def get_observed_stmts(target_agent, observed_agent_forms, fold_change):
stmts = []
for obsf in observed_agent_forms:
obs_agent = Agent(obsf.name, db_refs=obsf.db_refs)
# If the agent has a modification then we make Modification
# statements to check
if obsf.mods:
for mod in obsf.mods:
# Create a Phosphorylation statement corresponding to
# this drug/Ab pair
if fold_change < 1:
stmt = Phosphorylation(target_agent, obs_agent,
mod.residue, mod.position)
else:
stmt = Dephosphorylation(target_agent, obs_agent,
mod.residue, mod.position)
# Otherwise the observed change is in protein amounts so we make
# RegulateAmount statements
else:
if fold_change < 1:
stmt = IncreaseAmount(target_agent, obs_agent)
else:
stmt = DecreaseAmount(target_agent, obs_agent)
stmts.append(stmt)
return stmts
def test_find_contradicts():
st1 = Inhibition(Agent('a'), Agent('b'))
st2 = Activation(Agent('a'), Agent('b'))
st3 = IncreaseAmount(Agent('a'), Agent('b'))
st4 = DecreaseAmount(Agent('a'), Agent('b'))
pa = Preassembler(hierarchies, [st1, st2, st3, st4])
contradicts = pa.find_contradicts()
assert len(contradicts) == 2
for s1, s2 in contradicts:
assert {s1.uuid, s2.uuid} in ({st1.uuid,
st2.uuid}, {st3.uuid, st4.uuid})
def test_find_contradicts():
st1 = Inhibition(Agent('a'), Agent('b'))
st2 = Activation(Agent('a'), Agent('b'))
st3 = IncreaseAmount(Agent('a'), Agent('b'))
st4 = DecreaseAmount(Agent('a'), Agent('b'))
st5 = ActiveForm(
Agent('a', mods=[ModCondition('phosphorylation', None, None, True)]),
'kinase', True)
st6 = ActiveForm(
Agent('a', mods=[ModCondition('phosphorylation', None, None, True)]),
'kinase', False)
pa = Preassembler(hierarchies, [st1, st2, st3, st4, st5, st6])
contradicts = pa.find_contradicts()
assert len(contradicts) == 3
for s1, s2 in contradicts:
assert {s1.uuid,
s2.uuid} in ({st1.uuid,
st2.uuid}, {st3.uuid,
st4.uuid}, {st5.uuid, st6.uuid})
def process_decrease_expression_amount(self):
"""Looks for Negative_Regulation events with a specified Cause
and a Gene_Expression theme, and processes them into INDRA statements.
"""
statements = []
pwcs = self.find_event_parent_with_event_child('Negative_regulation',
'Gene_expression')
for pair in pwcs:
pos_reg = pair[0]
expression = pair[1]
cause = self.get_entity_text_for_relation(pos_reg, 'Cause')
target = self.get_entity_text_for_relation(expression, 'Theme')
if cause is not None and target is not None:
theme_node = self.get_related_node(expression, 'Theme')
assert (theme_node is not None)
evidence = self.node_to_evidence(theme_node, is_direct=False)
statements.append(
DecreaseAmount(s2a(cause), s2a(target), evidence=evidence))
return statements
else:
demo_idx = int(sys.argv[1])
if demo_idx in (1, 2):
# The content of the two independent models described in natural language
model_txts = [
'rainfall causes floods',
'floods cause displacement, and displacement reduces access to food'
]
# Read with Eidos and extract INDRA Statements for each NL model
stmts = [text_to_stmts(t) for t in model_txts]
# It makes sense to assume that floods go away naturally and that
# displacement is also decreased naturally over time. These are not
# really needed for the demo but are conceptually interesting to think
# about in terms of information missing from the original descriptions.
stmts[0].append(DecreaseAmount(None, Concept('flood')))
stmts[1].append(DecreaseAmount(None, Concept('displacement')))
# We now create the variable mappings and the assumed "root" variables
# for each demo case. In Demo 1, "rainfall" is assumed to be a root
# variable that is assumed to be fixed. In demo 2, "rainfall" is mapped
# to a corresponding Topoflow variable.
elif demo_idx == 3:
stmts = [eval_model.stmts]
bmi_models = []
if demo_idx == 1:
out_name_maps = [{}, {}]
input_vars = [[], ['flood']]
elif demo_idx == 2:
out_name_maps = [{
'atmosphere_water__rainfall_volume_flux': 'rainfall'
}, {}]