def dfs(model, up_to_cee=False):
'''
Performs a depth-first search in a space of equivalent models given by
syntactic operations. Two models are considered equivalent if they
generate the same reaction network.
Returns a model that cannot be further simplified by syntactic operations.
Parameters
----------
model : pysb.Model
Initial model.
up_to_cee : bool
If True, then it performes only bidirectional merging and context
enumeration elimination.
'''
# fast, finds one fix point
sort_key = get_rule_sort_key(model)
rn1 = parse_reaction_network(bng.generate_network(model))
node = model
while node:
m1 = node
node = None
for edge, m2 in search_model(m1, sort_key=sort_key,
up_to_cee=up_to_cee):
rn2 = parse_reaction_network(bng.generate_network(m2))
if rn1 == rn2:
print edge
node = m2
break
return m1
def check_generate_network(model):
"""Tests that network generation runs without error for the given model"""
success = False
try:
generate_network(model)
success = True
except:
pass
assert success, "Network generation failed on model %s:\n-----\n%s" % \
(model.name, traceback.format_exc())
def check_generate_network(model):
"""Tests that network generation occurs without error for the given
model."""
success = False
try:
generate_network(model)
success = True
except:
pass
assert success, "Network generation failed on model %s:\n-----\n%s" % \
(model.name, traceback.format_exc())
def check_generate_network(model):
"""Tests that network generation runs without error for the given model"""
success = False
try:
generate_network(model)
success = True
except Exception as e:
# Some example models are deliberately incomplete, so here we will treat
# any of these "expected" exceptions as a success.
model_base_name = model.name.rsplit('.', 1)[1]
exception_class = expected_exceptions.get(model_base_name)
if exception_class and isinstance(e, exception_class):
success = True
assert success, "Network generation failed on model %s:\n-----\n%s" % \
(model.name, traceback.format_exc())
def check_generate_network(model):
"""Tests that network generation runs without error for the given model"""
success = False
try:
generate_network(model)
success = True
except Exception as e:
# Some example models are deliberately incomplete, so here we will treat
# any of these "expected" exceptions as a success.
model_base_name = model.name.rsplit('.', 1)[1]
exception_classes = expected_exceptions.get(model_base_name)
if exception_classes and isinstance(e, exception_classes):
success = True
assert success, "Network generation failed on model %s:\n-----\n%s" % \
(model.name, traceback.format_exc())
def reaction_network(m):
'''
Call BNG to construct the reaction network of the model.
The resulting network is then parsed.
Parameters
----------
m : pysb.Model
Model from which the reaction net is to be constructed.
'''
return parse_reaction_network(bng.generate_network(m))
def export(self):
"""Generate the BNGL NET file for the PySB model associated with
the exporter. A wrapper around :py:func:`pysb.bng.generate_network`.
Returns
-------
string
The NET file output for the model, generated by BNG.
"""
net_str = ''
if self.docstring:
net_str += '# ' + self.docstring.replace('\n', '\n# ') + '\n'
net_str += generate_network(self.model, append_stdout=True)
return net_str
def test_generate_network(self):
generate_network(m8a.model)
from pysb.macros import *
from pysb import *
from pysb.bng import generate_network
Model()
Monomer('subunit', ['s1', 's2'])
assemble_pore_sequential(subunit, 's1', 's2', 4, [[1, 1], [1, 1], [1, 1]])
Initial(subunit(s1=None, s2=None), Parameter('subunit_0', 1))
Observable('sub', subunit())
generate_network(model)
def run(model):
return generate_network(model, append_stdout=True)
Model()
Monomer('C8', ['bf'])
Monomer('Bid', ['bf', 'state'], {'state': ['U', 'T']})
klist = [Parameter('kf', 1), Parameter('kr', 1), Parameter('kc', 1)]
Initial(C8(bf=None), Parameter('C8_0', 100))
Initial(Bid(bf=None, state='U'), Parameter('Bid_0', 100))
# This is the code shown for "Example Macro Call" (not printed here)
catalyze(C8, 'bf', Bid(state='U'), 'bf', Bid(state='T'), klist)
bng_code = generate_network(model)
# Merge continued lines
bng_code = bng_code.replace('\\\n', '')
generate_equations(model)
num_rules = len(model.rules)
num_odes = len(model.odes)
print("BNGL Rules")
print("==========")
for line in bng_code.split("\n"):
for rule in model.rules:
match = re.match(r'^\s*%s:\s*(.*)' % rule.name, line)
if match:
print(match.group(1))
print()
Model()
Monomer("C8", ["bf"])
Monomer("Bid", ["bf", "state"], {"state": ["U", "T"]})
klist = [Parameter("kf", 1), Parameter("kr", 1), Parameter("kc", 1)]
Initial(C8(bf=None), Parameter("C8_0", 100))
Initial(Bid(bf=None, state="U"), Parameter("Bid_0", 100))
# This is the code shown for "Example Macro Call" (not printed here)
catalyze(C8, "bf", Bid(state="U"), "bf", Bid(state="T"), klist)
bng_code = generate_network(model)
# Merge continued lines
bng_code = bng_code.replace("\\\n", "")
generate_equations(model)
num_rules = len(model.rules)
num_odes = len(model.odes)
print "BNGL Rules"
print "=========="
for line in bng_code.split("\n"):
for rule in model.rules:
match = re.match(r"^\s*%s:\s*(.*)" % rule.name, line)
if match:
print match.group(1)
print
from pysb import *
from pysb.macros import catalyze_one_step
from pysb.bng import generate_network, generate_equations
Model()
Monomer("C8", ["bf"])
Monomer("Bid", ["bf", "state"], {"state": ["U", "T"]})
kf = Parameter("kf", 1)
catalyze_one_step(C8(bf=None), Bid(state="U", bf=None), Bid(state="T", bf=None), kf)
Initial(C8(bf=None), Parameter("C8_0", 100))
Initial(Bid(bf=None, state="U"), Parameter("Bid_0", 100))
print(generate_network(model))
generate_equations(model)
print(zip(model.species, model.odes))
# Degrade the activated Apaf1 caspase 9 complex
Rule('degrade_Apaf1_C9c_XIAP', Apaf1(bC9=1) % C9(bXIAP=2, bApaf1=1, m='c') % XIAP(bC9C3=2) >> None, degrade_Apaf1_C9c_XIAP_kd)
# Degrade the activated Apaf1 caspase 9 XIAP complex
# ______________________________________________________________________________________________________________________________________________________________________________________________#
"""Set the observables and Setup a Simulation"""
Observable('ObsC3c', C3(bXIAP=None, m='c'))
Observable('Apaf1_obs', Apaf1(bC9=None))
generate_network(model)
generate_equations(model)
print(model.species)
for i,sp in enumerate(list(model.species)):
print i,":",sp
# for i,ode in enumerate(list(model.odes)):
# print i, ":", ode
#Dictionary to substitute in species names
species_dict = {0 : 'Apaf1',
1 : 'C9p',
2 : 'XIAP',
3 : 'C3p',
4 : 'Apaf1-C9p',
5 : 'C9p-XIAP',
