def test_WildtypeFilter(self):
f = data_query.WildtypeFilter()
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(variation='')))
self.assertEqual(f.score(None, ov), 1)
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(variation='Delta gene-01')))
self.assertEqual(f.score(None, ov), -1)
def test_filter_observed_results(self):
gen = self.ConcreteDataQueryGenerator()
gen.filters = [
data_query.TemperatureRangeFilter(min=36., max=38.),
]
observed_results = [
data_model.ObservedValue(metadata=data_model.ObservedResultMetadata(environment=data_model.Environment(temperature=36.5))),
data_model.ObservedValue(metadata=data_model.ObservedResultMetadata(environment=data_model.Environment(temperature=35.0))),
data_model.ObservedValue(metadata=data_model.ObservedResultMetadata(environment=data_model.Environment(temperature=37.0))),
]
result = gen.filter_observed_results(None, observed_results)
self.assertEqual(set(result.observed_results), set([observed_results[0], observed_results[2]]))
def test_score(self):
ov = [
data_model.ObservedValue(value=1, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37, ph=7))),
data_model.ObservedValue(value=1, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37, ph=6))),
data_model.ObservedValue(value=1, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=36, ph=7))),
data_model.ObservedValue(value=1, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=36, ph=6))),
data_model.ObservedValue(value=2, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37, ph=7))),
data_model.ObservedValue(value=2, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37, ph=6))),
data_model.ObservedValue(value=2, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=36, ph=7))),
data_model.ObservedValue(value=2, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=36, ph=6))),
]
f = [
data_query.TemperatureRangeFilter(min=36.5, max=37.5),
data_query.PhRangeFilter(min=6.5, max=7.5),
data_query.TemperatureNormalFilter(mean=37., std=1),
]
runner = data_query.FilterRunner(f[0])
self.assertEqual(list(runner.score(None, ov).ravel()), [1., 1., -1., -1., 1., 1., -1., -1.])
runner = data_query.FilterRunner(f[1])
self.assertEqual(list(runner.score(None, ov).ravel()), [1., -1., 1., -1., 1., -1., 1., -1.])
runner = data_query.FilterRunner(f[2])
s = 2 * scipy.stats.norm.cdf(-1)
numpy.testing.assert_almost_equal(list(runner.score(None, ov).ravel()), [1., 1., s, s, 1., 1., s, s], decimal=5)
def get_observed_concentrations(self, metabolite):
""" Find observed concentrations for the metabolite or similar metabolites
Args:
metabolite (:obj:`models.Metabolite`): metabolite to find data for
Returns:
:obj:`list` of :obj:`data_model.ObservedValue`: list of relevant observations
"""
concentrations = self.get_concentration_by_structure(metabolite.structure._value_inchi, only_formula_and_connectivity=False)
observed_values = []
for c in concentrations:
metadata = self.metadata_dump(c)
observable = data_model.Observable(
specie = self._port(metabolite),
compartment = data_model.Compartment(name = c._metadata.cell_compartment[0].name)
)
observed_values.append(data_model.ObservedValue(
metadata = metadata,
observable = observable,
value = c.value,
error = c.error,
units = c.units
))
return observed_values
def test(self):
c = data_model.Consensus(
observable=data_model.Observable(),
value=1.1,
error=0.3,
units='uM',
method=data_model.ConsensusMethod.mean,
evidence=[
data_model.Evidence(value=data_model.ObservedValue(),
relevance=10.),
data_model.Evidence(value=data_model.ObservedValue(),
relevance=5.),
],
user='******',
date=datetime.utcnow(),
)
def test(self):
ov = [
data_model.ObservedValue(value=1),
data_model.ObservedValue(value=2),
]
f = [
data_query.RangeFilter(('environment', 'temperature', )),
data_query.RangeFilter(('environment', 'ph', )),
]
s = [0.3, 0.5]
filter_result = data_query.FilterResult(copy.copy(ov), copy.copy(s), [1, 2], ov, s)
self.assertEqual(filter_result.observed_results, ov)
self.assertEqual(filter_result.scores, s)
self.assertEqual(filter_result.observed_value_indices, [1, 2])
self.assertEqual(filter_result.all_observed_results, ov)
self.assertEqual(filter_result.all_scores, s)
def test(self):
value = data_model.ObservedValue()
evidence = data_model.Evidence(
value=value,
relevance=10.,
)
self.assertEqual(evidence.value, value)
self.assertEqual(evidence.relevance, 10.)
def test_TaxonomicDistanceFilter(self):
# example 1
f = data_query.TaxonomicDistanceFilter('Mycoplasma pneumoniae M129')
self.assertEqual(f.max, 8.)
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(taxon='Mycoplasma pneumoniae M129')))
self.assertEqual(f.score(None, ov), 1.)
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(taxon='Mycoplasma pneumoniae')))
self.assertEqual(f.scale, 8./5)
self.assertEqual(f.score(None, ov), math.exp(-1/f.scale))
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(taxon='Escherichia coli')))
self.assertEqual(f.score(None, ov), math.exp(-5))
# example 2
f = data_query.TaxonomicDistanceFilter('Mycoplasma pneumoniae M129', max=5)
self.assertEqual(f.max, 5.)
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(taxon='Mycoplasma pneumoniae M129')))
self.assertEqual(f.score(None, ov), 1.)
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(taxon='Mycoplasma pneumoniae')))
self.assertEqual(f.score(None, ov), math.exp(-1/f.scale))
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(taxon='Escherichia coli')))
self.assertEqual(f.score(None, ov), -1)
# example 3
f = data_query.TaxonomicDistanceFilter('Mycoplasma genitalium')
self.assertEqual(f.max, 7.)
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(taxon='Mycoplasma genitalium G37')))
self.assertEqual(f.score(None, ov), 1.)
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(taxon='Mycoplasma genitalium')))
self.assertEqual(f.score(None, ov), 1)
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(taxon='Mycoplasma')))
self.assertEqual(f.score(None, ov), math.exp(-1/f.scale))
def test_group_observed_results_by_properties(self):
observed_results = [
data_model.ObservedValue(
observable=data_model.Observable(property='Km'),
value=1.,
units=''
)
]
gen = data_query.ConsensusGenerator()
observed_results
def test_SpecieSequenceSimilarityFilter(self):
seq1 = 'CTAACTCTACCTCGTATGTATGGAAGTTCGTCTATCTCTGGTCGGTTGCT'
seq2 = 'CTAACTCTACCTCGTATTATGGAAGTTCGTCTATCTTCTGGTCGGTTGCT'
ov = data_model.ObservedValue(observable=data_model.Observable(specie=data_model.PolymerSpecie(sequence=seq1)))
# min_similarity = 0
f = data_query.SpecieSequenceSimilarityFilter(min_similarity=0.)
self.assertEqual(f.score(data_model.PolymerSpecie(sequence=seq2), ov), 0.96)
# min_similarity = 0.75
f = data_query.SpecieSequenceSimilarityFilter(min_similarity=0.98)
self.assertEqual(f.score(data_model.PolymerSpecie(sequence=seq2), ov), -1)
def test_SpecieStructuralSimilarityFilter(self):
adp = 'NC1=C2N=CN(C3OC(COP([O-])(=O)OP([O-])([O-])=O)C(O)C3O)C2=NC=N1'
atp = 'NC1=C2N=CN(C3OC(COP([O-])(=O)OP([O-])(=O)OP([O-])([O-])=O)C(O)C3O)C2=NC=N1'
h2o = 'O'
# min_similarity = 0
f = data_query.SpecieStructuralSimilarityFilter(min_similarity=0.)
ov = data_model.ObservedValue(observable=data_model.Observable(specie=data_model.Specie(structure=adp)))
numpy.testing.assert_almost_equal(f.score(data_model.Specie(structure=atp), ov), 0.955, decimal=3)
ov = data_model.ObservedValue(observable=data_model.Observable(specie=data_model.Specie(structure=h2o)))
numpy.testing.assert_almost_equal(f.score(data_model.Specie(structure=atp), ov), 0, decimal=3)
# min_similarity = 0.75
f = data_query.SpecieStructuralSimilarityFilter(min_similarity=0.75)
ov = data_model.ObservedValue(observable=data_model.Observable(specie=data_model.Specie(structure=adp)))
numpy.testing.assert_almost_equal(f.score(data_model.Specie(structure=atp), ov), 0.955, decimal=3)
ov = data_model.ObservedValue(observable=data_model.Observable(specie=data_model.Specie(structure=h2o)))
numpy.testing.assert_almost_equal(f.score(data_model.Specie(structure=atp), ov), -1., decimal=3)
def get_observed_result(self, protein):
""" Find the observed values for protein abundance
Args:
protein (:obj:`models.ProteinSubunit`): Protein Subunit to find data for
Returns:
:obj:`list` of :obj:`data_model.ObservedValue`: list of relevant observed values
"""
abundances = self.get_abundance_by_uniprot(protein.uniprot_id)
observed_vals = []
for abundance in abundances:
metadata = self.metadata_dump(abundance.dataset)
observable = data_model.Observable(specie=data_model.ProteinSpecie(
name=protein.subunit_name,
uniprot_id=protein.uniprot_id,
entrez_id=protein.entrez_id,
gene_name=protein.gene_name,
length=protein.length,
mass=protein.mass,
sequence=protein.canonical_sequence))
observable.specie.cross_references = [
data_model.Resource(namespace='publication',
id=abundance.dataset.file_name),
data_model.Resource(
namespace='url',
id=abundance.dataset._metadata.resource[0]._id)
]
observed_vals.append(
data_model.ObservedValue(
metadata=metadata,
observable=observable,
value=abundance.abundance,
error=0,
units='PPM',
))
return observed_vals
def test_run(self):
ov = [
data_model.ObservedValue(value=1, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37, ph=7))),
data_model.ObservedValue(value=1, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37, ph=6))),
data_model.ObservedValue(value=1, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=36, ph=7))),
data_model.ObservedValue(value=1, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=36, ph=6))),
data_model.ObservedValue(value=2, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37.1, ph=7))),
data_model.ObservedValue(value=2, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37.1, ph=6))),
data_model.ObservedValue(value=2, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37.01, ph=7))),
data_model.ObservedValue(value=2, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37.01, ph=6))),
]
f = [
data_query.TemperatureNormalFilter(mean=37, std=1),
data_query.PhRangeFilter(min=6.5, max=7.5),
]
runner = data_query.FilterRunner(f)
# return_info=True
result = runner.run(None, ov, return_info=True)
self.assertEqual(result.all_observed_results, ov)
s1 = 2 * scipy.stats.norm.cdf(-1)
s01 = 2 * scipy.stats.norm.cdf(-0.1)
s001 = 2 * scipy.stats.norm.cdf(-0.01)
numpy.testing.assert_almost_equal(result.all_scores, numpy.array([[1., 1., s1, s1, s01, s01, s001, s001],
[1., -1., 1., -1., 1., -1., 1., -1.]], ndmin=2).transpose())
self.assertEqual(result.observed_results, [ov[0], ov[6], ov[4], ov[2]])
numpy.testing.assert_almost_equal(result.scores, numpy.array([[1., s001, s01, s1], [1., 1., 1., 1.]], ndmin=2).transpose())
self.assertEqual(result.observed_value_indices, [0, 6, 4, 2])
# return_info=False
ordered_observed_results = runner.run(None, ov, return_info=False)
self.assertEqual(ordered_observed_results, [ov[0], ov[6], ov[4], ov[2]])
def test_order(self):
ov = [
data_model.ObservedValue(value=1, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37, ph=7))),
data_model.ObservedValue(value=1, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37, ph=6))),
data_model.ObservedValue(value=1, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=36, ph=7))),
data_model.ObservedValue(value=1, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=36, ph=6))),
data_model.ObservedValue(value=2, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37, ph=7))),
data_model.ObservedValue(value=2, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37, ph=6))),
data_model.ObservedValue(value=2, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=36, ph=7))),
data_model.ObservedValue(value=2, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=36, ph=6))),
]
# one filter
s = numpy.array([0.9, -1, 0.5, 1, -1.1, 0, -1.3, 0.3], ndmin=2).transpose()
runner = data_query.FilterRunner([])
order_o, order_s, i_order_o = runner.order(ov, s)
self.assertEqual(order_o, [ov[3], ov[0], ov[2], ov[7], ov[5], ov[1], ov[4], ov[6]])
numpy.testing.assert_equal(order_s, numpy.array([1, 0.9, 0.5, 0.3, 0, -1, -1.1, -1.3], ndmin=2).transpose())
self.assertEqual(i_order_o, [3, 0, 2, 7, 5, 1, 4, 6])
# multiple filters
s = numpy.array([[0.9, -1, 0.5, 1, -1.1, 0, -1.3, 0.3], [0, 0, 0, 0, 0, 0, 0, 0]], ndmin=2).transpose()
runner = data_query.FilterRunner([])
order_o, order_s, i_order_o = runner.order(ov, s)
self.assertEqual(order_o, [ov[3], ov[0], ov[2], ov[7], ov[5], ov[1], ov[4], ov[6]])
numpy.testing.assert_equal(order_s, numpy.array(
[[1, 0.9, 0.5, 0.3, 0, -1, -1.1, -1.3], [0, 0, 0, 0, 0, 0, 0, 0]], ndmin=2).transpose())
self.assertEqual(i_order_o, [3, 0, 2, 7, 5, 1, 4, 6])
def test_filter(self):
ov = [
data_model.ObservedValue(value=1, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37, ph=7))),
data_model.ObservedValue(value=1, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37, ph=6))),
data_model.ObservedValue(value=1, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=36, ph=7))),
data_model.ObservedValue(value=1, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=36, ph=6))),
data_model.ObservedValue(value=2, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37, ph=7))),
data_model.ObservedValue(value=2, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=37, ph=6))),
data_model.ObservedValue(value=2, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=36, ph=7))),
data_model.ObservedValue(value=2, metadata=data_model.ObservedResultMetadata(
environment=data_model.Environment(temperature=36, ph=6))),
]
# one filter
s = numpy.array([1, -1, 0.5, 1, -1, 0, -1, 0.3], ndmin=2).transpose()
runner = data_query.FilterRunner([])
filt_o, filt_s, i_filt_o = runner.filter(ov, s)
self.assertEqual(filt_o, [ov[0], ov[2], ov[3], ov[5], ov[7]])
numpy.testing.assert_equal(filt_s, numpy.array([1, 0.5, 1, 0, 0.3], ndmin=2).transpose())
self.assertEqual(i_filt_o, [0, 2, 3, 5, 7])
# multiple filters
s = numpy.array([[1, -1, 0.5, 1, -1, 0, -1, 0.3], [0, 0, 0, 0, 0, 0, 0, -1]], ndmin=2).transpose()
runner = data_query.FilterRunner([])
filt_o, filt_s, i_filt_o = runner.filter(ov, s)
self.assertEqual(filt_o, [ov[0], ov[2], ov[3], ov[5]])
numpy.testing.assert_equal(filt_s, numpy.array([[1, 0.5, 1, 0], [0, 0, 0, 0]], ndmin=2).transpose())
self.assertEqual(i_filt_o, [0, 2, 3, 5])
def get_observed_result(self, metabolite):
""" Find observed concentrations for the metabolite or similar metabolites
Args:
metabolite (:obj:`models.Metabolite`): metabolite to find data for
Returns:
:obj:`list` of :obj:`data_model.ObservedValue`: list of relevant observations
"""
concentrations = self.get_concentration_by_structure(
metabolite.structure._value_inchi,
only_formula_and_connectivity=False).all()
observed_values = []
references = [
data_model.Resource(namespace=item.namespace, id=item._id)
for item in metabolite._metadata.resource
]
for c in concentrations:
metadata = self.metadata_dump(c)
observable = data_model.Observable(
specie=data_model.Specie(
name=metabolite.metabolite_name,
cross_references=references,
structure=metabolite.structure._value_inchi),
compartment=data_model.Compartment(
name=c._metadata.cell_compartment[0].name))
observed_values.append(
data_model.ObservedValue(metadata=metadata,
observable=observable,
value=c.value,
error=c.error,
units=c.units))
return observed_values
def test_OptionsFilter(self):
f = data_query.OptionsFilter(('metadata', 'genetics', 'variation', ), [''])
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(variation='')))
self.assertEqual(f.score(None, ov), 1)
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(variation='wildtype')))
self.assertEqual(f.score(None, ov), -1)
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(variation='Delta gene-01')))
self.assertEqual(f.score(None, ov), -1)
f = data_query.OptionsFilter(('metadata', 'genetics', 'variation', ), ['wildtype'])
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(variation='')))
self.assertEqual(f.score(None, ov), -1)
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(variation='wildtype')))
self.assertEqual(f.score(None, ov), 1)
ov = data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(genetics=data_model.Genetics(variation='Delta gene-01')))
self.assertEqual(f.score(None, ov), -1)
def obs_val(ph):
return data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(environment=data_model.Environment(ph=ph)))
def get_observed_result(self, reaction):
""" Find observed kinetics for the reaction or similar reactions
TODO: Add compartment infomrmation
1. Find kinetics observed for the reaction
a. Find the metabolite(s) of each participant
b. Find the reaction(s) which contain all of these metabolites
c. Find the kinetic laws associated with these reactions
2. Find kinetics observed for similar reactions
a. Find kinetics observed for the assigned EC number(s)
b. Find kinetics observed for EC number(s) predicted by tools such as E-zyme
Args:
reaction (:obj:`data_model.Reaction`): reaction to find data for
Returns:
:obj:`list` of :obj:`data_model.ObservedValue`: list of relevant observed values
"""
q_law = self.get_kinetic_laws_by_reaction(reaction)
observed_vals = []
for law in q_law:
common_schema_reaction_id = next(xr._id for xr in law._metadata.resource if xr.namespace == 'sabiork.reaction')
reaction = data_model.Reaction(
cross_references=[
data_model.Resource(namespace='common_schema.kinetic_law_id', id=str(law.kinetic_law_id)),
data_model.Resource(namespace='sabiork.reaction', id=common_schema_reaction_id),
],
)
species = {}
compartments = {}
cs_rxn = self.data_source.session.query(models.Reaction).filter_by(kinetic_law_id = law.kinetic_law_id)
reactants = cs_rxn.filter_by(_is_reactant = True).all()
products = cs_rxn.filter_by(_is_product = True).all()
modifiers = cs_rxn.filter_by(_is_modifier = True).all()
for reactant in reactants:
part = data_model.ReactionParticipant(coefficient=-1)
if reactant.metabolite_id not in species:
species[reactant.metabolite_id] = data_model.Specie(name=reactant.metabolite.metabolite_name)
part.specie = species[reactant.metabolite_id]
if reactant.metabolite.structure_id:
part.specie.structure = reactant.metabolite.structure._value_inchi
if reactant.compartment_id:
if reactant.compartment.name not in compartments:
compartments[reactant.compartment.name] = data_model.Compartment(name=reactant.compartment.name)
part.compartment = compartments[reactant.compartment.name]
reaction.participants.append(part)
for product in products:
part = data_model.ReactionParticipant(coefficient=1)
if product.metabolite_id not in species:
species[product.metabolite_id] = data_model.Specie(name=product.metabolite.metabolite_name)
part.specie = species[product.metabolite_id]
if product.metabolite.structure_id:
part.specie.structure = product.metabolite.structure._value_inchi
if product.compartment_id:
if product.compartment.name not in compartments:
compartments[product.compartment.name] = data_model.Compartment(name=product.compartment.name)
part.compartment = compartments[product.compartment.name]
reaction.participants.append(part)
for modifier in modifiers:
part = data_model.ReactionParticipant(coefficient=0)
if modifier.metabolite_id not in species:
species[modifier.metabolite_id] = data_model.Specie(name=modifier.metabolite.metabolite_name)
part.specie = species[modifier.metabolite_id]
if modifier.metabolite.structure_id:
part.specie.structure = modifier.metabolite.structure._value_inchi
if modifier.compartment_id:
if modifier.compartment.name not in compartments:
compartments[modifier.compartment.name] = data_model.Compartment(name=modifier.compartment.name)
part.compartment = compartments[modifier.compartment.name]
reaction.participants.append(part)
metadata = self.metadata_dump(law)
for parameter in law.parameter:
if parameter.value is None:
continue
observable = data_model.Observable(
interaction=reaction,
property=parameter.observed_name,
)
if parameter.metabolite_id:
observable.specie = species[parameter.metabolite_id]
# if parameter.compartment:
# observable.compartment = data_model.Compartment(
# id=parameter.compartment.name,
# )
observed_vals.append(data_model.ObservedValue(
metadata=metadata,
observable=observable,
value=parameter.value,
error=parameter.error,
units=parameter.units,
))
return observed_vals
def test_ObservedResultMetadata(self):
o = data_model.ObservedResultMetadata()
o.genetics = data_model.Genetics(taxon='Mycoplasma pneumoniae',
variation='ΔMPN001')
o.environment = data_model.Environment(temperature=37,
ph=7.,
media='Hayflick')
o.reference = data_model.Reference(title='title',
author='author',
year=2017,
volume=1,
number=1,
pages='1-10')
o.method = data_model.ExperimentalMethod(
name='assay', description='description of assay')
observable = data_model.Observable(
interaction=data_model.Reaction(id='AtpSynthase'),
specie=data_model.Specie(id='ATP'),
compartment=data_model.Compartment(id='c'),
property='K_m',
)
ov = data_model.ObservedValue(
observable=observable,
value=1.0,
error=0.5,
units='U/mg',
)
o.observed_result.append(ov)
o.validate()
self.assertEqual(o.observed_result, [ov])
self.assertEqual(ov.metadata, o)
self.assertEqual(ov.observable.interaction.id, 'AtpSynthase')
self.assertEqual(ov.observable.specie.id, 'ATP')
self.assertEqual(ov.observable.compartment.id, 'c')
self.assertEqual(ov.observable.property, 'K_m')
self.assertEqual(ov.value, 1.0)
self.assertEqual(ov.error, 0.5)
self.assertEqual(ov.units, 'U/mg')
self.assertEqual(o.genetics.taxon, 'Mycoplasma pneumoniae')
self.assertEqual(o.genetics.variation, 'ΔMPN001')
self.assertEqual(o.environment.temperature, 37.)
self.assertEqual(o.environment.ph, 7.)
self.assertEqual(o.environment.media, 'Hayflick')
self.assertEqual(o.reference.title, 'title')
self.assertEqual(o.reference.author, 'author')
self.assertEqual(o.reference.year, 2017)
self.assertEqual(o.reference.volume, 1)
self.assertEqual(o.reference.number, 1)
self.assertEqual(o.reference.pages, '1-10')
self.assertEqual(o.method.name, 'assay')
self.assertEqual(o.method.description, 'description of assay')
def test_ReactionSimilarityFilter(self):
atp_structure = (
'InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15(3-14-5)10-7(17)6(16)4(26-10)1-25-30(21,22)28-31(23,24)27-29(18,19)20'
'/h2-4,6-7,10,16-17H,1H2,(H,21,22)(H,23,24)(H2,11,12,13)(H2,18,19,20)/p-4/t4-,6-,7-,10-/m1/s1'
)
h2o_structure = 'InChI=1S/H2O/h1H2'
adp_structure = (
'InChI=1S/C10H15N5O10P2/c11-8-5-9(13-2-12-8)15(3-14-5)10-7(17)6(16)4(24-10)1-23-27(21,22)25-26(18,19)20'
'/h2-4,6-7,10,16-17H,1H2,(H,21,22)(H2,11,12,13)(H2,18,19,20)/p-3/t4-,6-,7-,10-/m1/s1'
)
pi_structure = 'InChI=1S/H3O4P/c1-5(2,3)4/h(H3,1,2,3,4)/p-2'
h_structure = 'InChI=1S/p+1/i/hH'
def get_reaction(pi_structure=pi_structure, ec='1.1.1.1'):
return data_model.Reaction(
participants=[
data_model.ReactionParticipant(coefficient=-1, specie=data_model.Specie(structure=atp_structure)),
data_model.ReactionParticipant(coefficient=-1, specie=data_model.Specie(structure=h2o_structure)),
data_model.ReactionParticipant(coefficient=1, specie=data_model.Specie(structure=adp_structure)),
data_model.ReactionParticipant(coefficient=1, specie=data_model.Specie(structure=pi_structure)),
data_model.ReactionParticipant(coefficient=1, specie=data_model.Specie(structure=h_structure)),
],
cross_references=[
data_model.Resource(namespace='ec-code', id=ec)
])
rxn = get_reaction()
f = data_query.ReactionSimilarityFilter(min_ec_level=3, scale=1)
# same participants
ov = data_model.ObservedValue(observable=data_model.Observable(interaction=get_reaction()))
numpy.testing.assert_almost_equal(f.score(rxn, ov), 1, decimal=3)
# similiar participants
ov = data_model.ObservedValue(observable=data_model.Observable(interaction=get_reaction(pi_structure='InChI=1S/H3O4P/c1-5(2,3)4')))
numpy.testing.assert_almost_equal(f.score(rxn, ov), 1, decimal=3)
# different participants, same 4-digit EC
ov = data_model.ObservedValue(observable=data_model.Observable(interaction=get_reaction(pi_structure='InChI=1S/H4O4P/c1-5(2,3)4')))
numpy.testing.assert_almost_equal(f.score(rxn, ov), math.exp(-1), decimal=3)
# different participants, same 3-digit EC
ov = data_model.ObservedValue(observable=data_model.Observable(
interaction=get_reaction(pi_structure='InChI=1S/H4O4P/c1-5(2,3)4', ec='1.1.1.2')))
numpy.testing.assert_almost_equal(f.score(rxn, ov), math.exp(-2), decimal=3)
ov = data_model.ObservedValue(observable=data_model.Observable(
interaction=get_reaction(pi_structure='InChI=1S/H4O4P/c1-5(2,3)4', ec='1.1.1')))
numpy.testing.assert_almost_equal(f.score(rxn, ov), math.exp(-2), decimal=3)
ov = data_model.ObservedValue(observable=data_model.Observable(
interaction=get_reaction(pi_structure='InChI=1S/H4O4P/c1-5(2,3)4', ec='1.1.1.')))
numpy.testing.assert_almost_equal(f.score(rxn, ov), math.exp(-2), decimal=3)
ov = data_model.ObservedValue(observable=data_model.Observable(
interaction=get_reaction(pi_structure='InChI=1S/H4O4P/c1-5(2,3)4', ec='1.1.1.-')))
numpy.testing.assert_almost_equal(f.score(rxn, ov), math.exp(-2), decimal=3)
# different participants, same 2-digit EC
ov = data_model.ObservedValue(observable=data_model.Observable(
interaction=get_reaction(pi_structure='InChI=1S/H4O4P/c1-5(2,3)4', ec='1.1.2.1')))
numpy.testing.assert_almost_equal(f.score(rxn, ov), -1, decimal=3)
# target reaction only has 3 digits
rxn_1_1_1 = get_reaction(ec='1.1.1')
rxn_1_1_1_1 = get_reaction(ec='1.1.1.1')
f1 = data_query.ReactionSimilarityFilter(min_ec_level=3, scale=1)
f2 = data_query.ReactionSimilarityFilter(min_ec_level=3, scale=1)
ov = data_model.ObservedValue(observable=data_model.Observable(
interaction=get_reaction(pi_structure='InChI=1S/H4O4P/c1-5(2,3)4', ec='1.1.1')))
numpy.testing.assert_almost_equal(f1.score(rxn_1_1_1, ov), math.exp(-2), decimal=3)
numpy.testing.assert_almost_equal(f2.score(rxn_1_1_1_1, ov), math.exp(-2), decimal=3)
ov = data_model.ObservedValue(observable=data_model.Observable(
interaction=get_reaction(pi_structure='InChI=1S/H4O4P/c1-5(2,3)4', ec='1.1.1.1')))
numpy.testing.assert_almost_equal(f1.score(rxn_1_1_1, ov), math.exp(-2), decimal=3)
numpy.testing.assert_almost_equal(f2.score(rxn_1_1_1_1, ov), math.exp(-1), decimal=3)
ov = data_model.ObservedValue(observable=data_model.Observable(
interaction=get_reaction(pi_structure='InChI=1S/H4O4P/c1-5(2,3)4', ec='1.1.2.1')))
numpy.testing.assert_almost_equal(f1.score(rxn_1_1_1, ov), -1, decimal=3)
numpy.testing.assert_almost_equal(f2.score(rxn_1_1_1_1, ov), -1, decimal=3)
# reverse direction, different numbers of reactants/products
f = data_query.ReactionSimilarityFilter(min_ec_level=3, scale=1)
for_rxn = get_reaction()
rev_rxn = get_reaction()
for part in rev_rxn.participants:
part.coefficient = -1 * part.coefficient
ov = data_model.ObservedValue(observable=data_model.Observable(interaction=for_rxn))
self.assertEqual(f.score(for_rxn, ov), 1.)
ov = data_model.ObservedValue(observable=data_model.Observable(interaction=rev_rxn))
self.assertEqual(f.score(for_rxn, ov), -1.)
# reverse direction, same numbers of reactants/products
for_rxn = data_model.Reaction(
participants=[
data_model.ReactionParticipant(coefficient=-1, specie=data_model.Specie(structure=atp_structure)),
data_model.ReactionParticipant(coefficient=1, specie=data_model.Specie(structure=adp_structure)),
],
cross_references=[
data_model.Resource(namespace='ec-code', id='1.1.1.1')
])
rev_rxn = data_model.Reaction(
participants=[
data_model.ReactionParticipant(coefficient=1, specie=data_model.Specie(structure=atp_structure)),
data_model.ReactionParticipant(coefficient=-1, specie=data_model.Specie(structure=adp_structure)),
],
cross_references=[
data_model.Resource(namespace='ec-code', id='1.1.1.1')
])
ov = data_model.ObservedValue(observable=data_model.Observable(interaction=for_rxn))
self.assertEqual(f.score(for_rxn, ov), 1.)
ov = data_model.ObservedValue(observable=data_model.Observable(interaction=rev_rxn))
self.assertEqual(f.score(for_rxn, ov), -1.)
def test_ReactionParticipantFilter(self):
atp = data_model.Specie(structure=(
'InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15(3-14-5)10-7(17)6(16)4(26-10)1-25-30(21,22)28-31(23,24)27-29(18,19)20'
'/h2-4,6-7,10,16-17H,1H2,(H,21,22)(H,23,24)(H2,11,12,13)(H2,18,19,20)/p-4/t4-,6-,7-,10-/m1/s1'
))
h2o = data_model.Specie(structure='InChI=1S/H2O/h1H2')
adp = data_model.Specie(structure=(
'InChI=1S/C10H15N5O10P2/c11-8-5-9(13-2-12-8)15(3-14-5)10-7(17)6(16)4(24-10)1-23-27(21,22)25-26(18,19)20'
'/h2-4,6-7,10,16-17H,1H2,(H,21,22)(H2,11,12,13)(H2,18,19,20)/p-3/t4-,6-,7-,10-/m1/s1'
))
pi = data_model.Specie(structure='InChI=1S/H3O4P/c1-5(2,3)4/h(H3,1,2,3,4)/p-2')
h = data_model.Specie(structure='InChI=1S/p+1/i/hH')
glc = data_model.Specie(structure='InChI=1S/C6H12O6/c7-1-2-3(8)4(9)5(10)6(11)12-2/h2-11H,1H2/t2-,3-,4+,5-,6?/m1/s1')
glc_2 = data_model.Specie(structure='InChI=1S/C6H12O6/c7-1-2-3(8)4(9)5(10)6(11)12-2')
gtp = data_model.Specie(structure=(
'InChI=1S/C10H16N5O14P3/c11-10-13-7-4(8(18)14-10)12-2-15(7)9-6(17)5(16)3(27-9)1-26-31(22,23)29-32(24,25)28-30(19,20)21'
'/h2-3,5-6,9,16-17H,1H2,(H,22,23)(H,24,25)(H2,19,20,21)(H3,11,13,14,18)/p-4/t3-,5-,6-,9-/m1/s1'
))
lactate = data_model.Specie(structure='InChI=1S/C3H6O3/c1-2(4)3(5)6/h2,4H,1H3,(H,5,6)/p-1')
def get_reaction(ntp=atp, glc=glc):
return data_model.Reaction(
participants=[
data_model.ReactionParticipant(coefficient=-1, specie=ntp),
data_model.ReactionParticipant(coefficient=-1, specie=h2o),
data_model.ReactionParticipant(coefficient=1, specie=adp),
data_model.ReactionParticipant(coefficient=1, specie=pi),
data_model.ReactionParticipant(coefficient=1, specie=h),
data_model.ReactionParticipant(coefficient=0, specie=glc),
])
f = data_query.ReactionParticipantFilter()
rxn_atp = get_reaction(ntp=atp)
rxn_gtp = get_reaction(ntp=gtp)
rxn_lac = get_reaction(ntp=lactate)
rxn_glc_2 = get_reaction(glc=glc_2)
# identical reactant
ov = data_model.ObservedValue(observable=data_model.Observable(property='Km', specie=atp, interaction=rxn_atp))
self.assertEqual(f.score(rxn_atp, ov), 1)
# identical product
ov = data_model.ObservedValue(observable=data_model.Observable(property='Ki', specie=adp, interaction=rxn_atp))
self.assertEqual(f.score(rxn_atp, ov), 1)
# similar modifier
ov = data_model.ObservedValue(observable=data_model.Observable(property='Ki', specie=glc_2, interaction=rxn_glc_2))
self.assertEqual(f.score(rxn_atp, ov), 1)
# similar species
ov = data_model.ObservedValue(observable=data_model.Observable(property='Km', specie=gtp, interaction=rxn_gtp))
numpy.testing.assert_almost_equal(f.score(rxn_atp, ov), 0.767, decimal=3)
# different species
ov = data_model.ObservedValue(observable=data_model.Observable(property='Km', specie=lactate, interaction=rxn_lac))
self.assertEqual(f.score(rxn_atp, ov), -1)
# property without species
ov = data_model.ObservedValue(observable=data_model.Observable(property='kcat', interaction=rxn_atp))
self.assertEqual(f.score(rxn_atp, ov), 1)
def obs_val(temperature):
return data_model.ObservedValue(
metadata=data_model.ObservedResultMetadata(environment=data_model.Environment(temperature=temperature)))
