def test_gibbs_energy(self):
warnings.simplefilter('ignore', ResourceWarning)
kegg_id_to_coeff = {'C00002' : -1, 'C00001' : -1,
'C00008' : 1, 'C00009' : 1} # ATP + H2O = ADP + Pi
kegg_id_to_conc = {'C00002' : 1e-3,
'C00009' : 1e-4}
reaction = Reaction(kegg_id_to_coeff)
cc = ComponentContribution(pH=7.0, ionic_strength=0.1)
dG0_prime, dG0_uncertainty = cc.dG0_prime(reaction)
self.assertAlmostEqual(dG0_prime, -26.4, 1)
self.assertAlmostEqual(dG0_uncertainty, 0.6, 1)
dG_prime, _ = cc.dG_prime(reaction, kegg_id_to_conc)
self.assertAlmostEqual(dG_prime, -32.1, 1)
dGm_prime, _ = cc.dGm_prime(reaction)
self.assertAlmostEqual(dGm_prime, -43.5, 1)
def predict_dG(model, pH=7, ionic_strength=0.1, fn=None):
eq_api = ComponentContribution(pH=7.0, ionic_strength=0.1)
lst = []
for r in model.reactions:
kegg_string = build_kegg_string(r)
if kegg_string:
try:
rxn = Reaction.parse_formula(kegg_string)
except KeyError:
print("eQuilibrator could not predict dG for {0}".format(r.id))
continue
# eq_api.dG0_prime(rxn)
try:
dgm = eq_api.dGm_prime(rxn)
dg0 = eq_api.dG0_prime(rxn)
except:
print("eQuilibrator could not predict dG for {0}".format(r.id))
else:
if dgm[-1] > dGMm_STD_THRESHOLD:
print(
"eQuilibrator could not predict dG for {0} with a reasonable uncertainty"
.format(r.id))
continue
print(r.id, dgm)
lst.append([
r.id, r.name, r.annotation["kegg.reaction"],
parse_reversibility(r), *dg0, *dgm
])
# Store as df
df = pd.DataFrame(lst)
df.columns = [
"Rxn", "Rxn Name", "Rxn KEGG ID", "Reversibility in model",
"dG0_prime", "dG0_prime_std", "dGm_prime", "dGm_prime_std"
]
if not fn:
fn = "eQuilibrator_reversibility.csv"
df.to_csv(fn, index=False, sep=",")
print("Found the dG for {0} of {1} reactions".format(
len(lst), len(model.reactions)))
return lst