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tellurium_examples.py
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tellurium_examples.py
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# coding: utf-8
# ### Activator system
# In[1]:
#!!! DO NOT CHANGE !!! THIS FILE WAS CREATED AUTOMATICALLY FROM NOTEBOOKS !!! CHANGES WILL BE OVERWRITTEN !!! CHANGE CORRESPONDING NOTEBOOK FILE !!!
import tellurium as te
# model Definition
r = te.loada ('''
#J1: S1 -> S2; Activator*kcat1*S1/(Km1+S1);
J1: S1 -> S2; SE2*kcat1*S1/(Km1+S1);
J2: S2 -> S1; Vm2*S2/(Km2+S2);
J3: T1 -> T2; S2*kcat3*T1/(Km3+T1);
J4: T2 -> T1; Vm4*T2/(Km4+T2);
J5: -> E2; Vf5/(Ks5+T2^h5);
J6: -> E3; Vf6*T2^h6/(Ks6+T2^h6);
#J7: -> E1;
J8: -> S; kcat8*E1
J9: E2 -> ; k9*E2;
J10:E3 -> ; k10*E3;
J11: S -> SE2; E2*kcat11*S/(Km11+S);
J12: S -> SE3; E3*kcat12*S/(Km12+S);
J13: SE2 -> ; SE2*kcat13;
J14: SE3 -> ; SE3*kcat14;
Km1 = 0.01; Km2 = 0.01; Km3 = 0.01; Km4 = 0.01; Km11 = 1; Km12 = 0.1;
S1 = 6; S2 =0.1; T1=6; T2 = 0.1;
SE2 = 0; SE3=0;
S=0;
E2 = 0; E3 = 0;
kcat1 = 0.1; kcat3 = 3; kcat8 =1; kcat11 = 1; kcat12 = 1; kcat13 = 0.1; kcat14=0.1;
E1 = 1;
k9 = 0.1; k10=0.1;
Vf6 = 1;
Vf5 = 3;
Vm2 = 0.1;
Vm4 = 2;
h6 = 2; h5=2;
Ks6 = 1; Ks5 = 1;
Activator = 0;
at (time > 100): Activator = 5;
''')
r.draw(width=300)
r.conservedMoietyAnalysis = True
result = r.simulate (0, 300, 2000, ['time', 'J11', 'J12']);
r.plot(result);
# In[2]:
### Feedback oscillations
# In[3]:
# http://tellurium.analogmachine.org/testing/
import tellurium as te
r = te.loada ('''
model feedback()
// Reactions:
J0: $X0 -> S1; (VM1 * (X0 - S1/Keq1))/(1 + X0 + S1 + S4^h);
J1: S1 -> S2; (10 * S1 - 2 * S2) / (1 + S1 + S2);
J2: S2 -> S3; (10 * S2 - 2 * S3) / (1 + S2 + S3);
J3: S3 -> S4; (10 * S3 - 2 * S4) / (1 + S3 + S4);
J4: S4 -> $X1; (V4 * S4) / (KS4 + S4);
// Species initializations:
S1 = 0; S2 = 0; S3 = 0;
S4 = 0; X0 = 10; X1 = 0;
// Variable initialization:
VM1 = 10; Keq1 = 10; h = 10; V4 = 2.5; KS4 = 0.5;
end''')
r.integrator.setValue('variable_step_size', True)
res = r.simulate(0, 40)
r.plot();
# ### Bistable System
# Example showing how to to multiple time course simulations, merging the data and plotting it onto one platting surface. Alternative is to use setHold()
#
# Model is a bistable system, simulations start with different initial conditions resulting in different steady states reached.
# In[4]:
import tellurium as te
import numpy as np
r = te.loada ('''
$Xo -> S1; 1 + Xo*(32+(S1/0.75)^3.2)/(1 +(S1/4.3)^3.2);
S1 -> $X1; k1*S1;
Xo = 0.09; X1 = 0.0;
S1 = 0.5; k1 = 3.2;
''')
print(r.selections)
initValue = 0.05
m = r.simulate (0, 4, 100, selections=["time", "S1"])
for i in range (0,12):
r.reset()
r['[S1]'] = initValue
res = r.simulate (0, 4, 100, selections=["S1"])
m = np.concatenate([m, res], axis=1)
initValue += 1
te.plotArray(m, color="black", alpha=0.7, loc=None,
xlabel="time", ylabel="[S1]", title="Bistable system");
# ### Add plot elements
# In[5]:
import tellurium as te
import numpy
import matplotlib.pyplot as plt
import roadrunner
# Example showing how to embelise a graph, change title, axes labels.
# Example also uses an event to pulse S1
r = te.loada ('''
$Xo -> S1; k1*Xo;
S1 -> $X1; k2*S1;
k1 = 0.2; k2 = 0.4; Xo = 1; S1 = 0.5;
at (time > 20): S1 = S1 + 0.35
''')
# Simulate the first part up to 20 time units
m = r.simulate (0, 50, 100, ["time", "S1"]);
plt.ylim ((0,1))
plt.xlabel ('Time')
plt.ylabel ('Concentration')
plt.title ('My First Plot ($y = x^2$)')
r.plot(m);
# ### Events
# In[6]:
import tellurium as te
import matplotlib.pyplot as plt
# Example showing use of events and how to set the y axis limits
r = te.loada ('''
$Xo -> S; Xo/(km + S^h);
S -> $w; k1*S;
# initialize
h = 1; # Hill coefficient
k1 = 1; km = 0.1;
S = 1.5; Xo = 2
at (time > 10): Xo = 5;
at (time > 20): Xo = 2;
''')
m1 = r.simulate (0, 30, 200, ['time', 'Xo', 'S'])
plt.ylim ((0,10))
r.plot(m1);
# ### Gene network
# In[7]:
import tellurium as te
import numpy
# Model desribes a cascade of two genes. First gene is activated
# second gene is repressed. Uses events to change the input
# to the gene regulatory network
r = te.loada ('''
v1: -> P1; Vm1*I^4/(Km1 + I^4);
v2: P1 -> ; k1*P1;
v3: -> P2; Vm2/(Km2 + P1^4);
v4: P2 -> ; k2*P2;
at (time > 60): I = 10;
at (time > 100): I = 0.01;
Vm1 = 5; Vm2 = 6; Km1 = 0.5; Km2 = 0.4;
k1 = 0.1; k2 = 0.1;
I = 0.01;
''')
result = r.simulate (0, 200, 100)
r.plot(result);
# ### Stoichiometric matrix
# In[8]:
import tellurium as te
# Example of using antimony to create a stoichiometry matrix
r = te.loada('''
J1: -> S1; v1;
J2: S1 -> S2; v2;
J3: S2 -> ; v3;
J4: S3 -> S1; v4;
J5: S3 -> S2; v5;
J6: -> S3; v6;
v1=1; v2=1; v3=1; v4=1; v5=1; v6=1;
''')
print(r.getFullStoichiometryMatrix())
r.draw()
# ### Lorenz attractor
# Example showing how to describe a model using ODES. Example implements the Lorenz attractor.
# In[9]:
import tellurium as te
r = te.loada ('''
x' = sigma*(y - x);
y' = x*(rho - z) - y;
z' = x*y - beta*z;
x = 0.96259; y = 2.07272; z = 18.65888;
sigma = 10; rho = 28; beta = 2.67;
''')
result = r.simulate (0, 20, 1000, ['time', 'x', 'y', 'z'])
r.plot(result);
# ### Time Course Parameter Scan
# Do 5 simulations on a simple model, for each simulation a parameter, `k1` is changed. The script merges the data together and plots the merged array on to one plot.
# In[10]:
import tellurium as te
import numpy as np
r = te.loada ('''
J1: $X0 -> S1; k1*X0;
J2: S1 -> $X1; k2*S1;
X0 = 1.0; S1 = 0.0; X1 = 0.0;
k1 = 0.4; k2 = 2.3;
''')
m = r.simulate (0, 4, 100, ["Time", "S1"])
for i in range (0,4):
r.k1 = r.k1 + 0.1
r.reset()
m = np.hstack([m, r.simulate(0, 4, 100, ['S1'])])
# use plotArray to plot merged data
te.plotArray(m);
# ### Merge multiple simulations
# Example of merging multiple simulations. In between simulations a parameter is changed.
# In[11]:
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])
r.plot(m);
# ### Relaxation oscillator
# Oscillator that uses positive and negative feedback. An example of a relaxation oscillator.
# In[12]:
import tellurium as te
r = te.loada ('''
v1: $Xo -> S1; k1*Xo;
v2: S1 -> S2; k2*S1*S2^h/(10 + S2^h) + k3*S1;
v3: S2 -> $w; k4*S2;
# Initialize
h = 2; # Hill coefficient
k1 = 1; k2 = 2; Xo = 1;
k3 = 0.02; k4 = 1;
''')
result = r.simulate(0, 100, 100)
r.plot(result);
# ### Scan hill coefficient
# Negative Feedback model where we scan over the value of the Hill coefficient.
# In[13]:
import tellurium as te
import numpy as np
r = te.loada ('''
// Reactions:
J0: $X0 => S1; (J0_VM1*(X0 - S1/J0_Keq1))/(1 + X0 + S1 + S4^J0_h);
J1: S1 => S2; (10*S1 - 2*S2)/(1 + S1 + S2);
J2: S2 => S3; (10*S2 - 2*S3)/(1 + S2 + S3);
J3: S3 => S4; (10*S3 - 2*S4)/(1 + S3 + S4);
J4: S4 => $X1; (J4_V4*S4)/(J4_KS4 + S4);
// Species initializations:
S1 = 0;
S2 = 0;
S3 = 0;
S4 = 0;
X0 = 10;
X1 = 0;
// Variable initializations:
J0_VM1 = 10;
J0_Keq1 = 10;
J0_h = 2;
J4_V4 = 2.5;
J4_KS4 = 0.5;
// Other declarations:
const J0_VM1, J0_Keq1, J0_h, J4_V4, J4_KS4;
''')
# time vector
result = r.simulate (0, 20, 201, ['time'])
h_values = [r.J0_h + k for k in range(0,8)]
for h in h_values:
r.reset()
r.J0_h = h
m = r.simulate(0, 20, 201, ['S1'])
result = numpy.hstack([result, m])
te.plotArray(result, labels=['h={}'.format(int(h)) for h in h_values]);
# ### Compare simulations
# In[14]:
import tellurium as te
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()
r.setSeed(1234)
m2 = r.gillespie(0, 20, 100, ['time', 'S1'])
# plot all the results together
te.plotArray(m1, color="black", show=False)
te.plotArray(m2, color="blue");
# ### Sinus injection
# Example that show how to inject a sinusoidal into the model and use events to switch it off and on.
# In[15]:
import tellurium as te
import numpy
r = te.loada ('''
# Inject sin wave into model
Xo := sin (time*0.5)*switch + 2;
# Model Definition
v1: $Xo -> S1; k1*Xo;
v2: S1 -> S2; k2*S1;
v3: S2 -> $X1; k3*S2;
at (time > 40): switch = 1;
at (time > 80): switch = 0.5;
# Initialize constants
k1 = 1; k2 = 1; k3 = 3; S1 = 3;
S2 = 0;
switch = 0;
''')
result = r.simulate (0, 100, 200, ['time', 'S1', 'S2'])
r.plot(result);
# ### Protein phosphorylation cycle
# Simple protein phosphorylation cycle. Steady state concentation of the phosphorylated protein is plotted as a funtion of the cycle kinase. In addition, the plot is repeated for various values of Km.
# In[16]:
import tellurium as te
import numpy as np
r = te.loada ('''
S1 -> S2; k1*S1/(Km1 + S1);
S2 -> S1; k2*S2/(Km2 + S2);
k1 = 0.1; k2 = 0.4; S1 = 10; S2 = 0;
Km1 = 0.1; Km2 = 0.1;
''')
r.conservedMoietyAnalysis = True
for i in range (1,8):
numbers = np.linspace (0, 1.2, 200)
result = np.empty ([0,2])
for value in numbers:
r.k1 = value
r.steadyState()
row = np.array ([value, r.S2])
result = np.vstack ((result, row))
te.plotArray(result, show=False, labels=['Km1={}'.format(r.Km1)],
resetColorCycle=False,
xlabel='k1', ylabel="S2",
title="Steady State S2 for different Km1 & Km2",
ylim=[-0.1, 11], grid=True)
r.k1 = 0.1
r.Km1 = r.Km1 + 0.5;
r.Km2 = r.Km2 + 0.5;
# In[17]: