def bounds_model(sbml_file, directory, doc_fba, annotations=None):
""""
Bounds model.
"""
bounds_notes = notes.format("""
<h2>BOUNDS submodel</h2>
<p>Submodel for dynamically calculating the flux bounds.
The dynamically changing flux bounds are the input to the
FBA model.</p>
""")
doc = builder.template_doc_bounds(settings.MODEL_ID)
model = doc.getModel()
utils.set_model_info(model,
notes=bounds_notes,
creators=creators,
units=units, main_units=main_units)
builder.create_dfba_dt(model, step_size=DT_SIM, time_unit=UNIT_TIME, create_port=True)
# compartment
compartment_id = 'extern'
builder.create_dfba_compartment(model, compartment_id=compartment_id, unit_volume=UNIT_VOLUME,
create_port=True)
# species
model_fba = doc_fba.getModel()
builder.create_dfba_species(model, model_fba, compartment_id=compartment_id, unit_amount=UNIT_AMOUNT,
hasOnlySubstanceUnits=True, create_port=True)
# exchange bounds
builder.create_exchange_bounds(model, model_fba=model_fba, unit_flux=UNIT_FLUX, create_ports=True)
objects = [
# exchange bounds
# FIXME: readout the FBA network bounds
mc.Parameter(sid="lb_default", value=builder.LOWER_BOUND_DEFAULT, unit=UNIT_FLUX, constant=True),
# kinetic bound parameter & calculation
mc.Parameter(sid='ub_R1', value=1.0, unit=UNIT_FLUX, constant=False, sboTerm="SBO:0000625"),
mc.Parameter(sid='k1', value=-0.2, unit="per_s", name="k1", constant=False),
mc.RateRule(sid="ub_R1", value="k1*ub_R1"),
# bound assignment rules
mc.AssignmentRule(sid="lb_EX_A", value='max(lb_default, -A/dt)'),
mc.AssignmentRule(sid="lb_EX_C", value='max(lb_default, -C/dt)'),
]
mc.create_objects(model, objects)
# ports
comp.create_ports(model, portType=comp.PORT_TYPE_PORT,
idRefs=["ub_R1"])
if annotations:
annotator.annotate_sbml_doc(doc, annotations)
sbmlio.write_sbml(doc, filepath=os.path.join(directory, sbml_file), validate=True)
def bounds_model(sbml_file, directory, doc_fba=None):
""""
Submodel for dynamically calculating the flux bounds.
The dynamically changing flux bounds are the input to the
FBA model.
"""
doc = builder.template_doc_bounds(settings.MODEL_ID)
model = doc.getModel()
bounds_notes = notes.format("""
<h2>BOUNDS submodel</h2>
<p>Submodel for dynamically calculating the flux bounds.
The dynamically changing flux bounds are the input to the
FBA model.</p>
""")
utils.set_model_info(model, notes=bounds_notes, creators=creators, units=units, main_units=main_units)
# dt
compartment_id = "blood"
builder.create_dfba_dt(model, time_unit=UNIT_TIME, create_port=True)
# compartment
builder.create_dfba_compartment(model, compartment_id=compartment_id, unit_volume=UNIT_VOLUME, create_port=True)
# dynamic species
model_fba = doc_fba.getModel()
builder.create_dfba_species(model, model_fba, compartment_id=compartment_id, unit_amount=UNIT_AMOUNT,
create_port=True)
# bounds
builder.create_exchange_bounds(model, model_fba=model_fba, unit_flux=UNIT_FLUX, create_ports=True)
# bounds
fba_prefix = "fba"
model_fba = doc_fba.getModel()
objects = []
ex_rids = utils.find_exchange_reactions(model_fba)
for ex_rid, sid in ex_rids.items():
r = model_fba.getReaction(ex_rid)
# lower & upper bound parameters
r_fbc = r.getPlugin(builder.SBML_FBC_NAME)
lb_id = r_fbc.getLowerFluxBound()
fba_lb_id = fba_prefix + lb_id
lb_value = model_fba.getParameter(lb_id).getValue()
objects.extend([
# default bounds from fba
mc.Parameter(sid=fba_lb_id, value=lb_value, unit=UNIT_FLUX, constant=False),
# uptake bounds (lower bound)
mc.AssignmentRule(sid=lb_id, value="max({}, -{}*{}/dt)".format(fba_lb_id, compartment_id, sid)),
])
mc.create_objects(model, objects)
sbmlio.write_sbml(doc, filepath=pjoin(directory, sbml_file), validate=True)
def top_model(sbml_file, directory, emds, doc_fba, annotations=None):
""" Create top comp model.
Creates full comp model by combining fba, update and bounds
model with additional kinetics in the top model.
"""
top_notes = notes.format("""
<h2>TOP model</h2>
<p>Main comp DFBA model by combining fba, update and bounds
model with additional kinetics in the top model.</p>
""")
working_dir = os.getcwd()
os.chdir(directory)
doc = builder.template_doc_top(settings.MODEL_ID, emds)
model = doc.getModel()
utils.set_model_info(model,
notes=top_notes,
creators=creators,
units=units,
main_units=main_units)
# dt
builder.create_dfba_dt(model,
step_size=DT_SIM,
time_unit=UNIT_TIME,
create_port=False)
# compartment
compartment_id = "cell"
builder.create_dfba_compartment(model,
compartment_id=compartment_id,
unit_volume=UNIT_VOLUME,
create_port=False)
# dynamic species
model_fba = doc_fba.getModel()
builder.create_dfba_species(model,
model_fba,
compartment_id=compartment_id,
hasOnlySubstanceUnits=False,
unit_amount=UNIT_AMOUNT,
create_port=False)
# dummy species
builder.create_dummy_species(model,
compartment_id=compartment_id,
hasOnlySubstanceUnits=False,
unit_amount=UNIT_AMOUNT)
# exchange flux bounds
builder.create_exchange_bounds(model,
model_fba=model_fba,
unit_flux=UNIT_FLUX,
create_ports=False)
# dummy reactions & flux assignments
builder.create_dummy_reactions(model,
model_fba=model_fba,
unit_flux=UNIT_FLUX)
# replacedBy (fba reactions)
builder.create_top_replacedBy(model, model_fba=model_fba)
# replaced
builder.create_top_replacements(model,
model_fba,
compartment_id=compartment_id)
objects = [
# kinetic parameters
mc.Parameter(sid="Vmax_RATP", value=1, unit=UNIT_FLUX, constant=True),
mc.Parameter(sid='k_RATP',
value=0.1,
unit=UNIT_CONCENTRATION,
constant=True),
# balancing rules
mc.AssignmentRule(sid="atp_tot",
value="atp + adp",
unit=UNIT_CONCENTRATION),
mc.AssignmentRule(sid="c3_tot",
value="2 dimensionless * glc + pyr",
unit="mM")
]
mc.create_objects(model, objects)
ratp = mc.create_reaction(model,
rid="RATP",
name="atp -> adp",
fast=False,
reversible=False,
reactants={"atp": 1},
products={"adp": 1},
compartment=compartment_id,
formula='Vmax_RATP * atp/(k_RATP + atp)')
# initial concentrations for fba exchange species
initial_c = {'atp': 2.0, 'adp': 1.0, 'glc': 5.0, 'pyr': 0.0}
for sid, value in initial_c.items():
species = model.getSpecies(sid)
species.setInitialConcentration(value)
# write SBML file
if annotations:
annotator.annotate_sbml_doc(doc, annotations)
sbmlio.write_sbml(doc,
filepath=os.path.join(directory, sbml_file),
validate=True)
# change back the working dir
os.chdir(working_dir)
'-',
constant=True,
name='volume fraction erythrocytes'),
]
##############################################################
# Assignments
##############################################################
assignments = []
##############################################################
# AssignmentRules
##############################################################
rules = [
# volumes
mc.AssignmentRule('Vrbc', 'Vpl * alpha', UNIT_KIND_LITRE),
]
##############################################################
# Reactions
##############################################################
reactions = [
ReactionTemplate(
rid="GLCIM",
name="GLCIM (glc_ext -> glcRBC)",
equation="glc_ext -> glcRBC",
compartment='Vrbc',
pars=[
mc.Parameter('GLCIM_Vmax',
100,
'mmole_per_h',
def xpp2sbml(
xpp_file: Path,
sbml_file: Path,
force_lower: bool = False,
validate: bool = True,
debug: bool = False,
):
"""Reads given xpp_file and converts to SBML file.
:param xpp_file: xpp input ode file
:param sbml_file: sbml output file
:param force_lower: force lower case for all lines
:param validate: perform validation on the generated SBML file
:return:
"""
print("-" * 80)
print("xpp2sbml: ", xpp_file, "->", sbml_file)
print("-" * 80)
doc = libsbml.SBMLDocument(3, 1)
model = doc.createModel()
parameters = []
initial_assignments = []
rate_rules = []
assignment_rules = []
functions = [
# definition of min and max
fac.Function("max",
"lambda(x,y, piecewise(x,gt(x,y),y) )",
name="minimum"),
fac.Function("min",
"lambda(x,y, piecewise(x,lt(x,y),y) )",
name="maximum"),
# heav (heavyside)
fac.Function(
"heav",
"lambda(x, piecewise(0,lt(x,0), 0.5, eq(x, 0), 1,gt(x,0), 0))",
name="heavyside",
),
# mod (modulo)
fac.Function("mod", "lambda(x,y, x % y)", name="modulo"),
]
function_definitions = []
events = []
def replace_fdef():
""" Replace all arguments within the formula definitions."""
changes = False
for k, fdata in enumerate(function_definitions):
for i in range(len(function_definitions)):
if i != k:
# replace i with k
formula = function_definitions[i]["formula"]
new_formula = xpp_helpers.replace_formula(
formula,
fid=function_definitions[k]["fid"],
old_args=function_definitions[k]["old_args"],
new_args=function_definitions[k]["new_args"],
)
if new_formula != formula:
function_definitions[i]["formula"] = new_formula
function_definitions[i]["new_args"] = list(
sorted(
set(function_definitions[i]["new_args"] +
function_definitions[k]["new_args"])))
changes = True
return changes
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)))
###########################################################################
# First iteration to parse relevant lines and get the replacement patterns
###########################################################################
parsed_lines = []
# with open(xpp_file, encoding="utf-8") as f:
with open(xpp_file) as f:
lines = f.readlines()
# add info to sbml
text = escape_string("".join(lines))
fac.set_notes(model, NOTES.format(text))
old_line = None
for line in lines:
if force_lower:
line = line.lower()
# clean up the ends
line = line.rstrip("\n").strip()
# handle douple continuation characters in some models
line = line.replace("\\\\", "\\")
# handle semicolons
line = line.rstrip(";")
# join continuation
if old_line:
line = old_line + line
old_line = None
# empty line
if len(line) == 0:
continue
# comment line
if line[0] in XPP_COMMENT_CHARS:
continue
# xpp setting
if line.startswith(XPP_SETTING_CHAR):
continue
# end word
if line == XPP_END_WORD:
continue
# line continuation
if line.endswith(XPP_CONTINUATION_CHAR):
old_line = line.rstrip(XPP_CONTINUATION_CHAR)
continue
# handle the power function
line = line.replace("**", "^")
# handle if(...)then(...)else()
pattern_ite = re.compile(
r"if\s*\((.*)\)\s*then\s*\((.*)\)\s*else\s*\((.*)\)")
pattern_ite_sub = re.compile(
r"if\s*\(.*\)\s*then\s*\(.*\)\s*else\s*\(.*\)")
groups = re.findall(pattern_ite, line)
for group in groups:
condition = group[0]
assignment = group[1]
otherwise = group[2]
f_piecewise = "piecewise({}, {}, {})".format(
assignment, condition, otherwise)
line = re.sub(pattern_ite_sub, f_piecewise, line)
################################
# Function definitions
################################
""" Functions are defined in xpp via fid(arguments) = formula
f(x,y) = x^2/(x^2+y^2)
They can have up to 9 arguments.
The difference to SBML functions is that xpp functions have access to the global parameter values
"""
f_pattern = re.compile(r"(.*)\s*\((.*)\)\s*=\s*(.*)")
groups = re.findall(f_pattern, line)
if groups:
# function definitions found
fid, args, formula = groups[0]
# handles the initial assignments which look like function definitions
if args == "0":
parsed_lines.append(line)
continue
# necessary to find the additional arguments from the ast_node
ast = libsbml.parseL3Formula(formula)
names = set(xpp_helpers.find_names_in_ast(ast))
old_args = [t.strip() for t in args.split(",")]
new_args = [a for a in names if a not in old_args]
# handle special functions
if fid == "power":
warnings.warn(
"power function cannot be added to model, rename function."
)
else:
# store functions with additional arguments
function_definitions.append({
"fid": fid,
"old_args": old_args,
"new_args": new_args,
"formula": formula,
})
# don't append line, function definition has been handeled
continue
parsed_lines.append(line)
if debug:
print("\n\nFUNCTION_DEFINITIONS")
pprint(function_definitions)
# functions can use functions so this also must be replaced
changes = True
while changes:
changes = replace_fdef()
# clean the new arguments
for fdata in function_definitions:
fdata["new_args"] = list(sorted(set(fdata["new_args"])))
if debug:
print("\nREPLACED FUNCTION_DEFINITIONS")
pprint(function_definitions)
# Create function definitions
for k, fdata in enumerate(function_definitions):
fid = fdata["fid"]
formula = fdata["formula"]
arguments = ",".join(fdata["old_args"] + fdata["new_args"])
functions.append(
fac.Function(fid, "lambda({}, {})".format(arguments, formula)), )
###########################################################################
# Second iteration
###########################################################################
if debug:
print("\nPARSED LINES")
pprint(parsed_lines)
print("\n\n")
for line in parsed_lines:
# replace function definitions in lines
new_line = line
for fdata in function_definitions:
new_line = xpp_helpers.replace_formula(new_line, fdata["fid"],
fdata["old_args"],
fdata["new_args"])
if new_line != line:
if False:
print("\nReplaced FD", fdata["fid"], ":", new_line)
print("->", new_line, "\n")
line = new_line
if debug:
# line after function replacements
print("*" * 3, line, "*" * 3)
################################
# Start parsing the given line
################################
# check for the equal sign
tokens = line.split("=")
tokens = [t.strip() for t in tokens]
#######################
# Line without '=' sign
#######################
# wiener
if len(tokens) == 1:
items = [t.strip() for t in tokens[0].split(" ") if len(t) > 0]
# keyword, value
if len(items) == 2:
xid, sid = items[0], items[1]
xpp_type = parse_keyword(xid)
# wiener
if xpp_type == XPP_WIE:
"""Wiener parameters are normally distributed numbers with zero mean
and unit standard deviation. They are useful in stochastic simulations since
they automatically scale with change in the integration time step.
Their names are listed separated by commas or spaces."""
# FIXME: this should be encoded using dist
parameters.append(fac.Parameter(sid=sid, value=0.0))
continue # line finished
else:
warnings.warn("XPP line not parsed: '{}'".format(line))
#####################
# Line with '=' sign
#####################
# parameter, aux, ode, initial assignments
elif len(tokens) >= 2:
left = tokens[0]
items = [t.strip() for t in left.split(" ") if len(t) > 0]
# keyword based information, i.e 2 items are on the left of the first '=' sign
if len(items) == 2:
xid = items[0] # xpp keyword
xpp_type = parse_keyword(xid)
expression = (" ".join(items[1:]) + "=" + "=".join(tokens[1:])
) # full expression after keyword
parts = parts_from_expression(expression)
if False:
print("xid:", xid)
print("expression:", expression)
print("parts:", parts)
# parameter & numbers
if xpp_type in [XPP_PAR, XPP_NUM]:
"""Parameter values are optional; if not they are set to zero.
Number declarations are like parameter declarations, except that they cannot be
changed within the program and do not appear in the parameter window."""
for part in parts:
sid, value = sid_value_from_part(part)
create_initial_assignment(sid, value)
# aux
elif xpp_type == XPP_AUX:
"""Auxiliary quantities are expressions that depend on all of your dynamic
variables which you want to keep track of. Energy is one such example. They are declared
like fixed quantities, but are prefaced by aux ."""
for part in parts:
sid, value = sid_value_from_part(part)
if sid == value:
# avoid circular dependencies (no information in statement)
pass
else:
assignment_rules.append(
fac.AssignmentRule(sid=sid, value=value))
# init
elif xpp_type == XPP_INIT:
for part in parts:
sid, value = sid_value_from_part(part)
create_initial_assignment(sid, value)
# table
elif xpp_type == XPP_TAB:
"""The Table declaration allows the user to specify a function of 1 variable in terms
of a lookup table which uses linear interpolation. The name of the function follows the
declaration and this is followed by (i) a filename (ii) or a function of "t"."""
warnings.warn(
"XPP_TAB not supported: XPP line not parsed: '{}'".
format(line))
else:
warnings.warn("XPP line not parsed: '{}'".format(line))
elif len(items) >= 2:
xid = items[0]
xpp_type = parse_keyword(xid)
# global
if xpp_type == XPP_GLO:
"""Global flags are expressions that signal events when they change sign, from less than
to greater than zero if sign=1 , greater than to less than if sign=-1 or either way
if sign=0. The condition should be delimited by braces {} The events are of the form
variable=expression, are delimited by braces, and separated by semicolons. When the
condition occurs all the variables in the event set are changed possibly discontinuously.
"""
# global sign {condition} {name1 = form1; ...}
pattern_global = re.compile(
r"([+,-]{0,1}\d{1})\s+\{{0,1}(.*)\{{0,1}\s+\{(.*)\}")
groups = re.findall(pattern_global, line)
if groups:
g = groups[0]
sign = int(g[0])
trigger = g[1]
# FIXME: handle sign=-1, sign=0, sign=+1
if sign == -1:
trigger = g[1] + ">= 0"
elif sign == 1:
trigger = g[1] + ">= 0"
elif sign == 0:
trigger = g[1] + ">= 0"
assignment_parts = [t.strip() for t in g[2].split(";")]
assignments = {}
for p in assignment_parts:
key, value = p.split("=")
assignments[key] = value
events.append(
fac.Event(
sid="e{}".format(len(events)),
trigger=trigger,
assignments=assignments,
))
else:
warnings.warn(
"global expression could not be parsed: {}".format(
line))
else:
warnings.warn("XPP line not parsed: '{}'".format(line))
# direct assignments
elif len(items) == 1:
right = tokens[1]
# init
if left.endswith("(0)"):
sid, value = left[0:-3], right
create_initial_assignment(sid, value)
# difference equations
elif left.endswith("(t+1)"):
warnings.warn(
"Difference Equations not supported: XPP line not parsed: '{}'"
.format(line))
# ode
elif left.endswith("'"):
sid = left[0:-1]
rate_rules.append(fac.RateRule(sid=sid, value=right))
elif left.endswith("/dt"):
sid = left[1:-3]
rate_rules.append(fac.RateRule(sid=sid, value=right))
# assignment rules
else:
assignment_rules.append(
fac.AssignmentRule(sid=left, value=right))
else:
warnings.warn("XPP line not parsed: '{}'".format(line))
# add time
assignment_rules.append(
fac.AssignmentRule(sid="t", value="time", name="model time"))
# create SBML objects
objects = (parameters + initial_assignments + functions + rate_rules +
assignment_rules + events)
fac.create_objects(model, obj_iter=objects, debug=False)
"""
Parameter values are optional; if not they are set to zero in xpp.
Many models do not encode the initial zeros.
"""
for p in doc.getModel().getListOfParameters():
if not p.isSetValue():
p.setValue(0.0)
sbml.write_sbml(
doc,
sbml_file,
validate=validate,
program_name="sbmlutils",
program_version=__version__,
units_consistency=False,
)
##############################################################
# Assignments
##############################################################
assignments = []
##############################################################
# AssignmentRules
##############################################################
rules = []
# add concentrations
for s in species:
rules.append(
mc.AssignmentRule(f'C{s.sid[1:]}', f'{s.sid}/{s.compartment}',
'mM', name=f'{s.name} concentration ({s.compartment})'),
)
##############################################################
# Reactions
##############################################################
reactions = [
ReactionTemplate(
rid="GLCIM",
name="GLCIM (glc_ext <-> glc)",
equation="Aext_glc <-> Ali_glc",
compartment='Vli',
pars=[
mc.Parameter('GLCIM_Vmax', 1E3, 'mmole_per_hl',
name='Glucose utilization brain'),
# C6H1206 (0) + C10H12N5O13P3 (-4) <-> C6H11O9P (-2) + C10H12N5O10P2 (-3) + H (1)
compartment='cyto',
pars=[
mc.Parameter('GK_n_gkrp', 2, 'dimensionless'),
mc.Parameter('GK_k_glc1', 15, 'mM'),
mc.Parameter('GK_k_fru6p', 0.010, 'mM'),
mc.Parameter('GK_b', 0.7, 'dimensionless'),
mc.Parameter('GK_n', 1.6, 'dimensionless'),
mc.Parameter('GK_k_glc', 7.5, 'mM'),
mc.Parameter('GK_k_atp', 0.26, 'mM'),
mc.Parameter('GK_Vmax', 25.2, 'mmole_per_min'),
],
rules=[
mc.AssignmentRule(
'GK_gc_free',
'(glc^GK_n_gkrp / (glc^GK_n_gkrp + GK_k_glc1^GK_n_gkrp) ) * '
'(1 dimensionless - GK_b*fru6p/(fru6p + GK_k_fru6p))',
'dimensionless'),
],
formula=
('f_gly * GK_Vmax * GK_gc_free * (atp/(GK_k_atp + atp)) * (glc^GK_n/(glc^GK_n + GK_k_glc^GK_n))',
'mmole_per_min'))
G6PASE = ReactionTemplate(
rid='G6PASE',
name='D-Glucose-6-phosphate Phosphatase',
equation='Aglc6p + Ah2o => Aglc + Aphos []',
# C6H11O9P (-2) + H2O (0) -> C6H12O6 (0) + HO4P (-2)
compartment='cyto',
pars=[
mc.Parameter('G6PASE_k_glc6p', 2, 'mM'),
mc.Species('Aadp_mito', compartment='mito', initialConcentration=0.8000, unit='mmole', boundaryCondition=True, hasOnlySubstanceUnits=True, name='ADP'),
mc.Species('Agtp_mito', compartment='mito', initialConcentration=0.2900, unit='mmole', boundaryCondition=False, hasOnlySubstanceUnits=True, name='GTP'),
mc.Species('Agdp_mito', compartment='mito', initialConcentration=0.1000, unit='mmole', boundaryCondition=False, hasOnlySubstanceUnits=True, name='GDP'),
mc.Species('Acoa_mito', compartment='mito', initialConcentration=0.0550, unit='mmole', boundaryCondition=True, hasOnlySubstanceUnits=True, name='coenzyme A'),
mc.Species('Anadh_mito', compartment='mito', initialConcentration=0.2400, unit='mmole', boundaryCondition=True, hasOnlySubstanceUnits=True, name='NADH'),
mc.Species('Anad_mito', compartment='mito', initialConcentration=0.9800, unit='mmole', boundaryCondition=True, hasOnlySubstanceUnits=True, name='NAD+'),
mc.Species('Ah2o_mito', compartment='mito', initialConcentration=0.0, unit='mmole', boundaryCondition=True, hasOnlySubstanceUnits=True, name='H20'),
mc.Species('Ah_mito', compartment='mito', initialConcentration=0.0, unit='mmole', boundaryCondition=True, hasOnlySubstanceUnits=True, name='H+'),
])
# ---------------------------------
# Concentration
# ---------------------------------
for s in species:
aid = s.sid[1:]
rules.append(
mc.AssignmentRule(f'{aid}', f'{s.sid}/{s.compartment}', 'mM', name=f'{s.name} concentration'),
)
##############################################################
# Parameters
##############################################################
parameters.extend([
mc.Parameter('V_cyto', 1.0, UNIT_KIND_LITRE, True, name='cytosolic volume'),
mc.Parameter('f_mito', 0.2, 'dimensionless', True, name='mitochondrial volume factor'),
mc.Parameter('Vliver', 1.5, UNIT_KIND_LITRE, True, name='liver volume'),
mc.Parameter('fliver', 0.583333333333334, 'dimensionless', True, name='parenchymal fraction liver'),
mc.Parameter('bodyweight', 70, 'kg', True, name='bodyweight'),
mc.Parameter('sec_per_min', 60, 's_per_min', True, name='time conversion'),
mc.Parameter('mumole_per_mmole', 1000, 'mumole_per_mmole', True, name='amount conversion'),
##############################################################
# 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'),
# volumes
mc.AssignmentRule('Vgu', 'BW*FVgu', UNIT_KIND_LITRE),
mc.AssignmentRule('Vki', 'BW*FVki', UNIT_KIND_LITRE),
mc.AssignmentRule('Vli', 'BW*FVli', UNIT_KIND_LITRE),
mc.AssignmentRule('Vlu', 'BW*FVlu', UNIT_KIND_LITRE),
mc.AssignmentRule('Vsp', 'BW*FVsp', UNIT_KIND_LITRE),
mc.AssignmentRule('Vve', 'BW*FVve', UNIT_KIND_LITRE),
mc.AssignmentRule('Var', 'BW*FVar', UNIT_KIND_LITRE),
mc.AssignmentRule('Vpl', 'BW*FVpl', UNIT_KIND_LITRE),
mc.AssignmentRule('Vre', 'BW*FVre', UNIT_KIND_LITRE),
mc.RateRule('Gp', 'EGP +Ra -U_ii -E -k_1*Gp +k_2*Gt', 'mg_per_kgmin'),
mc.RateRule('Gt', '-U_id + k_1*Gp -k_2*Gt', 'mg_per_kgmin'),
mc.RateRule('Il', '-(m_1+m_3)*Il + m_2*Ip + S', 'pmol_per_kg'),
mc.RateRule('Ip', '-(m_2+m_4)*Ip + m_1*Il', 'pmol_per_kg'),
mc.RateRule('Qsto1', '-k_gri*Qsto1', '?'),
mc.RateRule('Qsto2', '(-k_empt*Qsto2)+k_gri*Qsto1', '?'),
mc.RateRule('Qgut', '(-k_abs*Qgut)+k_empt*Qsto2', '?'),
mc.RateRule('I1', '-k_i*(I1-I)', '?'),
mc.RateRule('Id', '-k_i*(Id-I1)', '?'),
mc.RateRule('INS', '(-p_2U*INS)+p_2U*(I-I_b)', '?'),
mc.RateRule('Ipo', '(-gamma*Ipo)+S_po', '?'),
mc.RateRule('Y', '-alpha*(Y-beta*(G-G_b))', '?'),
]
rules = [
mc.AssignmentRule('aa', '5/2/(1-b)/D', '?'),
mc.AssignmentRule('cc', '5/2/d/D', '?'),
mc.AssignmentRule('EGP',
'k_p1-k_p2*Gp-k_p3*Id-k_p4*Ipo',
'mg_per_kgmin',
name='EGP endogenous glucose production'),
mc.AssignmentRule('V_mmax', '(1-part)*(V_m0+V_mX*INS)', '?'),
mc.AssignmentRule('V_fmax', 'part*(V_f0+V_fX*INS)', '?'),
mc.AssignmentRule('E', '0', 'mg_per_kgmin', 'renal excretion'),
mc.AssignmentRule('S',
'gamma*Ipo',
'pmol_per_kgmin',
name='S insulin secretion'),
mc.AssignmentRule('I', 'Ip/V_I', 'pmol_per_l', name='I plasma insulin'),
mc.AssignmentRule('G', 'Gp/V_G', 'mg_per_dl', name='G plasma Glucose'),
mc.AssignmentRule('HE',
unit='-',
constant=True,
name='{} plasma partition coefficient'.format(c_name)))
##############################################################
# Assignments
##############################################################
assignments = []
##############################################################
# AssignmentRules
##############################################################
rules = [
# Rest body volume
mc.AssignmentRule(
'FVre',
'1.0 litre_per_kg - (FVgu + FVki + FVli + FVlu + FVsp + FVve + FVar + FVpl)',
'litre_per_kg'),
# Rest body perfusion
mc.AssignmentRule('FQre', '1.0 dimensionless - (FQgu + FQki + FQh + FQsp)',
'dimensionless'),
# Body surface area (Haycock1978)
mc.AssignmentRule(
'BSA',
'0.024265 m2 * power(BW/1 kg, 0.5378) * power(HEIGHT/1 cm, 0.3964)',
'm2'),
# cardiac output (depending on heart rate and bodyweight)
mc.AssignmentRule('CO', 'BW*COBW + (HR-HRrest)*COHRI / 60 s_per_min',
'ml_per_s'),
# cardiac output (depending on bodyweight)
