Stochastic simulation

Stochastic simulations can be run by changing the current integrator type to ‘gillespie’ or by using the r.gillespie function.

import tellurium as te
import numpy as np

r = te.loada('S1 -> S2; k1*S1; k1 = 0.1; S1 = 40')
r.integrator = 'gillespie'
r.integrator.seed = 1234

results = []
for k in range(1, 50):
    r.reset()
    s = r.simulate(0, 40)
    results.append(s)
    r.plot(s, show=False, alpha=0.7)
te.show()

Seed

Setting the identical seed for all repeats results in identical traces in each simulation.

results = []
for k in range(1, 20):
    r.reset()
    r.setSeed(123456)
    s = r.simulate(0, 40)
    results.append(s)
    r.plot(s, show=False, loc=None, color='black', alpha=0.7)
te.show()

Combining Simulations

You can combine two timecourse simulations and change e.g. parameter values in between each simulation. The gillespie method simulates up to the given end time 10, after which you can make arbitrary changes to the model, then simulate again.

When using the r.plot function, you can pass the parameter labels, which controls the names that will be used in the figure legend, and tag, which ensures that traces with the same tag will be drawn with the same color (each species within each trace will be plotted in its own color, but these colors will match trace to trace).

import tellurium as te

r = te.loada('S1 -> S2; k1*S1; k1 = 0.02; S1 = 100')
r.setSeed(1234)
for k in range(1, 20):
    r.resetToOrigin()
    res1 = r.gillespie(0, 10)
    r.plot(res1, show=False) # plot first half of data

    # change in parameter after the first half of the simulation
    # We could have also used an Event in the antimony model,
    # which are described further in the Antimony Reference section
    r.k1 = r.k1*20
    res2 = r.gillespie (10, 20)

    r.plot(res2, show=False) # plot second half of data

te.show()