def __init__(self, sbtab_dict,
ecf_version='ECF3_1SP',
dG0_source=DEFAULT_DG0_SOURCE,
kcat_source=DEFAULT_KCAT_SOURCE):
self._sbtab_dict = sbtab_dict
self.kegg2met = self._sbtab_dict.GetDictFromTable('Compound',
'Compound:Identifiers:kegg.compound', 'Name')
self.kegg2rxn = self._sbtab_dict.GetDictFromTable('Reaction',
'Reaction', 'NameForPlots')
self.kegg_model = ECMmodel.GenerateKeggModel(self._sbtab_dict)
# a dictionary indicating which compound is external or not
if '!External' in self._sbtab_dict['Compound'].columns:
ext_col_name = 'External'
else:
ext_col_name = 'IsConstant'
self.cid2external = self._sbtab_dict.GetDictFromTable(
'Compound', 'Compound:Identifiers:kegg.compound', ext_col_name,
value_mapping=str2bool)
self._ReadConcentrationBounds()
self.kegg_model.check_S_balance(fix_water=True)
rid2crc_gmean, rid2crc_fwd, rid2crc_rev, rid_cid2KMM, rid2keq = \
ECMmodel._ReadKineticParameters(self._sbtab_dict)
if dG0_source == 'keq_table':
self._CalcGibbsEnergiesFromKeq(rid2keq)
elif dG0_source == 'component_contribution':
self._CalcGibbsEnerigesFromComponentContribution()
elif dG0_source == 'dG0r_table':
self._CalcGibbsEnerigesFromdG0ReactionTable()
else:
raise ValueError('unrecognized dG0 source: ' + dG0_source)
# read flux values and convert them to M/s
flux_units = self._sbtab_dict.GetTableAttribute('Flux', 'Unit')
flux_mapping = ECMmodel._MappingToCanonicalFluxUnits(flux_units)
self.rid2flux = self._sbtab_dict.GetDictFromTable(
'Flux', 'Reaction', 'Flux', value_mapping=flux_mapping)
flux = np.matrix(map(self.rid2flux.get, self.kegg_model.rids)).T
if kcat_source == 'gmean':
kcat = np.matrix(map(rid2crc_gmean.get, self.kegg_model.rids)).T
elif kcat_source == 'fwd':
# get the relevant kcat (fwd/rev) depending on the direction of flux
kcat = []
for r, rid in enumerate(self.kegg_model.rids):
if flux[r] >= 0:
kcat.append(rid2crc_fwd[rid])
else:
kcat.append(rid2crc_rev[rid])
kcat = np.matrix(kcat).T
else:
raise ValueError('unrecognized kcat source: ' + kcat_source)
dG0 = np.matrix(map(self.rid2dG0.get, self.kegg_model.rids)).T
KMM = ECMmodel._GenerateKMM(self.kegg_model.cids,
self.kegg_model.rids, rid_cid2KMM)
c_bounds = np.array(zip(map(self.cid2min_bound.get, self.kegg_model.cids),
map(self.cid2max_bound.get, self.kegg_model.cids)))
lnC_bounds = np.log(c_bounds) # assume bounds are in M
# we need all fluxes to be positive, so for every negative flux,
# we multiply it and the corresponding column in S by (-1)
dir_mat = np.matrix(np.diag(np.sign(flux + 1e-10).flat))
flux = dir_mat * flux
S = self.kegg_model.S * dir_mat
dG0 = dir_mat * dG0
self.ecf = EnzymeCostFunction(S, flux=flux, kcat=kcat, dG0=dG0, KMM=KMM,
lnC_bounds=lnC_bounds, ecf_version=ecf_version)
class ECMmodel(object):
def __init__(self, sbtab_dict,
ecf_version='ECF3_1SP',
dG0_source=DEFAULT_DG0_SOURCE,
kcat_source=DEFAULT_KCAT_SOURCE):
self._sbtab_dict = sbtab_dict
self.kegg2met = self._sbtab_dict.GetDictFromTable('Compound',
'Compound:Identifiers:kegg.compound', 'Name')
self.kegg2rxn = self._sbtab_dict.GetDictFromTable('Reaction',
'Reaction', 'NameForPlots')
self.kegg_model = ECMmodel.GenerateKeggModel(self._sbtab_dict)
# a dictionary indicating which compound is external or not
if '!External' in self._sbtab_dict['Compound'].columns:
ext_col_name = 'External'
else:
ext_col_name = 'IsConstant'
self.cid2external = self._sbtab_dict.GetDictFromTable(
'Compound', 'Compound:Identifiers:kegg.compound', ext_col_name,
value_mapping=str2bool)
self._ReadConcentrationBounds()
self.kegg_model.check_S_balance(fix_water=True)
rid2crc_gmean, rid2crc_fwd, rid2crc_rev, rid_cid2KMM, rid2keq = \
ECMmodel._ReadKineticParameters(self._sbtab_dict)
if dG0_source == 'keq_table':
self._CalcGibbsEnergiesFromKeq(rid2keq)
elif dG0_source == 'component_contribution':
self._CalcGibbsEnerigesFromComponentContribution()
elif dG0_source == 'dG0r_table':
self._CalcGibbsEnerigesFromdG0ReactionTable()
else:
raise ValueError('unrecognized dG0 source: ' + dG0_source)
# read flux values and convert them to M/s
flux_units = self._sbtab_dict.GetTableAttribute('Flux', 'Unit')
flux_mapping = ECMmodel._MappingToCanonicalFluxUnits(flux_units)
self.rid2flux = self._sbtab_dict.GetDictFromTable(
'Flux', 'Reaction', 'Flux', value_mapping=flux_mapping)
flux = np.matrix(map(self.rid2flux.get, self.kegg_model.rids)).T
if kcat_source == 'gmean':
kcat = np.matrix(map(rid2crc_gmean.get, self.kegg_model.rids)).T
elif kcat_source == 'fwd':
# get the relevant kcat (fwd/rev) depending on the direction of flux
kcat = []
for r, rid in enumerate(self.kegg_model.rids):
if flux[r] >= 0:
kcat.append(rid2crc_fwd[rid])
else:
kcat.append(rid2crc_rev[rid])
kcat = np.matrix(kcat).T
else:
raise ValueError('unrecognized kcat source: ' + kcat_source)
dG0 = np.matrix(map(self.rid2dG0.get, self.kegg_model.rids)).T
KMM = ECMmodel._GenerateKMM(self.kegg_model.cids,
self.kegg_model.rids, rid_cid2KMM)
c_bounds = np.array(zip(map(self.cid2min_bound.get, self.kegg_model.cids),
map(self.cid2max_bound.get, self.kegg_model.cids)))
lnC_bounds = np.log(c_bounds) # assume bounds are in M
# we need all fluxes to be positive, so for every negative flux,
# we multiply it and the corresponding column in S by (-1)
dir_mat = np.matrix(np.diag(np.sign(flux + 1e-10).flat))
flux = dir_mat * flux
S = self.kegg_model.S * dir_mat
dG0 = dir_mat * dG0
self.ecf = EnzymeCostFunction(S, flux=flux, kcat=kcat, dG0=dG0, KMM=KMM,
lnC_bounds=lnC_bounds, ecf_version=ecf_version)
def WriteMatFile(self, file_name):
mdict = self.ecf.Serialize()
mdict['cids'] = self.kegg_model.cids
mdict['rids'] = self.kegg_model.rids
with open(file_name, 'wb') as fp:
savemat(fp, mdict, format='5')
@staticmethod
def GenerateKeggModel(sbtab_dict):
met2kegg = sbtab_dict.GetDictFromTable('Compound', 'Compound',
'Compound:Identifiers:kegg.compound')
reaction_names = sbtab_dict.GetColumnFromTable('Reaction', 'Reaction')
reaction_formulas = sbtab_dict.GetColumnFromTable('Reaction', 'SumFormula')
sparse_reactions = map(ParseReaction, reaction_formulas)
map_met2kegg = lambda spr : {met2kegg.get(k): v for (k,v) in spr.iteritems()}
sparse_reactions_kegg = map(map_met2kegg, sparse_reactions)
kegg_reactions = [KeggReaction(s, rid=rid) for (s, rid)
in zip(sparse_reactions_kegg, reaction_names)]
model = KeggModel.from_kegg_reactions(kegg_reactions, has_reaction_ids=True)
return model
@staticmethod
def _ReadKineticParameters(sbtab_dict, table_name='RateConstant'):
cols = ['QuantityType',
'Value',
'Compound:Identifiers:kegg.compound',
'Reaction',
'Unit']
rid2keq = {}
rid2crc_gmean = {} # catalytic rate constant geomertic mean
rid2crc_fwd = {} # catalytic rate constant forward
rid2crc_rev = {} # catalytic rate constant reverse
crctype2dict = {'catalytic rate constant geometric mean': rid2crc_gmean,
'substrate catalytic rate constant': rid2crc_fwd,
'product catalytic rate constant': rid2crc_rev}
rid_cid2KMM = {} # Michaelis-Menten constants
for i, row in enumerate(sbtab_dict.GetColumnsFromTable(table_name, cols)):
try:
typ, val, cid, rid, unit = row
val = float(val)
if typ in crctype2dict:
if unit != '1/s':
raise AssertionError('Catalytic rate constants must be '
'in units of 1/s, not %s' % unit)
crctype2dict[typ][rid] = val
elif typ == 'Michaelis constant':
value_mapping = ECMmodel._MappingToCanonicalConcentrationUnits(unit)
rid_cid2KMM[rid, cid] = value_mapping(val)
elif typ == 'equilibrium constant':
assert unit == ''
rid2keq[rid] = val
elif typ == 'concentration of enzyme':
pass
else:
raise AssertionError('unrecognized Rate Constant Type: ' + typ)
except AssertionError as e:
raise ValueError('Syntax error in SBtab table %s, row %d - %s' %
(table_name, i, str(e)))
return rid2crc_gmean, rid2crc_fwd, rid2crc_rev, rid_cid2KMM, rid2keq
def _GibbsEnergyFromMilliMolarToMolar(self, dGm):
"""
In the current SBtab file format, the 'standard Gibbs energies'
are actually dGm (since mM is used as the reference concentration).
We need to convert them back to M and that requires using the
stoichiometric matrix (self.kegg_model.S).
"""
# Assume that all concentrations are 1 mM
mM_conc_v = 1e-3 * np.matrix(np.ones((self.kegg_model.S.shape[0], 1)))
# remember to set the concentration of H2O to 1
mM_conc_v[self.kegg_model.cids.index('C00001')] = 1
# subtract the effect of the concentrations to return back to dG0
dG0 = dGm - self.kegg_model.S.T * default_RT * np.log(mM_conc_v)
return dG0
def _CalcGibbsEnergiesFromKeq(self, rid2keq):
keq = np.matrix(map(rid2keq.get, self.kegg_model.rids)).T
dGm_prime = - default_RT * np.log(keq)
dG0_prime = self._GibbsEnergyFromMilliMolarToMolar(dGm_prime)
self.rid2dG0 = dict(zip(self.kegg_model.rids, dG0_prime.flat))
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))
def _CalcGibbsEnerigesFromdG0ReactionTable(self):
"""
it's better to take the values from the SBtab since they are processed
by the parameter balancing funcion
"""
dG0_units = self._sbtab_dict.GetTableAttribute('GibbsEnergyOfReaction', 'Unit')
value_mapping = ECMmodel._MappingToCanonicalEnergyUnits(dG0_units)
rid2dGm = self._sbtab_dict.GetDictFromTable(
'GibbsEnergyOfReaction', 'Reaction', 'Value', value_mapping=value_mapping)
dGm_prime = np.matrix(map(rid2dGm.get, self.kegg_model.rids), dtype=float).T
dG0_prime = self._GibbsEnergyFromMilliMolarToMolar(dGm_prime)
self.rid2dG0 = dict(zip(self.kegg_model.rids, dG0_prime.flat))
def _ReadConcentrationBounds(self):
"""
read the lower and upper bounds and convert them to M.
verify that the values make sense.
"""
bound_units = self._sbtab_dict.GetTableAttribute('ConcentrationConstraint', 'Unit')
bound_mapping = ECMmodel._MappingToCanonicalConcentrationUnits(bound_units)
self.cid2min_bound = self._sbtab_dict.GetDictFromTable(
'ConcentrationConstraint', 'Compound:Identifiers:kegg.compound', 'Concentration:Min',
value_mapping=bound_mapping)
self.cid2max_bound = self._sbtab_dict.GetDictFromTable(
'ConcentrationConstraint', 'Compound:Identifiers:kegg.compound', 'Concentration:Max',
value_mapping=bound_mapping)
if 'C00001' in self.cid2min_bound:
assert np.round(self.cid2min_bound['C00001'], 2) == 1
else:
self.cid2min_bound['C00001'] = 1
if 'C00001' in self.cid2max_bound:
assert np.round(self.cid2max_bound['C00001'], 2) == 1
else:
self.cid2max_bound['C00001'] = 1
for cid in self.cid2min_bound.keys():
assert self.cid2min_bound[cid] <= self.cid2max_bound[cid]
@staticmethod
def _GenerateKMM(cids, rids, rid_cid2KMM):
KMM = np.ones((len(cids), len(rids)))
for i, cid in enumerate(cids):
for j, rid in enumerate(rids):
KMM[i, j] = rid_cid2KMM.get((rid,cid), 1)
return KMM
def MDF(self):
mdf, params = self.ecf.MDF() # note that MDF is given in units of RT
if np.isnan(mdf) or mdf < 0.0:
logging.error('Negative MDF value: %.1f RT (= %.1f kJ/mol)' %
(mdf, mdf * default_RT))
logging.error('The reactions with shadow prices are:')
for rid, sp in zip(self.kegg_model.rids, params['reaction prices'].flat):
if sp:
logging.error('\t%s : %g' % (rid, sp))
conc = map(np.exp, params['ln concentrations'].flat)
for cid, sp, C in zip(self.kegg_model.cids, params['compound prices'].flat, conc):
if sp and not self.cid2external[cid]:
logging.error('\t[%30s] : %5.1e < %5.1e < %5.1e M' %
(self.kegg2met[cid], self.cid2min_bound[cid], C, self.cid2max_bound[cid]))
raise ThermodynamicallyInfeasibleError()
return params['ln concentrations']
def ECM(self, lnC0=None):
return self.ecf.ECM(lnC0 or self.MDF())
def ECF(self, lnC):
return self.ecf.ECF(lnC)
@staticmethod
def _nanfloat(x):
if type(x) == float:
return x
if type(x) == int:
return float(x)
if type(x) in types.StringTypes:
if x.lower() in ['', 'nan']:
return np.nan
else:
return float(x)
else:
raise ValueError('unrecognized type for value: ' + str(type(x)))
@staticmethod
def _MappingToCanonicalEnergyUnits(unit):
"""
Assuming the canonical units for concentration are Molar
Returns:
A function that converts a single number or string to the
canonical units
"""
if unit == 'kJ/mol':
return lambda x: ECMmodel._nanfloat(x)
if unit == 'kcal/mol':
return lambda x: ECMmodel._nanfloat(x)*4.184
raise ValueError('Cannot convert these units to kJ/mol: ' + unit)
@staticmethod
def _MappingToCanonicalConcentrationUnits(unit):
"""
Assuming the canonical units for concentration are Molar
Returns:
A function that converts a single number or string to the
canonical units
"""
if unit == 'M':
return lambda x: ECMmodel._nanfloat(x)
if unit == 'mM':
return lambda x: ECMmodel._nanfloat(x)*1e-3
if unit == 'uM':
return lambda x: ECMmodel._nanfloat(x)*1e-6
if unit == 'nM':
return lambda x: ECMmodel._nanfloat(x)*1e-9
raise ValueError('Cannot convert these units to M: ' + unit)
@staticmethod
def _MappingToCanonicalFluxUnits(unit):
"""
Assuming the canonical units for concentration are [M/s]
Returns:
A function that converts a single number or string to the
canonical units
Note: CELL_VOL_PER_DW is given in [L/gCDW]
"""
if unit == 'M/s':
return lambda x: ECMmodel._nanfloat(x)
if unit == 'mM/s':
return lambda x: ECMmodel._nanfloat(x)*1e-3
if unit == 'mmol/gCDW/h':
return lambda x: ECMmodel._nanfloat(x) / (CELL_VOL_PER_DW * 3600 * 1e3)
if unit == 'mol/gCDW/h':
return lambda x: ECMmodel._nanfloat(x) / (CELL_VOL_PER_DW * 3600)
if unit == 'umol/gCDW/min':
return lambda x: ECMmodel._nanfloat(x) / (CELL_VOL_PER_DW * 60 * 1e6)
if unit == 'mmol/gCDW/min':
return lambda x: ECMmodel._nanfloat(x) / (CELL_VOL_PER_DW * 60 * 1e3)
raise ValueError('Cannot convert these units to M/s: ' + unit)
def _GetMeasuredMetaboliteConcentrations(self):
unit = self._sbtab_dict.GetTableAttribute('Concentration', 'Unit')
value_mapping = ECMmodel._MappingToCanonicalConcentrationUnits(unit)
# assume concentrations are in mM
return self._sbtab_dict.GetDictFromTable(
'Concentration', 'Compound:Identifiers:kegg.compound',
'Concentration', value_mapping=value_mapping)
def _GetMeasuredEnzymeConcentrations(self):
unit = self._sbtab_dict.GetTableAttribute('EnzymeConcentration', 'Unit')
value_mapping = ECMmodel._MappingToCanonicalConcentrationUnits(unit)
return self._sbtab_dict.GetDictFromTable(
'EnzymeConcentration', 'Reaction',
'EnzymeConcentration', value_mapping=value_mapping)
def PlotEnzymeCosts(self, lnC, ax, top_level=3, plot_measured=False):
"""
A bar plot in log-scale showing the partitioning of cost between
the levels of kinetic costs:
1 - capacity
2 - thermodynamics
3 - saturation
4 - allosteric
"""
assert top_level in range(1, 5)
ecf_mat = self.ecf.GetEnzymeCostPartitions(lnC)
datamat = np.log(ecf_mat)
idx_fin = np.where(np.all(np.isfinite(datamat), 1))[0]
idx_nan = np.where(np.any(np.isinf(datamat), 1))[0]
base = min(datamat[idx_fin, :].flat) - 1
bottoms = np.hstack([np.ones((datamat.shape[0], 1)) * base,
np.cumsum(datamat, 1)])
bottoms = np.exp(bottoms)
bottoms[idx_nan, :] = np.exp(base)
steps = np.diff(bottoms)
labels = EnzymeCostFunction.ECF_LEVEL_NAMES[0:top_level]
ind = np.arange(ecf_mat.shape[0]) # the x locations for the groups
width = 0.8
ax.set_yscale('log')
if plot_measured:
all_labels = ['measured'] + labels
meas_enz2conc = self._GetMeasuredEnzymeConcentrations()
meas_conc = np.matrix(map(meas_enz2conc.get, self.kegg_model.rids), dtype=float).T
cmap = colors.ColorMap(all_labels, saturation=0.7, value=1.0, hues=[30.0/255, 170.0/255, 200.0/255, 5.0/255])
#ax.bar(ind, meas_conc, width, bottom=bottoms[:, 0],
# color=cmap['measured'], alpha=0.3)
ax.plot(ind+width/2.0, meas_conc, color=cmap['measured'], marker='o', markersize=5, linewidth=0,
markeredgewidth=0.3, markeredgecolor=(0.3,0.3,0.3))
else:
all_labels = labels
cmap = colors.ColorMap(labels, saturation=0.7, value=0.8, hues=[170.0/255, 200.0/255, 5.0/255])
for i, label in enumerate(labels):
ax.bar(ind, steps[:, i], width,
bottom=bottoms[:, i], color=cmap[label])
ax.set_xticks(ind + width/2)
xticks = map(self.kegg2rxn.get, self.kegg_model.rids)
ax.set_xticklabels(xticks, size='medium', rotation=90)
ax.legend(all_labels, loc='best', framealpha=0.2)
#ax.set_xlabel('reaction')
ax.set_ylabel('enzyme cost [M]')
ax.set_ylim(ymin=base)
def ValidateMetaboliteConcentrations(self, lnC, ax, scale='log'):
pred_conc = np.exp(lnC)
meas_met2conc = self._GetMeasuredMetaboliteConcentrations()
meas_conc = np.matrix(map(meas_met2conc.get, self.kegg_model.cids)).T
mask = (meas_conc > 0) & (pred_conc > 0) # remove NaNs and zeros
mask &= np.diff(self.ecf.lnC_bounds) > 1e-9 # remove compounds with fixed concentrations
labels = map(self.kegg2met.get, self.kegg_model.cids)
PlotCorrelation(ax, meas_conc, pred_conc, labels, mask, scale=scale)
ax.set_xlabel('measured [M]')
ax.set_ylabel('predicted [M]')
def ValidateEnzymeConcentrations(self, lnC, ax, scale='log'):
pred_conc = self.ecf.ECF(lnC)
meas_enz2conc = self._GetMeasuredEnzymeConcentrations()
meas_conc = np.matrix(map(meas_enz2conc.get, self.kegg_model.rids)).T
labels = map(self.kegg2rxn.get, self.kegg_model.rids)
PlotCorrelation(ax, meas_conc, pred_conc, labels, scale=scale)
ax.set_xlabel('measured [M]')
ax.set_ylabel('predicted [M]')
def WriteHtmlTables(self, lnC, html):
meas_enz2conc = self._GetMeasuredEnzymeConcentrations()
meas_conc = np.matrix(map(lambda r: meas_enz2conc.get(r, np.nan), self.kegg_model.rids)).T
data_mat = np.hstack([self.ecf.flux,
meas_conc,
self.ecf.ECF(lnC),
self.ecf._DrivingForce(lnC),
self.ecf.GetEnzymeCostPartitions(lnC)])
data_mat[:, 0] *= 1e3 # convert flux from M/s to mM/s
data_mat[:, 1] *= 1e6 # convert measured enzyme conc. from M to uM
data_mat[:, 2] *= 1e6 # convert predicted enzyme conc. from M to uM
data_mat[:, 4] *= 1e6 # convert capacity term from M to uM
headers = ['reaction', 'KEGG ID', 'flux [mM/s]',
'measured enz. conc. [uM]',
'predicted enz. conc. [uM]',
'driving force [kJ/mol]', 'capacity [uM]'] + \
EnzymeCostFunction.ECF_LEVEL_NAMES[1:]
values = zip(map(self.kegg2rxn.get, self.kegg_model.rids),
self.kegg_model.rids,
*data_mat.T.tolist())
values.append(['total', '', '', data_mat[:, 1].sum(), data_mat[:, 2].sum(),
'', '', '', '', ''])
rowdicst = [dict(zip(headers, v)) for v in values]
html.write_table(rowdicst, headers=headers, decimal=3)
meas_met2conc = self._GetMeasuredMetaboliteConcentrations()
meas_conc = np.matrix(map(lambda r: meas_met2conc.get(r, np.nan), self.kegg_model.cids)).T
data_mat = np.hstack([meas_conc,
np.exp(lnC),
np.exp(self.ecf.lnC_bounds)])
data_mat *= 1e3 # convert all concentrations from M to mM
headers = ['compound', 'KEGG ID', 'measured conc. [mM]',
'predicted conc. [mM]',
'lower bound [mM]', 'upper bound [mM]']
values = zip(map(self.kegg2met.get, self.kegg_model.cids),
self.kegg_model.cids,
*data_mat.T.tolist())
rowdicst = [dict(zip(headers, v)) for v in values]
headers = ['compound', 'KEGG ID', 'measured conc. [mM]',
'lower bound [mM]', 'predicted conc. [mM]', 'upper bound [mM]']
html.write_table(rowdicst, headers=headers)
