def po_bolus(simulator, r):
""" Oral bolus dose
oral dose (single dose, at given start with given dose).
Examples are the oral glucose tolerance test (OGTT) or the application of a drug orally,
e.g., codeine, midazolam, caffeine or paracetamol.
:return:
"""
# set initial concentration of somatostatin in all blood compartments
changes_init = pkpd.init_concentrations_changes(r, 'som', 0E-6) # [0 nmol/L]
# oral bolus dose
# FIXME: dosing changesets
changes_po_bolus = {
'PODOSE_som': 10.0E-6, # [mg]
}
# simulate
tcsims = ensemble(
TimecourseSim([
Timecourse(start=0, end=1200, steps=600,
changes={
**changes_init,
**changes_po_bolus}
)
]),
changeset=ChangeSet.parameter_sensitivity_changeset(r, 0.1)
)
return simulator.timecourses(tcsims)
def test_plotting():
r = load_model(MODEL_REPRESSILATOR)
simulator = Simulator(MODEL_REPRESSILATOR)
changeset = ChangeSet.parameter_sensitivity_changeset(r, sensitivity=0.5)
tcsims = ensemble(
TimecourseSim([
Timecourse(start=0, end=400, steps=400),
]), changeset)
result = simulator.timecourses(tcsims)
# create figure
fig, (ax1) = plt.subplots(nrows=1, ncols=1, figsize=(5, 5))
fig.subplots_adjust(wspace=0.3, hspace=0.3)
add_line(ax=ax1, data=result, xid='time', yid="X", label="X")
add_line(ax=ax1,
data=result,
xid='time',
yid="Y",
label="Y",
color="darkblue")
ax1.legend()
plt.show()
def run_parameter_scan(parallel=False):
"""Perform a parameter scan"""
# [2] value scan
scan_changeset = ChangeSet.scan_changeset('n',
values=np.linspace(start=2,
stop=10,
num=8))
tcsims = ensemble(sim=TimecourseSim([
Timecourse(start=0, end=100, steps=100, changes={}),
Timecourse(start=0, end=60, steps=100, changes={'[X]': 10}),
Timecourse(start=0, end=60, steps=100, changes={'X': 10}),
]),
changeset=scan_changeset)
print(tcsims[0])
if parallel:
simulator = SimulatorParallel(path=MODEL_REPRESSILATOR)
results = simulator.timecourses(tcsims)
assert isinstance(results, Result)
else:
simulator = SimulatorSerial(path=MODEL_REPRESSILATOR)
results = simulator.timecourses(tcsims)
assert isinstance(results, Result)
return results
def stepped_clamp(simulator, r):
changes_init = pkpd.init_concentrations_changes(r, 'som', 0E-6) # [0 nmol/L]
tcsims = ensemble(
TimecourseSim([
Timecourse(start=0, end=60, steps=120, changes=changes_init),
Timecourse(start=0, end=60, steps=120, changes={'Ri_som': 10.0E-6}), # [mg/min],
Timecourse(start=0, end=60, steps=120, changes={'Ri_som': 20.0E-6}), # [mg/min],
Timecourse(start=0, end=60, steps=120, changes={'Ri_som': 40.0E-6}), # [mg/min],
Timecourse(start=0, end=60, steps=120, changes={'Ri_som': 80.0E-6}), # [mg/min],
]), ChangeSet.parameter_sensitivity_changeset(r, 0.1)
)
return simulator.timecourses(tcsims)
def run_sensitivity():
""" Parameter sensitivity simulations.
:return:
"""
simulator = Simulator(MODEL_REPRESSILATOR)
# parameter sensitivity
# FIXME: make work with parallel
r = load_model(MODEL_REPRESSILATOR)
changeset = ChangeSet.parameter_sensitivity_changeset(r)
tc_sim = TimecourseSim([
Timecourse(start=0, end=100, steps=100),
Timecourse(start=0,
end=200,
steps=100,
model_changes={"boundary_condition": {
"X": True
}}),
Timecourse(start=0,
end=100,
steps=100,
model_changes={"boundary_condition": {
"X": False
}}),
])
tc_sims = ensemble(tc_sim, changeset=changeset)
result = simulator.timecourses(tc_sims)
# create figure
fig, (ax1) = plt.subplots(nrows=1, ncols=1, figsize=(5, 5))
fig.subplots_adjust(wspace=0.3, hspace=0.3)
add_line(ax=ax1, data=result, xid='time', yid="X", label="X")
add_line(ax=ax1,
data=result,
xid='time',
yid="Y",
label="Y",
color="darkblue")
add_line(ax=ax1,
data=result,
xid='time',
yid="Z",
label="Z",
color="darkorange")
ax1.legend()
plt.show()
def mix(simulator, r):
"""
[5] combination experiments
- somatostatin infusion + c-peptide bolus
- hyperinsulinemic, euglycemic clamp
"""
changes_init = pkpd.init_concentrations_changes(r, 'som', 0E-6) # [0 nmol/L]
tcsims = ensemble(
TimecourseSim([
Timecourse(start=0, end=60, steps=120, changes=changes_init),
Timecourse(start=0, end=60, steps=120, changes={'IVDOSE_som': 10.0E-6}),
Timecourse(start=0, end=60, steps=240, changes={'Ri_som': 10.0E-6}), # [mg/min],
Timecourse(start=0, end=60, steps=120, changes={'Ri_som': 10.0E-6, 'PODOSE_som': 10.0E-4}),
Timecourse(start=0, end=120, steps=240, changes={'Ri_som': 0.0}), # [mg/min],
]), ChangeSet.parameter_sensitivity_changeset(r, 0.1))
return simulator.timecourses(tcsims)
def iv_infusion(simulator, r):
""" [3] constant infusion (for given period, tstart, tend)
- somatostatin infusion
- insulin infusion
- glucose infusion
- glucagon infusion
:return:
"""
changes_init = pkpd.init_concentrations_changes(r, 'som', 0E-6) # [0 nmol/L]
tcsims = ensemble(
TimecourseSim([
Timecourse(start=0, end=60, steps=120, changes=changes_init),
Timecourse(start=0, end=120, steps=240, changes={'Ri_som': 10.0E-6}), # [mg/min],
Timecourse(start=0, end=120, steps=240, changes={'Ri_som': 0.0}), # [mg/min],
]), ChangeSet.parameter_sensitivity_changeset(r, 0.1)
)
return simulator.timecourses(tcsims)
def clamp(simulator, r):
"""
clamping of substance (e.g. glucose, tstart, tend)
- glucose clamping (euglycemic clamp)
- insulin clamping (insulin clamp)
:return:
"""
changes_init = pkpd.init_concentrations_changes(r, 'som', 0E-6) # [0 nmol/L]
# FIXME: some bug in the concentrations and assignments
tcsims = ensemble(
TimecourseSim([
Timecourse(start=0, end=60, steps=120, changes={**changes_init, **{'PODOSE_som': 1E-9}}),
Timecourse(start=0, end=120, steps=240, model_changes={'boundary_condition': {"Ave_som": True}}), # clamp venous som
Timecourse(start=0, end=120, steps=240, model_changes={'boundary_condition': {"Ave_som": False}}), # release venous som,
]), ChangeSet.parameter_sensitivity_changeset(r, 0.1)
)
return simulator.timecourses(tcsims)
def iv_bolus(simulator, r):
""" [2] bolus injection (at given time tstart, with given dose and given injection time, e.g. 5-10 min)
- c-peptide bolus
- ivGTT (glucose bolus)
- insulin injection
"""
changes_init = pkpd.init_concentrations_changes(r, 'som', 0E-6) # [0 nmol/L]
changes_iv_bolus = {
'IVDOSE_som': 10E-6, # [mg]
}
p_changeset = ChangeSet.parameter_sensitivity_changeset(r, 0.1)
tcsims = ensemble(
TimecourseSim([
Timecourse(start=0, end=1200, steps=600,
changes={
**changes_init,
**changes_iv_bolus}
)
]), p_changeset)
return simulator.timecourses(tcsims)
color=color,
label="sim {}".format(label))
else:
x = data[xid] * xf
ax.plot(x, data[yid], '-', color=color, label="sim {}".format(label))
if __name__ == "__main__":
from sbmlsim.tests.constants import MODEL_REPRESSILATOR
from sbmlsim.model import load_model
from sbmlsim.parametrization import ChangeSet
from sbmlsim.simulation_serial import TimecourseSimulation, Timecourse, timecourses
r = load_model(MODEL_REPRESSILATOR)
# parameter sensitivity
changeset = ChangeSet.parameter_sensitivity_changeset(r, sensitivity=0.05)
tc_sims = TimecourseSimulation([
Timecourse(start=0, end=100, steps=100),
Timecourse(start=0,
end=200,
steps=100,
model_changes={"boundary_condition": {
"X": True
}}),
Timecourse(start=0,
end=100,
steps=100,
model_changes={"boundary_condition": {
"X": False
}}),
]).ensemble(changeset=changeset)
# FIXME: not sure if this is doing the correct thing or custom implementation of copy and deepcopy needed
sim_new = deepcopy(sim)
# changes are mixed in the first timecourse
tc = sim_new.timecourses[0]
for key, value in changes.items():
tc.add_change(key, value)
sims.append(sim_new)
return sims
if __name__ == "__main__":
tcsim = TimecourseSim([
Timecourse(0, 100, steps=101),
Timecourse(0, 100, steps=101, changes={
'k': 100,
'p': 200
}),
Timecourse(0, 50, steps=51, changes={
'k': 100,
'p': 50
}),
])
import numpy as np
tcsims = ensemble(tcsim,
changeset=ChangeSet.scan_changeset(
"k2", np.linspace(0, 4, num=5)))
for tcs in tcsims:
print(tcs)