rhs[mnx_id] = math.fabs(rgt['coefficient'])
equation_str = ' + '.join([f'{value} {key}' for key, value in lhs.items()]) + \
" = " + \
' + '.join([f'{value} {key}' for key, value in rhs.items()])
equilibrator_reaction = Reaction.parse_formula(ccache.get_compound,
equation_str)
try:
result = equilibrator_calculator.standard_dg_prime(
equilibrator_reaction)
dG0_prime = str(result.value.to('kilocal / mole').magnitude)
uncertainty = str(result.error.to('kilocal / mole').magnitude)
ln_RI = equilibrator_calculator.ln_reversibility_index(
equilibrator_reaction)
if not type(ln_RI) == float:
ln_RI = str(ln_RI.magnitude)
output_handle.write("\t".join([rxn, dG0_prime, uncertainty, ln_RI]) +
"\n")
except Exception as e:
output_handle.write("\t".join([rxn, "Unable to retrieve energy"]) +
"\n")
print(e)
#These are all true, as per earlier condition
#print(OK)
print("Saving reactions")
reactions_helper.saveReactions(reactions_dict)
class rpEquilibrator:
"""Class containing collection of functions to intereact between rpSBML files and equilibrator. Includes a function to convert an rpSBML file to a SBtab format for MDF analysis
"""
def __init__(self, rpsbml=None, ph=7.5, ionic_strength=200, pMg=10.0, temp_k=298.15):
"""Constructor class for rpEquilibrator
:param rpsbml: rpSBML object (Default: None)
:param ph: pH of the cell input from the rpSBML model input (Default: 7.5)
:param ionic_strength: Ionic strength from the rpSBML model input (Default: 200)
:param pMg: pMg value from the rpSBML model input (Default: 10.0)
:param temp_k: Temperature from the rpSBML model input in Kelvin (Default: 298.15)
:type rpsbml: rpSBML
:type ph: float
:type ionic_strength: float
:type pMg: float
:type temp_k: float
.. document private functions
.. automethod:: _makeSpeciesStr
.. automethod:: _makeReactionStr
.. automethod:: _speciesCmpQuery
.. automethod:: _reactionCmpQuery
.. automethod:: _reactionStrQuery
"""
self.logger = logging.getLogger(__name__)
self.logger.debug('Started instance of rpEquilibrator')
self.cc = ComponentContribution()
self.cc.p_h = Q_(ph)
self.cc.ionic_strength = Q_(str(ionic_strength)+' mM')
self.cc.p_mg = Q_(pMg)
self.cc.temperature = Q_(str(temp_k)+' K')
self.ph = ph
self.ionic_strength = ionic_strength
self.pMg = pMg
self.temp_k = temp_k
#self.mnx_default_conc = json.load(open('data/mnx_default_conc.json', 'r'))
self.mnx_default_conc = json.load(open(os.path.join(os.path.dirname(os.path.abspath( __file__ )), 'data', 'mnx_default_conc.json'), 'r'))
self.rpsbml = rpsbml
self.calc_cmp = {}
##################################################################################
############################### PRIVATE ##########################################
##################################################################################
#TODO: metanetx.chemical:MNXM7 + bigg.metabolite:pi
def _makeSpeciesStr(self, libsbml_species, ret_type='xref'):
"""Private function that makes a Equilibrator friendly string of a species
:param libsbml_species: A libsbml species object
:param ret_type: Type of output. Valid output include: ['name', 'id', 'xref']
:type libsbml_species: libsbml.Species
:type ret_type: str
Take a libsbml species object, parse the MIRIAM or the brsynth (if present) to return
the equilibrator appropriate string. The order of preference is the following:
example input MIRIAM annotation: {'inchikey': ['GPRLSGONYQIRFK-UHFFFAOYSA-N'], 'seed': ['cpd00067'], 'sabiork': ['39'], 'reactome': ['R-ALL-74722', 'R-ALL-70106', 'R-ALL-5668577', 'R-ALL-428548', 'R-ALL-428040', 'R-ALL-427899', 'R-ALL-425999', 'R-ALL-425978', 'R-ALL-425969', 'R-ALL-374900', 'R-ALL-372511', 'R-ALL-351626', 'R-ALL-2872447', 'R-ALL-2000349', 'R-ALL-194688', 'R-ALL-193465', 'R-ALL-163953', 'R-ALL-156540', 'R-ALL-1470067', 'R-ALL-113529', 'R-ALL-1132304'], 'metacyc': ['PROTON'], 'hmdb': ['HMDB59597'], 'chebi': ['5584', '13357', '10744', '15378'], 'bigg': ['M_h', 'h'], 'metanetx': ['MNXM89553', 'MNXM145872', 'MNXM1', 'MNXM01']}
-KEGG
-CHEBI
#-bigg
#-MNX
-inchikey
ret_type -> valid options (xref, id, name)
:rtype: str
:return: The string id of the species or False if fail
"""
self.logger.debug('ret_type: '+str(ret_type))
if ret_type=='name':
return libsbml_species.getName()
elif ret_type=='id':
return libsbml_species.getId()
elif ret_type=='xref':
annot = libsbml_species.getAnnotation()
if not annot:
self.logger.error('Cannot retreive the annotation')
return False
miriam_dict = self.rpsbml.readMIRIAMAnnotation(annot)
self.logger.debug('miriam_dict: '+str(miriam_dict))
if not miriam_dict:
self.logger.warning('The object annotation does not have any MIRIAM entries')
return False
if 'kegg' in miriam_dict:
if miriam_dict['kegg']:
try:
#take the lowest value
int_list = [int(i.replace('C', '')) for i in miriam_dict['kegg']]
return 'KEGG:'+str(miriam_dict['kegg'][int_list.index(min(int_list))])
except ValueError:
self.logger.warning('There is a non int value in: '+str(miriam_dict['kegg']))
elif 'chebi' in miriam_dict:
if miriam_dict['chebi']:
try:
#take the lowest value
int_list = [int(i) for i in miriam_dict['chebi']]
return 'CHEBI:'+str(miriam_dict['chebi'][int_list.index(min(int_list))])
except ValueError:
self.logger.warning('There is a non int value in: '+str(miriam_dict['chebi']))
elif 'metanetx' in miriam_dict:
if miriam_dict['metanetx']:
try:
#take the lowest value
int_list = [int(i.replace('MNXM', '')) for i in miriam_dict['metanetx']]
return 'metanetx.chemical:'+str(miriam_dict['metanetx'][int_list.index(min(int_list))])
except ValueError:
self.logger.warning('There is a non int value in: '+str(miriam_dict['metanetx']))
elif 'inchikey' in miriam_dict:
if miriam_dict['inchikey']:
if len(miriam_dict['inchikey'])==1:
return miriam_dict['inchikey'][0]
else:
self.logger.warning('There are multiple values of inchikey: '+str(miriam_dict['inchikey']))
self.logger.warning('Taking the first one')
return miriam_dict['inchikey'][0]
else:
self.logger.warning('Could not extract string input for '+str(miriam_dict))
return False
self.logger.warning('The MIRIAM annotation does not have the required information')
return False
else:
self.logger.warning('Cannot determine ret_type: '+str(ret_type))
def _makeReactionStr(self, libsbml_reaction, ret_type='xref', ret_stoichio=True):
"""Make the reaction formulae string to query equilibrator
:param libsbml_reaction: A libsbml reaction object
:param ret_type: Type of output. Valid output include: ['name', 'id', 'xref'] (Default: xref)
:param ret_stoichio: Return the stoichio or not (Default: True)
:type libsbml_reaction: libsbml.Reaction
:type ret_type: str
:type ret_stoichio: bool
:rtype: str
:return: The string id of the reaction or False if fail
"""
reac_str = ''
for rea in libsbml_reaction.getListOfReactants():
rea_str = self._makeSpeciesStr(self.rpsbml.model.getSpecies(rea.getSpecies()), ret_type)
if rea_str:
if ret_stoichio:
reac_str += str(rea.getStoichiometry())+' '+str(rea_str)+' + '
else:
reac_str += str(rea_str)+' + '
else:
return False
reac_str = reac_str[:-2]
reac_str += '<=> ' #TODO: need to find a way to determine the reversibility of the reaction
for pro in libsbml_reaction.getListOfProducts():
pro_str = self._makeSpeciesStr(self.rpsbml.model.getSpecies(pro.getSpecies()), ret_type)
if pro_str:
if ret_stoichio:
reac_str += str(pro.getStoichiometry())+' '+str(pro_str)+' + '
else:
reac_str += str(pro_str)+' + '
else:
return False
reac_str = reac_str[:-2]
self.logger.debug('reac_str: '+str(reac_str))
return reac_str
################### Equilibrator component contribution queries instead of using the native functions ###########
def _speciesCmpQuery(self, libsbml_species):
"""Use the native equilibrator-api compound contribution method
:param libsbml_species: A libsbml species object
:type libsbml_species: libsbml.Reaction
:rtype: tuple
:return: Tuple of size two with mu and sigma values in that order or (None, None) if fail
"""
annot = libsbml_species.getAnnotation()
if not annot:
self.logger.warning('The annotation of '+str(libsbml_species)+' is None....')
return None, None
brs_annot = self.rpsbml.readBRSYNTHAnnotation(libsbml_species.getAnnotation())
#TODO: handle the condition where there are no inchi values but there are SMILES -- should rarely, if ever happen
self.logger.debug('libsbml_species: '+str(libsbml_species))
#self.logger.debug('brs_annot: '+str(brs_annot))
#Try to get the cmp from the ID
spe_id = self._makeSpeciesStr(libsbml_species)
spe_cmp = None
if spe_id:
self.logger.debug('Trying to find the CMP using the xref string: '+str(spe_id))
spe_cmp = self.cc.ccache.get_compound(self._makeSpeciesStr(libsbml_species))
if not spe_cmp:
self.logger.debug('Trying to find the CMP using the structure')
#try to find it in the local data - we do this because there are repeated species in many files
if brs_annot['inchi'] in self.calc_cmp:
spe_cmp = self.calc_cmp[brs_annot['inchi']]
elif brs_annot['smiles'] in self.calc_cmp:
spe_cmp = self.calc_cmp[brs_annot['smiles']]
else:
#if you cannot find it then calculate it
try:
spe_cmp = get_or_create_compound(self.cc.ccache, brs_annot['inchi'], mol_format='inchi')
self.calc_cmp[brs_annot['inchi']] = spe_cmp
self.calc_cmp[brs_annot['smiles']] = spe_cmp
except (OSError, KeyError, GroupDecompositionError) as e:
try:
spe_cmp = get_or_create_compound(self.cc.ccache, brs_annot['smiles'], mol_format='smiles')
self.calc_cmp[brs_annot['smiles']] = spe_cmp
self.calc_cmp[brs_annot['inchi']] = spe_cmp
except (OSError, KeyError, GroupDecompositionError) as e:
self.logger.warning('The following species does not have brsynth annotation InChI or SMILES: '+str(libsbml_species.getId()))
self.logger.warning('Or Equilibrator could not convert the structures')
self.logger.warning(e)
return None, None
if spe_cmp.id==4: #this is H+ and can be ignored
return 'h', 'h'
self.logger.debug('spe_cmp: '+str(spe_cmp))
#mu, sigma = self.cc.predictor.preprocess.get_compound_prediction(eq_cmp[0])
mu, sigma = self.cc.predictor.preprocess.get_compound_prediction(spe_cmp)
return mu, sigma
def _reactionCmpQuery(self, libsbml_reaction, write_results=False, physio_param=1e-3):
"""This method makes a list of structure compounds and uses equilibrator to return the reaction dG
:param libsbml_reaction: A libsbml reaction object
:param write_results: Write the results to the rpSBML file (Default: False)
:param physio_param: The physiological parameter, i.e. the concentration of the compounds to calculate the dG (Default: 1e-3)
:type libsbml_reaction: libsbml.Reaction
:type write_results: bool
:type physio_param: float
:rtype: tuple
:return: Tuple of size three with dfG_prime_o, dfG_prime_m, uncertainty values in that order or False if fail
"""
mus = []
sigma_vecs = []
S = []
dfG_prime_o = None
dfG_prime_m = None
uncertainty = None
for rea in libsbml_reaction.getListOfReactants():
self.logger.debug('------------------- '+str(rea.getSpecies())+' --------------')
mu, sigma = self._speciesCmpQuery(self.rpsbml.model.getSpecies(rea.getSpecies()))
self.logger.debug('mu: '+str(mu))
if not mu:
self.logger.warning('Failed to calculate the reaction mu thermodynamics using compound query')
if write_results:
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'dfG_prime_o', 0.0, 'kj_per_mol')
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'dfG_prime_m', 0.0, 'kj_per_mol')
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'dfG_uncert', 0.0, 'kj_per_mol')
return False
elif mu=='h': #skipping the Hydrogen
continue
mus.append(mu)
sigma_vecs.append(sigma)
S.append([-rea.getStoichiometry()])
for pro in libsbml_reaction.getListOfProducts():
mu, sigma = self._speciesCmpQuery(self.rpsbml.model.getSpecies(pro.getSpecies()))
if not mu:
self.logger.warning('Failed to calculate the reaction mu thermodynamics using compound query')
if write_results:
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'dfG_prime_o', 0.0, 'kj_per_mol')
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'dfG_prime_m', 0.0, 'kj_per_mol')
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'dfG_uncert', 0.0, 'kj_per_mol')
return False
elif mu=='h': #skipping the Hydrogen
continue
mus.append(mu)
sigma_vecs.append(sigma)
S.append([pro.getStoichiometry()])
mus = Q_(mus, 'kJ/mol')
sigma_vecs = Q_(sigma_vecs, 'kJ/mol')
np_S = np.array(S)
dfG_prime_o = np_S.T@mus
dfG_prime_o = float(dfG_prime_o.m[0])
###### adjust fot physio parameters to calculate the dGm'
#TODO: check with Elad
dfG_prime_m = float(dfG_prime_o)+float(self.cc.RT.m)*sum([float(sto[0])*float(np.log(co)) for sto, co in zip(S, [physio_param]*len(S))])
uncertainty = np_S.T@sigma_vecs
uncertainty = [email protected]
uncertainty = uncertainty.m[0][0]
if write_results:
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'dfG_prime_o', dfG_prime_o, 'kj_per_mol')
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'dfG_prime_m', dfG_prime_m, 'kj_per_mol')
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'dfG_uncert', uncertainty, 'kj_per_mol')
return dfG_prime_o, dfG_prime_m, uncertainty
'''
## Not sure if we should implement such a function -- recommended by Elad I geuss
#
#
def pathwayCmpQuery(self, write_results=False):
#1) build the stochio matrix taking into account all the species of the reaction -- must keep track
pass
#################### native equilibrator-api functions ###############
def speciesStrQuery(self, libsbml_species, write_results=False):
"""
Return the formation energy of a chemical species
"""
return False
'''
#TODO: when an inchikey is passed, (and you don't have any other xref) and equilibrator finds the correct species then update the MIRIAM annotations
def _reactionStrQuery(self, libsbml_reaction, write_results=False):
"""Build the string reaction from a libSBML reaction object to send to equilibrator and return the different thermodynamics analysis available
:param libsbml_reaction: A libsbml reaction object
:param write_results: Write the results to the rpSBML file (Default: False)
:type libsbml_reaction: libsbml.Reaction
:type write_results: bool
:rtype: bool
:return: Success or failue of the function
"""
reac_str = ''
try:
reac_str = self._makeReactionStr(libsbml_reaction)
self.logger.debug('The reaction string is: '+str(reac_str))
if not reac_str:
self.logger.warning('Could not generate the reaction string for: '+str(libsbml_reaction))
if write_results:
self.logger.warning('Writing the 0 results to the file')
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'dfG_prime_o', 0.0, 'kj_per_mol')
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'dfG_prime_m', 0.0, 'kj_per_mol')
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'dfG_uncert', 0.0, 'kj_per_mol')
#Are there default values for these?
#self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'reversibility_index', 0.0)
#self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'balanced', rxn.is_balanced())
return False
rxn = self.cc.parse_reaction_formula(reac_str)
standard_dg = self.cc.standard_dg(rxn)
standard_dg_prime = self.cc.standard_dg_prime(rxn)
physiological_dg_prime = self.cc.physiological_dg_prime(rxn)
ln_reversibility_index = self.cc.ln_reversibility_index(rxn)
if type(ln_reversibility_index)==float:
self.logger.warning('The reversibility index is infinite: '+str(ln_reversibility_index))
ln_reversibility_index = None
ln_reversibility_index_error = None
else:
ln_reversibility_index_error = ln_reversibility_index.error.m
ln_reversibility_index = ln_reversibility_index.value.m
self.logger.debug(rxn.is_balanced())
self.logger.debug('ln_reversibility_index: '+str(ln_reversibility_index))
self.logger.debug('standard_dg.value.m: '+str(standard_dg.value.m))
self.logger.debug('standard_dg.error.m: '+str(standard_dg.error.m))
self.logger.debug('standard_dg_prime.value.m: '+str(standard_dg_prime.value.m))
self.logger.debug('standard_dg_prime.error.m: '+str(standard_dg_prime.error.m))
self.logger.debug('physiological_dg_prime.value.m: '+str(physiological_dg_prime.value.m))
self.logger.debug('physiological_dg_prime.error.m: '+str(physiological_dg_prime.error.m))
if write_results:
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'dfG_prime_o', standard_dg_prime.value.m, 'kj_per_mol')
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'dfG_prime_m', physiological_dg_prime.value.m, 'kj_per_mol')
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'dfG_uncert', standard_dg.error.m, 'kj_per_mol')
if ln_reversibility_index:
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'reversibility_index', ln_reversibility_index)
self.rpsbml.addUpdateBRSynth(libsbml_reaction, 'balanced', rxn.is_balanced())
return (rxn.is_balanced(),
(ln_reversibility_index, ln_reversibility_index_error),
(float(standard_dg.value.m), float(standard_dg.error.m)),
(float(standard_dg_prime.value.m), float(standard_dg_prime.error.m)),
(float(physiological_dg_prime.value.m), float(physiological_dg_prime.error.m)))
except equilibrator_cache.exceptions.ParseException:
self.logger.warning('One of the reaction species cannot be parsed by equilibrator: '+str(reac_str))
return False
except equilibrator_cache.exceptions.MissingDissociationConstantsException:
self.logger.warning('Some of the species have not been pre-caclulated using ChemAxon')
################################################################################
########################### PUBLIC FUNCTIONS ###################################
################################################################################
#WARNING: taking the sum of the reaction thermodynamics is perhaps not the best way to do it
def pathway(self, pathway_id='rp_pathway', write_results=True):
"""Calculate the dG of a heterologous pathway
:param pathway_id: The id of the heterologous pathway of interest (Default: rp_pathway)
:param write_results: Write the results to the rpSBML file (Default: True)
:type pathway_id: str
:type write_results: bool
:rtype: tuple
:return: Tuple with the following information, in order: sum dG_prime, std dG_prime, sum dG_prime_o, std dG_prime_o, sum dG_prime_m, std dG_prime_m. Also False if function error.
"""
groups = self.rpsbml.model.getPlugin('groups')
rp_pathway = groups.getGroup(pathway_id)
if not rp_pathway:
self.logger.error('Cannot retreive the pathway: '+str(pathway_id))
return False
pathway_balanced = []
pathway_reversibility_index = []
pathway_reversibility_index_error = []
pathway_standard_dg = []
pathway_standard_dg_error = []
pathway_standard_dg_prime = []
pathway_standard_dg_prime_error = []
pathway_physiological_dg_prime = []
pathway_physiological_dg_prime_error = []
for react in [self.rpsbml.model.getReaction(i.getIdRef()) for i in rp_pathway.getListOfMembers()]:
self.logger.debug('Sending the following reaction to _reactionStrQuery: '+str(react))
res = self._reactionStrQuery(react, write_results)
self.logger.debug('The result is :'+str(res))
if res:
#WARNING: the uncertainty for the three thermo calculations should be the same
pathway_balanced.append(res[0])
if not res[1][0]==None:
pathway_reversibility_index.append(res[1][0])
else:
pathway_reversibility_index.append(0.0)
if not res[1][1]==None:
pathway_reversibility_index_error.append(res[1][1])
else:
pathway_reversibility_index_error.append(0.0)
#ignoring -- need to see if legacy component contribution can return these values
#pathway_standard_dg.append(res[2][0])
#pathway_standard_dg_error.append(res[2][1])
pathway_standard_dg_prime.append(res[3][0])
pathway_standard_dg_prime_error.append(res[3][1])
pathway_physiological_dg_prime.append(res[4][0])
pathway_physiological_dg_prime_error.append(res[4][1])
else:
self.logger.info('Native equilibrator string query failed')
self.logger.info('Trying equilibrator_api component contribution')
self.logger.debug('Trying to calculate using CC: '+str(react))
res = self._reactionCmpQuery(react, write_results)
if res:
pathway_standard_dg_prime.append(res[0])
pathway_standard_dg_prime_error.append(res[2])
pathway_physiological_dg_prime.append(res[1])
pathway_physiological_dg_prime_error.append(res[2])
#TODO: need to implement
pathway_balanced.append(None)
pathway_reversibility_index.append(None)
pathway_reversibility_index_error.append(None)
else:
self.logger.warning('Cannot calculate the thermodynmics for the reaction: '+str(react))
self.logger.warning('Setting everything to 0')
pathway_standard_dg_prime.append(0.0)
pathway_standard_dg_prime_error.append(0.0)
pathway_physiological_dg_prime.append(0.0)
pathway_physiological_dg_prime_error.append(0.0)
#TODO: need to implement
pathway_balanced.append(None)
pathway_reversibility_index.append(None)
pathway_reversibility_index_error.append(None)
#return False
#WARNING return is ignoring balanced and reversibility index -- need to implement in legacy to return it (however still writing these results to the SBML)
if write_results:
self.rpsbml.addUpdateBRSynth(rp_pathway, 'dfG_prime_o', np.sum(pathway_standard_dg_prime), 'kj_per_mol')
self.rpsbml.addUpdateBRSynth(rp_pathway, 'dfG_prime_o_std', np.std(pathway_standard_dg_prime), 'kj_per_mol')
self.rpsbml.addUpdateBRSynth(rp_pathway, 'dfG_prime_m', np.sum(pathway_physiological_dg_prime), 'kj_per_mol')
self.rpsbml.addUpdateBRSynth(rp_pathway, 'dfG_prime_m_std', np.std(pathway_physiological_dg_prime), 'kj_per_mol')
self.rpsbml.addUpdateBRSynth(rp_pathway, 'dfG_uncert', np.mean(pathway_standard_dg_prime_error), 'kj_per_mol')
self.rpsbml.addUpdateBRSynth(rp_pathway, 'dfG_uncert_std', np.std(pathway_standard_dg_prime_error), 'kj_per_mol')
return (np.sum(pathway_standard_dg_prime), np.std(pathway_standard_dg_prime)), (np.sum(pathway_physiological_dg_prime), np.std(pathway_physiological_dg_prime)), (np.sum(pathway_standard_dg_prime), np.std(pathway_standard_dg_prime))
def toNetworkSBtab(self, output, pathway_id='rp_pathway', thermo_id='dfG_prime_o', fba_id='fba_obj_fraction', stdev_factor=1.96):
"""Convert an SBML pathway to a simple network for input to equilibrator-pathway for MDF
:param output: Output path of the TSV file
:param pathway_id: The id of the heterologous pathway of interest (Default: rp_pathway)
:param thermo_id: The id of the thermodynamics result to be exported to the SBtab file (Default: dfG_prime_o, Valid Options: [dfG_prime_o, dfG_prime_m])
:param fba_id: The id of the FBA value to be exported to SBtab (Default: fba_obj_fraction)
:param stdev_factor: The standard deviation factor (Default: 1.96)
:type output: str
:type pathway_id: str
:type thermo_id: str
:type fba_id: str
:type stdev_factor: float
:rtype: bool
:return: Success or failure of the function
"""
groups = self.rpsbml.model.getPlugin('groups')
rp_pathway = groups.getGroup(pathway_id)
if not rp_pathway:
self.logger.error('Cannot retreive the pathway: '+str(pathway_id))
return False
with open(output, 'w') as fo:
####################### Make the header of the document ##############
fo.write("!!!SBtab DocumentName='E. coli central carbon metabolism - balanced parameters' SBtabVersion='1.0'\t\t\t\n")
fo.write("!!SBtab TableID='Configuration' TableType='Config'\t\t\t\n")
fo.write("!Option\t!Value\t!Comment\t\n")
fo.write("algorithm\tMDF\tECM, or MDF\t\n")
fo.write("p_h\t"+str(self.ph)+"\t\t\n")
fo.write("ionic_strength\t"+str(self.ionic_strength)+" mM\t\t\n")
fo.write("p_mg\t"+str(self.pMg)+"\t\t\n")
fo.write("stdev_factor "+str(stdev_factor)+"\n")
fo.write("\t\t\t\n")
####################### Make the reaction list ######################
fo.write("!!SBtab TableID='Reaction' TableType='Reaction'\t\t\t\n")
fo.write("!ID\t!ReactionFormula\t\t\n")
#TODO: need to sort it in the reverse order
#TODO: use the rpGraph to sort the pathway in the right order
ordered_react = [self.rpsbml.model.getReaction(i.getIdRef()) for i in rp_pathway.getListOfMembers()]
ordered_react.sort(key=lambda x: int(x.getId().replace('RP', '')), reverse=True)
#for react in [self.rpsbml.model.getReaction(i.getIdRef()) for i in rp_pathway.getListOfMembers()]:
for react in ordered_react:
react_str = self._makeReactionStr(react, 'id', True)
if react_str:
fo.write(str(react.getId())+"\t"+str(react_str)+"\n")
else:
self.logger.error('Cannot build the reaction: '+str(rect))
return False
fo.write("\t\t\t\n")
fo.write("\t\t\t\n")
########################## Make the species list ###################
fo.write("!!SBtab TableID='Compound' TableType='Compound'\t\t\t\n")
fo.write("!ID\t!Identifiers\t\t\n")
rp_species = self.rpsbml.readUniqueRPspecies(pathway_id)
for spe_id in rp_species:
spe = self.rpsbml.model.getSpecies(spe_id)
miriam_dict = self.rpsbml.readMIRIAMAnnotation(spe.getAnnotation())
if not miriam_dict:
self.logger.warning('The object annotation does not have any MIRIAM entries')
return False
iden_str = None
if 'kegg' in miriam_dict:
if miriam_dict['kegg']:
try:
#take the lowest value
int_list = [int(i.replace('C', '')) for i in miriam_dict['kegg']]
iden_str = 'KEGG:'+str(miriam_dict['kegg'][int_list.index(min(int_list))])
except ValueError:
self.logger.warning('There is a non int value in: '+str(miriam_dict['kegg']))
if 'chebi' in miriam_dict and not iden_str:
if miriam_dict['chebi']:
try:
#take the lowest value
int_list = [int(i) for i in miriam_dict['chebi']]
iden_str = 'CHEBI:'+str(miriam_dict['chebi'][int_list.index(min(int_list))])
except ValueError:
self.logger.warning('There is a non int value in: '+str(miriam_dict['chebi']))
if 'metanetx' in miriam_dict and not iden_str:
if miriam_dict['metanetx']:
try:
#take the lowest value
int_list = [int(i.replace('MNXM', '')) for i in miriam_dict['metanetx']]
iden_str = 'metanetx.chemical:'+str(miriam_dict['metanetx'][int_list.index(min(int_list))])
except ValueError:
self.logger.warning('There is a non int value in: '+str(miriam_dict['metanetx']))
if 'inchikey' in miriam_dict and not iden_str:
if miriam_dict['inchikey']:
if len(miriam_dict['inchikey'])==1:
iden_str = miriam_dict['inchikey'][0]
else:
self.logger.warning('There are multiple values of inchikey: '+str(miriam_dict['inchikey']))
self.logger.warning('Taking the first one')
iden_str = miriam_dict['inchikey'][0]
if not iden_str:
self.logger.warning('Could not extract string input for '+str(miriam_dict))
fo.write(str(spe_id)+"\t"+str(iden_str)+"\t\t\n")
fo.write("\t\t\t\n")
################## Add FBA values ##############################
#TODO: perhaps find a better way than just setting this to 1
fo.write("!!SBtab TableID='Flux' TableType='Quantity' Unit='mM/s'\t\t\t\n")
fo.write("!QuantityType\t!Reaction\t!Value\t\n")
for react in [self.rpsbml.model.getReaction(i.getIdRef()) for i in rp_pathway.getListOfMembers()]:
brs_annot = self.rpsbml.readBRSYNTHAnnotation(react.getAnnotation())
if fba_id:
if fba_id in brs_annot:
#the saved value is mmol_per_gDW_per_hr while rpEq required mmol_per_s
#WARNING: rpEq says that these values are mMol/s while here we have mMol/gDw/h. However not changing since this would mean doing /3600 and
#given the values that seems wrong
fo.write("rate of reaction\t"+str(react.getId())+"\t"+str(brs_annot[fba_id]['value'])+"\t\n")
else:
self.logger.warning('Cannot retreive the FBA value '+str(fba_id)+'. Setting a default value of 1.')
fo.write("rate of reaction\t"+str(react.getId())+"\t1\t\n")
else:
fo.write("rate of reaction\t"+str(react.getId())+"\t1\t\n")
################## Add the concentration bounds ##############################
fo.write("\t\t\t\n")
fo.write("!!SBtab TableID='ConcentrationConstraint' TableType='Quantity' Unit='mM'\t\t\t\n")
fo.write("!QuantityType\t!Compound\t!Min\t!Max\n")
for spe_id in rp_species:
self.logger.debug('========= '+str(spe_id)+' ========')
is_found = False
spe = self.rpsbml.model.getSpecies(spe_id)
miriam_dict = self.rpsbml.readMIRIAMAnnotation(spe.getAnnotation())
self.logger.debug(miriam_dict)
if not miriam_dict:
self.logger.warning('The object annotation does not have any MIRIAM entries')
continue
if 'metanetx' in miriam_dict:
self.logger.debug(miriam_dict['metanetx'])
for mnx in miriam_dict['metanetx']:
if mnx in list(self.mnx_default_conc.keys()) and not is_found:
self.logger.debug('Found default concentration range for '+str(spe.getId())+' ('+str(mnx)+'): '+str(self.mnx_default_conc[mnx]))
fo.write("concentration\t"+spe.getId()+"\t"+str(self.mnx_default_conc[mnx]['c_min'])+"\t"+str(self.mnx_default_conc[mnx]['c_max'])+"\n")
is_found = True
if not is_found:
self.logger.debug('Using default values for '+str(spe.getId()))
fo.write("concentration\t"+spe.getId()+"\t0.001\t10\n")
fo.write("\t\t\t\n")
############################ Add the thermo value ###########################
#TODO: perform on the fly thermodynamic calculations when the values are not included within the SBML file
if thermo_id:
fo.write("!!SBtab TableID='Thermodynamics' TableType='Quantity' StandardConcentration='M'\t\t\t\n")
fo.write("!QuantityType\t!Reaction\t!Compound\t!Value\t!Unit\n")
for react in [self.rpsbml.model.getReaction(i.getIdRef()) for i in rp_pathway.getListOfMembers()]:
brs_annot = self.rpsbml.readBRSYNTHAnnotation(react.getAnnotation())
try:
#TODO: switch to dfG_prime_m when you are sure how to calculate it using the native equilibrator function
if thermo_id in brs_annot:
if brs_annot[thermo_id]:
fo.write("reaction gibbs energy\t"+str(react.getId())+"\t\t"+str(brs_annot['dfG_prime_o']['value'])+"\tkJ/mol\n")
else:
self.logger.error(str(thermo_id)+' is empty. Was rpThermodynamics run on this SBML? Aborting...')
return False
else:
self.logger.error('There is no '+str(thermo_id)+' in the reaction '+str(react.getId()))
return False
except KeyError:
self.logger.error('The reaction '+str(react.getId())+' does not seem to have the following thermodynamic value: '+str(thermo_id))
return False
return True
def MDF(self, pathway_id='rp_pathway', thermo_id='dfG_prime_o', fba_id='fba_obj_fraction', stdev_factor=1.96, write_results=True):
"""Perform MDF analysis on the rpSBML file
:param pathway_id: The id of the heterologous pathway of interest (Default: rp_pathway)
:param thermo_id: The id of the thermodynamics result to be exported to the SBtab file (Default: dfG_prime_o, Valid Options: [dfG_prime_o, dfG_prime_m])
:param fba_id: The id of the FBA value to be exported to SBtab (Default: fba_obj_fraction)
:param stdev_factor: The standard deviation factor (Default: 1.96)
:param write_results: Write the results to the rpSBML file (Default: True)
:type pathway_id: str
:type thermo_id: str
:type fba_id: str
:type stdev_factor: float
:type write_results: bool
:rtype: float
:return: MDF of the pathway
"""
to_ret_mdf = None
groups = self.rpsbml.model.getPlugin('groups')
rp_pathway = groups.getGroup(pathway_id)
with tempfile.TemporaryDirectory() as tmpOutputFolder:
path_sbtab = os.path.join(tmpOutputFolder, 'tmp_sbtab.tsv')
sbtab_status = self.toNetworkSBtab(path_sbtab, pathway_id=pathway_id, thermo_id=thermo_id, fba_id=fba_id, stdev_factor=stdev_factor)
if not sbtab_status:
self.logger.error('There was a problem generating the SBtab... aborting')
return 0.0
try:
pp = Pathway.from_sbtab(path_sbtab, comp_contrib=self.cc)
pp.update_standard_dgs()
try:
mdf_sol = pp.calc_mdf()
except:
self.logger.warning('The calc_mdf function failed')
self.logger.warning('Exception: Cannot solve MDF primal optimization problem')
self.rpsbml.addUpdateBRSynth(rp_pathway, 'MDF', 0.0, 'kj_per_mol')
return 0.0
#mdf_sol = pp.mdf_analysis()
#plt_reac_plot = mdf_sol.reaction_plot
#plt_cmp_plot = mdf_sol.compound_plot
to_ret_mdf = float(mdf_sol.mdf.m)
if write_results:
self.rpsbml.addUpdateBRSynth(rp_pathway, 'MDF', float(mdf_sol.mdf.m), 'kj_per_mol')
except KeyError as e:
self.logger.warning('Cannot calculate MDF')
self.logger.warning(e)
self.rpsbml.addUpdateBRSynth(rp_pathway, 'MDF', 0.0, 'kj_per_mol')
return 0.0
except equilibrator_cache.exceptions.MissingDissociationConstantsException as e:
self.logger.warning('Some species are invalid: '+str(e))
self.rpsbml.addUpdateBRSynth(rp_pathway, 'MDF', 0.0, 'kj_per_mol')
return 0.0
return to_ret_mdf