def __init__(self):
self.model = create_cobra_model_from_sbml_file("../data/iJO1366.xml")
# Modify model
convert_to_irreversible(self.model)
self.rxns = dict([(r.id, r) for r in self.model.reactions])
self.genes = dict([(g.id, g) for g in self.model.genes])
add_to_model(self.model)
self.include_specific_isozmyes()
self.gc = pd.DataFrame.from_csv("../data/growth_conditions.csv")
flux = pd.DataFrame.from_csv('../data/flux[mmol_gCDW_h].csv')
self.v = self._convert_mmol_gCDW_h_to_mmol_gCDW_s(flux)
# PPKr_reverse reaction is used for ATP generation from ADP
# in the FBA model. Nevertheless, acording to EcoCyc, it is used to
# to generate polyP (inorganic phosphate) chains from ATP and it is not
# part of the oxidative phosphorilation, thus removed from rate calculations
if 'PPKr_reverse' in self.v.index:
self.v.drop('PPKr_reverse', axis=0, inplace=True)
self.enzymatic_reactions = self._enzymatic_reactions()
self.homomeric_reactions = self.reactions_by_homomeric_enzymes()
proteins_copies_fL = pd.DataFrame.from_csv(
'../data/meta_abundance[copies_fL].csv')
self.proteins_mmol_gCDW = self._convert_copies_fL_to_mmol_gCDW(
proteins_copies_fL)
self.E = self.map_expression_by_reaction()
self.kapp = self.get_kapp() # per subunit
self.SA = self.get_specific_activity()
self.kcat = pd.DataFrame.from_csv("../data/kcat_data.csv")
self.p_per_as = (self.kcat['polypeptides per complex'] /
self.kcat['catalytic sites per complex'])
self.kmax = self.get_kmax(self.kapp)
self.SAmax = self.get_maximum_specific_activity(self.SA)
def __init__(self):
self.model = create_cobra_model_from_sbml_file("../data/iJO1366.xml")
# Modify model
convert_to_irreversible(self.model)
self.rxns = dict([(r.id, r) for r in self.model.reactions])
self.genes = dict([(g.id, g) for g in self.model.genes])
add_to_model(self.model)
self.include_specific_isozmyes()
self.gc = pd.DataFrame.from_csv("../data/growth_conditions.csv")
flux = pd.DataFrame.from_csv('../data/flux[mmol_gCDW_h].csv')
self.v = self._convert_mmol_gCDW_h_to_mmol_gCDW_s(flux)
# PPKr_reverse reaction is used for ATP generation from ADP
# in the FBA model. Nevertheless, acording to EcoCyc, it is used to
# to generate polyP (inorganic phosphate) chains from ATP and it is not
# part of the oxidative phosphorilation, thus removed from rate calculations
if 'PPKr_reverse' in self.v.index:
self.v.drop('PPKr_reverse', axis=0, inplace=True)
self.enzymatic_reactions = self._enzymatic_reactions()
self.homomeric_reactions = self.reactions_by_homomeric_enzymes()
proteins_copies_fL = pd.DataFrame.from_csv('../data/meta_abundance[copies_fL].csv')
self.proteins_mmol_gCDW = self._convert_copies_fL_to_mmol_gCDW(proteins_copies_fL)
self.E = self.map_expression_by_reaction()
self.kapp = self.get_kapp() # per subunit
self.SA = self.get_specific_activity()
self.kcat = pd.DataFrame.from_csv("../data/kcat_data.csv")
self.p_per_as = (self.kcat['polypeptides per complex']
/ self.kcat['catalytic sites per complex'])
self.kmax = self.get_kmax(self.kapp)
self.SAmax = self.get_maximum_specific_activity(self.SA)
# reversibility index
N_P = sum([v for v in r.metabolites.itervalues() if v > 0])
N_S = sum([-v for v in r.metabolites.itervalues() if v < 0])
N = N_P + N_S
fixed_conc = 0.1
r.logRI = (2 / N) * (r.logKeq + (N_P - N_S) * np.log(fixed_conc))
for r in set(reactions) - set(self.reactions):
r.dG0_prime = np.NaN
r.dGm_prime = np.NaN
r.logKeq = np.NaN
r.logRI = np.NaN
return dG0_prime
if __name__ == "__main__":
from model_addons import add_to_model
model_fname = "../shared_data/iJO1366.xml"
model = create_cobra_model_from_sbml_file(model_fname)
add_to_model(model)
reactions = [
'MDH', 'FBA', 'TPI', 'FBP', 'PGM', 'SERAT', 'TMDS', 'DBTS', 'DM_4CRSOL'
]
reactions = ['PPS']
reactions = map(model.reactions.get_by_id, reactions)
# reactions = model.reactions
Th = reaction_thermodynamics(reactions)
def filter_reactions(metabolite, only_irreversible=True):
metabolite_forming_reactions = [r for r in metabolite.reactions
if metabolite in r.products]
homomeric_reactions = filter(lambda r: 'and' not in r.gene_reaction_rule,
metabolite_forming_reactions)
reactions = {r:r.dG0_prime for r in homomeric_reactions}
if only_irreversible:
reactions = {k:v for k,v in reactions.iteritems() if v<10}
return reactions
#model = create_cobra_model_from_sbml_file('../data/iJO1366.xml')
model = create_cobra_model_from_sbml_file('/home/yinonbaron/git/shared_data/iJO1366.xml')
add_to_model(model)
tr = reaction_thermodynamics(model.reactions)
nadh = model.metabolites.get_by_id('nadh_c')
# reactions which produce NADH, and are thermodynamically favorable
# Also, all reactions are catalyzed by homomeric enzymes.
nadh_forming = filter_reactions(nadh, only_irreversible=True)
water = model.metabolites.get_by_id('h2o_c')
first_hop = defaultdict(list)
for r in nadh_forming:
for m in r.reactants:
if m not in [water, nadh]:
homomeric_and_favorable = filter_reactions(m)
for k in homomeric_and_favorable.iterkeys():
if k not in nadh_forming:
document = parse(model_fname)
ec_list = []
for r_elem in document.getElementsByTagName('reaction'):
for p_elem in r_elem.getElementsByTagName('p'):
val = p_elem.childNodes[0].nodeValue
if val.find('EC Number:') != -1:
ec = val[11:].encode('utf-8')
ec_list.append(ec)
return ec_list
a = map_model_reactions_to_EC("../data/iJO1366.xml")
m = create_cobra_model_from_sbml_file("../data/iJO1366.xml")
add_to_model(m)
#b = set(filter(None, a))
brenda = pd.DataFrame.from_csv("../data/BRENDA_data.csv")
brenda = brenda[brenda["Organism ID"] == 6] # ecoli reactions
brenda = brenda[brenda["kcat"] > 0]
measured_ecs = set(brenda.index)
out = pd.DataFrame(index=a)
out['reaction'] = [r.id for r in m.reactions]
out['subsystem'] = [r.subsystem for r in m.reactions]
out['kcat'] = [1 if ec in brenda.index else 'NaN' for ec in out.index]
out[out['kcat'] != 1]
out.drop('', inplace=True)
out.drop('', inplace=True)
out['EC'] = out.index
''' parse the sbml model file to extract EC numbers of reactions '''
document = parse(model_fname)
ec_list = []
for r_elem in document.getElementsByTagName('reaction'):
for p_elem in r_elem.getElementsByTagName('p'):
val = p_elem.childNodes[0].nodeValue
if val.find('EC Number:') != -1:
ec = val[11:].encode('utf-8')
ec_list.append(ec)
return ec_list
a = map_model_reactions_to_EC("../data/iJO1366.xml")
m = create_cobra_model_from_sbml_file("../data/iJO1366.xml")
add_to_model(m)
#b = set(filter(None, a))
brenda = pd.DataFrame.from_csv("../data/BRENDA_data.csv")
brenda = brenda[brenda["Organism ID"]==6] # ecoli reactions
brenda = brenda[brenda["kcat"]>0]
measured_ecs = set(brenda.index)
out = pd.DataFrame(index=a)
out['reaction'] = [r.id for r in m.reactions]
out['subsystem'] = [r.subsystem for r in m.reactions]
out['kcat'] = [1 if ec in brenda.index else 'NaN' for ec in out.index]
out[out['kcat']!=1]
out.drop('', inplace=True)
out.drop('', inplace=True)
out['EC'] = out.index