def generate_network(model, cleanup=True, append_stdout=False):
"""Run a model through BNG's generate_network function and return
the content of the resulting .net file as a string"""
gen = BngGenerator(model)
bng_filename = '%s_%d_%d_temp.bngl' % (model.name, os.getpid(), random.randint(0, 10000))
net_filename = bng_filename.replace('.bngl', '.net')
output = StringIO()
try:
bng_file = open(bng_filename, 'w')
bng_file.write(gen.get_content())
bng_file.write(generate_network_code)
bng_file.close()
p = subprocess.Popen(['perl', bng_path, bng_filename],
stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(p_out, p_err) = p.communicate()
if p.returncode:
raise GenerateNetworkError(p_out.rstrip()+"\n"+p_err.rstrip())
net_file = open(net_filename, 'r')
output.write(net_file.read())
net_file.close()
if append_stdout:
output.write("#\n# BioNetGen execution log follows\n# ==========")
output.write(re.sub(r'(^|\n)', r'\n# ', p_out))
finally:
if cleanup:
for filename in [bng_filename, net_filename]:
if os.access(filename, os.F_OK):
os.unlink(filename)
return output.getvalue()
def generate_network(model, cleanup=True, append_stdout=False):
"""Run a model through BNG's generate_network function and return
the content of the resulting .net file as a string"""
gen = BngGenerator(model)
bng_filename = '%s_%d_%d_temp.bngl' % (model.name, os.getpid(),
random.randint(0, 10000))
net_filename = bng_filename.replace('.bngl', '.net')
output = StringIO()
try:
bng_file = open(bng_filename, 'w')
bng_file.write(gen.get_content())
bng_file.write(generate_network_code)
bng_file.close()
p = subprocess.Popen(['perl', bng_path, bng_filename],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
(p_out, p_err) = p.communicate()
if p.returncode:
raise GenerateNetworkError(p_out.rstrip() + "\n" + p_err.rstrip())
net_file = open(net_filename, 'r')
output.write(net_file.read())
net_file.close()
if append_stdout:
output.write("#\n# BioNetGen execution log follows\n# ==========")
output.write(re.sub(r'(^|\n)', r'\n# ', p_out))
finally:
if cleanup:
for filename in [bng_filename, net_filename]:
if os.access(filename, os.F_OK):
os.unlink(filename)
return output.getvalue()
def generate_network(model, cleanup=True, append_stdout=False):
"""
Return the output from BNG's generate_network function given a model.
The output is a BNGL model definition with additional sections 'reactions'
and 'groups', and the 'species' section expanded to contain all possible
species. BNG refers to this as a 'net' file.
Parameters
----------
model : Model
Model to pass to generate_network.
cleanup : bool, optional
If True (default), delete the temporary files after the simulation is
finished. If False, leave them in place (in `output_dir`). Useful for
debugging.
append_stdout : bool, optional
If True, provide BNG.pl's standard output stream as comment lines
appended to the net file contents. If False (default), do not append it.
"""
gen = BngGenerator(model)
if not model.initial_conditions:
raise NoInitialConditionsError()
if not model.rules:
raise NoRulesError()
bng_filename = '%s_%d_%d_temp.bngl' % (model.name, os.getpid(),
random.randint(0, 10000))
net_filename = bng_filename.replace('.bngl', '.net')
output = StringIO()
try:
bng_file = open(bng_filename, 'w')
bng_file.write(gen.get_content())
bng_file.write(_generate_network_code)
bng_file.close()
p = subprocess.Popen(['perl', _get_bng_path(), bng_filename],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
(p_out, p_err) = p.communicate()
if p.returncode:
raise GenerateNetworkError(p_out.rstrip() + "\n" + p_err.rstrip())
net_file = open(net_filename, 'r')
output.write(net_file.read())
net_file.close()
if append_stdout:
output.write("#\n# BioNetGen execution log follows\n# ==========")
output.write(re.sub(r'(^|\n)', r'\n# ', p_out))
finally:
if cleanup:
for filename in [bng_filename, net_filename]:
if os.access(filename, os.F_OK):
os.unlink(filename)
return output.getvalue()
def run_ssa(model, t_end=10, n_steps=100, output_dir='/tmp', cleanup=True):
"""Run a model through BNG's SSA simulator and return
the simulation data as a numpy record array.
"""
run_ssa_code = """
begin actions
generate_network({overwrite=>1});
simulate_ssa({t_end=>%f, n_steps=>%d});\n
end actions
""" % (t_end, n_steps)
gen = BngGenerator(model)
bng_filename = '%s_%d_%d_temp.bngl' % (model.name, os.getpid(),
random.randint(0, 10000))
gdat_filename = bng_filename.replace('.bngl', '.gdat')
cdat_filename = bng_filename.replace('.bngl', '.cdat')
net_filename = bng_filename.replace('.bngl', '.net')
output = StringIO()
try:
#import pdb; pdb.set_trace()
working_dir = os.getcwd()
os.chdir(output_dir)
bng_file = open(bng_filename, 'w')
bng_file.write(gen.get_content())
bng_file.write(run_ssa_code)
bng_file.close()
p = subprocess.Popen(['perl', bng_path, bng_filename],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
(p_out, p_err) = p.communicate()
if p.returncode:
raise GenerateNetworkError(p_out.rstrip() + "\n" + p_err.rstrip())
output_arr = _parse_bng_outfile(gdat_filename)
#ssa_file = open(ssa_filename, 'r')
#output.write(ssa_file.read())
#net_file.close()
#if append_stdout:
# output.write("#\n# BioNetGen execution log follows\n# ==========")
# output.write(re.sub(r'(^|\n)', r'\n# ', p_out))
finally:
if cleanup:
for filename in [
bng_filename, gdat_filename, cdat_filename, net_filename
]:
if os.access(filename, os.F_OK):
os.unlink(filename)
os.chdir(working_dir)
return output_arr
def generate_network(model, cleanup=True, append_stdout=False):
"""
Return the output from BNG's generate_network function given a model.
The output is a BNGL model definition with additional sections 'reactions'
and 'groups', and the 'species' section expanded to contain all possible
species. BNG refers to this as a 'net' file.
Parameters
----------
model : Model
Model to pass to generate_network.
cleanup : bool, optional
If True (default), delete the temporary files after the simulation is
finished. If False, leave them in place (in `output_dir`). Useful for
debugging.
append_stdout : bool, optional
If True, provide BNG.pl's standard output stream as comment lines
appended to the net file contents. If False (default), do not append it.
"""
gen = BngGenerator(model)
if not model.initial_conditions:
raise NoInitialConditionsError()
if not model.rules:
raise NoRulesError()
bng_filename = '%s_%d_%d_temp.bngl' % (model.name, os.getpid(), random.randint(0, 10000))
net_filename = bng_filename.replace('.bngl', '.net')
output = StringIO()
try:
bng_file = open(bng_filename, 'w')
bng_file.write(gen.get_content())
bng_file.write(_generate_network_code)
bng_file.close()
p = subprocess.Popen(['perl', _get_bng_path(), bng_filename],
stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(p_out, p_err) = p.communicate()
if p.returncode:
raise GenerateNetworkError(p_out.rstrip()+"\n"+p_err.rstrip())
net_file = open(net_filename, 'r')
output.write(net_file.read())
net_file.close()
if append_stdout:
output.write("#\n# BioNetGen execution log follows\n# ==========")
output.write(re.sub(r'(^|\n)', r'\n# ', p_out))
finally:
if cleanup:
for filename in [bng_filename, net_filename]:
if os.access(filename, os.F_OK):
os.unlink(filename)
return output.getvalue()
def run_ssa(model, t_end=10, n_steps=100, output_dir='/tmp', cleanup=True):
"""Run a model through BNG's SSA simulator and return
the simulation data as a numpy record array.
"""
run_ssa_code = """
begin actions
generate_network({overwrite=>1});
simulate_ssa({t_end=>%f, n_steps=>%d});\n
end actions
""" % (t_end, n_steps)
gen = BngGenerator(model)
bng_filename = '%s_%d_%d_temp.bngl' % (model.name,
os.getpid(), random.randint(0, 10000))
gdat_filename = bng_filename.replace('.bngl', '.gdat')
cdat_filename = bng_filename.replace('.bngl', '.cdat')
net_filename = bng_filename.replace('.bngl', '.net')
output = StringIO()
try:
#import pdb; pdb.set_trace()
working_dir = os.getcwd()
os.chdir(output_dir)
bng_file = open(bng_filename, 'w')
bng_file.write(gen.get_content())
bng_file.write(run_ssa_code)
bng_file.close()
p = subprocess.Popen(['perl', bng_path, bng_filename],
stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(p_out, p_err) = p.communicate()
if p.returncode:
raise GenerateNetworkError(p_out.rstrip("at line")+"\n"+p_err.rstrip())
output_arr = _parse_bng_outfile(gdat_filename)
#ssa_file = open(ssa_filename, 'r')
#output.write(ssa_file.read())
#net_file.close()
#if append_stdout:
# output.write("#\n# BioNetGen execution log follows\n# ==========")
# output.write(re.sub(r'(^|\n)', r'\n# ', p_out))
finally:
if cleanup:
for filename in [bng_filename, gdat_filename,
cdat_filename, net_filename]:
if os.access(filename, os.F_OK):
os.unlink(filename)
os.chdir(working_dir)
return output_arr
def export(self):
"""Generate the corresponding BNGL for the PySB model associated
with the exporter. A wrapper around ``pysb.generator.bng.BngGenerator``.
Returns
-------
string
The BNGL output for the model.
"""
bngl_str = ''
if self.docstring:
bngl_str += '# ' + self.docstring.replace('\n', '\n# ') + '\n'
gen = BngGenerator(self.model)
bngl_str += gen.get_content()
return bngl_str
def __init__(self,
model=None,
verbose=False,
cleanup=False,
output_prefix=None,
output_dir=None,
model_additional_species=None,
model_population_maps=None):
self._logger = get_logger(__name__, model=model, log_level=verbose)
self._base_file_stem = 'pysb'
self.cleanup = cleanup
self.output_prefix = 'tmpBNG' if output_prefix is None else \
output_prefix
if model:
self.generator = BngGenerator(
model,
additional_initials=model_additional_species,
population_maps=model_population_maps)
self.model = self.generator.model
self._check_model()
else:
self.generator = None
self.model = None
self.base_directory = tempfile.mkdtemp(prefix=self.output_prefix,
dir=output_dir)
self._logger.debug('{} instantiated in directory {}'.format(
self.__class__, self.base_directory))
def __init__(self, model, verbose=False, cleanup=False,
output_prefix=None, output_dir=None):
self.verbose = verbose
self.cleanup = cleanup
self.output_prefix = 'tmpBNG' if output_prefix is None else \
output_prefix
self.generator = BngGenerator(model)
self._check_model()
self.base_directory = tempfile.mkdtemp(prefix=self.output_prefix,
dir=output_dir)
def run_ssa(model, t_end=10, n_steps=100, output_dir='/tmp',
output_file_basename=None, cleanup=True):
"""
Simulate a model with BNG's SSA simulator and return the trajectories.
Parameters
----------
model : Model
Model to simulate.
t_end : number, optional
Final time point of the simulation.
n_steps : int, optional
Number of steps in the simulation.
output_dir : string, optional
Location for temporary files generated by BNG. Defaults to '/tmp'.
output_file_basename : string, optional
The basename for the .bngl, .gdat, .cdat, and .net files that are
generated by BNG. If None (the default), creates a basename from the
model name, process ID, and a random integer in the range (0, 100000).
cleanup : bool, optional
If True (default), delete the temporary files after the simulation is
finished. If False, leave them in place (in `output_dir`). Useful for
debugging.
"""
run_ssa_code = """
begin actions
generate_network({overwrite=>1});
simulate_ssa({t_end=>%f, n_steps=>%d});\n
end actions
""" % (t_end, n_steps)
gen = BngGenerator(model)
if output_file_basename is None:
output_file_basename = '%s_%d_%d_temp' % (model.name,
os.getpid(), random.randint(0, 100000))
if os.path.exists(output_file_basename + '.bngl'):
print "WARNING! File %s already exists!" % (output_file_basename + '.bngl')
output_file_basename += '_1'
bng_filename = output_file_basename + '.bngl'
gdat_filename = output_file_basename + '.gdat'
cdat_filename = output_file_basename + '.cdat'
net_filename = output_file_basename + '.net'
output = StringIO()
try:
working_dir = os.getcwd()
os.chdir(output_dir)
bng_file = open(bng_filename, 'w')
bng_file.write(gen.get_content())
bng_file.write(run_ssa_code)
bng_file.close()
p = subprocess.Popen(['perl', _get_bng_path(), bng_filename],
stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(p_out, p_err) = p.communicate()
if p.returncode:
raise GenerateNetworkError(p_out.rstrip("at line")+"\n"+p_err.rstrip())
print "Wrote " + gdat_filename # FIXME
output_arr = _parse_bng_outfile(gdat_filename)
#ssa_file = open(ssa_filename, 'r')
#output.write(ssa_file.read())
#net_file.close()
#if append_stdout:
# output.write("#\n# BioNetGen execution log follows\n# ==========")
# output.write(re.sub(r'(^|\n)', r'\n# ', p_out))
finally:
if cleanup:
for filename in [bng_filename, gdat_filename,
cdat_filename, net_filename]:
if os.access(filename, os.F_OK):
os.unlink(filename)
os.chdir(working_dir)
return output_arr
def run(model):
gen = BngGenerator(model)
return gen.get_content()
def run_ssa(model,
t_end=10,
n_steps=100,
output_dir='/tmp',
output_file_basename=None,
cleanup=True):
"""
Simulate a model with BNG's SSA simulator and return the trajectories.
Parameters
----------
model : Model
Model to simulate.
t_end : number, optional
Final time point of the simulation.
n_steps : int, optional
Number of steps in the simulation.
output_dir : string, optional
Location for temporary files generated by BNG. Defaults to '/tmp'.
output_file_basename : string, optional
The basename for the .bngl, .gdat, .cdat, and .net files that are
generated by BNG. If None (the default), creates a basename from the
model name, process ID, and a random integer in the range (0, 100000).
cleanup : bool, optional
If True (default), delete the temporary files after the simulation is
finished. If False, leave them in place (in `output_dir`). Useful for
debugging.
"""
run_ssa_code = """
begin actions
generate_network({overwrite=>1});
simulate_ssa({t_end=>%f, n_steps=>%d});\n
end actions
""" % (t_end, n_steps)
gen = BngGenerator(model)
if output_file_basename is None:
output_file_basename = '%s_%d_%d_temp' % (model.name, os.getpid(),
random.randint(0, 100000))
if os.path.exists(output_file_basename + '.bngl'):
print "WARNING! File %s already exists!" % (output_file_basename +
'.bngl')
output_file_basename += '_1'
bng_filename = output_file_basename + '.bngl'
gdat_filename = output_file_basename + '.gdat'
cdat_filename = output_file_basename + '.cdat'
net_filename = output_file_basename + '.net'
output = StringIO()
try:
working_dir = os.getcwd()
os.chdir(output_dir)
bng_file = open(bng_filename, 'w')
bng_file.write(gen.get_content())
bng_file.write(run_ssa_code)
bng_file.close()
p = subprocess.Popen(['perl', _get_bng_path(), bng_filename],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
(p_out, p_err) = p.communicate()
if p.returncode:
raise GenerateNetworkError(
p_out.rstrip("at line") + "\n" + p_err.rstrip())
print "Wrote " + gdat_filename # FIXME
output_arr = _parse_bng_outfile(gdat_filename)
#ssa_file = open(ssa_filename, 'r')
#output.write(ssa_file.read())
#net_file.close()
#if append_stdout:
# output.write("#\n# BioNetGen execution log follows\n# ==========")
# output.write(re.sub(r'(^|\n)', r'\n# ', p_out))
finally:
if cleanup:
for filename in [
bng_filename, gdat_filename, cdat_filename, net_filename
]:
if os.access(filename, os.F_OK):
os.unlink(filename)
os.chdir(working_dir)
return output_arr
Compartment('membrane', parent=extracellular, dimension=2)
Monomer('egf', ['R'])
Monomer('egfr', ['L', 'D', 'C'])
Parameter('Kf_egfr_egf', 1.2)
Parameter('Kr_egfr_egf', 1.1)
Rule('egfr_egf',
egfr(L=None) + egf(R=None) <>
egfr(L=1) % egf(R=1),
Kf_egfr_egf, Kr_egfr_egf)
Observable('free_egf', egf(R=None))
Observable('free_egfr', egfr(L=None))
Observable('bound', egf(R=1) % egfr(L=1))
Parameter('egf_0', 6.0)
Parameter('egfr_0', 10.0)
Initial(egf(R=None) ** extracellular, egf_0)
Initial(egfr(L=None, D=None, C=None) ** membrane, egfr_0)
if __name__ == '__main__':
from pysb.generator.bng import BngGenerator
gen = BngGenerator(model)
print gen.get_content()
print "begin actions"
print " generate_network({overwrite=>1});"
print "end actions"
BAX(inh=1) % MCL1(b=1),
kbaxmcl1f, kbaxmcl1r)
# Initial conditions
Parameter('BAX_0', 1e3)
Initial(BAX(t1=None, t2=None, inh=None), BAX_0)
Parameter('MCL1_0', 1e2)
Initial(MCL1(b=None), MCL1_0)
# We must fully specify all four BAX-BAX bonds, otherwise the pattern
# is too loose, match a given species multiple times (beyond the
# factor of four expected due to the rotational symmetry of the
# tetramer), resulting in erroneously high values.
Observable('BAX4', BAX(t1=1, t2=3) % BAX(t1=4, t2=1) % BAX(t1=2, t2=4) % BAX(t1=3, t2=2))
# Same all-bonds requirement here. However since the BAX tetramer is
# considered inhibited when even one subunit has an inhibitor bound,
# we only need to explicitly write inh=ANY on one of the monomer
# patterns.
Observable('BAX4_inh', BAX(inh=ANY, t1=1, t2=3) % BAX(t1=4, t2=1) % BAX(t1=2, t2=4) % BAX(t1=3, t2=2))
if __name__ == '__main__':
from pysb.generator.bng import BngGenerator
gen = BngGenerator(model)
print gen.get_content()
print ""
print "begin actions"
print " generate_network({overwrite=>1});"
print " simulate_ode({t_end=>21600,n_steps=>360});" # 6 hours, 1-minute steps
print "end actions"
