def test_gecko_adjustment_sanchez_etal():
mmol_gdw = pd.Series.from_csv(
os.path.join(os.path.dirname(__file__),
'../geckopy/data_files/sanchez-mmol_gdw.csv'))
ggdw = pd.Series(PROTEIN_PROPERTIES.loc[mmol_gdw.index, 'mw'] /
1000.) * pd.Series(mmol_gdw)
model = GeckoModel('multi-pool')
growth_rate_unlimited_protein = model.slim_optimize()
model.limit_proteins(ggdw=pd.Series(ggdw))
growth_rate_limited_protein = model.slim_optimize()
# should be smaller, but how much..
assert growth_rate_limited_protein < 0.8 * growth_rate_unlimited_protein
measured_in_model = set(mmol_gdw.index).intersection(model.proteins)
assert sum(model.concentrations[p] - ggdw[p]
for p in measured_in_model) < 1e-10
assert sum(
abs(rxn.upper_bound - mmol_gdw[rxn.annotation['uniprot']])
for rxn in model.individual_protein_exchanges) < 1e-6
assert abs(model.p_measured - 0.296) < 1e-2 # section 3.2.6 reports 0.283
assert sum(rxn.metabolites[model.common_protein_pool] +
PROTEIN_PROPERTIES.loc[rxn.annotation['uniprot'], 'mw'] / 1000.
for rxn in model.pool_protein_exchanges) < 1e-6
# FIXME: section 3.2.6 reports 0.2154
assert abs(model.f_mass_fraction_measured_matched_to_total - 0.291) < 1e-2
# FIXME: provided model had 0.0168, value including p_base term is 0.0507 as in matlab..
assert abs(model.protein_pool_exchange.upper_bound - 0.0203) < 1e-2
sanchez_biomass = pd.Series.from_csv(
os.path.join(os.path.dirname(__file__),
'../geckopy/data_files/sanchez-biomass.csv'))
biomass = pd.Series(
dict((m.id, v) for m, v in model.reactions.r_4041.metabolites.items()))
# FIXME: poor match with provided model for ATP, ADP, H+, H20, P
assert sum((biomass - sanchez_biomass).abs()) < 20
def test_gecko_adjustment_sanchez_etal():
mmol_gdw = pd.read_csv(os.path.join(
os.path.dirname(__file__),
'../geckopy/data_files/sanchez-mmol_gdw.csv'),
index_col=0,
header=None,
squeeze=True)
ggdw = pd.Series(PROTEIN_PROPERTIES.loc[mmol_gdw.index, 'mw'] /
1000.) * pd.Series(mmol_gdw)
model = GeckoModel('multi-pool')
growth_rate_unlimited_protein = model.slim_optimize()
model.limit_proteins(ggdw=pd.Series(ggdw))
growth_rate_limited_protein = model.slim_optimize()
# should be smaller, but how much..
assert growth_rate_limited_protein < 0.8 * growth_rate_unlimited_protein
measured_in_model = set(mmol_gdw.index).intersection(model.proteins)
assert sum(model.concentrations[p] - ggdw[p]
for p in measured_in_model) < 1e-10
assert sum(
abs(rxn.upper_bound - mmol_gdw[rxn.annotation['uniprot']])
for rxn in model.individual_protein_exchanges) < 1e-6
assert sum(rxn.metabolites[model.common_protein_pool] +
PROTEIN_PROPERTIES.loc[rxn.annotation['uniprot'], 'mw'] / 1000.
for rxn in model.pool_protein_exchanges) < 1e-6
assert model.p_measured > 0.25 # With yeast 8.1.3 -> p_measured = 0.296
assert model.f_mass_fraction_measured_matched_to_total > 0.25 # With yeast 8.1.3 -> f = 0.304
assert model.protein_pool_exchange.upper_bound > 0.015 # With yeast 8.1.3 -> pool_exchange = 0.0212
def test_adjust_pool_bounds():
essential = {'P00498': 0., 'P00815': 0.}
in_model = {'P00549': 0.1, 'P31373': 0.1, 'P31382': 0.1, 'P39708': 0.1, 'P39714': 0.1, 'P39726': 0.1, 'Q01574': 0.1}
expected = set('prot_{}_exchange'.format(pool_id) for pool_id in essential)
measurements = pd.concat([pd.Series(in_model), pd.Series(essential)])
model = GeckoModel('multi-pool')
model.limit_proteins(fractions=pd.Series(measurements))
assert model.slim_optimize() < 1e-3
model.adjust_pool_bounds(inplace=False)
assert model.slim_optimize() < 1e-3
adjustment = model.adjust_pool_bounds(inplace=True)
observed = set(adjustment['reaction'])
assert abs(model.slim_optimize() - 0.05) < 1e-3
assert expected == observed