def calculate_deltaG0s(model,
kegg_compounds,
pH=default_pH,
I=default_I,
T=default_T):
""" Calculate standard Gibbs Energy for reactions in model (as many as possible) using eQuilibrator.
Args:
model (CBModel): model
kegg_compounds (list): KEGG compounds accepted by eQuilibrator
pH (float): pH (default: 7.0)
I (float): ionic strenght (default: 0.25)
T (float): temperature (default: 298.15 K (25.0 C))
Returns:
dict: standard Gibbs Energies indexed by reaction ids
dict: estimation error indexed by reaction ids
"""
kegg_rxns = build_kegg_reactions(model, kegg_compounds)
kmodel = KeggModel.from_formulas(kegg_rxns.values(), raise_exception=True)
kmodel.add_thermo(CC.init())
dG0, sdG0, _ = kmodel.get_transformed_dG0(pH, I, T)
dG0 = dict(zip(kegg_rxns.keys(), dG0.A1))
sdG0 = dict(zip(kegg_rxns.keys(), sdG0.A1))
return dG0, sdG0
def __init__(self, reactions):
self.cc = ComponentContribution.init()
self.pH = 7.5
self.I = 0.2
self.T = default_T
self.R = R
self._not_balanced = []
self.reactions = []
self.Kmodel = self.generate_kegg_model(reactions)
self.dG0_prime = self.add_thermodynamics(reactions)
def __init__(self, reactions):
self.cc = ComponentContribution.init()
self.pH = 7.5
self.I = 0.2
self.T = default_T
self.R = R
self._not_balanced = []
self.reactions = []
self.Kmodel = self.generate_kegg_model(reactions)
self.dG0_prime = self.add_thermodynamics(reactions)
def __init__(self):
self.prepare_mappings()
reactions = self.get_reactions_from_model()
self.reactions = []
self._not_balanced = []
self.rstrings = []
for r in reactions:
k = r.kegg_reaction
if k:
if k.is_balanced() and not k.is_empty():
self.rstrings.append(k.write_formula())
self.reactions.append(r)
else:
self._not_balanced.append(r)
self.Kmodel = KeggModel.from_formulas(self.rstrings)
self.cc = ComponentContribution.init()
self.pH = 7.3
self.I = 0.25
self.RT = R * default_T
def __init__(self):
self.prepare_mappings()
reactions = self.get_reactions_from_model()
self.reactions = []
self._not_balanced = []
self.rstrings = []
for r in reactions:
k = r.kegg_reaction
if k:
if k.is_balanced() and not k.is_empty():
self.rstrings.append(k.write_formula())
self.reactions.append(r)
else:
self._not_balanced.append(r)
self.Kmodel = KeggModel.from_formulas(self.rstrings)
self.cc = ComponentContribution.init()
self.pH = 7.3
self.I = 0.25
self.RT = R * default_T
model.rids = rids
pH = fields.GetFloatField('PH', 7.5)
I = fields.GetFloatField('I', 0.2)
T = fields.GetFloatField('T', 298.15)
pathways.append({'entry': entry, 'model': model, 'fluxes': fluxes,
'bounds': bounds, 'pH': pH, 'I': I, 'T': T})
return pathways
if __name__ == '__main__':
#fname = sys.argv[1]
fname = 'mdf_pathways'
REACTION_FNAME = 'scripts/%s.txt' % fname
pathways = KeggFile2ModelList(REACTION_FNAME)
html_writer = HtmlWriter('res/%s.html' % fname)
cc = ComponentContribution.init()
matdict = {}
for p in pathways:
html_writer.write('<h2>%s</h2>' % p['entry'])
p['model'].add_thermo(cc)
mdf = MaxMinDrivingForce(p['model'], p['fluxes'], p['bounds'],
pH=p['pH'], I=p['I'], T=p['T'],
html_writer=html_writer)
mdf_solution, dGm_prime, dG0_std = mdf.Solve()
logging.info('Pathway %s: MDF = %.1f' % (p['entry'], mdf_solution))
matdict[p['entry'] + '.dGm_prime'] = dGm_prime
def _CalcGibbsEnerigesFromComponentContribution(self):
cc = ComponentContribution.init()
self.kegg_model.add_thermo(cc)
dG0_prime, dG0_std, sqrt_Sigma = self.kegg_model.get_transformed_dG0(
pH=7.5, I=0.1, T=298.15)
self.rid2dG0 = dict(zip(self.kegg_model.rids, dG0_prime.flat))
