Check GRASP models

Format checks

These checks mostly check if the file syntax is valid.

Check the order of reactions and metabolites

Checks if the order of metabolites and reactions in all excel sheets is consistent with the order in the stoic sheet.

from set_up_grasp_models.check_models.format_checks import check_met_rxn_order

model_name = 'glycolysis_v3'
file_in = os.path.join('example_files', 'output', model_name + '.xlsx')
data_dict = pd.read_excel(file_in, sheet_name=None)


# check if the order of metabolites and reactions in all excel sheets is consistent
check_met_rxn_order(data_dict)

Check list separators in the kinetics sheet

Checks if metabolite lists are separated by commas, semi-colons, or dots. These should be separated by a single space.

from set_up_grasp_models.check_models.format_checks import check_kinetics_met_separators

model_name = 'glycolysis_v3'
file_in = os.path.join('example_files', 'output', model_name + '.xlsx')
data_dict = pd.read_excel(file_in, sheet_name=None)

# check metabolite lists separators in kinetics sheet
check_kinetics_met_separators(data_dict)

Check substrate order and product order columns in the kinetics sheet

Checks if the metabolites specified in the substrate order and product order columns are indeed substrates/products of the respective reaction. Metabolites marked as not active in the mets sheet are not considered.

from set_up_grasp_models.check_models.format_checks import check_kinetics_subs_prod_order

model_name = 'glycolysis_v3'
file_in = os.path.join('example_files', 'output', model_name + '.xlsx')
data_dict = pd.read_excel(file_in, sheet_name=None)

# checks if metabolite names in subtrate/product order columns are indeed
#  substrates/products of the respective reaction
check_kinetics_subs_prod_order(data_dict)

Checks if reactions with enzymatic mechanisms come before reaction with non-enzymatic mechanisms

In particular it checks if reactions with fixedExchange mechanisms are the last ones and reactions with massAction, freeExchange, and diffusion come after reactions with enzymatic mechanisms (e.g. orderedBiBi) and before fixedExchange.

from set_up_grasp_models.check_models.format_checks import check_rxn_mechanism_order

model_name = 'glycolysis_v3'
file_in = os.path.join('example_files', 'output', model_name + '.xlsx')
data_dict = pd.read_excel(file_in, sheet_name=None)

# checks if massAction/diffusion/freeExchange mechanism come after other enzyme
#  mechanisms and fixedExchange comes at the end
check_rxn_mechanism_order(data_dict)

Thermodynamic feasibility checks

This check checks thermodynamic feasibility, i.e. consistency between Gibbs free energies and fluxes.

Check if Gibbs energies and fluxes are compatible

Checks if the mean flux for each reaction is compatible with the respective Gibbs free energy range.

If not all fluxes are defined in the measRates sheet, but enough fluxes are defined it calculates the remaining fluxes by solving \(S_ {unknown} * v_{unknown} = S_{known} * v_{known}\).

from set_up_grasp_models.check_models.thermodynamics_checks import check_thermodynamic_feasibility

model_name = 'glycolysis_v3'
file_in = os.path.join('example_files', 'output', model_name + '.xlsx')
data_dict = pd.read_excel(file_in, sheet_name=None)

# checks if massAction/diffusion/freeExchange mechanism come after other enzyme
#  mechanisms and fixedExchange comes at the end
flag, flux_df, dG_df = check_thermodynamic_feasibility(data_dict)

Mass balance checks

These checks check metabolite mass balance.

Check if metabolites are balanced based on stoichiometric matrix only

Goes through the stoichiometric matrix and checks:

• if a metabolite is both consumed and produced and if so, whether or not it is marked as balanced in the mets sheet;

• if a metabolite is only consumed and produced and if so, whether or not it is marked as balanced and fixed in the mets sheet;

The idea is that if a met is only consumed or produced it should be marked as unbalanced and fixed and if it is both consumed and produced it might actually be balanced. However, in the latter case it is possible to have many false positives, since having a metabolite that is both consumed and produced doesn’t mean it is mass balanced. To decide that flux data is needed.

from set_up_grasp_models.check_models.mass_balance_checks import check_balanced_metabolites

model_name = 'glycolysis_v3'
file_in = os.path.join('example_files', 'output', model_name + '.xlsx')
data_dict = pd.read_excel(file_in, sheet_name=None)

# check consistency between stoic sheet and mets sheet
check_balanced_metabolites(data_dict)

Check if metabolites are mass balanced based on reaction fluxes

If all reactions fluxes are defined in the measRates sheet, checks if all metabolites are mass balanced.

If enough fluxes are defined it calculates the remaining fluxes by solving \(S_ {unknown} * v_{unknown} = S_{known} * v_{known}\), and checks if all metabolites are mass balanced.

from set_up_grasp_models.check_models.mass_balance_checks import check_flux_balance

model_name = 'glycolysis_v3'
file_in = os.path.join('example_files', 'output', model_name + '.xlsx')
data_dict = pd.read_excel(file_in, sheet_name=None)

# check if all metabolites marked as balanced are indeed mass balanced
check_flux_balance(data_dict)