def test_eidos_to_pysb():
stmts = __get_stmts_from_remote_jsonld()
pa = PysbAssembler()
# Make sure these don't error
pa.add_statements(stmts)
pa.make_model()
for fmt in ['kappa', 'sbml', 'sbgn']:
exp_str = pa.export_model(fmt)
assert exp_str, "Got no exported model from eidos->psyb to %s." % fmt
return
def post(self):
"""Assemble INDRA Statements and return PySB model string.
Parameters
----------
statements : list[indra.statements.Statement.to_json()]
A list of INDRA Statements to assemble.
export_format : str
The format to export into, for instance "kappa", "bngl",
"sbml", "matlab", "mathematica", "potterswheel". See
http://pysb.readthedocs.io/en/latest/modules/export/index.html
for a list of supported formats. In addition to the formats
supported by PySB itself, this method also provides "sbgn"
output.
Returns
-------
image or model
Assembled exported model. If export_format is kappa_im or kappa_cm,
image is returned. Otherwise model string is returned.
"""
args = request.json
stmts_json = args.get('statements')
export_format = args.get('export_format')
stmts = stmts_from_json(stmts_json)
pa = PysbAssembler()
pa.add_statements(stmts)
pa.make_model()
try:
for m in pa.model.monomers:
pysb_assembler.set_extended_initial_condition(pa.model, m, 0)
except Exception as e:
logger.exception(e)
if not export_format:
model_str = pa.print_model()
elif export_format in ('kappa_im', 'kappa_cm'):
fname = 'model_%s.png' % export_format
root = os.path.dirname(os.path.abspath(fname))
graph = pa.export_model(format=export_format, file_name=fname)
with open(fname, 'rb') as fh:
data = 'data:image/png;base64,%s' % \
base64.b64encode(fh.read()).decode()
return {'image': data}
else:
try:
model_str = pa.export_model(format=export_format)
except Exception as e:
logger.exception(e)
model_str = ''
res = {'model': model_str}
return res
def indra_processing(statements):
pa = PysbAssembler()
pa.add_statements(statements)
# pa.make_model(policies='two_step')
pa.make_model()
print(10 * '=', 'Monomer', 10 * '=')
for monomer in pa.model.monomers:
print(monomer)
for rule in pa.model.rules:
print(rule)
for parameter in pa.model.parameters:
print(parameter)
return pa
def assemble_pysb(self, mode='local', bucket=EMMAA_BUCKET_NAME):
"""Assemble the model into PySB and return the assembled model."""
if not self.assembled_stmts:
self.run_assembly()
pa = PysbAssembler()
pa.add_statements(self.assembled_stmts)
pysb_model = pa.make_model()
if mode == 's3':
for exp_f in self.export_formats:
if exp_f not in {
'sbml', 'kappa', 'kappa_im', 'kappa_cm', 'bngl',
'sbgn', 'pysb_flat', 'gromet'
}:
continue
elif exp_f == 'gromet':
# Export gromet here if there's no separate "dynamic" pysb
if 'dynamic' not in self.assembly_config:
fname = f'gromet_{self.date_str}.json'
pysb_to_gromet(pysb_model, self.name,
self.assembled_stmts, fname)
else:
fname = f'{exp_f}_{self.date_str}.{exp_f}'
pa.export_model(exp_f, fname)
logger.info(f'Uploading {fname}')
client = get_s3_client(unsigned=False)
client.upload_file(fname, bucket,
f'exports/{self.name}/{fname}')
return pysb_model
def assemble_model(stmts):
pa = PysbAssembler()
pa.add_statements(stmts)
model = pa.make_model(policies='one_step')
pa.add_default_initial_conditions(100.0)
try:
targeted_agents = get_targeted_agents(stmts)
no_upstream_active_agents = get_no_upstream_active_agents(stmts)
except:
targeted_agents = []
no_upstream_active_agents = []
try:
chemical_agents = get_chemical_agents(stmts)
except:
chemical_agents = []
for m in model.monomers:
try:
if m.name in targeted_agents or m.name in no_upstream_active_agents:
pysb_assembler.set_base_initial_condition(model,
model.monomers[m.name], 50.0)
pysb_assembler.set_extended_initial_condition(model, m, 50.0)
elif m.name in chemical_agents:
pysb_assembler.set_base_initial_condition(model,
model.monomers[m.name], 10000.0)
else:
pysb_assembler.set_extended_initial_condition(model, m, 0)
except:
pysb_assembler.set_extended_initial_condition(model, m, 0)
# Tweak parameters
for param in model.parameters:
if 'kf' in param.name and 'bind' in param.name:
param.value = param.value * 100
return model
def assemble_model(stmts):
pa = PysbAssembler()
pa.add_statements(stmts)
model = pa.make_model(policies='one_step')
pa.add_default_initial_conditions(100.0)
try:
targeted_agents = get_targeted_agents(stmts)
no_upstream_active_agents = get_no_upstream_active_agents(stmts)
except:
targeted_agents = []
no_upstream_active_agents = []
try:
chemical_agents = get_chemical_agents(stmts)
except:
chemical_agents = []
for m in model.monomers:
try:
if m.name in targeted_agents or m.name in no_upstream_active_agents:
pysb_assembler.set_base_initial_condition(model,
model.monomers[m.name], 50.0)
pysb_assembler.set_extended_initial_condition(model, m, 50.0)
elif m.name in chemical_agents:
pysb_assembler.set_base_initial_condition(model,
model.monomers[m.name], 10000.0)
else:
pysb_assembler.set_extended_initial_condition(model, m, 0)
except:
pysb_assembler.set_extended_initial_condition(model, m, 0)
# Tweak parameters
for param in model.parameters:
if 'kf' in param.name and 'bind' in param.name:
param.value = param.value * 100
return model
def get_subnetwork(statements, nodes):
"""Return a PySB model based on a subset of given INDRA Statements.
Statements are first filtered for nodes in the given list and other nodes
are optionally added based on relevance in a given network. The filtered
statements are then assembled into an executable model using INDRA's
PySB Assembler.
Parameters
----------
statements : list[indra.statements.Statement]
A list of INDRA Statements to extract a subnetwork from.
nodes : list[str]
The names of the nodes to extract the subnetwork for.
Returns
-------
model : pysb.Model
A PySB model object assembled using INDRA's PySB Assembler from
the INDRA Statements corresponding to the subnetwork.
"""
filtered_statements = _filter_statements(statements, nodes)
pa = PysbAssembler()
pa.add_statements(filtered_statements)
model = pa.make_model()
return model
def test_check_model():
explain = Activation(raf, erk)
mek_active = Agent('MEK', db_refs={'FPLX': 'MEK'},
activity=ActivityCondition('activity', True))
model_stmts = [Activation(raf, mek), Activation(mek_active, erk)]
# Build the pysb model
pa = PysbAssembler()
pa.add_statements(model_stmts)
pa.make_model(policies='one_step')
md = ModelDiagnoser(model_stmts, pa.model, explain)
result = md.check_explanation()
assert result['has_explanation'] is True
path = result['explanation_path']
assert len(path) == 2
assert path[0] == model_stmts[0]
assert path[1] == model_stmts[1]
def assemble_pysb(self):
"""Assemble the model into PySB and return the assembled model."""
if not self.assembled_stmts:
self.run_assembly()
pa = PysbAssembler()
pa.add_statements(self.assembled_stmts)
pysb_model = pa.make_model()
return pysb_model
def test_check_model():
explain = Activation(raf, erk)
mek_active = Agent('MEK',
db_refs={'FPLX': 'MEK'},
activity=ActivityCondition('activity', True))
model_stmts = [Activation(raf, mek), Activation(mek_active, erk)]
# Build the pysb model
pa = PysbAssembler()
pa.add_statements(model_stmts)
pa.make_model(policies='one_step')
md = ModelDiagnoser(model_stmts, pa.model, explain)
result = md.check_explanation()
assert result['has_explanation'] is True
path = result['explanation_path']
assert len(path) == 2
assert path[0] == model_stmts[0]
assert path[1] == model_stmts[1]
def test_nl_modeling():
# 1 code chunk
from indra.sources import trips
model_text = 'MAP2K1 phosphorylates MAPK1 and DUSP6 dephosphorylates MAPK1.'
tp = trips.process_text(model_text)
# 2nd code chunk
for st in tp.statements:
assert st.evidence[0].text # Replaces a print statement in the doc
# 3rd code chunk
from indra.assemblers.pysb import PysbAssembler
pa = PysbAssembler()
pa.add_statements(tp.statements)
pa.make_model(policies='two_step')
# 4th code chunk
for monomer in pa.model.monomers:
assert monomer # This replaces a print statements in the doc
# 5th code chunk
for rule in pa.model.rules:
assert rule # This replaces a print statements in the doc
# 6th code chunk
for parameter in pa.model.parameters:
assert parameter # This replaces a print statements in the doc
# 7th code chunk
for annotation in pa.model.annotations:
assert annotation # This replaces a print statements in the doc
# 8th code chunk (this code is currently in a commented out section)
pa.set_context('A375_SKIN')
for monomer_pattern, parameter in pa.model.initial_conditions:
assert monomer_pattern
assert parameter.value
# 9th code chunk
_ = pa.export_model('sbml')
assert _
_ = pa.export_model('bngl')
assert _
def assemble_pysb(stmts, data_genes, out_file):
"""Return an assembled PySB model."""
base_file, _ = os.path.splitext(out_file)
#stmts = ac.load_statements('%s.pkl' % base_file)
stmts = preprocess_stmts(stmts, data_genes)
# Make a SIF model equivalent to the PySB model
# Useful for making direct comparisons in pathfinding
sa = SifAssembler(stmts)
sa.make_model(use_name_as_key=True, include_mods=True,
include_complexes=True)
sif_str = sa.print_model(include_unsigned_edges=True)
with open('%s_pysb.sif' % base_file, 'wt') as f:
f.write(sif_str)
# This is the "final" set of statements going into the assembler so it
# makes sense to cache these.
# This is also the point where index cards can be generated
ac.dump_statements(stmts, '%s_before_pa.pkl' % base_file)
assemble_index_cards(stmts, 'output/index_cards')
# Save a version of statements with no evidence for faster loading
for s in stmts:
s.evidence = []
for ss in s.supports + s.supported_by:
ss.evidence = []
ac.dump_statements(stmts, '%s_no_evidence.pkl' % base_file)
# Assemble model
pa = PysbAssembler()
pa.add_statements(stmts)
pa.make_model(reverse_effects=False)
#ac.dump_statements(pa.statements, '%s_after_pa.pkl' % base_file)
# Set context
set_context(pa)
# Add observables
add_observables(pa.model)
pa.save_model(out_file)
with open('korkut_pysb.pkl', 'wb') as fh:
pickle.dump(pa.model, fh)
#pa.export_model('kappa', '%s.ka' % base_file)
return pa.model
def processText(Text):
pa = PysbAssembler()
# Process a natural language description of a mechanism
trips_processor = trips.process_text(Text)
# Collect extracted mechanisms in PysbAssembler
pa.add_statements(trips_processor.statements)
# Assemble the model
model = pa.make_model(policies='two_step')
return model
def test_grounded_active_pattern():
a = Agent('A', db_refs={'HGNC': '1234'})
b = Agent('B', db_refs={'HGNC': '5678'})
b_phos = Agent('B', mods=[ModCondition('phosphorylation', 'S', '100')],
db_refs={'HGNC': '5678'})
b_act = Agent('B', activity=ActivityCondition('activity', True),
db_refs={'HGNC': '5678'})
st1 = Phosphorylation(a, b, 'S', '100')
st2 = ActiveForm(b_phos, 'activity', True)
pysba = PysbAssembler([st1, st2])
model = pysba.make_model(policies='one_step')
mps = list(pa.grounded_monomer_patterns(model, b_act))
def test_propose_statement():
jun = Agent('JUN', db_refs={'HGNC':'6204', 'UP': 'P05412'})
explain = Activation(raf, jun)
erk_active = Agent('ERK', db_refs={'FPLX': 'ERK'},
activity=ActivityCondition('activity', True))
# Leave out MEK activates ERK
model_stmts = [Activation(raf, mek), Activation(erk_active, jun)]
# Build the pysb model
pa = PysbAssembler()
pa.add_statements(model_stmts)
pa.make_model(policies='one_step')
md = ModelDiagnoser(model_stmts, pa.model, explain)
result = md.check_explanation()
assert result['has_explanation'] is False
assert result.get('explanation_path') is None
inf_prop = result.get('connect_rules')
assert inf_prop == ('RAF_activates_MEK_activity',
'ERK_act_activates_JUN_activity'), inf_prop
stmt_prop = result.get('connect_stmts')
assert stmt_prop == (model_stmts[0], model_stmts[1])
stmt_suggestions = md.suggest_statements(*stmt_prop)
def test_grounded_active_pattern():
a = Agent('A', db_refs={'HGNC': '1234'})
b = Agent('B', db_refs={'HGNC': '5678'})
b_phos = Agent('B', mods=[ModCondition('phosphorylation', 'S', '100')],
db_refs={'HGNC': '5678'})
b_act = Agent('B', activity=ActivityCondition('activity', True),
db_refs={'HGNC': '5678'})
st1 = Phosphorylation(a, b, 'S', '100')
st2 = ActiveForm(b_phos, 'activity', True)
pysba = PysbAssembler([st1, st2])
model = pysba.make_model(policies='one_step')
mps = list(pa.grounded_monomer_patterns(model, b_act))
def test_eidos_to_pysb():
stmts = __get_stmts_from_remote_jsonld()
pa = PysbAssembler()
# Make sure these don't error
pa.add_statements(stmts)
model = pa.make_model()
assert model.rules, model.rules
for fmt in ['kappa', 'sbml', 'sbgn']:
exp_str = pa.export_model(fmt)
assert exp_str, "Got no exported model from eidos->psyb to %s." % fmt
return
def test_getting_started7_8():
# Chunk 7
stmts = gn_stmts # Added only in this test, not in docs
from indra.assemblers.pysb import PysbAssembler
pa = PysbAssembler()
pa.add_statements(stmts)
model = pa.make_model()
assert model
# Chunk 8
sbml_model = pa.export_model('sbml')
assert sbml_model
def test_readme_using_indra1():
from indra.sources import trips
from indra.assemblers.pysb import PysbAssembler
pa = PysbAssembler()
# Process a natural language description of a mechanism
trips_processor = trips.process_text(
'MEK2 phosphorylates ERK1 at Thr-202 and Tyr-204')
# Collect extracted mechanisms in PysbAssembler
pa.add_statements(trips_processor.statements)
# Assemble the model
model = pa.make_model(policies='two_step')
assert model
def assemble_pysb():
"""Assemble INDRA Statements and return PySB model string."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
export_format = body.get('export_format')
stmts = stmts_from_json(stmts_json)
pa = PysbAssembler()
pa.add_statements(stmts)
pa.make_model()
if not export_format:
model_str = pa.print_model()
else:
try:
model_str = pa.export_model(format=export_format)
except Exception as e:
logger.exception(e)
model_str = ''
res = {'model': model_str}
return res
def test_propose_statement():
jun = Agent('JUN', db_refs={'HGNC': '6204', 'UP': 'P05412'})
explain = Activation(raf, jun)
erk_active = Agent('ERK',
db_refs={'FPLX': 'ERK'},
activity=ActivityCondition('activity', True))
# Leave out MEK activates ERK
model_stmts = [Activation(raf, mek), Activation(erk_active, jun)]
# Build the pysb model
pa = PysbAssembler()
pa.add_statements(model_stmts)
pa.make_model(policies='one_step')
md = ModelDiagnoser(model_stmts, pa.model, explain)
result = md.check_explanation()
assert result['has_explanation'] is False
assert result.get('explanation_path') is None
inf_prop = result.get('connect_rules')
assert inf_prop == ('RAF_activates_MEK_activity',
'ERK_act_activates_JUN_activity'), inf_prop
stmt_prop = result.get('connect_stmts')
assert stmt_prop == (model_stmts[0], model_stmts[1])
stmt_suggestions = md.suggest_statements(*stmt_prop)
def assemble_pysb():
"""Assemble INDRA Statements and return PySB model string."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
export_format = body.get('export_format')
stmts = stmts_from_json(stmts_json)
pa = PysbAssembler()
pa.add_statements(stmts)
pa.make_model()
try:
for m in pa.model.monomers:
pysb_assembler.set_extended_initial_condition(pa.model, m, 0)
except Exception as e:
logger.exception(e)
if not export_format:
model_str = pa.print_model()
elif export_format in ('kappa_im', 'kappa_cm'):
fname = 'model_%s.png' % export_format
root = os.path.dirname(os.path.abspath(fname))
graph = pa.export_model(format=export_format, file_name=fname)
with open(fname, 'rb') as fh:
data = 'data:image/png;base64,%s' % \
base64.b64encode(fh.read()).decode()
return {'image': data}
else:
try:
model_str = pa.export_model(format=export_format)
except Exception as e:
logger.exception(e)
model_str = ''
res = {'model': model_str}
return res
def assemble_pysb():
"""Assemble INDRA Statements and return PySB model string."""
if request.method == 'OPTIONS':
return {}
response = request.body.read().decode('utf-8')
body = json.loads(response)
stmts_json = body.get('statements')
export_format = body.get('export_format')
stmts = stmts_from_json(stmts_json)
pa = PysbAssembler()
pa.add_statements(stmts)
pa.make_model()
try:
for m in pa.model.monomers:
pysb_assembler.set_extended_initial_condition(pa.model, m, 0)
except Exception as e:
logger.exception(e)
if not export_format:
model_str = pa.print_model()
elif export_format in ('kappa_im', 'kappa_cm'):
fname = 'model_%s.png' % export_format
root = os.path.dirname(os.path.abspath(fname))
graph = pa.export_model(format=export_format, file_name=fname)
with open(fname, 'rb') as fh:
data = 'data:image/png;base64,%s' % \
base64.b64encode(fh.read()).decode()
return {'image': data}
else:
try:
model_str = pa.export_model(format=export_format)
except Exception as e:
logger.exception(e)
model_str = ''
res = {'model': model_str}
return res
def assemble_dynamic_pysb(self, **kwargs):
"""Assemble a version of a PySB model for dynamic simulation."""
# First need to run regular assembly
if not self.assembled_stmts:
self.run_assembly()
if 'dynamic' in self.assembly_config:
logger.info('Assembling dynamic PySB model')
ap = AssemblyPipeline(self.assembly_config['dynamic'])
# Not overwrite assembled stmts
stmts = deepcopy(self.assembled_stmts)
new_stmts = ap.run(stmts)
pa = PysbAssembler()
pa.add_statements(new_stmts)
pysb_model = pa.make_model()
return pysb_model
logger.info('Did not find dynamic assembly steps')
def assemble_model(model_name, statements):
# Pysb assembly
pa = PysbAssembler()
pa.add_statements(statements)
ts = time.time()
model = pa.make_model()
te = time.time()
print('Assembly took %.2fs' % (te-ts))
model.name = model_name
add_observable(model)
set_parameters(model)
# Save and return model
pa.model = model
pa.save_model('%s.py' % model_name)
return model
def remove_kappa_dead_rules(stmts, model, dead_rules):
# FIXME: we should probably check that a statement we remove has all its
# generated rules recognized as dead. If it has at least one live rule
# coming from it, we shouldn't remove it. But the dead rules should still
# be removed somehow from the final model.
dead_uuids = set()
for rule in dead_rules:
for ann in model.annotations:
if ann.subject == rule and ann.predicate == 'from_indra_statement':
dead_uuids.add(ann.object)
all_uuids = {stmt.uuid for stmt in stmts}
live_uuids = all_uuids - dead_uuids
stmts = ac.filter_uuid_list(stmts, live_uuids)
pa = PysbAssembler()
pa.add_statements(stmts)
model = pa.make_model()
return stmts, model
def assemble_model(model_name, statements):
# Pysb assembly
pa = PysbAssembler()
pa.add_statements(statements)
ts = time.time()
model = pa.make_model()
te = time.time()
print('Assembly took %.2fs' % (te - ts))
model.name = model_name
add_observable(model)
set_parameters(model)
# Save and return model
pa.model = model
pa.save_model('%s.py' % model_name)
return model
def get_subnetwork(statements,
nodes,
relevance_network=None,
relevance_node_lim=10):
"""Return a PySB model based on a subset of given INDRA Statements.
Statements are first filtered for nodes in the given list and other nodes
are optionally added based on relevance in a given network. The filtered
statements are then assembled into an executable model using INDRA's
PySB Assembler.
Parameters
----------
statements : list[indra.statements.Statement]
A list of INDRA Statements to extract a subnetwork from.
nodes : list[str]
The names of the nodes to extract the subnetwork for.
relevance_network : Optional[str]
The UUID of the NDEx network in which nodes relevant to the given
nodes are found.
relevance_node_lim : Optional[int]
The maximal number of additional nodes to add to the subnetwork
based on relevance.
Returns
-------
model : pysb.Model
A PySB model object assembled using INDRA's PySB Assembler from
the INDRA Statements corresponding to the subnetwork.
"""
if relevance_network is not None:
relevant_nodes = _find_relevant_nodes(nodes, relevance_network,
relevance_node_lim)
all_nodes = nodes + relevant_nodes
else:
all_nodes = nodes
filtered_statements = _filter_statements(statements, all_nodes)
pa = PysbAssembler()
pa.add_statements(filtered_statements)
model = pa.make_model()
return model
def get_subnetwork(statements, nodes, relevance_network=None,
relevance_node_lim=10):
"""Return a PySB model based on a subset of given INDRA Statements.
Statements are first filtered for nodes in the given list and other nodes
are optionally added based on relevance in a given network. The filtered
statements are then assembled into an executable model using INDRA's
PySB Assembler.
Parameters
----------
statements : list[indra.statements.Statement]
A list of INDRA Statements to extract a subnetwork from.
nodes : list[str]
The names of the nodes to extract the subnetwork for.
relevance_network : Optional[str]
The UUID of the NDEx network in which nodes relevant to the given
nodes are found.
relevance_node_lim : Optional[int]
The maximal number of additional nodes to add to the subnetwork
based on relevance.
Returns
-------
model : pysb.Model
A PySB model object assembled using INDRA's PySB Assembler from
the INDRA Statements corresponding to the subnetwork.
"""
if relevance_network is not None:
relevant_nodes = _find_relevant_nodes(nodes, relevance_network,
relevance_node_lim)
all_nodes = nodes + relevant_nodes
else:
all_nodes = nodes
filtered_statements = _filter_statements(statements, all_nodes)
pa = PysbAssembler()
pa.add_statements(filtered_statements)
model = pa.make_model()
return model
def replace_agent(self, agent_name, agent_replacement_names, model_id):
"""Replace an agent in a model with other agents.
This is used, for instance, to expand a protein family to
multiple specific proteins.
"""
for stmt in self.statements[model_id-1]:
agent_key = [i for i, m in enumerate(stmt.agent_list())
if m is not None and m.name == agent_name]
if agent_key:
self.statements[model_id-1].remove(stmt)
for p in agent_replacement_names:
s = copy.deepcopy(stmt)
if isinstance(stmt, Complex):
s.members[agent_key[0]].name = p
else:
s.__dict__[agent_key[0]].name = p
self.extend_statements([s], model_id)
pa = PysbAssembler()
pa.add_statements(self.statements[model_id-1])
model = pa.make_model()
pa.add_default_initial_conditions(self.default_initial_amount)
return model
def replace_agent(self, agent_name, agent_replacement_names, model_id):
"""Replace an agent in a model with other agents.
This is used, for instance, to expand a protein family to
multiple specific proteins.
"""
for stmt in self.statements[model_id-1]:
agent_key = [i for i, m in enumerate(stmt.agent_list())
if m is not None and m.name == agent_name]
if agent_key:
self.statements[model_id-1].remove(stmt)
for p in agent_replacement_names:
s = copy.deepcopy(stmt)
if isinstance(stmt, Complex):
s.members[agent_key[0]].name = p
else:
s.__dict__[agent_key[0]].name = p
self.extend_statements([s], model_id)
pa = PysbAssembler()
pa.add_statements(self.statements[model_id-1])
model = pa.make_model()
pa.add_default_initial_conditions(self.default_initial_amount)
return model
def assemble_dynamic_pysb(self, mode='local', bucket=EMMAA_BUCKET_NAME):
"""Assemble a version of a PySB model for dynamic simulation."""
# First need to run regular assembly
if not self.assembled_stmts:
self.run_assembly()
if 'dynamic' in self.assembly_config:
logger.info('Assembling dynamic PySB model')
ap = AssemblyPipeline(self.assembly_config['dynamic'])
# Not overwrite assembled stmts
stmts = deepcopy(self.assembled_stmts)
self.dynamic_assembled_stmts = ap.run(stmts)
pa = PysbAssembler()
pa.add_statements(self.dynamic_assembled_stmts)
pysb_model = pa.make_model()
if mode == 's3' and 'gromet' in self.export_formats:
fname = f'gromet_{self.date_str}.json'
pysb_to_gromet(pysb_model, self.name,
self.dynamic_assembled_stmts, fname)
logger.info(f'Uploading {fname}')
client = get_s3_client(unsigned=False)
client.upload_file(fname, bucket,
f'exports/{self.name}/{fname}')
return pysb_model
logger.info('Did not find dynamic assembly steps')
out_name_maps = [{
'atmosphere_water__rainfall_volume_flux': 'rainfall'
}, {}]
input_vars = [['rainfall'], ['flood']]
else:
out_name_maps = [{
'atmosphere_water__rainfall_volume_flux':
'Precipitation'
}]
input_vars = [['Precipitation']]
# We now assemble PySB models from the INDRA Statements and then
# instantiate these models as BMI-wrapped models along with a simulator
for idx, model_stmts in enumerate(stmts):
pa = PysbAssembler()
pa.add_statements(model_stmts)
model = pa.make_model()
if demo_idx in (1, 2):
model.name = 'indra_model%d' % idx
else:
model.name = 'indra_eval_model'
bm = BMIModel(model,
inputs=input_vars[idx],
stop_time=50000,
outside_name_map=out_name_maps[idx])
bmi_models.append(bm)
# Example 1: two NL models co-simulated
if demo_idx == 1:
# We make the model component repository without Topoflow
make_component_repo(bmi_models, False)
# We instantiate the EMELI framework and then run the simulations
def make_bmi_model():
pa = PysbAssembler()
pa.add_statements(stmts)
model = pa.make_model()
bm = BMIModel(model, inputs=['rainfall'])
return bm
def make_bmi_model():
pa = PysbAssembler()
pa.add_statements(stmts)
model = pa.make_model()
bm = BMIModel(model, inputs=['rainfall'])
return bm
extension = (f if f != 'pysb_flat' else 'py')
fname = 'hello_indra_model.%s' % extension
with open(fname, 'wb') as fh:
fh.write(model_export.encode('utf-8'))
# User defines text
text = 'MEK1 phosphorylates ERK2 on threonine 185 and tyrosine 187.'
# Process text using TRIPS processor
tp = trips.process_text(text)
# Get the list of extracted Statements
stmts = tp.statements
# Assemble a PySB model
pa = PysbAssembler()
pa.add_statements(stmts)
pa.make_model()
# Run simulation
t = np.linspace(0, 300)
sol = Solver(pa.model, t)
sol.run()
# Plot the result
plot_result(pa.model, sol)
# Export model
export_hello(pa.model, ['sbml', 'bngl', 'kappa', 'pysb_flat'])
def test_gene_network():
# Chunk 1: this is tested in _get_gene_network_stmts
# from indra.tools.gene_network import GeneNetwork
# gn = GeneNetwork(['H2AX'])
# biopax_stmts = gn.get_biopax_stmts()
# bel_stmts = gn.get_bel_stmts()
# Chunk 2
from indra import literature
pmids = literature.pubmed_client.get_ids_for_gene('H2AX')
# Chunk 3
from indra import literature
paper_contents = {}
for pmid in pmids:
content, content_type = literature.get_full_text(pmid, 'pmid')
if content_type == 'abstract':
paper_contents[pmid] = content
if len(paper_contents) == 5: # Is 10 in actual code
break
# Chunk 4
from indra.sources import reach
literature_stmts = []
for pmid, content in paper_contents.items():
rp = reach.process_text(content, url=reach.local_text_url)
literature_stmts += rp.statements
print('Got %d statements' % len(literature_stmts))
assert literature_stmts # replaces a print statements
# Chunk 6
from indra.tools import assemble_corpus as ac
# stmts = biopax_stmts + bel_stmts + literature_stmts # tested elsewhere
stmts = gn_stmts + literature_stmts # Added instead of above line
stmts = ac.map_grounding(stmts)
stmts = ac.map_sequence(stmts)
stmts = ac.run_preassembly(stmts)
assert stmts
# Chunk 7
from indra.assemblers.cx import CxAssembler
from indra.databases import ndex_client
cxa = CxAssembler(stmts)
cx_str = cxa.make_model()
assert cx_str
# Chunk 8
# ndex_cred = {'user': '******', 'password': '******'}
# network_id = ndex_client.create_network(cx_str, ndex_cred)
# print(network_id)
# Chunk 9
from indra.assemblers.indranet import IndraNetAssembler
indranet_assembler = IndraNetAssembler(statements=stmts)
indranet = indranet_assembler.make_model()
assert len(indranet.nodes) > 0, 'indranet conatins no nodes'
assert len(indranet.edges) > 0, 'indranet conatins no edges'
# Chunk 10
import networkx as nx
paths = nx.single_source_shortest_path(G=indranet, source='H2AX', cutoff=1)
assert paths
# Chunk 11
from indra.assemblers.pysb import PysbAssembler
pysb = PysbAssembler(statements=stmts)
pysb_model = pysb.make_model()
assert pysb_model
def add_initial(model, pattern, value):
"""Add initial condition; if an initial condition for this pattern
already exists, override it."""
complex_pattern = pysb.as_complex_pattern(pattern)
for other_cp, other_value in model.initial_conditions:
if complex_pattern.is_equivalent_to(other_cp):
model.initial_conditions.remove((other_cp, other_value))
model.initial(complex_pattern, value)
# Generate model rules via indra
pa = PysbAssembler()
bp = bel.process_belrdf('RAS_combined.rdf')
pa.add_statements(bp.statements)
model = pa.make_model(initial_conditions=False)
# Useful shortcuts to access model components
m = model.monomers
p = model.parameters
# Add ligand to the model
model.add_component(Monomer('EGF'))
model.add_component(Parameter('kf_ee_act', 1e-6))
model.add_component(
Rule('EGF_activates_EGFR',
m['EGF']() + m['EGFR']({'Kinase':'inactive'}) >>
m['EGF']() + m['EGFR']({'Kinase':'active'}),
p['kf_ee_act']))
# Add initial conditions
extension = (f if f != 'pysb_flat' else 'py')
fname = 'hello_indra_model.%s' % extension
with open(fname, 'wb') as fh:
fh.write(model_export)
# User defines text
text = 'MEK1 phosphorylates ERK2 on threonine 185 and tyrosine 187.'
# Process text using TRIPS processor
tp = trips.process_text(text)
# Get the list of extracted Statements
stmts = tp.statements
# Assemble a PySB model
pa = PysbAssembler()
pa.add_statements(stmts)
pa.make_model()
# Run simulation
t = np.linspace(0, 300)
sol = Solver(pa.model, t)
sol.run()
# Plot the result
plot_result(pa.model, sol)
# Export model
export_hello(pa.model, ['sbml', 'bngl', 'kappa', 'pysb_flat'])
def assemble_pysb(self, stmts):
pa = PysbAssembler()
pa.add_statements(stmts)
pa.make_model(policies=self.default_policy)
pa.add_default_initial_conditions(self.default_initial_amount)
return pa.model
def assemble_pysb(stmts, data_genes, contextualize=False):
# Filter the INDRA Statements to be put into the model
stmts = ac.filter_by_type(stmts, Complex, invert=True)
stmts = ac.filter_direct(stmts)
stmts = ac.filter_belief(stmts, 0.95)
stmts = ac.filter_top_level(stmts)
# Strip the extraneous supports/supported by here
strip_supports(stmts)
stmts = ac.filter_gene_list(stmts, data_genes, 'all')
stmts = ac.filter_enzyme_kinase(stmts)
stmts = ac.filter_mod_nokinase(stmts)
stmts = ac.filter_transcription_factor(stmts)
# Simplify activity types
ml = MechLinker(stmts)
ml.gather_explicit_activities()
ml.reduce_activities()
ml.gather_modifications()
ml.reduce_modifications()
stmts = normalize_active_forms(ml.statements)
# Replace activations when possible
ml = MechLinker(stmts)
ml.gather_explicit_activities()
ml.replace_activations()
# Require active forms
ml.require_active_forms()
num_stmts = len(ml.statements)
while True:
# Remove inconsequential PTMs
ml.statements = ac.filter_inconsequential_mods(ml.statements,
get_mod_whitelist())
ml.statements = ac.filter_inconsequential_acts(ml.statements,
get_mod_whitelist())
if num_stmts <= len(ml.statements):
break
num_stmts = len(ml.statements)
stmts = ml.statements
# Save the Statements here
ac.dump_statements(stmts, prefixed_pkl('pysb_stmts'))
# Add drug target Statements
drug_target_stmts = get_drug_target_statements()
stmts += drug_target_stmts
# Just generate the generic model
pa = PysbAssembler()
pa.add_statements(stmts)
model = pa.make_model()
with open(prefixed_pkl('pysb_model'), 'wb') as f:
pickle.dump(model, f)
# Run this extra part only if contextualize is set to True
if not contextualize:
return
cell_lines_no_data = ['COLO858', 'K2', 'MMACSF', 'MZ7MEL', 'WM1552C']
for cell_line in cell_lines:
if cell_line not in cell_lines_no_data:
stmtsc = contextualize_stmts(stmts, cell_line, data_genes)
else:
stmtsc = stmts
pa = PysbAssembler()
pa.add_statements(stmtsc)
model = pa.make_model()
if cell_line not in cell_lines_no_data:
contextualize_model(model, cell_line, data_genes)
ac.dump_statements(stmtsc, prefixed_pkl('pysb_stmts_%s' % cell_line))
with open(prefixed_pkl('pysb_model_%s' % cell_line), 'wb') as f:
pickle.dump(model, f)
def make_pysb_model(statements):
pa = PysbAssembler()
pa.add_statements(statements)
model = pa.make_model()
return model
def assemble_model(model_id, reread=False):
model_name = 'model%d' % model_id
# If model has already been read, just process the EKB XML
if os.path.exists(model_name + '.xml') and not reread:
tp = trips.process_xml(open(model_name + '.xml').read())
else:
# Start with the basic model
model_txt = open('model1.txt').read()
# Apply patches one by one to get to the current model text
for j in range(1, model_id):
patch_txt = open('model%d_from%d.txt' % (j+1, j)).read()
model_txt = apply_patch(model_txt, patch_txt)
print('Reading model %d text:' % model_id)
print(model_txt)
# Process model text and save result EKB XML
tp = trips.process_text(model_txt, model_name + '.xml')
print('Assembling statements:')
for i, st in enumerate(tp.statements):
print('%d: %s' % (i, st))
# Assemble the PySB model
pa = PysbAssembler()
pa.add_statements(tp.statements)
model = pa.make_model(policies='two_step')
# Set initial conditions
erk = model.monomers['ERK']
obs = Observable('ERK_p', erk(phospho='p'))
model.add_component(obs)
vem = model.monomers['VEMURAFENIB']
obs = Observable('Vem_free', vem(map3k=None))
model.add_component(obs)
ras = model.monomers['RAS']
obs = Observable('RAS_active', ras(gtp=ANY))
model.add_component(obs)
braf = model.monomers['BRAF']
obs = Observable('BRAF_active', braf(vemurafenib=None))
model.add_component(obs)
model.parameters['BRAF_0'].value = 0
egf = model.monomers['EGF']
obs = Observable('EGF_free', egf(erbb=None))
model.add_component(obs)
# Add mutated form of BRAF as initial condition
sites_dict = {}
for site in braf.sites:
if site in braf.site_states:
sites_dict[site] = braf.site_states[site][0]
else:
sites_dict[site] = None
sites_dict['V600'] = 'E'
model.add_component(Parameter('BRAF_mut_0', 1e5))
model.initial(braf(**sites_dict), model.parameters['BRAF_mut_0'])
# Set up model parameters
model.parameters['kf_ee_bind_1'].value = 1
model.parameters['kr_ee_bind_1'].value = 0.1
model.parameters['kf_ee_bind_2'].value = 1
model.parameters['kr_ee_bind_2'].value = 0.1
model.parameters['kf_eg_bind_1'].value = 1
model.parameters['kr_eg_bind_1'].value = 0.1
model.parameters['kf_gs_bind_1'].value = 1
model.parameters['kr_gs_bind_1'].value = 0.1
model.parameters['kf_sr_bind_1'].value = 1
model.parameters['kr_sr_bind_1'].value = 50
model.parameters['kf_rg_bind_1'].value = 50
model.parameters['kr_rg_bind_1'].value = 0.5
model.parameters['kf_rb_bind_1'].value = 1
model.parameters['kr_rb_bind_1'].value = 0.5
model.parameters['kf_vb_bind_1'].value = 10
model.parameters['kr_vb_bind_1'].value = 1
model.parameters['kf_bm_bind_1'].value = 1
model.parameters['kr_bm_bind_1'].value = 0.1
model.parameters['kc_bm_phosphorylation_1'].value = 3
model.parameters['kf_pm_bind_1'].value = 1
model.parameters['kr_pm_bind_1'].value = 0.001
model.parameters['kc_pm_dephosphorylation_1'].value = 10
model.parameters['kf_me_bind_1'].value = 1
model.parameters['kr_me_bind_1'].value = 0.1
model.parameters['kc_me_phosphorylation_1'].value = 10
model.parameters['kf_de_bind_1'].value = 1
model.parameters['kr_de_bind_1'].value = 0.001
model.parameters['kc_de_dephosphorylation_1'].value = 10
model.parameters['VEMURAFENIB_0'].value = 0
model.parameters['EGF_0'].value = 1e3
model.parameters['EGFR_0'].value = 1e5
model.parameters['SOS_0'].value = 1e3
model.parameters['GRB2_0'].value = 1e5
model.parameters['RAS_0'].value = 2e5
model.parameters['GTP_0'].value = 1e7
model.parameters['MEK_0'].value = 1e5
model.parameters['ERK_0'].value = 1e5
model.parameters['DUSP6_0'].value = 1e3
model.parameters['PPP2CA_0'].value = 1e5
if model_id >= 2:
model.parameters['Phosphatase_0'].value = 1e2
model.parameters['kf_es_bind_1'].value = 1e-05
model.parameters['kr_es_bind_1'].value = 1e-04
model.parameters['kc_es_phosphorylation_1'].value = 1
model.parameters['kf_ps_bind_1'].value = 1
model.parameters['kr_ps_bind_1'].value = 0.1
model.parameters['kc_ps_dephosphorylation_1'].value = 1e-04
if model_id >= 3:
model.parameters['kf_bb_bind_1'].value = 10
model.parameters['kr_bb_bind_1'].value = 1
model.parameters['kf_vb_bind_2'].value = 1e-04
pa.model = model
pa.save_model('model%d.py' % model_id)
return model