def make_13CEnsemble(self,formula_str_I):
'''Make formula for m + 0 to m + # carbons'''
# input:
# formula_str_I = string of formula
# output:
# mass_ensemble_O = ensemble of distributions
formula_str = re.sub('[+-]', '', formula_str_I) # remove '-' or '+'
formula = Formula(formula_str);
mass_ensemble_O = {};
if 'C' in formula._elements:
nC = formula._elements['C'][0]; # count the number of carbon;
for c in range(nC+1):
tmp = Formula(formula_str);
if c==0:tmp._elements['C'] = {0:nC-c};
elif nC-c==0:tmp._elements['C'] = {13:c};
else:tmp._elements['C'] = {0:nC-c, 13:c};
mass_ensemble_O[c] = Formula(tmp.formula);
return mass_ensemble_O;
else:
nC = 0;
mass_ensemble_O = {0:formula};
return mass_ensemble_O
def execute_scheduledMRMPro_quant(self,met_ids_I):
'''generate the MRMs for each compound for the scheduled MRM pro acquisition method'''
# input:
# met_ids_I = [{'met_id': , 'precursor_formula':, 'product_formula':},]
# ouptput:
# dictionary of MRMs
mrms_O = [];
# loop over each met_id
for met in met_ids_I:
# query transition from the tuning method
trans = self.get_row_MSComponents_metIDAndFormula(met['met_id'],met['precursor_formula'],met['product_formula'],'tuning');
transUC13 = trans.copy();
# make component_name, group_name
trans['component_name'] = met['met_id'] + '.' + met['met_id'] + '_' + str(trans['quantifier']) + '.Light';
trans['ms_group'] = met['met_id'];
trans['ms_methodtype'] = 'quantification';
# make UC13 component_name, group_name
transUC13['component_name'] = met['met_id'] + '.' + met['met_id'] + '_' + str(trans['quantifier']) + '.Heavy';
transUC13['met_ID'] = met['met_id'] + '-UC13';
transUC13['met_name'] = transUC13['met_name'] + '-UC13';
transUC13['ms_group'] = met['met_id'] + '-UC13';
transUC13['ms_methodtype'] = 'quantification';
# make UC13 equivalent: q1/q3_mass, precursor/product_formula, precursor/product_exactmass
if trans['precursor_formula']:
trans_precursor_formula = Formula(re.sub('[+-]', '', trans['precursor_formula'])) # remove '-' or '+'
trans['precursor_formula'] = trans_precursor_formula.formula + trans['ms_mode'];
trans['precursor_exactmass'] = trans_precursor_formula.isotope.mass;
if 'C' in list(trans_precursor_formula._elements.keys()):
nC = trans_precursor_formula._elements['C'][0];
tmp = Formula(trans_precursor_formula.formula);
tmp._elements['C'] = {13:nC};
transUC13_precursor_formula = Formula(tmp.formula);
else:
transUC13_precursor_formula = trans_precursor_formula;
transUC13['precursor_formula'] = transUC13_precursor_formula.formula + trans['ms_mode'];
transUC13['precursor_exactmass'] = transUC13_precursor_formula.isotope.mass;
# substitute for algorithm that checks for unique q1_masses
# therefore, must ensure that each q1_mass/q3_mass is unique for a given mode in ms_components
transUC13['q1_mass'] = trans['q1_mass'] + transUC13_precursor_formula.isotope.mass - trans_precursor_formula.isotope.mass;
if trans['product_formula']:
trans_product_formula = Formula(re.sub('[+-]', '', trans['product_formula'])) # remove '-' or '+'
trans['product_formula'] = trans_product_formula.formula + trans['ms_mode'];
trans['product_exactmass'] = trans_product_formula.isotope.mass;
if 'C' in list(trans_product_formula._elements.keys()):
nC = trans_product_formula._elements['C'][0];
tmp = Formula(trans_product_formula.formula);
tmp._elements['C'] = {13:nC};
transUC13_product_formula = Formula(tmp.formula);
else:
transUC13_product_formula = trans_product_formula;
transUC13['product_formula'] = transUC13_product_formula.formula + trans['ms_mode'];
transUC13['product_exactmass'] = transUC13_product_formula.isotope.mass;
# substitute for algorithm that checks for unique q1_masses
# therefore, must ensure that each q1_mass/q3_mass is unique for a given mode in ms_components
transUC13['q3_mass'] = trans['q3_mass'] + transUC13_product_formula.isotope.mass - trans_product_formula.isotope.mass;
# set defaults: window = 120 sec, dwell = 1, priority, ms_include = False
mrms_O.append(trans);
mrms_O.append(transUC13);
self.add_MSComponents(mrms_O);
def _parse_model_sbml(self,model_id_I,date_I,filename_I):
# Read in the sbml file and define the model conditions
cobra_model = create_cobra_model_from_sbml_file(filename_I, print_time=True)
model_data = [];
model_data_tmp = {};
# parse out model metadata
model_data_tmp['model_id'] = model_id_I;
model_data_tmp['model_name'] = None;
model_data_tmp['date'] = date_I;
model_data_tmp['model_description'] = cobra_model.description;
with open(filename_I, 'r') as f:
model_data_tmp['model_file'] = f.read();
model_data_tmp['file_type'] = 'sbml'
model_data.append(model_data_tmp)
reaction_data = [];
# parse out reaction data
for r in cobra_model.reactions:
reaction_data_dict = {};
reaction_data_dict['model_id'] = model_id_I
reaction_data_dict['rxn_id'] = r.id
reaction_data_dict['rxn_name'] = r.name
reaction_data_dict['equation'] = r.build_reaction_string()
reaction_data_dict['subsystem'] = r.subsystem
reaction_data_dict['gpr'] = r.gene_reaction_rule
reaction_data_dict['genes'] = []
genes = r.genes;
for g in genes:
reaction_data_dict['genes'].append(g.id);
reaction_data_dict['reactants_stoichiometry'] = [];
reaction_data_dict['reactants_ids'] = [];
reactants = r.reactants;
for react in reactants:
reaction_data_dict['reactants_stoichiometry'].append(r.get_coefficient(react.id));
reaction_data_dict['reactants_ids'].append(react.id);
reaction_data_dict['products_stoichiometry'] = [];
reaction_data_dict['products_ids'] = [];
products = r.products;
for prod in products:
reaction_data_dict['products_stoichiometry'].append(r.get_coefficient(prod.id));
reaction_data_dict['products_ids'].append(prod.id);
reaction_data_dict['lower_bound'] = r.lower_bound
reaction_data_dict['upper_bound'] = r.upper_bound
reaction_data_dict['objective_coefficient'] = r.objective_coefficient
reaction_data_dict['flux_units'] = 'mmol*gDW-1*hr-1'
reaction_data_dict['reversibility'] = r.reversibility
#reaction_data_dict['reactants_stoichiometry_tracked'] = None;
#reaction_data_dict['products_stoichiometry_tracked'] = None;
#reaction_data_dict['reactants_ids_tracked'] = None;
#reaction_data_dict['products_ids_tracked'] = None;
#reaction_data_dict['reactants_mapping'] = None;
#reaction_data_dict['products_mapping'] = None;
#reaction_data_dict['rxn_equation'] = None;
reaction_data_dict['fixed'] = None;
reaction_data_dict['free'] = None;
reaction_data_dict['weight'] = None;
reaction_data_dict['used_'] = True
reaction_data_dict['comment_'] = None;
reaction_data.append(reaction_data_dict);
metabolite_data = [];
# parse out metabolite data
for met in cobra_model.metabolites:
metabolite_data_tmp = {};
metabolite_data_tmp['model_id'] = model_id_I
metabolite_data_tmp['met_name'] = met.name;
metabolite_data_tmp['met_id'] = met.id;
formula = {};
for k,v in met.formula.elements.items():
formula[k] = {0:v};
tmp = Formula()
tmp._elements=formula
metabolite_data_tmp['formula'] = tmp.formula;
metabolite_data_tmp['charge'] = met.charge
metabolite_data_tmp['compartment'] = met.compartment
metabolite_data_tmp['bound'] = met._bound
metabolite_data_tmp['constraint_sense'] = met._constraint_sense
#metabolite_data_tmp['met_elements'] = None;
#metabolite_data_tmp['met_atompositions'] = None;
#metabolite_data_tmp['balanced'] = None;
#metabolite_data_tmp['met_symmetry'] = None;
#metabolite_data_tmp['met_symmetry_atompositions'] = None;
metabolite_data_tmp['used_'] = True
metabolite_data_tmp['comment_'] = None;
metabolite_data.append(metabolite_data_tmp);
return model_data,reaction_data,metabolite_data
def _parse_json_sbml_cobra_model(cobra_model, model_id, date, model_file_name,
filetype):
"""
Helper function for parse_cobra_model(), parses reaction- and metabolite
information out of an already loaded cobra model
Parameters
----------
cobra_model : cobra.Model
Cobra metabolic model as loaded by the file type specific import
function
model_id : str
Name of the model (for downstream reference)
date : str
Date of model processing (for downstream reference)
model_file_name : str or path + str
Filename or path to file + filename of the cobra metabolic model
filetype : str
Extension of the provided file
Returns
-------
model_data : pandas.DataFrame
General information about the processed metabolic model
reaction_data : pandas.DataFrame
Information about the reactions in the metabolic model
metabolite_data : pandas.DataFrame
Information about the metabolites in the metabolic model
"""
# Pre-process the model file information
with open(model_file_name, "r", encoding="utf-8") as f:
model_file = f.read()
# parse out model data
model_data = pd.DataFrame(
{
"model_id": model_id,
"date": date,
"model_description": cobra_model.description,
"model_file": model_file,
"file_type": filetype,
},
index=[0],
)
# parse out reaction data
reaction_data_temp = {}
for cnt, r in enumerate(cobra_model.reactions):
reaction_data_dict = {
"model_id":
model_id,
"rxn_id":
r.id,
"rxn_name":
r.name,
"equation":
r.build_reaction_string(),
"subsystem":
r.subsystem,
"gpr":
r.gene_reaction_rule,
"genes": [g.id for g in r.genes],
"reactants_stoichiometry":
[r.get_coefficient(react.id) for react in r.reactants],
"reactants_ids": [react.id for react in r.reactants],
"products_stoichiometry":
[r.get_coefficient(prod.id) for prod in r.products],
"products_ids": [prod.id for prod in r.products],
"lower_bound":
r.lower_bound,
"upper_bound":
r.upper_bound,
"objective_coefficient":
r.objective_coefficient,
"flux_units":
"mmol*gDW-1*hr-1",
"reversibility":
r.reversibility,
"used_":
True,
}
reaction_data_temp[cnt] = reaction_data_dict
reaction_data = pd.DataFrame.from_dict(reaction_data_temp, "index")
# parse out metabolite data
metabolite_data_tmp = {}
for cnt, met in enumerate(cobra_model.metabolites):
# Pre-process formulas using FIA-MS database methods
if is_valid(met):
formula = Formula(met.formula)
formula = str(formula)
else:
formula = None
# set up part of temp dict to transform into df later
metabolite_data_dict = {
"model_id": model_id,
"met_name": met.name,
"met_id": met.id,
"formula": formula,
"charge": met.charge,
"compartment": met.compartment,
"bound": met._bound,
"annotations": met.annotation,
"used_": True,
}
metabolite_data_tmp[cnt] = metabolite_data_dict
metabolite_data = pd.DataFrame.from_dict(metabolite_data_tmp, "index")
return model_data, reaction_data, metabolite_data
def sort_parameter_info(self, f_par_info, simulation_info, fittedData):
"""
Seperate the information from the original input, the
"get_fitted_parameters(f, simulation_info)"
function, the "extract_file_info(filename)" function and the
"extract_base_stats(f, simulation_id, info)" function
into appropriate rows
Parameters
----------
f_par_info : dict
output of the
get_fitted_parameters(f, simulation_info) function
simulation_info : pandas.DataFrame
the MS fragment file corresponding to
the simulation
fittedData : pandas.DataFrame
the output of the
"extract_base_stats(f, simulation_id, info)" function
Returns
-------
fittedFluxes : pandas.DataFrame
info about the parameters of the fluxes used
as an input for the simulation
fittedFragments : pandas.DataFrame
info about the parameters of the MS data
used as an input for the simulation
"""
fittedFluxes = {}
fittedFragments = {}
rxn_id = f_par_info["rxn_id"]
flux = f_par_info["flux"]
flux_stdev = f_par_info["flux_stdev"]
par_type = f_par_info["par_type"]
flux_lb = f_par_info["flux_lb"]
flux_ub = f_par_info["flux_ub"]
flux_units = f_par_info["flux_units"]
fit_alf = f_par_info["fit_alf"]
free = f_par_info["free"]
# f_par_chi2s = f_par_info["fit_chi2s"]
# f_par_cor = f_par_info["fit_cor"]
# f_par_cov = f_par_info["fit_cov"]
simulation_id = fittedData["simulation_id"].unique()[0]
simulation_dateAndTime = fittedData["simulation_dateAndTime"].unique(
)[0]
for cnt, p_type in enumerate(par_type):
if p_type == "Net flux":
fittedFluxes[cnt] = {
"simulation_id": simulation_id,
"simulation_dateAndTime": simulation_dateAndTime,
"rxn_id": rxn_id[cnt],
"flux": flux[cnt],
"flux_stdev": flux_stdev[cnt],
"flux_lb": flux_lb[cnt],
"flux_ub": flux_ub[cnt],
"flux_units": flux_units[cnt],
"fit_alf": fit_alf[cnt],
"fit_chi2s": None,
"fit_cor": None,
"fit_cov": None,
"free": free[cnt],
"used_": True,
"comment_": None,
}
elif p_type == "Norm":
# parse the id
id_list = rxn_id[cnt].split(" ")
expt = id_list[0]
fragment_id = id_list[1]
fragment_string = id_list[2]
units = id_list[3]
# parse the id into fragment_id and mass
fragment_string = re.sub("_DASH_", "-", fragment_string)
fragment_string = re.sub("_LPARANTHES_", "[(]",
fragment_string)
fragment_string = re.sub("_RPARANTHES_", "[)]",
fragment_string)
fragment_list = fragment_string.split("_")
if not len(fragment_list) > 5 or not ("MRM" in fragment_list or
"EPI" in fragment_list):
fragment_mass = Formula(fragment_list[2]).mass + float(
fragment_list[3])
time_point = fragment_list[4]
else:
fragment_mass = Formula(fragment_list[2]).mass + float(
fragment_list[4])
time_point = fragment_list[5]
if expt in list(simulation_info["experiment_id"]):
fittedFragments[cnt] = {
"simulation_id":
simulation_id,
"simulation_dateAndTime":
simulation_dateAndTime,
"experiment_id":
expt,
"sample_name_abbreviation":
simulation_info["sample_name_abbreviation"][0],
"time_point":
time_point,
"fragment_id":
fragment_id,
"fragment_mass":
fragment_mass,
"fit_val":
flux[cnt],
"fit_stdev":
flux_stdev[cnt],
"fit_units":
units,
"fit_alf":
fit_alf[cnt],
"fit_cor":
None,
"fit_cov":
None,
"free":
free[cnt],
"used_":
True,
"comment_":
None,
}
elif expt in list(simulation_info["sample_name_abbreviation"]):
fittedFragments[cnt] = {
"simulation_id": simulation_id,
"simulation_dateAndTime": simulation_dateAndTime,
"experiment_id": simulation_info["experiment_id"][0],
"sample_name_abbreviation": expt,
"time_point": time_point,
"fragment_id": fragment_id,
"fragment_mass": fragment_mass,
"fit_val": flux[cnt],
"fit_stdev": flux_stdev[cnt],
"fit_units": units,
"fit_alf": fit_alf[cnt],
"fit_cor": None,
"fit_cov": None,
"free": free[cnt],
"used_": True,
"comment_": None,
}
else:
print("type not recognized")
fittedFluxes = pd.DataFrame.from_dict(fittedFluxes, "index")
fittedFragments = pd.DataFrame.from_dict(fittedFragments, "index")
return fittedFluxes, fittedFragments
def sort_residual_info(self, f_mnt_res_info, simulation_info, fittedData):
"""
Seperate the information from the original input, the
"get_residuals_info(f)" function, the "extract_file_info(filename)"
function and the "extract_base_stats(f, simulation_id, info)" function
into appropriate rows
Parameters
----------
f_mnt_res_info : dict
the output of the "get_residuals_info(f)" function
simulation_info : pandas.DataFrame
the MS fragment file corresponding to
the simulation
fittedData : pandas.DataFrame
the output of the
"extract_base_stats(f, simulation_id, info)" function
Returns
-------
fittedMeasuredFluxResiduals : pandas.DataFrame
info about the residuals of the
fluxes used as an input for the simulation
fittedMeasuredFragmentResiduals : pandas.DataFrame
info about the residuals of
the fragments in the MS data used as an input for
the simulation
"""
fittedMeasuredFluxResiduals = {}
fittedMeasuredFragmentResiduals = {}
expt_type = f_mnt_res_info["expt_type"]
experiment_id = f_mnt_res_info["experiment_id"]
time_point = f_mnt_res_info["time_point"]
rxn_id = f_mnt_res_info["rxn_id"]
res_data = f_mnt_res_info["res_data"]
res_fit = f_mnt_res_info["res_fit"]
res_peak = f_mnt_res_info["res_peak"]
res_stdev = f_mnt_res_info["res_stdev"]
res_val = f_mnt_res_info["res_val"]
simulation_id = fittedData["simulation_id"].unique()[0]
simulation_dateAndTime = fittedData["simulation_dateAndTime"].unique(
)[0]
for cnt, x_type in enumerate(expt_type):
if x_type == "Flux":
if experiment_id[cnt] in list(
simulation_info["experiment_id"]):
fittedMeasuredFluxResiduals[cnt] = {
"simulation_id":
simulation_id,
"simulation_dateAndTime":
simulation_dateAndTime,
"experiment_id":
experiment_id[cnt],
"sample_name_abbreviation":
simulation_info["sample_name_abbreviation"][0],
"time_point":
time_point[cnt],
"rxn_id":
rxn_id[cnt],
"res_data":
float(res_data[cnt]),
"res_fit":
float(res_fit[cnt]),
"res_peak":
res_peak[cnt],
"res_stdev":
float(res_stdev[cnt]),
"res_val":
float(res_val[cnt]),
"res_msens":
None,
"res_esens":
None,
"used_":
True,
"comment_":
None,
}
elif experiment_id[cnt] in list(
simulation_info["sample_name_abbreviation"]):
fittedMeasuredFluxResiduals[cnt] = {
"simulation_id": simulation_id,
"simulation_dateAndTime": simulation_dateAndTime,
"experiment_id": simulation_info["experiment_id"][0],
"sample_name_abbreviation": experiment_id[cnt],
"time_point": time_point[cnt],
"rxn_id": rxn_id[cnt],
"res_data": float(res_data[cnt]),
"res_fit": float(res_fit[cnt]),
"res_peak": res_peak[cnt],
"res_stdev": float(res_stdev[cnt]),
"res_val": float(res_val[cnt]),
"res_msens": None,
"res_esens": None,
"used_": True,
"comment_": None,
}
elif x_type == "MS":
# parse the id into fragment_id and mass
fragment_string = rxn_id[cnt]
fragment_string = re.sub("_DASH_", "-", fragment_string)
fragment_string = re.sub("_LPARANTHES_", "[(]",
fragment_string)
fragment_string = re.sub("_RPARANTHES_", "[)]",
fragment_string)
fragment_list = fragment_string.split("_")
if not len(fragment_list) > 5 or not ("MRM" in fragment_list or
"EPI" in fragment_list):
fragment_id = "_".join(
[fragment_list[0], fragment_list[1], fragment_list[2]])
fragment_mass = Formula(fragment_list[2]).mass + float(
fragment_list[3])
time_point = fragment_list[4]
else:
fragment_id = "_".join([
fragment_list[0],
fragment_list[1],
fragment_list[2],
fragment_list[3],
])
fragment_mass = Formula(fragment_list[2]).mass + float(
fragment_list[4])
time_point = fragment_list[5]
fragment_id = re.sub("-", "_DASH_", fragment_id)
fragment_id = re.sub("[(]", "_LPARANTHES_", fragment_id)
fragment_id = re.sub("[)]", "_RPARANTHES_", fragment_id)
if experiment_id[cnt] in list(
simulation_info["experiment_id"]):
fittedMeasuredFragmentResiduals[cnt] = {
"simulation_id":
simulation_id,
"simulation_dateAndTime":
simulation_dateAndTime,
"experiment_id":
experiment_id[cnt],
"sample_name_abbreviation":
simulation_info["sample_name_abbreviation"][0],
"time_point":
time_point,
"fragment_id":
fragment_id,
"fragment_mass":
fragment_mass,
"res_data":
float(res_data[cnt]),
"res_fit":
float(res_fit[cnt]),
"res_peak":
res_peak[cnt],
"res_stdev":
float(res_stdev[cnt]),
"res_val":
float(res_val[cnt]),
"res_msens":
None,
"res_esens":
None,
"used_":
True,
"comment_":
None,
}
elif experiment_id[cnt] in list(
simulation_info["sample_name_abbreviation"]):
fittedMeasuredFragmentResiduals[cnt] = {
"simulation_id": simulation_id,
"simulation_dateAndTime": simulation_dateAndTime,
"experiment_id": simulation_info["experiment_id"][0],
"sample_name_abbreviation": experiment_id[cnt],
"time_point": time_point[cnt],
"fragment_id": fragment_id,
"fragment_mass": fragment_mass,
"res_data": float(res_data[cnt]),
"res_fit": float(res_fit[cnt]),
"res_peak": res_peak[cnt],
"res_stdev": float(res_stdev[cnt]),
"res_val": float(res_val[cnt]),
"res_msens": None,
"res_esens": None,
"used_": True,
"comment_": None,
}
else:
print("type not recognized")
fittedMeasuredFluxResiduals = pd.DataFrame.from_dict(
fittedMeasuredFluxResiduals, "index")
fittedMeasuredFragmentResiduals = pd.DataFrame.from_dict(
fittedMeasuredFragmentResiduals, "index")
return fittedMeasuredFluxResiduals, fittedMeasuredFragmentResiduals