def _add_observation_model(self):
"""Extend PySB model by observation model as defined in the PEtab
observables table"""
import pysb
# add any required output parameters
local_syms = {sp.Symbol.__str__(comp): comp for comp in
self.pysb_model.components if
isinstance(comp, sp.Symbol)}
for formula in [*self.observable_df[OBSERVABLE_FORMULA],
*self.observable_df[NOISE_FORMULA]]:
sym = sp.sympify(formula, locals=local_syms)
for s in sym.free_symbols:
if not isinstance(s, pysb.Component):
p = pysb.Parameter(str(s), 1.0)
local_syms[sp.Symbol.__str__(p)] = p
# add observables and sigmas to pysb model
for (observable_id, observable_formula, noise_formula) \
in zip(self.observable_df.index,
self.observable_df[OBSERVABLE_FORMULA],
self.observable_df[NOISE_FORMULA]):
# No observableTransformation so far
if OBSERVABLE_TRANSFORMATION in self.observable_df:
trafo = self.observable_df.loc[observable_id,
OBSERVABLE_TRANSFORMATION]
if trafo and trafo != LIN:
raise NotImplementedError(
"Observable transformation currently unsupported "
"for PySB models")
obs_symbol = sp.sympify(observable_formula, locals=local_syms)
obs_expr = pysb.Expression(observable_id, obs_symbol)
local_syms[observable_id] = obs_expr
sigma_id = f"{observable_id}_sigma"
sigma_symbol = sp.sympify(
noise_formula,
locals=local_syms
)
sigma_expr = pysb.Expression(sigma_id, sigma_symbol)
local_syms[sigma_id] = sigma_expr
def _add_observation_model(self):
"""Extend PySB model by observation model as defined in the PEtab
observables table"""
# add any required output parameters
local_syms = {
sp.Symbol.__str__(comp): comp
for comp in self.pysb_model.components
if isinstance(comp, sp.Symbol)
}
for formula in [
*self.observable_df[OBSERVABLE_FORMULA],
*self.observable_df[NOISE_FORMULA]
]:
sym = sp.sympify(formula, locals=local_syms)
for s in sym.free_symbols:
if not isinstance(s, pysb.Component):
p = pysb.Parameter(str(s), 1.0, _export=False)
self.pysb_model.add_component(p)
local_syms[sp.Symbol.__str__(p)] = p
# add observables and sigmas to pysb model
for (observable_id, observable_formula, noise_formula) \
in zip(self.observable_df.index,
self.observable_df[OBSERVABLE_FORMULA],
self.observable_df[NOISE_FORMULA]):
obs_symbol = sp.sympify(observable_formula, locals=local_syms)
if observable_id in self.pysb_model.expressions.keys():
obs_expr = self.pysb_model.expressions[observable_id]
else:
obs_expr = pysb.Expression(observable_id,
obs_symbol,
_export=False)
self.pysb_model.add_component(obs_expr)
local_syms[observable_id] = obs_expr
sigma_id = f"{observable_id}_sigma"
sigma_symbol = sp.sympify(noise_formula, locals=local_syms)
sigma_expr = pysb.Expression(sigma_id, sigma_symbol, _export=False)
self.pysb_model.add_component(sigma_expr)
local_syms[sigma_id] = sigma_expr
def load_model(
pathway_name: str,
force_compile: bool = True,
add_observables: bool = False
) -> Tuple[amici.AmiciModel, amici.AmiciSolver]:
model = load_pathway(pathway_name)
outdir = os.path.join(basedir, 'amici_models', model.name)
# extend observables
if add_observables:
for obs in model.observables:
if re.match(r'[p|t][A-Z0-9]+[SYT0-9_]*', obs.name):
offset = pysb.Parameter(obs.name + '_offset', 0.0)
scale = pysb.Parameter(obs.name + '_scale', 1.0)
pysb.Expression(obs.name + '_obs',
sp.log(scale * (obs + offset)))
if force_compile or not os.path.exists(
os.path.join(outdir, model.name, model.name + '.py')):
os.makedirs(outdir, exist_ok=True)
amici.pysb_import.pysb2amici(model,
outdir,
verbose=logging.DEBUG,
observables=[
expr.name
for expr in model.expressions
if expr.name.endswith('_obs')
],
constant_parameters=[])
model_module = amici.import_model_module(model.name, outdir)
amici_model = model_module.getModel()
solver = amici_model.getSolver()
solver.setMaxSteps(int(1e5))
solver.setAbsoluteToleranceSteadyState(1e-2)
solver.setRelativeToleranceSteadyState(1e-8)
return amici_model, solver
parameter = pysb.Parameter(p.name, p.value, _export=False)
model.add_component(parameter)
for r in sbml_model.reactions:
assert 1 <= len(r.reactants) <= 2
assert len(r.products) == 1
lterms = [model.monomers[s.name]() for s in r.reactants]
lhs = sum(lterms[1:], lterms[0]())
rhs = model.monomers[r.products[0].name]()
kf = model.parameters[r.kf.name]
kr = model.parameters[r.kr.name]
if len(lterms) == 2 and r.reactants[0] == r.reactants[1]:
expr_name = kf.name + '_symmetric'
try:
kf = model.expressions[expr_name]
except KeyError:
kf = pysb.Expression(expr_name, kf * 2, _export=False)
model.add_component(kf)
if kr.get_value() == 0:
rule = pysb.Rule(r.name, lhs >> rhs, kf, _export=False)
else:
rule = pysb.Rule(r.name, lhs <> rhs, kf, kr, _export=False)
model.add_component(rule)
sbml_perb11_names = [
'c483',
'c136',
'c23',
'c7',
'c25',
'c88',
'c27',
'c89',