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run_ssa_model_clb.py
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import numpy as np
import matplotlib.pyplot as plt
from parameters import *
from models import *
def simulate_stochastic_clb(params, Y0, Omega, T_end, dt = 1):
state = np.array(Y0)
Y_total = np.zeros([1+T_end//dt, len(state)])
T = np.zeros(1+T_end//dt)
t = 0
Y_total[0, :] = state
T[0] = t
N = CLB_generate_stoichiometry()
i = 1
last_time = t
while t < T_end:
"""
if t < T_end/3:
rho_I0_a, rho_I0_b, rho_I1_a, rho_I1_b, rho_I2_a, rho_I2_b, rho_I3_a, rho_I3_b = 0, 5, 5, 0, 5, 0, 5, 0
elif t < 2*T_end/3:
rho_I0_a, rho_I0_b, rho_I1_a, rho_I1_b, rho_I2_a, rho_I2_b, rho_I3_a, rho_I3_b = 0, 0, 0, 0, 0, 0, 0, 0
else:
rho_I0_a, rho_I0_b, rho_I1_a, rho_I1_b, rho_I2_a, rho_I2_b, rho_I3_a, rho_I3_b = 5, 0, 0, 5, 0, 0, 0, 0
params[-8:] = rho_I0_a, rho_I0_b, rho_I1_a, rho_I1_b, rho_I2_a, rho_I2_b, rho_I3_a, rho_I3_b
if t > T_end/2:
S = np.array([1, 0])
state[24:26] = S*Omega
"""
if t > T_end/2:
#params[-8:] = rho_I0_a, rho_I0_b, rho_I1_a, rho_I1_b, rho_I2_a, rho_I2_b, rho_I3_a, rho_I3_b
params[-8:] = 0, 0, 0, 0, 0, 0, 0, 0
#choose two random numbers
r = np.random.uniform(size=2)
r1 = r[0]
r2 = r[1]
a = CLB_model_stochastic(state, params, Omega)
asum = np.cumsum(a)
a0 = np.sum(a)
#get tau
tau = (1.0/a0)*np.log(1.0/r1)
#print(t)
#select reaction
reaction_number = np.argwhere(asum > r2*a0)[0,0] #get first element
#update concentrations
state = state + N[:,reaction_number]
#update time
t = t + tau
if (t - last_time >= dt) or (t >= T_end):
last_time = t
Y_total[i, :] = state
T[i] = t
i += 1
return T[:i], Y_total[:i,:]
Y0 = np.zeros(59)
# number of cells: toggle switches
N_I0 = np.array([1,1])
N_I1 = np.array([1,1])
N_I2 = np.array([1,1])
N_I3 = np.array([1,1])
Y0[4:6] = N_I0
Y0[10:12] = N_I1
Y0[16:18] = N_I2
Y0[22:24] = N_I3
# number of cells: mux
#Y0[22-4+24:38-4+24] = 1 # number of cells
Y0[42:58] = 1 # number of cells
# reaction space volume for the whole cell population
# N_cells should be set to 1
Omega = 10
t_end = 500
states = [([0,0], [0,0,0,0]),
([0,0], [1,0,0,0]),
([1,0], [1,0,0,0]),
([1,0], [0,1,0,0]),
([0,1], [0,1,0,0]),
([0,1], [0,0,1,0]),
([1,1], [0,0,1,0]),
([1,1], [0,0,0,1])]
"""
states = [([0,0], [0,0,0,0]), ([0,0], [1,0,0,0]),
([1,0], [1,0,0,0]), ([1,0], [1,1,0,0]),
([0,1], [1,1,0,0]), ([0,1], [1,1,1,0]),
([1,1], [1,1,1,0]), ([1,1], [1,1,1,1])]
"""
for iteration, state in enumerate(states):
S = state[0]
I = state[1]
I0, I1, I2, I3 = I
rho_x = 0
rho_y = 0
if iteration > 0 and states[iteration-1][1] == I:
#rho_I0_a, rho_I0_b, rho_I1_a, rho_I1_b, rho_I2_a, rho_I2_b, rho_I3_a, rho_I3_b = (1-I0) * 5, I0*5, (1-I1)*5, I1*5, (1-I2)*5, I2*5, (1-I3)*5, I3*5
rho_I0_a, rho_I0_b, rho_I1_a, rho_I1_b, rho_I2_a, rho_I2_b, rho_I3_a, rho_I3_b = 0, 0, 0, 0, 0, 0, 0, 0
else:
rho_I0_a, rho_I0_b, rho_I1_a, rho_I1_b, rho_I2_a, rho_I2_b, rho_I3_a, rho_I3_b = (1-I0) * 5, I0*5, (1-I1)*5, I1*5, (1-I2)*5, I2*5, (1-I3)*5, I3*5
#rho_I0_a, rho_I0_b, rho_I1_a, rho_I1_b, rho_I2_a, rho_I2_b, rho_I3_a, rho_I3_b = 5, 0, 5, 0, 5, 0, 5, 0
params = [delta_L, gamma_L_X, n_y, theta_L_X, eta_x, omega_x, m_x, delta_x, delta_y, rho_x, rho_y, gamma_x, theta_x, r_X, r_Y,
rho_I0_a, rho_I0_b, rho_I1_a, rho_I1_b, rho_I2_a, rho_I2_b, rho_I3_a, rho_I3_b]
if iteration:
Y0 = Y_full[-1,:]
#else:
# Y0 *= N_cells
#print(Y0)
Y0[24:26] = np.array(S) * Omega
T, Y = simulate_stochastic_clb(params, Y0, Omega, t_end)
if not iteration:
Y_full = Y
T_full = T
else:
Y_full = np.append(Y_full, Y, axis = 0)
T_full = np.append(T_full, T + T_full[-1], axis = 0)
Y = Y_full
T = T_full
"""
results
"""
out = Y[:,-1]
S0, S1 = Y[:,24], Y[:,25]
I0_a, I0_b = Y[:,2], Y[:,3]
I1_a, I1_b = Y[:,8], Y[:,9]
I2_a, I2_b = Y[:,14], Y[:,15]
I3_a, I3_b = Y[:,20], Y[:,21]
# plot
"""
ax1 = plt.subplot(241)
ax1.plot(T, I0_a)
ax1.plot(T, I0_b)
ax1.legend(["I0_a = I0", "I0_b"])
ax1.set_title('I0 toggle')
ax2 = plt.subplot(242)
ax2.plot(T, I1_a)
ax2.plot(T, I1_b)
ax2.legend(["I1_a = I1", "I1_b"])
ax2.set_title('I1 toggle')
ax3 = plt.subplot(243)
ax3.plot(T, I2_a)
ax3.plot(T, I2_b)
ax3.legend(["I2_a = I2", "I2_b"])
ax3.set_title('I2 toggle')
ax4 = plt.subplot(244)
ax4.plot(T, I3_a)
ax4.plot(T, I3_b)
ax4.legend(["I3_a = I3", "I3_b"])
ax4.set_title('I3 toggle')
ax5 = plt.subplot(212)
ax5.plot(T,out)
ax5.set_title('out')
plt.suptitle(f"S = [{S[1]},{S[0]}]")
plt.show()
"""
# plot
ax1 = plt.subplot(341)
ax1.plot(T, I0_a, color="#800000ff", alpha=0.75)
ax1.plot(T, I0_b, color="#999999ff", alpha=0.75)
ax1.legend(["$I_0$", "$\\overline{I_0}$"])
#ax1.set_title('$I_0$ toggle')
ax1.set_xlabel("Time [min]")
ax1.set_ylabel("Molecules")
ax2 = plt.subplot(342)
ax2.plot(T, I1_a, color = "#00ff00ff", alpha=0.75)
ax2.plot(T, I1_b, color = "#666666ff")#, alpha=0.75)
ax2.legend(["$I_1$", "$\\overline{I_1}$"])
#ax2.set_title('$I_1$ toggle')
ax2.set_xlabel("Time [min]")
ax2.set_ylabel("Molecules")
ax3 = plt.subplot(343)
ax3.plot(T, I2_a, color = "#0000ffff", alpha=0.75)
ax3.plot(T, I2_b, color = "#ecececfe")#, alpha=0.75)
ax3.legend(["$I_2$", "$\\overline{I_2}$"])
#ax3.set_title('$I_2$ toggle')
ax3.set_xlabel("Time [min]")
ax3.set_ylabel("Molecules")
ax4 = plt.subplot(344)
ax4.plot(T, I3_a, color = "#800080ff", alpha=0.75)
ax4.plot(T, I3_b, color = "#999999fc")#, alpha=0.75)
ax4.legend(["$I_3$", "$\\overline{I_3}$"])
#ax4.set_title('$I_3$ toggle')
ax4.set_xlabel("Time [min]")
ax4.set_ylabel("Molecules")
ax5 = plt.subplot(312)
ax5.plot(T,S0, color = "#ff6600ff", alpha=0.75)
ax5.plot(T,S1, color = "#ffff00ff")#, alpha=0.75)
ax5.legend(["$S_0$", "$S_1$"])
#ax5.set_title('Select inputs')
ax5.set_xlabel("Time [min]")
ax5.set_ylabel("Molecules")
ax6 = plt.subplot(313)
ax6.plot(T,out, color = "#8080805a", alpha=0.75)
#ax6.set_title('out')
ax6.legend(['out'])
ax6.set_xlabel("Time [min]")
ax6.set_ylabel("Molecules")
#step = int(self.N)
#ax6.plot(T[step::step], out[step::step], 'x')
#plt.suptitle("$out = \\overline{S}_1 \\overline{S}_0 I_0 \\vee \\overline{S}_1 S_0 I_1 \\vee S_1 \\overline{S}_0 I_2 \\vee S_1 S_0 I_3$")
plt.gcf().set_size_inches(15,10)
#plt.savefig("figs\\CBLB_ssa.pdf", bbox_inches = 'tight')
plt.show()