Perturbations. Applying Perturbations in Tellurium 2 import tellurium as te import numpy r = te.loada (``` # Model Definition v1: $Xo -> S1; k1*Xo; v2:

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Presentation transcript:

Perturbations

Applying Perturbations in Tellurium 2 import tellurium as te import numpy r = te.loada (``` # Model Definition v1: $Xo -> S1; k1*Xo; v2: S1 -> $w; k2*S1; # Initialize constants k1 = 1; k2 = 1; S1 = 15; Xo = 1; ```) # Time course simulation m1 = r.simulate (0, 15, 100, [“Time”,”S1”]); r.k1 = r.k1 * 6; m2 = r.simulate (15, 40, 100, [“Time”,”S1”]); r.k1 = r.k1 / 6; m3 = r.simulate (40, 60, 100, [“Time”>,”S1”]); m = numpy.vstack ((m1, m2, m3)); # Merge data r.plot (m) m1 m2 m vstack ((m1, m2)) -> m (augment by row)

Perturbations to Parameters

Perturbations to Variables import tellurium as te import numpy r = te.loada (''' $Xo -> S1; k1*Xo; S1 -> $X1; k2*S1; k1 = 0.2; k2 = 0.4; Xo = 1; S1 = 0.5; ''') # Simulate the first part up to 20 time units m1 = r.simulate (0, 20, 100, ["time", "S1"]); # Perturb the concentration of S1 by 0.35 units r.S1 = r.S ; # Continue simulating from last end point m2 = r.simulate (20, 50, 100, ["time", "S1"]); # Merge and plot the two halves of the simulation r.plot (numpy.vstack ((m1, m2)));

5 Perturbations to Variables

More on Plotting import tellurium as te import numpy import matplotlib.pyplot as plt r = te.loada (''' $Xo -> S1; k1*Xo; S1 -> $X1; k2*S1; k1 = 0.2; k2 = 0.4; Xo = 1; S1 = 0.5; ''') # Simulate the first part up to 20 time units m1 = r.simulate (0, 20, 100, ["time", "S1"]); r.S1 = r.S ; m2 = r.simulate (20, 50, 100, ["time", "S1"]); plt.ylim ((0,1)) plt.xlabel ('Time') plt.ylabel ('Concentration') plt.title ('My First Plot ($y = x^2$)') r.plot (numpy.vstack ((m1, m2)));

Three Important Plot Commands r.plot (result) # Plots a legend te.plotArray (result) # No legend te.setHold (True) # Overlay plots

Example of Hold import tellurium as te import numpy import matplotlib.pyplot as plt # model Definition r = te.loada (''' v1: $Xo -> S1; k1*Xo; v2: S1 -> $w; k2*S1; //initialize. Deterministic process. k1 = 1; k2 = 1; S1 = 20; Xo = 1; ''') m1 = r.simulate (0,20,100); # Stochastic process. r.resetToOrigin() m2 = r.gillespie (0, 20, 100, ['time', 'S1']) # plot all the results together te.setHold (True) te.plotArray (m1) te.plotArray (m2)