def create_initial_assignment(sid, value):
""" Helper for creating initial assignments """
# check if valid identifier
if "(" in sid:
warnings.warn(
"sid is not valid: {}. Initial assignment is not generated".
format(sid))
return
try:
f_value = float(value)
parameters.append(
fac.Parameter(
sid=sid,
value=f_value,
name="{} = {}".format(sid, value),
constant=False,
))
except ValueError:
"""
Initial data are optional, XPP sets them to zero by default (many xpp model don't write the p(0)=0.
"""
parameters.append(
fac.Parameter(sid=sid, value=0.0, name=sid, constant=False))
initial_assignments.append(
fac.InitialAssignment(sid=sid,
value=value,
name="{} = {}".format(sid, value)))
def create_initial_assignment(sid: str, value: str) -> None:
"""Create initial assignments helper."""
# check if valid identifier
if "(" in sid:
warnings.warn(
f"sid is not valid: {sid}. Initial assignment is not generated"
)
return
try:
f_value = float(value)
parameters.append(
fac.Parameter(
sid=sid,
value=f_value,
name=f"{sid} = {value}",
constant=False,
))
except ValueError:
"""
Initial data are optional, XPP sets them to zero by default (many
xpp model don't write the p(0)=0.
"""
parameters.append(
fac.Parameter(sid=sid, value=0.0, name=sid, constant=False))
initial_assignments.append(
fac.InitialAssignment(sid=sid,
value=value,
name=f"{sid} = {value}"))
mc.Parameter('x_glu4', 6.40, 'dimensionless', True),
mc.Parameter('x_epi1', 6090, 'pM', True),
mc.Parameter('x_epi2', 100, 'pM', True),
mc.Parameter('x_epi3', 3.10, 'mM', True),
mc.Parameter('x_epi4', 8.40, 'dimensionless', True),
mc.Parameter('K_val', 0.1, 'dimensionless', True),
mc.Parameter('epi_f', 0.8, 'dimensionless', True),
])
##############################################################
# Assignments
##############################################################
assignments.extend([
mc.InitialAssignment('V_mito', 'f_mito * V_cyto', 'm3', name='mitochondrial volume'),
mc.InitialAssignment('flux_conversion', 'mumole_per_mmole/bodyweight', 'mumole_per_mmole_kg'),
# scaling factors
mc.InitialAssignment('scale', '1 dimensionless', 'dimensionless', name='scaling factor rates'),
mc.InitialAssignment('f_gly', 'scale', 'dimensionless', name='scaling factor glycolysis'),
mc.InitialAssignment('f_glyglc', 'scale', 'dimensionless', name='scaling factor glycogen metabolism'),
])
##############################################################
# Rules
##############################################################
rules.extend([
# hormonal regulation
mc.AssignmentRule('ins', 'x_ins2 + (x_ins1-x_ins2) * glc_ext^x_ins4/(glc_ext^x_ins4 + x_ins3^x_ins4)', 'pM',
0,
'litre_per_h',
constant=True,
name='renal clearance [L/hr] caffeine'),
mc.Parameter('CLrenal_px',
0,
'litre_per_h',
constant=True,
name='renal clearance [L/hr] paraxanthine'),
]
##############################################################
# Assignments
##############################################################
assignments = [
mc.InitialAssignment('Ave_caf', 'IVDOSE_caf', 'mg'),
mc.InitialAssignment('D_caf', 'PODOSE_caf', 'mg'),
mc.InitialAssignment('Ave_px', 'IVDOSE_px', 'mg'),
mc.InitialAssignment('D_px', 'PODOSE_px', 'mg'),
]
##############################################################
# Rules
##############################################################
rules = [
# absorption px identical to caf
mc.AssignmentRule('Ka_px', 'Ka_caf', 'per_h'),
# clearance of px relative to caf (both via Cyp1a2)
mc.AssignmentRule('HLM_CLint_px', 'fcl_px_caf*HLM_CLint_caf',
'mulitre_per_min_mg'),