info['sample_name_abbreviation'],0,0)
tcc = thermodynamics_dG_r_data(dG0_r_I = dG0_r,
dG_r_coverage_I = measured_dG_f_coverage,
metabolomics_coverage_I = measured_concentration_coverage,
thermodynamic_consistency_check_I = feasible);
# apply tfba constraints
tfba = thermodynamics_tfba()
thermodynamic_constraints_check,diagnose_variables_1,diagnose_variables_2,diagnose_variables_3 = tfba.check_conc_ln_constraints_transport(cobra_model_copy,
metabolomics_data.measured_concentrations, metabolomics_data.estimated_concentrations,
tcc.dG0_r, other_data.pH,other_data.temperature,tcc.metabolomics_coverage,
tcc.dG_r_coverage, tcc.thermodynamic_consistency_check,
measured_concentration_coverage_criteria_I, measured_dG_f_coverage_criteria_I,
n_checks_I = 5,
diagnose_solver_I=None,diagnose_threshold_I=0.98,diagnose_break_I=0.1);
return thermodynamic_constraints_check,diagnose_variables_1,diagnose_variables_2,diagnose_variables_3;
def _main_():
##PART 1: Working
#-------
# Read in the model sbml file and define the model conditions
# Aerobic specific changes
cobra_model_oxic = load_thermoModel(anoxic = False)
convert_to_irreversible(cobra_model_oxic)
##PART 2: Working
#-------
# make/load simulated data for aerobic conditions
data_fva_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_fva_irrev_oxic.json'
data_srd_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_srd_irrev_oxic.json'
simulated_data_oxic = thermodynamics_simulatedData()
# simulated_data_oxic.generate_sra_data(cobra_model_oxic) # perform single reaction deletion analysis
# simulated_data_oxic.generate_fva_data(cobra_model_oxic) # perform flux variability analysis
# simulated_data_oxic.export_sra_data(data_srd_oxic) # save results for later use
# simulated_data_oxic.export_fva_data(data_fva_oxic) # save results for later use
simulated_data_oxic.import_sra_data(data_srd_oxic)
simulated_data_oxic.import_fva_data(data_fva_oxic)
simulated_data_oxic.check_data()
##PART 3: Working
#-------
# load pH, ionic_strength, and temperature parameters
other_data = thermodynamics_otherData()
other_data.load_defaultData()
other_data.check_data()
# generate dG_f data for all model compounds
# calculate the dG_f for each compound in each compartment
# export/load and check the dG_f data
#data_dG0_transformed = 'thermodynamics_data/ijo1366_dG_f.json'
data_dG0_transformed = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_dG_f01.json'
dG_f_data = thermodynamics_dG_f_data(id2KEGGID_filename_I='/home/user/code/thermodynamics/thermodynamics_data/id2KEGGID.csv')
# # # dG_f_data.make_dG0_f_pH0() # only if the data has not been generated previously!
# dG_f_data.get_transformed_dG_f('/home/user/code/thermodynamics/thermodynamics_data/compounds_dG0_f.json',cobra_model_oxic,other_data.pH,other_data.temperature,other_data.ionic_strength) # adjust the non-transformed dG0_f data to physiological pH, temperature, and ionic strength (this step has already been completed)
#dG_f_data.export_dG_f(data_dG0_transformed) # save results for later use
dG_f_data.import_dG_f(data_dG0_transformed)
dG_f_data.format_dG_f()
dG_f_data.generate_estimated_dG_f(cobra_model_oxic)
dG_f_data.check_data()
##PART 4: Working
#-------
# load metabolomics data for oxic conditions
data_concentrations_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_geo01.json'
metabolomics_data_oxic = thermodynamics_metabolomicsData()
metabolomics_data_oxic.import_metabolomics_data(data_concentrations_oxic)
metabolomics_data_oxic.format_metabolomics_data() # add compartment identifiers to metabolite ids
metabolomics_data_oxic.generate_estimated_metabolomics_data(cobra_model_oxic)
##PART 5: Working
#-------
# calculate dG_r and perform a consistency check based on model simulations for oxic conditions
data_ta_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_irrev_ta.csv'
data_dG0_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_orrev_dG0.json'
data_dG_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_irrev_dG.json'
data_tcc_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_irrev_tcc.json'
tcc_oxic = thermodynamics_dG_r_data()
tcc_oxic.import_dG0_r_json(data_dG0_oxic)
tcc_oxic.import_dG_r_json(data_dG_oxic)
tcc_oxic.import_tcc_json(data_tcc_oxic)
# tcc_oxic.calculate_dG0_r(cobra_model_oxic, dG_f_data.measured_dG_f, dG_f_data.estimated_dG_f, other_data.temperature) # calculate the change in free energy of reaction without accounting for metabolite concentrations
# tcc_oxic.calculate_dG_r(cobra_model_oxic,metabolomics_data_oxic.measured_concentrations, metabolomics_data_oxic.estimated_concentrations,
# other_data.pH, other_data.ionic_strength, other_data.temperature) # adjust the change in free energy of reaction for intracellular metabolite concentrations
# tcc_oxic.check_thermodynamicConsistency(cobra_model_oxic,simulated_data_oxic.fva_data,
# metabolomics_data_oxic.measured_concentrations,
# metabolomics_data_oxic.estimated_concentrations,
# other_data.pH,other_data.ionic_strength,other_data.temperature) # check the thermodynamic consistency of the data
# tcc_oxic.export_dG0_r_json(data_dG0_oxic) # save for later use
# tcc_oxic.export_dG_r_json(data_dG_oxic) # save for later use
# tcc_oxic.export_tcc_json(data_tcc_oxic) # save for later use
# tcc_oxic.export_summary(cobra_model_oxic,simulated_data_oxic.fva_data,data_ta_oxic) # write summary of the analysis to csv file
##PART 8:
#-------
# Diagnose model variables and constraints prior to FBA, FVA, and sampling (Oxic condition only)
# identified inconsistent concentrations/dG_f/tcc values
inconsistent_concentrations_I = []
inconsistent_dG_f_I = []
inconsistent_tcc_I = ['EX_glc_LPAREN_e_RPAREN__reverse']
# remove an inconsistent dGf values
dG_f_data.remove_measured_dG_f(inconsistent_dG_f_I)
# remove an inconsistent concentration values
metabolomics_data_oxic.remove_measured_concentrations(inconsistent_concentrations_I)
# remove an inconcsistent tcc
tcc_oxic.change_feasibleReactions(inconsistent_tcc_I)
# diagnose tfba constraints
tfba = thermodynamics_tfba()
# thermodynamic_constraints_check,\
# inconsistent_tcc,diagnose_variables_1,\
# diagnose_variables_2,\
# diagnose_variables_3 = tfba.check_conc_ln_constraints_transport(
# cobra_model_oxic,
# metabolomics_data_oxic.measured_concentrations, metabolomics_data_oxic.estimated_concentrations,
# tcc_oxic.dG0_r, other_data.pH,other_data.temperature,tcc_oxic.metabolomics_coverage,
# tcc_oxic.dG_r_coverage, tcc_oxic.thermodynamic_consistency_check,
# 0.5, 0.99,n_checks_I = 0,
# diagnose_solver_I=None,diagnose_threshold_I=0.98,diagnose_break_I=0.1)
# tfba._add_conc_ln_constraints_transport(cobra_model_oxic,
# metabolomics_data_oxic.measured_concentrations, metabolomics_data_oxic.estimated_concentrations,
# tcc_oxic.dG0_r, other_data.pH,other_data.temperature,tcc_oxic.metabolomics_coverage,
# tcc_oxic.dG_r_coverage, tcc_oxic.thermodynamic_consistency_check,
# 0.5, 0.99)
# # cobra_model_oxic.optimize()
# from cobra.flux_analysis import flux_variability_analysis
# fva_data = flux_variability_analysis(cobra_model_oxic, fraction_of_optimum=0.9,
# objective_sense='maximize',
# reaction_list=[cobra_model_oxic.reactions.get_by_id('conc_lnv_fum_c')],
# )
# #PART 9:
# -------
# perform thermodynamic FBA and FVA (Oxic condition only)
# # run TFBA
# cobra_model_copy = cobra_model_oxic.copy()
# tfba.tfba(cobra_model_copy,
# tcc_oxic.dG0_r,other_data.temperature,
# tcc_oxic.dG_r_coverage, tcc_oxic.thermodynamic_consistency_check,
# use_measured_dG_r=True, solver='glpk',)
# cobra_model_copy = cobra_model_oxic.copy()
# tfba.tfba_conc_ln(cobra_model_copy,
# metabolomics_data_oxic.measured_concentrations, metabolomics_data_oxic.estimated_concentrations,
# tcc_oxic.dG0_r,other_data.temperature,tcc_oxic.metabolomics_coverage,
# tcc_oxic.dG_r_coverage, tcc_oxic.thermodynamic_consistency_check,
# measured_concentration_coverage_criteria = 0.5, measured_dG_f_coverage_criteria = 0.99,
# use_measured_concentrations=True,use_measured_dG0_r=True, solver='glpk',)
# run TFVA
data_tfva_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_irrev_tfva.csv'
data_tfva_analysis_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_irrev_tfva_analysis.csv'
data_tfva_dG_r_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_orrev_tfva_dG_r.json'
data_tfva_concentrations_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_irrev_tfva_concentrations.json'
cobra_model_copy = cobra_model_oxic.copy()
tfba.tfva(cobra_model_copy,
tcc_oxic.dG0_r,other_data.temperature,
tcc_oxic.dG_r_coverage, tcc_oxic.thermodynamic_consistency_check,
use_measured_dG0_r=True, reaction_list=None,fraction_of_optimum=1.0, solver='glpk',
objective_sense="maximize")
tfba.export_tfva_data(data_tfva_oxic)
tfba.analyze_tfva_results(flux_threshold=1e-6)
tfba.export_tfva_analysis(data_tfva_analysis_oxic)
cobra_model_copy = cobra_model_oxic.copy()
tfba.tfva_dG_r(cobra_model_copy,
tcc_oxic.dG0_r,other_data.temperature,
tcc_oxic.dG_r_coverage, tcc_oxic.thermodynamic_consistency_check,
use_measured_dG0_r=True, fraction_of_optimum=1.0, solver='glpk',
objective_sense="maximize")
tfba.export_tfva_dG_r_data(data_tfva_dG_r_oxic)
cobra_model_copy = cobra_model_oxic.copy()
tfba.tfva_concentrations(cobra_model_copy,
metabolomics_data_oxic.measured_concentrations, metabolomics_data_oxic.estimated_concentrations,
tcc_oxic.dG0_r,other_data.temperature,tcc_oxic.metabolomics_coverage,
tcc_oxic.dG_r_coverage, tcc_oxic.thermodynamic_consistency_check,
measured_concentration_coverage_criteria = 0.5, measured_dG_f_coverage_criteria = 0.99,
use_measured_concentrations=True,use_measured_dG0_r=True,fraction_of_optimum=1.0, solver='glpk',
objective_sense="maximize")
tfba.export_tfva_concentrations_data(data_tfva_concentrations_oxic)
# ##PART 10:
# #-------
# # perform thermodynamic Tsampling (Oxic condition only)
# # NOTE: requires optGpSampler
# # run Tsampling
# data_dir = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo'
# sampling = optGpSampler_sampling(data_dir_I = data_dir);
# simulation_id_I = 'aerobicAnaerobic01_oxic_tsampling'
# filename_model = simulation_id_I + '.json';
# filename_script = simulation_id_I + '.py';
# filename_points = simulation_id_I + '_points' + '.json';
# filename_warmup = simulation_id_I + '_warmup' + '.json';
# sampling.export_sampling_optGpSampler(cobra_model=cobra_model_oxic,
# filename_model=filename_model,
# filename_script=filename_script,
# filename_points=filename_points,
# filename_warmup=filename_warmup,
# solver_id_I = 'optGpSampler',
# n_points_I = 2*len(cobra_model_oxic.reactions),
# n_steps_I = 5000,
# n_threads_I = 2)
##PART 11:
#-------
# Analyze thermodynamic sampling (Oxic condition only)
sampling = optGpSampler_sampling(
data_dir_I = data_dir,
model_I=cobra_model_oxic);
sampling.get_points_json(filename_points);
sampling.get_warmup_json(filename_warmup);
sampling.calculate_mixFraction();
# check if the model contains loops
#loops_bool = self.sampling.check_loops();
sampling.simulate_loops(
data_fva='/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/loops_fva_tmp.json',
solver_I = 'optGpSampler');
sampling.find_loops(data_fva='/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/loops_fva_tmp.json');
sampling.remove_loopsFromPoints();
# calculate the flux descriptive statistics
sampling.descriptive_statistics(points_I='flux');
# calculate descriptive stats for metabolites
sampling.convert_points2MetabolitePoints();
sampling.descriptive_statistics(points_I='metabolite');
# calculate descriptive stats for subsystems
sampling.convert_points2SubsystemPoints();
sampling.descriptive_statistics(points_I='subsystem');
# visualize the results
##TODO: methods are defined, but an example is not yet given
def _main_():
##PART 1: Working
#-------
# Read in the model sbml file and define the model conditions
# Aerobic specific changes
cobra_model_oxic = load_thermoModel(anoxic=False)
convert_to_irreversible(cobra_model_oxic)
##PART 2: Working
#-------
# make/load simulated data for aerobic conditions
data_fva_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_fva_irrev_oxic.json'
data_srd_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_srd_irrev_oxic.json'
simulated_data_oxic = thermodynamics_simulatedData()
# simulated_data_oxic.generate_sra_data(cobra_model_oxic) # perform single reaction deletion analysis
# simulated_data_oxic.generate_fva_data(cobra_model_oxic) # perform flux variability analysis
# simulated_data_oxic.export_sra_data(data_srd_oxic) # save results for later use
# simulated_data_oxic.export_fva_data(data_fva_oxic) # save results for later use
simulated_data_oxic.import_sra_data(data_srd_oxic)
simulated_data_oxic.import_fva_data(data_fva_oxic)
simulated_data_oxic.check_data()
##PART 3: Working
#-------
# load pH, ionic_strength, and temperature parameters
other_data = thermodynamics_otherData()
other_data.load_defaultData()
other_data.check_data()
# generate dG_f data for all model compounds
# calculate the dG_f for each compound in each compartment
# export/load and check the dG_f data
#data_dG0_transformed = 'thermodynamics_data/ijo1366_dG_f.json'
data_dG0_transformed = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_dG_f01.json'
dG_f_data = thermodynamics_dG_f_data(
id2KEGGID_filename_I=
'/home/user/code/thermodynamics/thermodynamics_data/id2KEGGID.csv')
# # # dG_f_data.make_dG0_f_pH0() # only if the data has not been generated previously!
# dG_f_data.get_transformed_dG_f('/home/user/code/thermodynamics/thermodynamics_data/compounds_dG0_f.json',cobra_model_oxic,other_data.pH,other_data.temperature,other_data.ionic_strength) # adjust the non-transformed dG0_f data to physiological pH, temperature, and ionic strength (this step has already been completed)
#dG_f_data.export_dG_f(data_dG0_transformed) # save results for later use
dG_f_data.import_dG_f(data_dG0_transformed)
dG_f_data.format_dG_f()
dG_f_data.generate_estimated_dG_f(cobra_model_oxic)
dG_f_data.check_data()
##PART 4: Working
#-------
# load metabolomics data for oxic conditions
data_concentrations_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_geo01.json'
metabolomics_data_oxic = thermodynamics_metabolomicsData()
metabolomics_data_oxic.import_metabolomics_data(data_concentrations_oxic)
metabolomics_data_oxic.format_metabolomics_data(
) # add compartment identifiers to metabolite ids
metabolomics_data_oxic.generate_estimated_metabolomics_data(
cobra_model_oxic)
##PART 5: Working
#-------
# calculate dG_r and perform a consistency check based on model simulations for oxic conditions
data_ta_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_irrev_ta.csv'
data_dG0_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_orrev_dG0.json'
data_dG_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_irrev_dG.json'
data_tcc_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_irrev_tcc.json'
tcc_oxic = thermodynamics_dG_r_data()
tcc_oxic.import_dG0_r_json(data_dG0_oxic)
tcc_oxic.import_dG_r_json(data_dG_oxic)
tcc_oxic.import_tcc_json(data_tcc_oxic)
# tcc_oxic.calculate_dG0_r(cobra_model_oxic, dG_f_data.measured_dG_f, dG_f_data.estimated_dG_f, other_data.temperature) # calculate the change in free energy of reaction without accounting for metabolite concentrations
# tcc_oxic.calculate_dG_r(cobra_model_oxic,metabolomics_data_oxic.measured_concentrations, metabolomics_data_oxic.estimated_concentrations,
# other_data.pH, other_data.ionic_strength, other_data.temperature) # adjust the change in free energy of reaction for intracellular metabolite concentrations
# tcc_oxic.check_thermodynamicConsistency(cobra_model_oxic,simulated_data_oxic.fva_data,
# metabolomics_data_oxic.measured_concentrations,
# metabolomics_data_oxic.estimated_concentrations,
# other_data.pH,other_data.ionic_strength,other_data.temperature) # check the thermodynamic consistency of the data
# tcc_oxic.export_dG0_r_json(data_dG0_oxic) # save for later use
# tcc_oxic.export_dG_r_json(data_dG_oxic) # save for later use
# tcc_oxic.export_tcc_json(data_tcc_oxic) # save for later use
# tcc_oxic.export_summary(cobra_model_oxic,simulated_data_oxic.fva_data,data_ta_oxic) # write summary of the analysis to csv file
##PART 8:
#-------
# Diagnose model variables and constraints prior to FBA, FVA, and sampling (Oxic condition only)
# identified inconsistent concentrations/dG_f/tcc values
inconsistent_concentrations_I = []
inconsistent_dG_f_I = []
inconsistent_tcc_I = ['EX_glc_LPAREN_e_RPAREN__reverse']
# remove an inconsistent dGf values
dG_f_data.remove_measured_dG_f(inconsistent_dG_f_I)
# remove an inconsistent concentration values
metabolomics_data_oxic.remove_measured_concentrations(
inconsistent_concentrations_I)
# remove an inconcsistent tcc
tcc_oxic.change_feasibleReactions(inconsistent_tcc_I)
# diagnose tfba constraints
tfba = thermodynamics_tfba()
# thermodynamic_constraints_check,\
# inconsistent_tcc,diagnose_variables_1,\
# diagnose_variables_2,\
# diagnose_variables_3 = tfba.check_conc_ln_constraints_transport(
# cobra_model_oxic,
# metabolomics_data_oxic.measured_concentrations, metabolomics_data_oxic.estimated_concentrations,
# tcc_oxic.dG0_r, other_data.pH,other_data.temperature,tcc_oxic.metabolomics_coverage,
# tcc_oxic.dG_r_coverage, tcc_oxic.thermodynamic_consistency_check,
# 0.5, 0.99,n_checks_I = 0,
# diagnose_solver_I=None,diagnose_threshold_I=0.98,diagnose_break_I=0.1)
# tfba._add_conc_ln_constraints_transport(cobra_model_oxic,
# metabolomics_data_oxic.measured_concentrations, metabolomics_data_oxic.estimated_concentrations,
# tcc_oxic.dG0_r, other_data.pH,other_data.temperature,tcc_oxic.metabolomics_coverage,
# tcc_oxic.dG_r_coverage, tcc_oxic.thermodynamic_consistency_check,
# 0.5, 0.99)
# # cobra_model_oxic.optimize()
# from cobra.flux_analysis import flux_variability_analysis
# fva_data = flux_variability_analysis(cobra_model_oxic, fraction_of_optimum=0.9,
# objective_sense='maximize',
# reaction_list=[cobra_model_oxic.reactions.get_by_id('conc_lnv_fum_c')],
# )
# #PART 9:
# -------
# perform thermodynamic FBA and FVA (Oxic condition only)
# # run TFBA
# cobra_model_copy = cobra_model_oxic.copy()
# tfba.tfba(cobra_model_copy,
# tcc_oxic.dG0_r,other_data.temperature,
# tcc_oxic.dG_r_coverage, tcc_oxic.thermodynamic_consistency_check,
# use_measured_dG_r=True, solver='glpk',)
# cobra_model_copy = cobra_model_oxic.copy()
# tfba.tfba_conc_ln(cobra_model_copy,
# metabolomics_data_oxic.measured_concentrations, metabolomics_data_oxic.estimated_concentrations,
# tcc_oxic.dG0_r,other_data.temperature,tcc_oxic.metabolomics_coverage,
# tcc_oxic.dG_r_coverage, tcc_oxic.thermodynamic_consistency_check,
# measured_concentration_coverage_criteria = 0.5, measured_dG_f_coverage_criteria = 0.99,
# use_measured_concentrations=True,use_measured_dG0_r=True, solver='glpk',)
# run TFVA
data_tfva_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_irrev_tfva.csv'
data_tfva_analysis_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_irrev_tfva_analysis.csv'
data_tfva_dG_r_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_orrev_tfva_dG_r.json'
data_tfva_concentrations_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_irrev_tfva_concentrations.json'
cobra_model_copy = cobra_model_oxic.copy()
tfba.tfva(cobra_model_copy,
tcc_oxic.dG0_r,
other_data.temperature,
tcc_oxic.dG_r_coverage,
tcc_oxic.thermodynamic_consistency_check,
use_measured_dG0_r=True,
reaction_list=None,
fraction_of_optimum=1.0,
solver='glpk',
objective_sense="maximize")
tfba.export_tfva_data(data_tfva_oxic)
tfba.analyze_tfva_results(flux_threshold=1e-6)
tfba.export_tfva_analysis(data_tfva_analysis_oxic)
cobra_model_copy = cobra_model_oxic.copy()
tfba.tfva_dG_r(cobra_model_copy,
tcc_oxic.dG0_r,
other_data.temperature,
tcc_oxic.dG_r_coverage,
tcc_oxic.thermodynamic_consistency_check,
use_measured_dG0_r=True,
fraction_of_optimum=1.0,
solver='glpk',
objective_sense="maximize")
tfba.export_tfva_dG_r_data(data_tfva_dG_r_oxic)
cobra_model_copy = cobra_model_oxic.copy()
tfba.tfva_concentrations(cobra_model_copy,
metabolomics_data_oxic.measured_concentrations,
metabolomics_data_oxic.estimated_concentrations,
tcc_oxic.dG0_r,
other_data.temperature,
tcc_oxic.metabolomics_coverage,
tcc_oxic.dG_r_coverage,
tcc_oxic.thermodynamic_consistency_check,
measured_concentration_coverage_criteria=0.5,
measured_dG_f_coverage_criteria=0.99,
use_measured_concentrations=True,
use_measured_dG0_r=True,
fraction_of_optimum=1.0,
solver='glpk',
objective_sense="maximize")
tfba.export_tfva_concentrations_data(data_tfva_concentrations_oxic)
# ##PART 10:
# #-------
# # perform thermodynamic Tsampling (Oxic condition only)
# # NOTE: requires optGpSampler
# # run Tsampling
# data_dir = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo'
# sampling = optGpSampler_sampling(data_dir_I = data_dir);
# simulation_id_I = 'aerobicAnaerobic01_oxic_tsampling'
# filename_model = simulation_id_I + '.json';
# filename_script = simulation_id_I + '.py';
# filename_points = simulation_id_I + '_points' + '.json';
# filename_warmup = simulation_id_I + '_warmup' + '.json';
# sampling.export_sampling_optGpSampler(cobra_model=cobra_model_oxic,
# filename_model=filename_model,
# filename_script=filename_script,
# filename_points=filename_points,
# filename_warmup=filename_warmup,
# solver_id_I = 'optGpSampler',
# n_points_I = 2*len(cobra_model_oxic.reactions),
# n_steps_I = 5000,
# n_threads_I = 2)
##PART 11:
#-------
# Analyze thermodynamic sampling (Oxic condition only)
sampling = optGpSampler_sampling(data_dir_I=data_dir,
model_I=cobra_model_oxic)
sampling.get_points_json(filename_points)
sampling.get_warmup_json(filename_warmup)
sampling.calculate_mixFraction()
# check if the model contains loops
#loops_bool = self.sampling.check_loops();
sampling.simulate_loops(
data_fva=
'/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/loops_fva_tmp.json',
solver_I='optGpSampler')
sampling.find_loops(
data_fva=
'/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/loops_fva_tmp.json'
)
sampling.remove_loopsFromPoints()
# calculate the flux descriptive statistics
sampling.descriptive_statistics(points_I='flux')
# calculate descriptive stats for metabolites
sampling.convert_points2MetabolitePoints()
sampling.descriptive_statistics(points_I='metabolite')
# calculate descriptive stats for subsystems
sampling.convert_points2SubsystemPoints()
sampling.descriptive_statistics(points_I='subsystem')
def _main_():
##PART 1: Working
#-------
# Read in the model sbml file and define the model conditions
# Anaerobic specific changes:
cobra_model_anoxic = load_thermoModel(anoxic = True)
#convert_to_irreversible(cobra_model_anoxic)
# Aerobic specific changes
cobra_model_oxic = load_thermoModel(anoxic = False)
#convert_to_irreversible(cobra_model_oxic)
##PART 2: Working
#-------
# make/load simulated data for anaerobic conditions
data_fva_anoxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_fva_anoxic.json'
data_srd_anoxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_srd_anoxic.json'
simulated_data_anoxic = thermodynamics_simulatedData()
# simulated_data_anoxic.generate_sra_data(cobra_model_anoxic) # perform single reaction deletion analysis
# simulated_data_anoxic.generate_fva_data(cobra_model_anoxic) # perform flux variability analysis
# simulated_data_anoxic.export_sra_data(data_srd_anoxic) # save results for later use
# simulated_data_anoxic.export_fva_data(data_fva_anoxic) # save results for later use
simulated_data_anoxic.import_sra_data(data_srd_anoxic)
simulated_data_anoxic.import_fva_data(data_fva_anoxic)
simulated_data_anoxic.check_data()
# make/load simulated data for aerobic conditions
data_fva_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_fva_oxic.json'
data_srd_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_srd_oxic.json'
simulated_data_oxic = thermodynamics_simulatedData()
# simulated_data_oxic.generate_sra_data(cobra_model_oxic) # perform single reaction deletion analysis
# simulated_data_oxic.generate_fva_data(cobra_model_oxic) # perform flux variability analysis
# simulated_data_oxic.export_sra_data(data_srd_oxic) # save results for later use
# simulated_data_oxic.export_fva_data(data_fva_oxic) # save results for later use
simulated_data_oxic.import_sra_data(data_srd_oxic)
simulated_data_oxic.import_fva_data(data_fva_oxic)
simulated_data_oxic.check_data()
##PART 3: Working
#-------
# load pH, ionic_strength, and temperature parameters
other_data = thermodynamics_otherData()
other_data.load_defaultData()
other_data.check_data()
# generate dG_f data for all model compounds
# calculate the dG_f for each compound in each compartment
# export/load and check the dG_f data
#data_dG0_transformed = 'thermodynamics_data/ijo1366_dG_f.json'
data_dG0_transformed = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_dG_f01.json'
dG_f_data = thermodynamics_dG_f_data(id2KEGGID_filename_I='/home/user/code/thermodynamics/thermodynamics_data/id2KEGGID.csv')
# # # dG_f_data.make_dG0_f_pH0() # only if the data has not been generated previously!
# dG_f_data.get_transformed_dG_f('/home/user/code/thermodynamics/thermodynamics_data/compounds_dG0_f.json',cobra_model_oxic,other_data.pH,other_data.temperature,other_data.ionic_strength) # adjust the non-transformed dG0_f data to physiological pH, temperature, and ionic strength (this step has already been completed)
#dG_f_data.export_dG_f(data_dG0_transformed) # save results for later use
dG_f_data.import_dG_f(data_dG0_transformed)
dG_f_data.format_dG_f()
dG_f_data.generate_estimated_dG_f(cobra_model_oxic)
dG_f_data.check_data()
##PART 4: Working
#-------
# load metabolomics data for anoxic conditions
data_concentrations_anoxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_anoxic_geo01.json'
metabolomics_data_anoxic = thermodynamics_metabolomicsData()
metabolomics_data_anoxic.import_metabolomics_data(data_concentrations_anoxic)
metabolomics_data_anoxic.format_metabolomics_data() # add compartment identifiers to metabolite ids
metabolomics_data_anoxic.generate_estimated_metabolomics_data(cobra_model_anoxic)
# load metabolomics data for oxic conditions
data_concentrations_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_geo01.json'
metabolomics_data_oxic = thermodynamics_metabolomicsData()
metabolomics_data_oxic.import_metabolomics_data(data_concentrations_oxic)
metabolomics_data_oxic.format_metabolomics_data() # add compartment identifiers to metabolite ids
metabolomics_data_oxic.generate_estimated_metabolomics_data(cobra_model_oxic)
#PART 5: Working
#-------
# calculate dG_r and perform a consistency check based on model simulations for anoxic conditions
data_ta_anoxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_anoxic_ta.csv'
data_dG0_anoxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_anoxic_dG0.json'
data_dG_anoxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_anoxic_dG.json'
data_tcc_anoxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_anoxic_tcc.json'
tcc_anoxic = thermodynamics_dG_r_data()
tcc_anoxic.calculate_dG0_r(cobra_model_anoxic, dG_f_data.measured_dG_f, dG_f_data.estimated_dG_f, other_data.temperature) # calculate the change in free energy of reaction without accounting for metabolite concentrations
tcc_anoxic.calculate_dG_r(cobra_model_anoxic,metabolomics_data_anoxic.measured_concentrations, metabolomics_data_anoxic.estimated_concentrations,
other_data.pH, other_data.ionic_strength, other_data.temperature) # adjust the change in free energy of reaction for intracellular metabolite concentrations
tcc_anoxic.check_thermodynamicConsistency(cobra_model_anoxic,simulated_data_anoxic.fva_data,
metabolomics_data_anoxic.measured_concentrations,
metabolomics_data_anoxic.estimated_concentrations,
other_data.pH,other_data.ionic_strength,other_data.temperature) # check the thermodynamic consistency of the data
tcc_anoxic.export_dG0_r_json(data_dG0_anoxic) # save for later use
tcc_anoxic.export_dG_r_json(data_dG_anoxic) # save for later use
tcc_anoxic.export_tcc_json(data_ta_anoxic) # save for later use
tcc_anoxic.export_summary(cobra_model_anoxic,simulated_data_anoxic.fva_data,data_ta_anoxic) # write summary of the analysis to csv file
# calculate dG_r and perform a consistency check based on model simulations for oxic conditions
data_ta_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_ta.csv'
data_dG0_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_dG0.json'
data_dG_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_dG.json'
data_tcc_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_tcc.json'
tcc_oxic = thermodynamics_dG_r_data()
# tcc_oxic.import_dG0_r_json(data_dG0_oxic)
# tcc_oxic.import_dG_r_json(data_dG_oxic)
# tcc_oxic.import_tcc_json(data_tcc_oxic)
tcc_oxic.calculate_dG0_r(cobra_model_oxic, dG_f_data.measured_dG_f, dG_f_data.estimated_dG_f, other_data.temperature) # calculate the change in free energy of reaction without accounting for metabolite concentrations
tcc_oxic.calculate_dG_r(cobra_model_oxic,metabolomics_data_oxic.measured_concentrations, metabolomics_data_oxic.estimated_concentrations,
other_data.pH, other_data.ionic_strength, other_data.temperature) # adjust the change in free energy of reaction for intracellular metabolite concentrations
tcc_oxic.check_thermodynamicConsistency(cobra_model_oxic,simulated_data_oxic.fva_data,
metabolomics_data_oxic.measured_concentrations,
metabolomics_data_oxic.estimated_concentrations,
other_data.pH,other_data.ionic_strength,other_data.temperature) # check the thermodynamic consistency of the data
tcc_oxic.export_dG0_r_json(data_dG0_oxic) # save for later use
tcc_oxic.export_dG_r_json(data_dG_oxic) # save for later use
tcc_oxic.export_tcc_json(data_tcc_oxic) # save for later use
tcc_oxic.export_summary(cobra_model_oxic,simulated_data_oxic.fva_data,data_ta_oxic) # write summary of the analysis to csv file
##PART 6: Working
#-------
# inspect the thermodynamic analysis results
# constrain the model solution and simulate optimal growth
gr_analysis_anoxic = simulate_thermoConstraints(cobra_model_anoxic,['PGCD','ACACT1r','NDPK2'])
gr_analysis_oxic = simulate_thermoConstraints(cobra_model_oxic,['PGCD','ACACT1r'])
# expand the reaction set of the anoxic model to reflect the enzyme permiscuity of pykA
add_pykA(cobra_model_anoxic)
gr_analysis_anoxic = simulate_thermoConstraints(cobra_model_anoxic,['PGCD','ACACT1r','NDPK2'])
##PART 7: Working
#-------
# calculate the dG for biosynthetic pathways
# calculate the dG for biosynthetic pathways for anoxic conditions
tccp_anoxic = thermodynamics_dG_p_data()
tccp_anoxic.calculate_dG_p(cobra_model_anoxic,tcc_anoxic.dG0_r,tcc_anoxic.dG_r)
# calculate the dG for biosynthetic pathways for oxic conditions
tccp_oxic = thermodynamics_dG_p_data()
tccp_oxic.calculate_dG_p(cobra_model_oxic,tcc_oxic.dG0_r,tcc_oxic.dG_r)
# visualize the results
##TODO: methods are defined, but an example is not yet given
def _main_():
##PART 1: Working
#-------
# Read in the model sbml file and define the model conditions
# Anaerobic specific changes:
cobra_model_anoxic = load_thermoModel(anoxic=True)
#convert_to_irreversible(cobra_model_anoxic)
# Aerobic specific changes
cobra_model_oxic = load_thermoModel(anoxic=False)
#convert_to_irreversible(cobra_model_oxic)
##PART 2: Working
#-------
# make/load simulated data for anaerobic conditions
data_fva_anoxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_fva_anoxic.json'
data_srd_anoxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_srd_anoxic.json'
simulated_data_anoxic = thermodynamics_simulatedData()
# simulated_data_anoxic.generate_sra_data(cobra_model_anoxic) # perform single reaction deletion analysis
# simulated_data_anoxic.generate_fva_data(cobra_model_anoxic) # perform flux variability analysis
# simulated_data_anoxic.export_sra_data(data_srd_anoxic) # save results for later use
# simulated_data_anoxic.export_fva_data(data_fva_anoxic) # save results for later use
simulated_data_anoxic.import_sra_data(data_srd_anoxic)
simulated_data_anoxic.import_fva_data(data_fva_anoxic)
simulated_data_anoxic.check_data()
# make/load simulated data for aerobic conditions
data_fva_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_fva_oxic.json'
data_srd_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_srd_oxic.json'
simulated_data_oxic = thermodynamics_simulatedData()
# simulated_data_oxic.generate_sra_data(cobra_model_oxic) # perform single reaction deletion analysis
# simulated_data_oxic.generate_fva_data(cobra_model_oxic) # perform flux variability analysis
# simulated_data_oxic.export_sra_data(data_srd_oxic) # save results for later use
# simulated_data_oxic.export_fva_data(data_fva_oxic) # save results for later use
simulated_data_oxic.import_sra_data(data_srd_oxic)
simulated_data_oxic.import_fva_data(data_fva_oxic)
simulated_data_oxic.check_data()
##PART 3: Working
#-------
# load pH, ionic_strength, and temperature parameters
other_data = thermodynamics_otherData()
other_data.load_defaultData()
other_data.check_data()
# generate dG_f data for all model compounds
# calculate the dG_f for each compound in each compartment
# export/load and check the dG_f data
#data_dG0_transformed = 'thermodynamics_data/ijo1366_dG_f.json'
data_dG0_transformed = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_dG_f01.json'
dG_f_data = thermodynamics_dG_f_data(
id2KEGGID_filename_I=
'/home/user/code/thermodynamics/thermodynamics_data/id2KEGGID.csv')
# # # dG_f_data.make_dG0_f_pH0() # only if the data has not been generated previously!
# dG_f_data.get_transformed_dG_f('/home/user/code/thermodynamics/thermodynamics_data/compounds_dG0_f.json',cobra_model_oxic,other_data.pH,other_data.temperature,other_data.ionic_strength) # adjust the non-transformed dG0_f data to physiological pH, temperature, and ionic strength (this step has already been completed)
#dG_f_data.export_dG_f(data_dG0_transformed) # save results for later use
dG_f_data.import_dG_f(data_dG0_transformed)
dG_f_data.format_dG_f()
dG_f_data.generate_estimated_dG_f(cobra_model_oxic)
dG_f_data.check_data()
##PART 4: Working
#-------
# load metabolomics data for anoxic conditions
data_concentrations_anoxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_anoxic_geo01.json'
metabolomics_data_anoxic = thermodynamics_metabolomicsData()
metabolomics_data_anoxic.import_metabolomics_data(
data_concentrations_anoxic)
metabolomics_data_anoxic.format_metabolomics_data(
) # add compartment identifiers to metabolite ids
metabolomics_data_anoxic.generate_estimated_metabolomics_data(
cobra_model_anoxic)
# load metabolomics data for oxic conditions
data_concentrations_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_geo01.json'
metabolomics_data_oxic = thermodynamics_metabolomicsData()
metabolomics_data_oxic.import_metabolomics_data(data_concentrations_oxic)
metabolomics_data_oxic.format_metabolomics_data(
) # add compartment identifiers to metabolite ids
metabolomics_data_oxic.generate_estimated_metabolomics_data(
cobra_model_oxic)
#PART 5: Working
#-------
# calculate dG_r and perform a consistency check based on model simulations for anoxic conditions
data_ta_anoxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_anoxic_ta.csv'
data_dG0_anoxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_anoxic_dG0.json'
data_dG_anoxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_anoxic_dG.json'
data_tcc_anoxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_anoxic_tcc.json'
tcc_anoxic = thermodynamics_dG_r_data()
tcc_anoxic.calculate_dG0_r(
cobra_model_anoxic, dG_f_data.measured_dG_f, dG_f_data.estimated_dG_f,
other_data.temperature
) # calculate the change in free energy of reaction without accounting for metabolite concentrations
tcc_anoxic.calculate_dG_r(
cobra_model_anoxic, metabolomics_data_anoxic.measured_concentrations,
metabolomics_data_anoxic.estimated_concentrations, other_data.pH,
other_data.ionic_strength, other_data.temperature
) # adjust the change in free energy of reaction for intracellular metabolite concentrations
tcc_anoxic.check_thermodynamicConsistency(
cobra_model_anoxic, simulated_data_anoxic.fva_data,
metabolomics_data_anoxic.measured_concentrations,
metabolomics_data_anoxic.estimated_concentrations, other_data.pH,
other_data.ionic_strength, other_data.temperature
) # check the thermodynamic consistency of the data
tcc_anoxic.export_dG0_r_json(data_dG0_anoxic) # save for later use
tcc_anoxic.export_dG_r_json(data_dG_anoxic) # save for later use
tcc_anoxic.export_tcc_json(data_ta_anoxic) # save for later use
tcc_anoxic.export_summary(
cobra_model_anoxic, simulated_data_anoxic.fva_data,
data_ta_anoxic) # write summary of the analysis to csv file
# calculate dG_r and perform a consistency check based on model simulations for oxic conditions
data_ta_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_ta.csv'
data_dG0_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_dG0.json'
data_dG_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_dG.json'
data_tcc_oxic = '/home/user/code/thermodynamics/thermodynamics_data/aerobicAnaerobic01_geo/aerobicAnaerobic01_oxic_tcc.json'
tcc_oxic = thermodynamics_dG_r_data()
# tcc_oxic.import_dG0_r_json(data_dG0_oxic)
# tcc_oxic.import_dG_r_json(data_dG_oxic)
# tcc_oxic.import_tcc_json(data_tcc_oxic)
tcc_oxic.calculate_dG0_r(
cobra_model_oxic, dG_f_data.measured_dG_f, dG_f_data.estimated_dG_f,
other_data.temperature
) # calculate the change in free energy of reaction without accounting for metabolite concentrations
tcc_oxic.calculate_dG_r(
cobra_model_oxic, metabolomics_data_oxic.measured_concentrations,
metabolomics_data_oxic.estimated_concentrations, other_data.pH,
other_data.ionic_strength, other_data.temperature
) # adjust the change in free energy of reaction for intracellular metabolite concentrations
tcc_oxic.check_thermodynamicConsistency(
cobra_model_oxic, simulated_data_oxic.fva_data,
metabolomics_data_oxic.measured_concentrations,
metabolomics_data_oxic.estimated_concentrations, other_data.pH,
other_data.ionic_strength, other_data.temperature
) # check the thermodynamic consistency of the data
tcc_oxic.export_dG0_r_json(data_dG0_oxic) # save for later use
tcc_oxic.export_dG_r_json(data_dG_oxic) # save for later use
tcc_oxic.export_tcc_json(data_tcc_oxic) # save for later use
tcc_oxic.export_summary(
cobra_model_oxic, simulated_data_oxic.fva_data,
data_ta_oxic) # write summary of the analysis to csv file
##PART 6: Working
#-------
# inspect the thermodynamic analysis results
# constrain the model solution and simulate optimal growth
gr_analysis_anoxic = simulate_thermoConstraints(
cobra_model_anoxic, ['PGCD', 'ACACT1r', 'NDPK2'])
gr_analysis_oxic = simulate_thermoConstraints(cobra_model_oxic,
['PGCD', 'ACACT1r'])
# expand the reaction set of the anoxic model to reflect the enzyme permiscuity of pykA
add_pykA(cobra_model_anoxic)
gr_analysis_anoxic = simulate_thermoConstraints(
cobra_model_anoxic, ['PGCD', 'ACACT1r', 'NDPK2'])
##PART 7: Working
#-------
# calculate the dG for biosynthetic pathways
# calculate the dG for biosynthetic pathways for anoxic conditions
tccp_anoxic = thermodynamics_dG_p_data()
tccp_anoxic.calculate_dG_p(cobra_model_anoxic, tcc_anoxic.dG0_r,
tcc_anoxic.dG_r)
# calculate the dG for biosynthetic pathways for oxic conditions
tccp_oxic = thermodynamics_dG_p_data()
tccp_oxic.calculate_dG_p(cobra_model_oxic, tcc_oxic.dG0_r, tcc_oxic.dG_r)