def _set_metabolite_concentrations(self):
'''
set the metabolite concentrations to values given in the sbtab
(if specified)
'''
for s in self._model.getListOfSpecies():
if misc.is_enzyme(s): # enzymes are treated separately
continue
conc = float(self._get_sbtab_entry('met_conc', species=s))
try:
conc = float(self._get_sbtab_entry('met_conc', species=s))
s.setInitialConcentration(conc)
except:
self._print_warning('''No initial concentration found for
species %s. Setting value to 1.''' % s.getId())
s.setInitialConcentration(1.)
def _set_metabolite_concentrations(self):
'''
set the metabolite concentrations to values given in the sbtab
(if specified)
'''
for s in self._model.getListOfSpecies():
if misc.is_enzyme(s): # enzymes are treated separately
continue
conc = float(self._get_sbtab_entry('met_conc', species=s))
try:
conc = float(self._get_sbtab_entry('met_conc', species=s))
s.setInitialConcentration(conc)
except:
self._print_warning('''No initial concentration found for
species %s. Setting value to 1.''' % s.getId())
s.setInitialConcentration(1.)
def _create_global_params(self, g_params, mode):
'''
create the global parameters
@type g_params: list
@param g_params: list of global parameters, as generated by
pack_parameters function
@type mode: string
@param mode: parametrisation type ('cat' | 'weg' | 'hal')
'''
# Hack, add temperature and gas constant if parametrization is 'weg'
if mode == 'weg':
p = self._model.createParameter()
p.setId('temp')
p.setValue(300)
p = self._model.createParameter()
p.setId('R')
p.setValue(8.31)
# iterate over all global parameter types
global_param_count = 0
for p_type in self.mode2params[mode]:
scope, deps, id_temp, required = self.param2options[p_type]
if scope == 'local':
continue
p_value = g_params[global_param_count]
global_param_count += 1
# decide whether parameter depends on species / reaction and add it
if deps == 's' or deps == 'k':
i = 0
for s in self._model.getListOfSpecies():
if misc.is_enzyme(s):
continue
self._add_param(p_type,
p_value[i],
reaction=None,
species=s)
i += 1
elif deps == 'r':
for i, r in enumerate(self._model.getListOfReactions):
self._add_param(p_type,
p_value[i],
reaction=r,
species=None)
def _create_global_params(self, g_params, mode):
'''
create the global parameters
@type g_params: list
@param g_params: list of global parameters, as generated by
pack_parameters function
@type mode: string
@param mode: parametrisation type ('cat' | 'weg' | 'hal')
'''
# Hack, add temperature and gas constant if parametrization is 'weg'
if mode == 'weg':
p = self._model.createParameter()
p.setId('temp')
p.setValue(300)
p = self._model.createParameter()
p.setId('R')
p.setValue(8.31)
# iterate over all global parameter types
global_param_count = 0
for p_type in self.mode2params[mode]:
scope, deps, id_temp, required = self.param2options[p_type]
if scope == 'local':
continue
p_value = g_params[global_param_count]
global_param_count += 1
# decide whether parameter depends on species / reaction and add it
if deps == 's' or deps == 'k':
i = 0
for s in self._model.getListOfSpecies():
if misc.is_enzyme(s):
continue
self._add_param(p_type, p_value[i], reaction=None,
species=s)
i += 1
elif deps == 'r':
for i, r in enumerate(self._model.getListOfReactions):
self._add_param(p_type, p_value[i], reaction=r,
species=None)
def _get_regulation(self, reaction):
'''
get regulational term for a reaction
@type reaction: libsbml.reaction
@param reaction: reaction
@rtype: tuple
@return: (prefactor, denominator_term), where prefactor is a
list of prefactors to the kinetic law and denominator
term is a list of terms added to the denominator (one
list entry per modifier)
'''
prefacs = []
denom_terms = []
# generate one term for each modifier
for m in reaction.getListOfModifiers():
if misc.is_enzyme(self._model.getSpecies(m.getSpecies())):
continue
# get the ids for the possiby involved parameters
ka = self.param2options['k_a_vec'][2].replace(
'$REAC$', reaction.getId()).replace('$SPECIES$',
m.getSpecies())
ki = self.param2options['k_i_vec'][2].replace(
'$REAC$', reaction.getId()).replace('$SPECIES$',
m.getSpecies())
act_ratio = self.param2options['act_ratio_vec'][2].replace(
'$REAC$', reaction.getId()).replace('$SPECIES$',
m.getSpecies())
inh_ratio = self.param2options['inh_ratio_vec'][2].replace(
'$REAC$', reaction.getId()).replace('$SPECIES$',
m.getSpecies())
act_term = '(%s/%s)' % (m.getSpecies(), ka)
inh_term = '(%s/%s)' % (m.getSpecies(), ki)
# decide for regulation type
sbo = m.getSBOTerm()
try:
reg_type = self.sbo2type[sbo]
except:
if reaction.getKineticLaw().getParameter(ka):
reg_type = self._default_act
elif reaction.getKineticLaw().getParameter(ki):
reg_type = self._default_inh
else:
continue
if 'act' in reg_type:
if not reaction.getKineticLaw().getParameter(ka):
raise Exception('Parameter %s not given' % ka)
if 'inh' in reg_type:
if not reaction.getKineticLaw().getParameter(ki):
raise Exception('Parameter %s not given' % ki)
if reg_type == 'partial_act' \
and not reaction.getKineticLaw().getParameter(act_ratio):
raise Exception('Parameter %s not given' % act_ratio)
if reg_type == 'partial_inh' \
and not reaction.getKineticLaw().getParameter(inh_ratio):
raise Exception('Parameter %s not given' % inh_ratio)
reaction.getKineticLaw().setSBOTerm(self.type2sbo[reg_type])
# generate the actual terms
if reg_type == 'partial_act':
term = '(%s + (1 - %s) * ( (%s) / (1 + %s)))' % (
act_ratio, act_ratio, act_term, act_term)
elif reg_type == 'partial_inh':
term = '(%s + (1 - %s) * ( 1 / (1 + %s)))' % (
inh_ratio, inh_ratio, inh_term)
elif reg_type == 'complete_act':
term = '((%s)/(1 + %s))' % (act_term, act_term)
elif reg_type == 'complete_inh':
term = '(1/(1 + %s))' % (inh_term)
elif reg_type == 'specific_act':
t = '(%s/%s)' % (ka, m.getSpecies())
denom_terms.append(t)
continue
elif reg_type == 'specific_inh':
denom_terms.append(inh_term)
continue
else:
raise Exception("Unknown regulation type: typo in code")
prefacs.append(term)
prefac = ' * '.join(prefacs)
denom_term = ' + '.join(denom_terms)
return (prefac, denom_term)
def _pack_parameters(self, mode):
'''
get the parameters from the sbtab and pack them in a way that they can
be added easily. Parameters are separated in local ones (associated
with a reaction) and global ones.
@type mode: string
@param mode: parametrisation type ('cat' | 'weg' | 'hal')
@rtype: tuple
@return: tuple of (global_parameters, local_parameters) where
global parameters is the list of global params (1 entry
for each param type) and local_parameter is the list of
local params (1 entry for each reaction)
'''
# get the global params
global_params = []
# get global params for all parameters specified for this mode
for p_type in self.mode2params[mode]:
p_vec = []
scope, deps, dummy, required = self.param2options[p_type]
if scope == 'local':
continue
# if param. depends on species, get one param for each species, if
# it depends on reaction get one for each reaction
if deps == 's' or deps == 'k':
for s in self._model.getListOfSpecies():
if misc.is_enzyme(s):
continue
p_value = self._get_sbtab_entry(p_type, species=s)
p_vec.append(p_value)
elif deps == 'r':
for r in self._model.getListOfReactions():
p_value = self._get_sbtab_entry(p_type, reaction=r)
p_vec.append(p_value)
global_params.append(p_vec)
# get local parameters (one set for each reaction)
local_params = []
for r in self._model.getListOfReactions():
reaction_params = []
for p_type in self.mode2params[mode]:
scope, deps, dummy, required = self.param2options[p_type]
if scope == 'global':
continue
if deps == '':
p_vec = self._get_sbtab_entry(p_type, reaction=r)
elif deps == 's':
p_vec = []
for s in misc.get_participants(r):
p_value = self._get_sbtab_entry(
p_type,
reaction=r,
species=self._model.getSpecies(s.getSpecies()))
p_vec.append(p_value)
elif deps == 'k':
p_vec = []
for s in misc.get_modifiers(r):
p_value = self._get_sbtab_entry(
p_type,
reaction=r,
species=self._model.getSpecies(s.getSpecies()))
p_vec.append(p_value)
elif deps == 'm':
p_vec = []
for m in r.getListOfModifiers():
p_value = self._get_sbtab_entry(
p_type,
reaction=r,
species=self._model.getSpecies(m.getSpecies()))
p_vec.append(p_value)
reaction_params.append(p_vec)
local_params.append(reaction_params)
return (global_params, local_params)
def _get_regulation(self, reaction):
'''
get regulational term for a reaction
@type reaction: libsbml.reaction
@param reaction: reaction
@rtype: tuple
@return: (prefactor, denominator_term), where prefactor is a
list of prefactors to the kinetic law and denominator
term is a list of terms added to the denominator (one
list entry per modifier)
'''
prefacs = []
denom_terms = []
# generate one term for each modifier
for m in reaction.getListOfModifiers():
if misc.is_enzyme(self._model.getSpecies(m.getSpecies())):
continue
# get the ids for the possiby involved parameters
ka = self.param2options['k_a_vec'][2].replace('$REAC$',
reaction.getId()).replace('$SPECIES$',
m.getSpecies())
ki = self.param2options['k_i_vec'][2].replace('$REAC$',
reaction.getId()).replace('$SPECIES$',
m.getSpecies())
act_ratio = self.param2options['act_ratio_vec'][2].replace('$REAC$',
reaction.getId()).replace('$SPECIES$',
m.getSpecies())
inh_ratio = self.param2options['inh_ratio_vec'][2].replace('$REAC$',
reaction.getId()).replace('$SPECIES$',
m.getSpecies())
act_term = '(%s/%s)' % (m.getSpecies(), ka)
inh_term = '(%s/%s)' % (m.getSpecies(), ki)
# decide for regulation type
sbo = m.getSBOTerm()
try:
reg_type = self.sbo2type[sbo]
except:
if reaction.getKineticLaw().getParameter(ka):
reg_type = self._default_act
elif reaction.getKineticLaw().getParameter(ki):
reg_type = self._default_inh
else:
continue
if 'act' in reg_type:
if not reaction.getKineticLaw().getParameter(ka):
raise Exception('Parameter %s not given' % ka)
if 'inh' in reg_type:
if not reaction.getKineticLaw().getParameter(ki):
raise Exception('Parameter %s not given' % ki)
if reg_type == 'partial_act' \
and not reaction.getKineticLaw().getParameter(act_ratio):
raise Exception('Parameter %s not given' % act_ratio)
if reg_type == 'partial_inh' \
and not reaction.getKineticLaw().getParameter(inh_ratio):
raise Exception('Parameter %s not given' % inh_ratio)
reaction.getKineticLaw().setSBOTerm(self.type2sbo[reg_type])
# generate the actual terms
if reg_type == 'partial_act':
term = '(%s + (1 - %s) * ( (%s) / (1 + %s)))' % (act_ratio,
act_ratio,
act_term,
act_term)
elif reg_type == 'partial_inh':
term = '(%s + (1 - %s) * ( 1 / (1 + %s)))' % (inh_ratio,
inh_ratio,
inh_term)
elif reg_type == 'complete_act':
term = '((%s)/(1 + %s))' % (act_term, act_term)
elif reg_type == 'complete_inh':
term = '(1/(1 + %s))' % (inh_term)
elif reg_type == 'specific_act':
t = '(%s/%s)' % (ka, m.getSpecies())
denom_terms.append(t)
continue
elif reg_type == 'specific_inh':
denom_terms.append(inh_term)
continue
else:
raise Exception("Unknown regulation type: typo in code")
prefacs.append(term)
prefac = ' * '.join(prefacs)
denom_term = ' + '.join(denom_terms)
return (prefac, denom_term)
def _pack_parameters(self, mode):
'''
get the parameters from the sbtab and pack them in a way that they can
be added easily. Parameters are separated in local ones (associated
with a reaction) and global ones.
@type mode: string
@param mode: parametrisation type ('cat' | 'weg' | 'hal')
@rtype: tuple
@return: tuple of (global_parameters, local_parameters) where
global parameters is the list of global params (1 entry
for each param type) and local_parameter is the list of
local params (1 entry for each reaction)
'''
# get the global params
global_params = []
# get global params for all parameters specified for this mode
for p_type in self.mode2params[mode]:
p_vec = []
scope, deps, dummy, required = self.param2options[p_type]
if scope == 'local':
continue
# if param. depends on species, get one param for each species, if
# it depends on reaction get one for each reaction
if deps == 's' or deps == 'k':
for s in self._model.getListOfSpecies():
if misc.is_enzyme(s):
continue
p_value = self._get_sbtab_entry(p_type, species=s)
p_vec.append(p_value)
elif deps == 'r':
for r in self._model.getListOfReactions():
p_value = self._get_sbtab_entry(p_type, reaction=r)
p_vec.append(p_value)
global_params.append(p_vec)
# get local parameters (one set for each reaction)
local_params = []
for r in self._model.getListOfReactions():
reaction_params = []
for p_type in self.mode2params[mode]:
scope, deps, dummy, required = self.param2options[p_type]
if scope == 'global':
continue
if deps == '':
p_vec = self._get_sbtab_entry(p_type, reaction=r)
elif deps == 's':
p_vec = []
for s in misc.get_participants(r):
p_value = self._get_sbtab_entry(p_type, reaction=r,
species=self._model.getSpecies(s.getSpecies()))
p_vec.append(p_value)
elif deps == 'k':
p_vec = []
for s in misc.get_modifiers(r):
p_value = self._get_sbtab_entry(p_type, reaction=r,
species=self._model.getSpecies(s.getSpecies()))
p_vec.append(p_value)
elif deps == 'm':
p_vec = []
for m in r.getListOfModifiers():
p_value = self._get_sbtab_entry(p_type, reaction=r,
species=self._model.getSpecies(m.getSpecies()))
p_vec.append(p_value)
reaction_params.append(p_vec)
local_params.append(reaction_params)
return (global_params, local_params)