def ParseReactionFormula(name, formula):
""" parse a two-sided formula such as: 2 C00001 = C00002 + C00003
return the set of substrates, products and the direction of the reaction
"""
try:
left, right = formula.split(' = ', 1)
except ValueError:
raise KeggParseException("There should be exactly one '=' sign")
sparse_reaction = {}
for cid, amount in NistRowData.ParseReactionFormulaSide(
left).iteritems():
sparse_reaction[cid] = -amount
for cid, amount in NistRowData.ParseReactionFormulaSide(
right).iteritems():
if (cid in sparse_reaction):
raise KeggParseException(
"C%05d appears on both sides of this formula" % cid)
sparse_reaction[cid] = amount
reaction = Reaction([name], sparse_reaction, None, '=>')
kegg = Kegg.getInstance()
rid = kegg.reaction2rid(reaction) or kegg.reaction2rid(
reaction.reverse())
reaction.rid = rid
return reaction
def row2string(S_row, cids):
active_cids = list(np.nonzero(S_row)[0].flat)
sparse = dict((cids[c], S_row[c])
for c in active_cids
if abs(S_row[c]) > 1e-10)
r = Reaction("", sparse)
return r.FullReactionString(show_cids=False)
def GetReactionString(self, r, show_cids=False):
rid = self.rids[r]
sparse = dict([(self.cids[c], self.S[c, r])
for c in self.S[:, r].nonzero()[0].flat])
if self.fluxes[0, r] >= 0:
direction = '=>'
else:
direction = '<='
reaction = Reaction(self.kegg.rid2string(rid), sparse, rid=rid,
direction=direction)
return reaction.to_hypertext(show_cids=show_cids)
def GetReactionString(self, r, show_cids=False):
rid = self.rids[r]
sparse = dict([(self.cids[c], self.S[c, r])
for c in self.S[:, r].nonzero()[0].flat])
if self.fluxes[0, r] >= 0:
direction = '=>'
else:
direction = '<='
reaction = Reaction(self.kegg.rid2string(rid), sparse, rid=rid,
direction=direction)
return reaction.to_hypertext(show_cids=show_cids)
def stoichiometric_matrix2html(html_writer, A, cids, eps=1e-10):
"""
Print a table in HTML format.
A is a stoichiometric matrix where each row is a reaction and
each column is a compound, corresponding in position to the list "cids".
"""
dict_list = []
for i in xrange(A.shape[0]):
sparse_reaction = dict([(cids[j], A[i, j]) for j in xrange(A.shape[1]) if abs(A[i, j]) > eps])
r = Reaction("reaction%d" % i, sparse_reaction=sparse_reaction)
dict_list.append({"reaction": r.to_hypertext()})
html_writer.write_ul(
["%d rows" % A.shape[0], "%d columns" % A.shape[1], "%d rank" % LinearRegression.MatrixRank(A)]
)
html_writer.write_table(dict_list, headers=["#", "reaction"])
def runBeta2Alpha(thermo, reactionList):
pl = Pathologic(db=SqliteDatabase('../res/gibbs.sqlite', 'r'),
public_db=SqliteDatabase('../data/public_data.sqlite'),
html_writer=HtmlWriter('../res/Beta2Alpha.html'),
thermo=thermo,
max_solutions=None,
max_reactions=15,
maximal_dG=0.0,
thermodynamic_method=OptimizationMethods.GLOBAL,
update_file=None)
add_cofactor_reactions(pl)
add_redox_reactions(pl)
for r in reactionList:
pl.add_reaction(Reaction.FromFormula(r, "Auto generate #%s" % hash(r)))
r = Reaction.FromFormula("C00099 => C01401")
pl.find_path("Beta2Alpha", r)
def GetC1Thermodynamics(
html_writer,
reaction_fname='../data/thermodynamics/c1_reaction_thermodynamics.csv'
):
html_writer.write("<h1>C1 thermodynamics</h1>\n")
dict_list = []
db_public = SqliteDatabase('../data/public_data.sqlite')
alberty = PsuedoisomerTableThermodynamics.FromDatabase(\
db_public, 'alberty_pseudoisomers', name='alberty')
alberty.AddPseudoisomer(101, nH=23, z=0, nMg=0, dG0=0)
reacthermo = ReactionThermodynamics(alberty, 'C1')
reacthermo.pH = 7
reacthermo.I = 0.1
reacthermo.T = 298.15
reacthermo.pMg = 14
c1_reactions = []
for row in csv.DictReader(open(reaction_fname, 'r')):
r = Reaction.FromFormula(row['formula'])
r.Balance(balance_water=False)
r.SetNames(row['enzyme'])
dG0_r_prime = float(row['dG0_r_prime'])
pH, I, pMg, T = [float(row[k]) for k in ['pH', 'I', 'pMg', 'T']]
reacthermo.AddReaction(r, dG0_r_prime, pH=pH, I=I, pMg=pMg, T=T)
c1_reactions.append(r)
row['formula'] = r.to_hypertext(show_cids=False)
dict_list.append(row)
html_writer.write_table(
dict_list, headers=['acronym', 'enzyme', 'formula', 'dG0_r_prime'])
reacthermo._Recalculate()
return reacthermo
def GetFullOxidationReaction(cid):
kegg = Kegg.getInstance()
basic_cids = [1, 7, 9, 11, 14] # H2O, O2, Pi, CO2, NH3
basic_elements = ['C', 'O', 'P', 'N', 'e-']
element_mat = np.matrix(np.zeros((len(basic_elements), len(basic_cids))))
for j in xrange(len(basic_cids)):
atom_bag = kegg.cid2atom_bag(basic_cids[j])
atom_bag['e-'] = kegg.cid2num_electrons(basic_cids[j])
for i in xrange(len(basic_elements)):
element_mat[i, j] = atom_bag.get(basic_elements[i], 0)
cs_element_vec = np.zeros((len(basic_elements), 1))
atom_bag = kegg.cid2atom_bag(cid)
atom_bag['e-'] = kegg.cid2num_electrons(cid)
for i in xrange(len(basic_elements)):
cs_element_vec[i, 0] = atom_bag.get(basic_elements[i], 0)
x = np.linalg.inv(element_mat) * cs_element_vec
sparse = dict([(basic_cids[i], np.round(x[i, 0], 3))
for i in xrange(len(basic_cids))])
sparse[cid] = -1
r = Reaction("complete oxidation of %s" % kegg.cid2name(cid), sparse)
return r
def combine(fname, text_out):
modules = {}
for row in csv.DictReader(open(fname, 'r')):
options = modules.setdefault(row['Module'], {})
enzymes = options.setdefault(row['Option'], [])
reaction = Reaction.FromFormula(row['Formula'])
reaction.Balance(balance_water=False, exception_if_unknown=False)
enzymes.append((row['Enzyme'], row['Formula'], float(row['Flux'])))
l_mod = []
for module, options in sorted(modules.iteritems()):
l_opt = []
for option, enzymes in sorted(options.iteritems()):
l_opt.append(("%s%s" % (module, option), enzymes))
l_mod.append(l_opt)
for combination in itertools.product(*l_mod):
entry = '_'.join([mod for mod, enzymes in combination])
text_out.write('ENTRY %s\n' % entry)
text_out.write('THERMO merged\n')
firstrow = True
for mod, enzymes in combination:
for enzyme, formula, flux in enzymes:
if firstrow:
text_out.write('REACTION %-6s %s (x%g)\n' %
(enzyme, formula, flux))
firstrow = False
else:
text_out.write(' %-6s %s (x%g)\n' %
(enzyme, formula, flux))
text_out.write('///\n')
def MakeReaction(self, rid, vec):
sparse_reaction = {}
for j, stoich in enumerate(vec):
if stoich == 0:
continue
sparse_reaction[self.compound_ids[j]] = stoich
return Reaction(rid, sparse_reaction)
def GetForamtionEnergies(self, thermo):
self.db.CreateTable(self.GIBBS_ENERGY_TABLE_NAME, "equation TEXT, dG0 REAL, dGc REAL", drop_if_exists=True)
self.db.CreateIndex('gibbs_equation_idx', self.GIBBS_ENERGY_TABLE_NAME, 'equation', unique=True, drop_if_exists=True)
all_equations = set()
for row in self.db.Execute("SELECT distinct(equation) FROM %s" %
(self.EQUATION_TABLE_NAME)):
all_equations.add(str(row[0]))
from pygibbs.kegg import Kegg
kegg = Kegg.getInstance()
all_kegg_cids = set(kegg.get_all_cids())
for equation in all_equations:
try:
rxn = Reaction.FromFormula(equation)
if not rxn.get_cids().issubset(all_kegg_cids):
raise KeggNonCompoundException
rxn.Balance(balance_water=True, exception_if_unknown=True)
dG0 = thermo.GetTransfromedKeggReactionEnergies([rxn], conc=1)[0, 0]
dGc = thermo.GetTransfromedKeggReactionEnergies([rxn], conc=1e-3)[0, 0]
self.db.Insert(self.GIBBS_ENERGY_TABLE_NAME, [equation, dG0, dGc])
except (KeggParseException, KeggNonCompoundException, KeggReactionNotBalancedException):
self.db.Insert(self.GIBBS_ENERGY_TABLE_NAME, [equation, None, None])
self.db.Commit()
def write_module_to_html(html_writer, S, rids, fluxes, cids):
from pygibbs.kegg_reaction import Reaction
reactions = []
for r in xrange(S.shape[0]):
sparse = dict([(cids[c], S[r, c]) for c in pylab.find(S[r, :])])
reaction = Reaction('R%05d' % rids[r], sparse, rid=rids[r])
reactions.append(reaction)
write_kegg_pathway(html_writer, reactions, fluxes)
def stoichiometric_matrix2html(html_writer, A, cids, eps=1e-10):
"""
Print a table in HTML format.
A is a stoichiometric matrix where each row is a reaction and
each column is a compound, corresponding in position to the list "cids".
"""
dict_list = []
for i in xrange(A.shape[0]):
sparse_reaction = dict([(cids[j], A[i, j]) for j in xrange(A.shape[1])
if abs(A[i, j]) > eps])
r = Reaction("reaction%d" % i, sparse_reaction=sparse_reaction)
dict_list.append({'reaction': r.to_hypertext()})
html_writer.write_ul([
'%d rows' % A.shape[0],
'%d columns' % A.shape[1],
'%d rank' % LinearRegression.MatrixRank(A)
])
html_writer.write_table(dict_list, headers=['#', 'reaction'])
def ParseReactionFormula(name, formula):
""" parse a two-sided formula such as: 2 C00001 = C00002 + C00003
return the set of substrates, products and the direction of the reaction
"""
try:
left, right = formula.split(' = ', 1)
except ValueError:
raise KeggParseException("There should be exactly one '=' sign")
sparse_reaction = {}
for cid, amount in NistRowData.ParseReactionFormulaSide(left).iteritems():
sparse_reaction[cid] = -amount
for cid, amount in NistRowData.ParseReactionFormulaSide(right).iteritems():
if (cid in sparse_reaction):
raise KeggParseException("C%05d appears on both sides of this formula" % cid)
sparse_reaction[cid] = amount
reaction = Reaction([name], sparse_reaction, None, '=>')
kegg = Kegg.getInstance()
rid = kegg.reaction2rid(reaction) or kegg.reaction2rid(reaction.reverse())
reaction.rid = rid
return reaction
def example_formate(thermo, product_cid=22, co2_conc=1e-5):
co2_hydration = Reaction.FromFormula("C00011 + C00001 => C00288")
co2_hydration_dG0_prime = float(thermo.GetTransfromedKeggReactionEnergies([co2_hydration]))
carbonate_conc = co2_conc * np.exp(-co2_hydration_dG0_prime / (R*default_T))
thermo.bounds[11] = (co2_conc, co2_conc)
thermo.bounds[288] = (carbonate_conc, carbonate_conc)
pl = Pathologic(db=SqliteDatabase('../res/gibbs.sqlite', 'r'),
public_db=SqliteDatabase('../data/public_data.sqlite'),
html_writer=HtmlWriter('../res/pathologic.html'),
thermo=thermo,
max_solutions=None,
max_reactions=20,
maximal_dG=0.0,
thermodynamic_method=OptimizationMethods.GLOBAL,
update_file=None)
add_cofactor_reactions(pl, free_ATP_hydrolysis=True)
add_redox_reactions(pl, NAD_only=False)
pl.delete_reaction(134) # formate:NADP+ oxidoreductase
pl.delete_reaction(519) # Formate:NAD+ oxidoreductase
pl.delete_reaction(24) # Rubisco
pl.delete_reaction(581) # L-serine:NAD+ oxidoreductase (deaminating)
pl.delete_reaction(220) # L-serine ammonia-lyase
pl.delete_reaction(13) # glyoxylate carboxy-lyase (dimerizing; tartronate-semialdehyde-forming)
pl.delete_reaction(585) # L-Serine:pyruvate aminotransferase
pl.delete_reaction(1440) # D-Xylulose-5-phosphate:formaldehyde glycolaldehydetransferase
pl.delete_reaction(5338) # 3-hexulose-6-phosphate synthase
pl.add_reaction(Reaction.FromFormula("C06265 => C00011", name="CO2 uptake"))
pl.add_reaction(Reaction.FromFormula("C06265 => C00288", name="carbonate uptake"))
pl.add_reaction(Reaction.FromFormula("C06265 => C00058", name="formate uptake"))
r = Reaction.FromFormula("5 C06265 + C00058 => C%05d" % product_cid) # at least one formate to product
#r.Balance()
kegg = Kegg.getInstance()
pl.find_path("formate to %s" % kegg.cid2name(product_cid), r)
def example_oxidative(thermo):
pl = Pathologic(db=SqliteDatabase('../res/gibbs.sqlite', 'r'),
public_db=SqliteDatabase('../data/public_data.sqlite'),
html_writer=HtmlWriter('../res/pathologic.html'),
thermo=thermo,
max_solutions=None,
max_reactions=10,
maximal_dG=0,
thermodynamic_method=OptimizationMethods.MAX_TOTAL,
update_file=None)
add_cofactor_reactions(pl)
add_redox_reactions(pl, NAD_only=False)
r = Reaction.FromFormula("C00022 => 3 C00011")
#r.Balance()
pl.find_path("oxidative", r)
def example_reductive(thermo):
pl = Pathologic(db=SqliteDatabase('../res/gibbs.sqlite', 'r'),
public_db=SqliteDatabase('../data/public_data.sqlite'),
html_writer=HtmlWriter('../res/pathologic.html'),
thermo=thermo,
max_solutions=None,
max_reactions=15,
maximal_dG=0.0,
thermodynamic_method=OptimizationMethods.GLOBAL,
update_file=None)
add_cofactor_reactions(pl)
add_redox_reactions(pl)
r = Reaction.FromFormula("3 C00011 => C00022")
#r.Balance()
pl.find_path("reductive", r)
def runPathologic(thermo, reactionList):
pl = Pathologic(db=SqliteDatabase('../res/gibbs.sqlite', 'r'),
public_db=SqliteDatabase('../data/public_data.sqlite'),
html_writer=HtmlWriter('../res/mog_finder.html'),
thermo=thermo,
max_solutions=None,
max_reactions=15,
maximal_dG=-3.0,
thermodynamic_method=OptimizationMethods.GLOBAL,
update_file=None)
add_cofactor_reactions(pl)
add_redox_reactions(pl)
for r in reactionList:
pl.add_reaction(Reaction.FromFormula(r, "Auto generate #%s" % hash(r)))
pl.delete_reaction(134)
pl.delete_reaction(344)
pl.delete_reaction(575)
pl.delete_reaction(212)
#pl.add_reaction(Reaction.FromFormula('C00149 + C00006 <=> C00036 + C00005 + C00080',
# 'malate + NADP+ = oxaloacetate + NADPH',343))
#pl.add_reaction(Reaction.FromFormula('C00222 + C00010 + C00006 <=> C00083 + C00005',
# 'malonate-semialdehyde + CoA + NADP+ = malonyl-CoA + NADPH',740))
r = Reaction.FromFormula("2 C00288 => C00048")
pl.find_path("MOG_finder", r)
def example_lower_glycolysis(thermo):
pl = Pathologic(db=SqliteDatabase('../res/gibbs.sqlite', 'r'),
public_db=SqliteDatabase('../data/public_data.sqlite'),
html_writer=HtmlWriter('../res/pathologic.html'),
thermo=thermo,
max_solutions=None,
max_reactions=8,
maximal_dG=0.0,
thermodynamic_method=OptimizationMethods.GLOBAL,
update_file=None)
add_cofactor_reactions(pl)
add_redox_reactions(pl)
#r = Reaction.FromFormula("C00003 + C00118 + C00001 => C00022 + C00004 + C00009")
r = Reaction.FromFormula("C00118 => C00022")
#r.Balance()
pl.find_path("GAP => PYR", r)
def example_three_acetate(thermo):
pl = Pathologic(db=SqliteDatabase('../res/gibbs.sqlite', 'r'),
public_db=SqliteDatabase('../data/public_data.sqlite'),
html_writer=HtmlWriter('../res/pathologic.html'),
thermo=thermo,
max_solutions=None,
max_reactions=20,
maximal_dG=0.0,
thermodynamic_method=OptimizationMethods.GLOBAL,
update_file=None)
add_cofactor_reactions(pl)
#add_redox_reactions(pl)
pl.delete_reaction(761) # F6P + Pi = E4P + acetyl-P
pl.delete_reaction(1621) # X5P + Pi = GA3P + acetyl-P
r = Reaction.FromFormula("C00031 => 3 C00033")
#r.Balance()
pl.find_path("three_acetate", r)
def example_glycolysis(thermo):
pl = Pathologic(db=SqliteDatabase('../res/gibbs.sqlite', 'r'),
public_db=SqliteDatabase('../data/public_data.sqlite'),
html_writer=HtmlWriter('../res/pathologic.html'),
thermo=thermo,
max_solutions=None,
max_reactions=15,
maximal_dG=0.0,
thermodynamic_method=OptimizationMethods.GLOBAL,
update_file=None)
add_cofactor_reactions(pl, free_ATP_hydrolysis=False)
ban_toxic_compounds(pl)
#add_carbon_counts(pl)
#r = Reaction.FromFormula("C00031 => 6 C06265")
r = Reaction.FromFormula("C00031 + 3 C00008 => 2 C00186 + 3 C00002")
#r.Balance()
pl.find_path("GLC => 2 LAC, 3 ATP, No methylglyoxal", r)
def example_rpi_bypass(thermo):
pl = Pathologic(db=SqliteDatabase('../res/gibbs.sqlite', 'r'),
public_db=SqliteDatabase('../data/public_data.sqlite'),
html_writer=HtmlWriter('../res/pathologic.html'),
thermo=thermo,
max_solutions=None,
max_reactions=10,
maximal_dG=0.0,
thermodynamic_method=OptimizationMethods.GLOBAL,
update_file=None)
add_cofactor_reactions(pl)
#add_redox_reactions(pl)
pl.delete_reaction(1056) # ribose-phosphate isomerase
pl.delete_reaction(1081) # ribose isomerase
r = Reaction.FromFormula("C00117 => C01182")
#r.Balance()
pl.find_path("rpi_bypass", r)
def FromCsv(csv_fname, formation_thermo):
data = []
for row in csv.DictReader(open(csv_fname, 'r')):
r = Reaction.FromFormula(row['formula'])
r.Balance(balance_water=False)
r.SetNames(row['enzyme'])
dG0_r_prime = float(row['dG0_r_prime'])
pH = float(row['pH'])
I = float(row['I'])
pMg = float(row.get('pMg', '10'))
T = float(row['T'])
data.append((r, dG0_r_prime, pH, I, pMg, T))
reacthermo = ReactionThermodynamics(formation_thermo)
reacthermo.SetConditions(pH=pH, I=I, pMg=pMg, T=T)
for r, dG0, pH, I, pMg, T in data:
reacthermo.AddReaction(r, dG0, pH=pH, I=I, pMg=pMg, T=T)
reacthermo._Recalculate()
return reacthermo
def example_glucose_to_ethanol_and_formate(thermo):
pl = Pathologic(db=SqliteDatabase('../res/gibbs.sqlite', 'r'),
public_db=SqliteDatabase('../data/public_data.sqlite'),
html_writer=HtmlWriter('../res/pathologic.html'),
thermo=thermo,
max_solutions=None,
max_reactions=15,
maximal_dG=0.0,
thermodynamic_method=OptimizationMethods.GLOBAL,
update_file=None)
#add_cofactor_reactions(pl)
#add_XTP_reactions(pl, '=>')
#add_redox_reactions(pl)
#pl.delete_reaction(761) # F6P + Pi = E4P + acetyl-P
#pl.delete_reaction(1621) # X5P + Pi = GA3P + acetyl-P
r = Reaction.FromFormula("2 C00031 + 3 C00001 => 6 C00058 + 3 C00469")
r.Balance()
pl.find_path("glucose_to_ethanol_and_formate", r)
def example_more_than_two_pyruvate(thermo):
pl = Pathologic(db=SqliteDatabase('../res/gibbs.sqlite', 'r'),
public_db=SqliteDatabase('../data/public_data.sqlite'),
html_writer=HtmlWriter('../res/pathologic.html'),
thermo=thermo,
max_solutions=None,
max_reactions=20,
maximal_dG=0.0,
thermodynamic_method=OptimizationMethods.GLOBAL,
update_file=None)
#add_cofactor_reactions(pl)
#add_XTP_reactions(pl, '=>')
#add_redox_reactions(pl)
#pl.delete_reaction(761) # F6P + Pi = E4P + acetyl-P
#pl.delete_reaction(1621) # X5P + Pi = GA3P + acetyl-P
r = Reaction.FromFormula("3 C00031 + 3 C00011 + C00003 => 7 C00022 + 3 C00001 + C00004")
r.Balance()
pl.find_path("more_than_two_pyr", r)
def GetStoichiometries(self):
self.db.CreateTable(self.STOICHIOMETRY_TABLE_NAME, "equation TEXT, compound TEXT, coefficient REAL", drop_if_exists=True)
self.db.CreateIndex('stoichiometry_equation_idx', self.STOICHIOMETRY_TABLE_NAME, 'equation', unique=False, drop_if_exists=True)
self.db.CreateIndex('stoichiometry_compound_idx', self.STOICHIOMETRY_TABLE_NAME, 'compound', unique=False, drop_if_exists=True)
all_kegg_reactions = []
all_equations = []
for row in self.db.Execute("SELECT distinct(equation) FROM %s" %
(self.EQUATION_TABLE_NAME)):
try:
r = Reaction.FromFormula(str(row[0]))
all_equations.append(str(row[0]))
all_kegg_reactions.append(r)
except (KeggParseException, KeggNonCompoundException):
pass
for i, equation in enumerate(all_equations):
for compound, coefficient in all_kegg_reactions[i].iteritems():
self.db.Insert(self.STOICHIOMETRY_TABLE_NAME,
[equation, "cpd:C%05d" % compound, coefficient])
self.db.Commit()
def AddRedoxCarriers(self, anchored=True):
redox_carriers = RedoxCarriers()
for name, rc in redox_carriers.iteritems():
# make sure all redox carriers have a pKa table.
# for some which don't (usually because their structure is not explicit)
# we assume that the table is empty (i.e. no pKas exist).
if self.dissociation.GetDissociationTable(rc.cid_ox) is None:
self.dissociation.SetOnlyPseudoisomer(rc.cid_ox, rc.nH_ox,
rc.z_ox)
if self.dissociation.GetDissociationTable(rc.cid_red) is None:
self.dissociation.SetOnlyPseudoisomer(rc.cid_red, rc.nH_red,
rc.z_red)
obs_id = name + " redox"
self.cid2nH_nMg[rc.cid_ox] = (rc.nH_ox, 0)
self.cid2nH_nMg[rc.cid_red] = (rc.nH_red, 0)
sparse = {rc.cid_ox: -1, rc.cid_red: 1}
dG0 = rc.ddG0_prime
if not self.transformed:
reaction = Reaction(obs_id, sparse)
ddG0 = self.dissociation.ReverseTransformReaction(
reaction,
pH=rc.pH,
I=0,
pMg=default_pMg,
T=default_T,
cid2nH_nMg=self.cid2nH_nMg)
dG0 -= ddG0
self.AddObservation(obs_id=obs_id,
obs_type=KeggObservation.TYPE_REDOX,
url="",
anchored=anchored,
dG0=dG0,
sparse=sparse)
def add_carbon_counts(pl):
"""Add reactions counting carbons in various
plausible fermentation products.
"""
reactions = [
#Reaction.FromFormula("C00246 => 4 C06265", name="Butyrate makeup"),
#Reaction.FromFormula("C02632 => 4 C06265", name="Isobutyrate makeup"),
Reaction.FromFormula("C00042 => 4 C06265", name="Succinate makeup"),
#Reaction.FromFormula("C00022 => 3 C06265", name="Pyruvate makeup"),
#Reaction.FromFormula("C00163 => 3 C06265", name="Propionate makeup"),
#Reaction.FromFormula("C01013 => 3 C06265", name="3-hydroxypropionate makeup"),
Reaction.FromFormula("C00186 => 3 C06265", name="Lactate makeup"),
Reaction.FromFormula("C00033 => 2 C06265", name="Acetate makeup"),
Reaction.FromFormula("C00469 => 2 C06265", name="Ethanol makeup"),
Reaction.FromFormula("C00058 => 1 C06265", name="Formate makeup"),
Reaction.FromFormula("C00011 => 1 C06265", name="CO2 makeup"),
]
for rxn in reactions:
pl.add_reaction(rxn)
"""
def pH_dependence():
analyze_this_reaction = []
I_mid = []
I_tolerance = []
T_mid = []
T_tolerance = []
analyze_this_reaction += [Reaction(['glucose kinase'], {2:-1, 31:-1, 8:1, 92:1})]
I_mid += [0.01]
I_tolerance += [0.02]
T_mid += [303.1]
T_tolerance += [0.1]
analyze_this_reaction += [Reaction(['L-serine kinase'], {1:-1, 1005:-1, 9:1, 65:1})]
I_mid += [0.25]
I_tolerance += [0.01]
T_mid += [311.15]
T_tolerance += [0.1]
analyze_this_reaction += [Reaction(['pyrophosphatase'], {1:-1, 13:-1, 9:2}, rid=4)]
I_mid += [0.05]
I_tolerance += [0.05]
T_mid += [298.1]
T_tolerance += [15]
analyze_this_reaction += [Reaction(['Fructose bisphosphate aldolase'], {354:-1, 111:1, 118:1})]
I_mid += [0.01]
I_tolerance += [0.011]
T_mid += [300]
T_tolerance += [12]
pylab.figure()
pylab.rcParams['text.usetex'] = True
pylab.rcParams['legend.fontsize'] = 4
pylab.rcParams['font.family'] = 'sans-serif'
pylab.rcParams['font.size'] = 6
pylab.rcParams['lines.linewidth'] = 0.5
pylab.rcParams['lines.markersize'] = 3
for i, reaction in enumerate(analyze_this_reaction):
pylab.subplot(2,2,i+1)
logging.info("Compound parameters from Alberty's table:")
for cid in reaction.get_cids():
A.cid2PseudoisomerMap(cid).Display()
logging.info("Compound parameters from Hatzimanikatis' table:")
for cid in reaction.get_cids():
H.cid2PseudoisomerMap(cid).Display()
M_obs = []
sys.stdout.write("%5s | %5s | %5s | %6s | %6s | %s\n" % ("Match", "pH", "I", "T", "dG0(N)", "link"))
for row in nist.data:
if (reaction != None and reaction != row.reaction):
continue
try:
#evaluation = row[3] # A, B, C, D
if (reaction == row.reaction and
abs(row.I-I_mid[i]) < I_tolerance[i] and
abs(row.T-T_mid[i]) < T_tolerance[i]):
M_obs.append([row.pH, row.dG0_r])
sys.stdout.write(" *** | ")
else:
sys.stdout.write(" | ")
sys.stdout.write("%5.2f | %5.2f | %6.1f | %6.2f | %s\n" % (row.pH, row.I, row.T, row.dG0_r, row.ref_id))
except MissingCompoundFormationEnergy:
continue
if len(M_obs) == 0:
sys.stderr.write("There are now data points matching this reaction with the specific I and T")
continue
M_obs = pylab.matrix(M_obs)
leg = ['NIST']
pH_range = pylab.arange(M_obs[:,0].min()-1, M_obs[:,0].max()+1, 0.01)
M_est = []
I_low = max(I_mid[i]-I_tolerance[i], 0.0)
I_high = I_mid[i]+I_tolerance[i]
for pH in pH_range:
predictions = []
for predictor in [A, H]:
predictions.append(reaction.PredictReactionEnergy(predictor, pH=pH, I=I_low ,T=T_mid[i]))
predictions.append(reaction.PredictReactionEnergy(predictor, pH=pH, I=I_mid[i] ,T=T_mid[i]))
predictions.append(reaction.PredictReactionEnergy(predictor, pH=pH, I=I_high ,T=T_mid[i]))
M_est.append(predictions)
M_est = pylab.matrix(M_est)
pylab.plot(M_obs[:,0], M_obs[:,1], 'co')
pylab.plot(pH_range, M_est[:,0], 'b:')
pylab.plot(pH_range, M_est[:,1], 'b-')
pylab.plot(pH_range, M_est[:,2], 'b:')
pylab.plot(pH_range, M_est[:,3], 'g:')
pylab.plot(pH_range, M_est[:,4], 'g-')
pylab.plot(pH_range, M_est[:,5], 'g:')
pylab.plot(pH_range, M_est[:,6], 'r:')
pylab.plot(pH_range, M_est[:,7], 'r-')
pylab.plot(pH_range, M_est[:,8], 'r:')
for predictor_name in ['Alberty', 'Hatzi', 'Rugged']:
for I in [I_low, I_mid[i], I_high]:
leg.append("%s (I = %.2f)" % (predictor_name, I))
pylab.legend(leg, loc='best')
if (i == 2 or i == 3):
pylab.xlabel('pH')
if (i == 0 or i == 2):
pylab.ylabel(r"$\Delta_r G'^\circ$ [kJ/mol]")
s_title = gc.kegg.reaction2string(reaction, cids=False) + "\n"
s_title += "($I = %.2f \pm %.2f$ $M$, $T = %.1f \pm %.1f$ $K$)" % (I_mid[i], I_tolerance[i], T_mid[i]-273.15, T_tolerance[i])
pylab.title(s_title, fontsize=5)
pylab.savefig('../res/compare_pH.pdf', format='pdf')
plt.legend(['value in iAF1260', 'UGCM estimation'])
plt.savefig(FIG_FNAME + "_fig3.svg", fmt='.svg')
db = SqliteDatabase('../res/gibbs.sqlite', 'w')
ugc = UnifiedGroupContribution(db)
ugc.LoadGroups(True)
ugc.LoadObservations(True)
ugc.LoadGroupVectors(True)
ugc.LoadData(True)
ugc.init()
r_list = []
#r_list += [Reaction.FromFormula("C00036 + C00044 = C00011 + C00035 + C00074")]
#r_list += [Reaction.FromFormula("C00003 + C00037 + C00101 = C00004 + C00011 + C00014 + C00080 + C00143")] # glycine synthase
r_list += [
Reaction.FromFormula(
"C00001 + C00002 + C00064 + C04376 => C00008 + C00009 + C00025 + C04640"
)
]
#r_list += [Reaction.FromFormula("C00001 + 2 C00002 + C00064 + C00288 <=> 2 C00008 + C00009 + C00025 + C00169")]
kegg = Kegg.getInstance()
S, cids = kegg.reaction_list_to_S(r_list)
logging.getLogger('').setLevel(logging.DEBUG)
dG0_prime = ugc.GetTransfromedReactionEnergies(S,
cids,
pH=pH,
I=I,
pMg=pMg,
T=T)
['C%05d' % all_cids[c],
'%8.1f' % dG0_f[c, 0], 'Calculated'])
#figure()
#plot(b, dot(S_red, dG0_f[unknown_columns]), '.')
figure()
plot(dG0_r[known_rows], dot(S[known_rows, :], dG0_f), '.')
show()
csv_out = csv.writer(open('../res/acetogens_reactions.csv', 'w'))
csv_out.writerow([
"RID", "EC", "REACTION", "dG0 measured", "dG0 calculated", "pH", "I", "T"
])
for r in xrange(len(reactions)):
(rid, ec, sparse, dG0, pH, I, T) = reactions[r]
reaction = Reaction(ec, sparse, rid=rid)
dG0_calculated = dot(S[r, :], dG0_f)
if r in known_rows:
csv_out.writerow([
rid, ec,
reaction.FullReactionString(),
'%.1f' % dG0,
'%.1f' % dG0_calculated, pH, I, T
])
else:
csv_out.writerow([
rid, ec,
reaction.FullReactionString(), 'N/A',
'%.1f' % dG0_calculated, pH, I, T
])
def GetTotalReactionString(self, show_cids=False):
total_S = self.S * self.fluxes.T
sparse = dict([(self.cids[c], total_S[c, 0])
for c in total_S.nonzero()[0].flat])
reaction = Reaction("Total", sparse, direction="=>")
return reaction.to_hypertext(show_cids=show_cids)
def write_current_solution(self, exp_html, lp, experiment_name,
output_kegg_file=None):
solution = lp.get_active_reaction_data()
solution_id = '%03d' % lp.solution_index
exp_html.write('%d reactions, flux = %g, \n' %
(len(solution.reactions),
float(solution.fluxes.sum(1))))
# draw network as a graph and link to it
Gdot = self.kegg_pathologic.draw_pathway(solution.reactions,
list(solution.fluxes.flat))
svg_fname = '%s/%s_graph' % (experiment_name, solution_id)
exp_html.embed_dot_inline(Gdot, width=240, height=320, name=svg_fname)
# write the solution for the concentrations in a table
if solution.concentrations is not None:
exp_html.insert_toggle(start_here=True)
rowdicts = []
for c, compound in enumerate(solution.compounds):
rowdict = {}
rowdict['KEGG ID'] = '<a href="%s">C%05d</a>' % (compound.get_link(), compound.cid)
rowdict['Compound'] = compound.name
rowdict[symbol_df_G0_prime + "[kJ/mol]"] = solution.dG0_f[0, c]
rowdict[symbol_df_G_prime + "[kJ/mol]"] = solution.dG_f[0, c]
if np.isfinite(solution.concentrations[0, c]):
rowdict['Conc. [M]'] = '%.1e' % solution.concentrations[0, c]
else:
rowdict['Conc. [M]'] = 'N/A'
rowdicts.append(rowdict)
headers=['KEGG ID', 'Compound', symbol_df_G0_prime + "[kJ/mol]",
symbol_df_G_prime + "[kJ/mol]", 'Conc. [M]']
exp_html.write('Compound Concentrations<br>\n')
exp_html.write_table(rowdicts, headers, decimal=1)
total_reaction = Reaction('total', {})
rowdicts = []
for r, reaction in enumerate(solution.reactions):
rowdict = {}
flux = solution.fluxes[0, r]
rowdict['KEGG ID'] = '<a href="%s">R%05d</a>' % (reaction.get_link(), reaction.rid)
rowdict['Reaction'] = reaction.to_hypertext(show_cids=False)
rowdict['Flux'] = flux
rowdict[symbol_dr_Gc_prime + "[kJ/mol]"] = solution.dGc_r[0, r]
rowdict[symbol_dr_G_prime + "[kJ/mol]"] = solution.dG_r[0, r]
rowdicts.append(rowdict)
total_reaction += (flux * reaction)
rowdict = {}
rowdict['KEGG ID'] = 'total'
rowdict['Reaction'] = total_reaction.to_hypertext(show_cids=False)
rowdict[symbol_dr_Gc_prime + "[kJ/mol]"] = float(solution.dGc_r.sum(1))
rowdict[symbol_dr_G_prime + "[kJ/mol]"] = float(solution.dG_r.sum(1))
rowdicts.append(rowdict)
headers=['KEGG ID', 'Reaction', symbol_dr_Gc_prime + "[kJ/mol]",
symbol_dr_G_prime + "[kJ/mol]", "Flux"]
exp_html.write('Reaction Gibbs energies<br>\n')
exp_html.write_table(rowdicts, headers, decimal=1)
exp_html.div_end()
# write the pathway in KEGG format
if output_kegg_file is not None:
write_kegg_pathway(output_kegg_file,
entry=experiment_name + ' ' + solution_id,
reactions=solution.reactions,
fluxes=list(solution.fluxes.flat))
exp_html.write('<br>\n')
ugc.init()
if args.test:
r_list = []
# r_list += [Reaction.FromFormula("C00002 + C00001 = C00008 + C00009")]
# r_list += [Reaction.FromFormula("C00036 + C00024 = C00022 + C00083")]
# r_list += [Reaction.FromFormula("C00036 + C00100 = C00022 + C00683")]
# r_list += [Reaction.FromFormula("C01013 + C00010 + C00002 = C05668 + C00020 + C00013")]
# r_list += [Reaction.FromFormula("C00091 + C00005 = C00232 + C00010 + C00006")]
# r_list += [Reaction.FromFormula("C00002 + C00493 = C00008 + C03175")]
# r_list += [Reaction.FromFormula("C00243 + C00125 = C05403 + C00126")]
# r_list += [Reaction.FromFormula("2 C00206 = C00360 + C00131")]
# r_list += [Reaction.FromFormula("C04171 + C00003 = C00196 + C00080 + C00004")]
# r_list += [Reaction.FromFormula("C00036 + C00044 = C00011 + C00035 + C00074")]
r_list += [
Reaction.FromFormula(
"C00001 + C00022 + C17569 = C00011 + C00033 + C00390")
]
kegg = Kegg.getInstance()
S, cids = kegg.reaction_list_to_S(r_list)
dG0_prime = ugc.GetTransfromedReactionEnergies(S, cids, pH=7.0, I=0.15)
ln_conc = np.matrix(np.ones((1, S.shape[0]))) * np.log(0.001)
if 1 in cids:
ln_conc[0,
cids.index(1)] = 0 # H2O should have a concentration of 1
RT = R * default_T
dGc_prime = dG0_prime + RT * ln_conc * S
for i in xrange(len(r_list)):
r_list[i].Balance()
def FromFiles():
feist = Feist()
reaction_file = '../data/metabolic_models/iAF1260_reactions.csv'
# Read compounds ids 2 Kegg cid's mapping file into a dict
counters = {
'kegg_error': 0,
'translocation': 0,
'exchange': 0,
'sink': 0,
'unbalanced': 0,
'okay': 0
}
for row in csv.DictReader(open(reaction_file, 'r')):
#if 'Transport' in row['subSystem']:
# counters['translocation'] += 1
# continue
if row['abbreviation'][0:3] == 'EX_':
counters['exchange'] += 1
continue
if row['abbreviation'][0:3] == 'DM_':
counters['sink'] += 1
continue
sparse = Feist.parse_equation(row['equation'])
kegg_sparse = {}
for biggID, coeff in sparse.iteritems():
keggID = feist.bigg2kegg[biggID]
kegg_sparse[keggID] = kegg_sparse.get(keggID, 0) + coeff
if 0 in kegg_sparse:
logging.debug('Some compounds are missing KEGG IDs in %s: %s' %
(row['abbreviation'], row['equation']))
counters['kegg_error'] += 1
continue
for keggID in [k for k, v in kegg_sparse.iteritems() if v == 0]:
del kegg_sparse[keggID]
directionality = row["directionality without uncertainty (pH 7.2)"]
#directionaliry = row['reconstruction directionality']
if directionality == 'reversible':
direction = '<=>'
elif directionality == 'forward only':
direction = '=>'
elif directionality == 'reverse only':
direction = '<='
else:
raise ValueError('unknown directionality tag: ' +
directionality)
reaction = Reaction(row['abbreviation'],
kegg_sparse,
direction=direction)
if row['delta G (pH 7)'] == 'Not calculated':
dG0 = np.nan
else:
dG0 = float(row['delta G (pH 7)']) * J_per_cal
try:
reaction.Balance(balance_water=True,
exception_if_unknown=False)
counters['okay'] += 1
except KeggReactionNotBalancedException as e:
logging.debug(str(e) + ' - ' + str(reaction))
counters['unbalanced'] += 1
continue
feist.reactions.append(reaction)
feist.dG0s.append(dG0)
logging.debug(" ; ".join(
["%s : %d" % (key, val) for (key, val) in counters.iteritems()]))
return feist
H_nopka = Hatzi(use_pKa=False)
H_withpka = Hatzi(use_pKa=True)
H_withpka.ToDatabase(db, 'hatzi_thermodynamics')
#H.ToDatabase(db, 'hatzi_gc')
#H.I = 0.25
#H.T = 300;
#sparse_reaction = {13:-1, 1:-1, 9:2}
#sparse_reaction = {36:-1, 3981:1}
#sparse_reaction = {6:-1, 143:-1, 234:1, 5:1}
#sparse_reaction = {1:-1, 499:-1, 603:1, 86:1}
#sparse_reaction = {1:-1, 6:-1, 311:-1, 288:1, 5:1, 80:2, 26:1}
#sparse_reaction = {408:-1, 6:-1, 4092:1, 5:1}
#sparse_reaction = {588:-1, 1:-1, 114:1, 9:1}
#sparse_reaction = {1:-1, 3:-1, 149:-1, 288:1, 4:1, 80:2, 22:1}
react = Reaction("reaction", {408:-1, 6:-1, 4092:1, 5:1})
#sys.stdout.write("The dG0_r of PPi + H20 <=> 2 Pi: \n\n")
react.Balance()
print react.FullReactionString()
sys.stdout.write("%5s | %5s | %6s | %6s\n" % ("pH", "I", "T", "dG0_r"))
for pH in np.arange(5, 10.01, 0.25):
H_withpka.pH = pH
sys.stdout.write("%5.2f | %5.2f | %6.1f | %6.2f\n" %
(H_withpka.pH, H_withpka.I, H_withpka.T,
react.PredictReactionEnergy(H_withpka)))
for cid in react.get_cids():
print '-'*50
