

In [1]:

    
%matplotlib notebook
from libsignetsim import Model, TimeseriesSimulation, Experiment, ModelVsTimeseriesOptimization
from libsignetsim.figure.SigNetSimFigure import SigNetSimFigure

Settings up a model with a Michaelis-Menten reaction



In [2]:

    
m = Model()
m.setName("Enzymatic Reaction")

e = m.listOfSpecies.new("E")
s = m.listOfSpecies.new("S")
p = m.listOfSpecies.new("P")

m.listOfParameters.new("vmax", 1)
m.listOfParameters.new("km", 1)

r = m.listOfReactions.new("Enzymatic reaction")
r.listOfReactants.add(s)
r.listOfModifiers.add(e)
r.listOfProducts.add(p)
r.kineticLaw.setPrettyPrintMathFormula("vmax*E*S/(km+S)")

e.setValue(10)
s.setValue(12)
p.setValue(0)

Simulating it



In [3]:

    
sim = TimeseriesSimulation([m], time_min=0, time_ech=0.1, time_max=20)
sim.run()

Setting up experimental data



In [4]:

    
reference_times = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20]
reference_data = [0.0, 2, 3.5, 5.5, 7, 9, 10, 11.5, 12, 12, 12, 12, 12, 12, 12, 12, 12]

# Building the experiment
experiment = Experiment()
condition = experiment.createCondition()
for i, data in enumerate(reference_data):
    condition.addObservation(reference_times[i], 'P', data)

Comparing simulation vs experimental data



In [5]:

    
figure = SigNetSimFigure()
plots = experiment.plot(figure=figure, suffix=" (data)", marker='.')
sim.plot(figure=figure, plot=plots[0], variables=['P'], suffix=" (model)")









    














    











    Out[5]:





<libsignetsim.figure.SigNetSimFigure.SigNetSimFigure at 0x7f15f109ef10>

Fitting the model



In [6]:

    
selected_parameters = []
for parameter in m.listOfParameters:
    selected_parameters.append((parameter, 1, 1e-6, 1e+6, 4))

fit = ModelVsTimeseriesOptimization(
    workingModel=m,
    list_of_experiments=[experiment],
    parameters_to_fit=selected_parameters,
    nb_procs=2
)

score = fit.runOptimization(2)
parameters = fit.readOptimizationOutput()









    



Inside runOptimization, writing files
inside writeOptimizationFiles
Writing model
Calling build
Writing...
Writing...(2)
Writing...(3)
Writing... (4)
Done, now runOptimization
Entry of runOptimization
Calling compile
Compiling
Compile returned
Calling run
Executing
call returned
self.run returned



In [7]:

    
for parameter, value in parameters.items():
    parameter.setValue(value)



In [8]:

    
sim2 = TimeseriesSimulation([m], time_min=0, time_ech=0.1, time_max=20)
sim2.run()

Comparing data with the fitted model



In [9]:

    
figure = SigNetSimFigure()
plots = experiment.plot(figure=figure, suffix=" (data)", marker='.')
sim2.plot(figure=figure, plot=plots[0], variables=['P'], suffix=" (model)")









    














    











    Out[9]:





<libsignetsim.figure.SigNetSimFigure.SigNetSimFigure at 0x7f15f0fae090>



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