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 _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_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
