Monday, February 19, 2024

Another way to find unstable steady states

James Glazier recently told me of a trick he uses to find unstable steady states. Consider the following model which has two stable and one unstable steady state (it's a bistable system using postiive feedback). Note that $Xo means the species Xo is fixed.  import tellurium as te r = te.loada('''$Xo -> S1; (0.1 + k1*S1^4/(k2+S1^4));
S1 ->; k3*S1;

k1 = 0.9; k2 = 0.3; k3 = 0.7; S1 = 0.5;
''')

If we run a simulation of this system it evolves to one of the stable steady states, in this case 0.144635. If we set the initial conditon to S1 = 10, we can also get the other stable state at S1 = 1.3095. There is the code to do that:

import tellurium as te

$Xo -> S1; ( 0.1 + k1*S1^4/(k2+S1^4)); S1 ->; k3*S1; k1 = 0.9; k2 = 0.3; k3 = 0.7; S1 = 0.5; ''') r.steadyState() print (r.S1) # Find theother steady state r.S1 = 10 r.steadyState() print (r.S1)  But how can we find the unstable one? There as an old trick where if one integrates backwards in time, stable states became repelers and unstable states attractors. However we don't allow someone to specfiy a start time that is bigger then the eed time. Instead James Glazier realized one chould just a put minus sign in front of every rate law to mimic the same effect. For example, a simulation of the following modiified model:  import tellurium as te r = te.loada('''$Xo -> S1; -(0.1 + k1*S1^4/(k2+S1^4));
S1 ->; -k3*S1;

k1 = 0.9; k2 = 0.3; k3 = 0.7; S1 = 0.5;
''')
m = r.simulate (0, 50, 100)
print (r.S1)

will yield the unstable state at S1 = 0.68256.

Friday, January 5, 2024

Generating random networks

Here is a simple script that will generate a large number of random mass-action models and plot simulations of each in a grid.

Each grid has a number written on it so that you can grab the associated model if you find some interesting beahvior. In this case we see model 81 is oscillatory. You'll need the teUtils package for this in order to access the random network generator.


import tellurium as te
import teUtils as tu
import matplotlib.pyplot as plt
import numpy as np

numRows = 15
numCols = 15
plt.subplots(numRows, numCols, figsize=(19,16))
count = 0
models = []
while count < numRows*numCols:
model = tu.buildNetworks.getRandomNetwork(10,20)
try:
m = r.simulate (0, 160, 200)
try:
models.append (model)
if count % 20 == 0:
print (count)
ax = plt.subplot (numRows, numCols, count+1)
ax.tick_params(axis='both', which='major', labelsize=7)
ax.tick_params(axis='both', which='minor', labelsize=7)
ax.set_xlabel('Time')
te.plotArray(m, show=False)
maxy = ax.get_ylim ()
ax.text (50, maxy[1]/2, str(count), fontsize=14)
count = count + 1
except:
# failed to find a steady state so probably a bad model
pass
except:
print ('Something very wrong with the model')
plt.show()


There is one run I did and you'll model 81 ihas some intersting dynamics:
This is model 81 pulled out and resimulated to show the dynamics more clearly:

import tellurium as te

# Get the model using:
#   print (models[81])

# Then copy and paste the model as below:

var S0, S1, S2, S4, S5, S6, S7, S8, S9
ext S3;
J0: S9 + S8 -> S6; E0*(k0*S9*S8);
J1: S6 -> S7 + S9; E1*(k1*S6);
J2: S2 + S0 -> S8; E2*(k2*S2*S0);
J3: S4 + S4 -> S6; E3*(k3*S4*S4);
J4: S5 -> S2 + S1; E4*(k4*S5);
J5: S9 -> S4 + S0; E5*(k5*S9);
J6: S1 -> S0 + S5; E6*(k6*S1);
J7: S0 -> S4 + S4; E7*(k7*S0);
J8: S3 -> S9; E8*(k8*S3);
J9: S5 -> S1 + S9; E9*(k9*S5);
J10: S5 -> S0 + S4; E10*(k10*S5);
J11: S5 -> S4 + S9; E11*(k11*S5);
J12: S5 + S1 -> S0; E12*(k12*S5*S1);
J13: S2 + S9 -> S4; E13*(k13*S2*S9);
J14: S1 -> S8; E14*(k14*S1);
J15: S8 + S0 -> S1; E15*(k15*S8*S0);
J16: S7 -> S8; E16*(k16*S7);
J17: S8 + S6 -> S7; E17*(k17*S8*S6);
J18: S4 -> S6 + S6; E18*(k18*S4);
J19: S5 -> S1 + S4; E19*(k19*S5);

k0 = 0.6158; k1 = 0.0524;  k2 = 0.7206;  k3 = 0.0261
k4 = 0.4946; k5 = 0.2428;  k6 = 0.3249;  k7 = 0.4854
k8 = 0.6743; k9 = 0.6320;  k10 = 0.8954; k11 = 0.435
k12 = 0.580; k13 = 0.0298; k14 = 0.850;  k15 = 0.342
k16 = 0.556; k17 = 0.3221; k18 = 0.584;  k19 = 0.681

E0 = 1;  E1 = 1;  E2 = 1;  E3 = 1
E4 = 1;  E5 = 1;  E6 = 1;  E7 = 1
E8 = 1;  E9 = 1;  E10 = 1; E11 = 1
E12 = 1; E13 = 1; E14 = 1; E15 = 1
E16 = 1; E17 = 1; E18 = 1; E19 = 1

S3 = 1
S0 = 6; S1 = 3; S2 = 3; S4 = 2
S5 = 6; S6 = 2; S7 = 6; S8 = 2
S9 = 1
""")

m = r.simulate (0, 180, 100)
r.plot()


Here is the simulation from running the above code. Note that not every reaction is contributing to this behavior. You can remove J2, J3, J4, J6, J9, J10, J11, J12, J13, J14, J15, and J19 and the system will still oscillate.
What you can now is use another function in teUtils to look at a single model and plot a grid of simulations using random parameter values for that model. To do this just call plotRandSimGrid as follows. We apply it to model 81:

r = te.loada (models[81])
tu.plotting.plotRandSimGrid (r, endTime-500, ngrid=8, maxRange=1)


maxRange limits the range of random parmateer values. In this model, if the range is bigger, sometimes we get bad parameter sets which don't simulate. A maxRange of 1 seems to gives simulatable models. Most of the time the defaults are sufficient and you only have to pass in the roadrunner object, r. Here is a sample run: