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create_graphs_for_figure_4b.py
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from matplotlib import pyplot as plt
from simple_driven_network import simple_reaction_network, reaction
import numpy as np
dt = 0.01
steps = 100000
N = 9
plot = True
forcings = [500.0, 600.0, 700.0, 750.0, 800.0, 850.0, 900.0, 950.0, 1000.0, 1050.0, 1100.0, 1150.0, 1200.0, 1250.0, 1300.0, 1350.0, 1400.0, 1450.0, 1500.0, 2000.0, 2500.0, 3000.0]
i = 0
efficiencies = np.zeros( (len(forcings),3) )
#### Simulate deterministic CRNs and calculate efficiencies
for i, forcing in enumerate(forcings):
print('Calculating efficiencies for forcing %f' % forcing)
for state in range(3):
CRN = simple_reaction_network(N = N, M = 35, steps = int(1e6), ks = [], dt = 0.01, create_reactions = False)
# Intermediates for different relaxation channels
# Channel 1
CRN.reactions.append( reaction(N, [0], [6], [1,1], [1e-5]) )
CRN.reactions.append( reaction(N, [4], [6], None, [1.0]) )
# Channel 2
CRN.reactions.append( reaction(N, [0], [7], [2,2], [1e-5]) )
CRN.reactions.append( reaction(N, [4], [7], None, [1.0]) )
# Channel 3
CRN.reactions.append( reaction(N, [0], [8], [3,3], [1e-5]) )
CRN.reactions.append( reaction(N, [4], [8], None, [1.0]) )
# Reservoir for chemical switches
CRN.reactions.append( reaction(N, [6], [5], [1,1], [1e-6]) )
CRN.reactions.append( reaction(N, [7], [5], [2,2], [3e-7]) )
CRN.reactions.append( reaction(N, [8], [5], [3,3], [1e-7]) )
# Chemical switch 1
CRN.reactions.append( reaction(N, [5], [1], [1,1], [1e-6]) )
CRN.reactions.append( reaction(N, [1], [4], [2,3], [0.001]) )
# Chemical switch 2
CRN.reactions.append( reaction(N, [5], [2], [2,2], [1e-6]) )
CRN.reactions.append( reaction(N, [2], [4], [1,3], [0.001]) )
# Chemical switch 3
CRN.reactions.append( reaction(N, [5], [3], [3,3], [1e-6]) )
CRN.reactions.append( reaction(N, [3], [4], [1,2], [0.001]) )
if state == 0:
CRN.A[1] = forcing
CRN.A[2] = 5.0
CRN.A[3] = 5.0
CRN.A[5] = forcing
CRN.A[6] = forcing
CRN.A[7] = 5.0
CRN.A[8] = 5.0
elif state == 1:
CRN.A[1] = 5.0
CRN.A[2] = forcing
CRN.A[3] = 5.0
CRN.A[5] = forcing
CRN.A[6] = 5.0
CRN.A[7] = forcing
CRN.A[8] = 5.0
else:
CRN.A[1] = 5.0
CRN.A[2] = 5.0
CRN.A[3] = forcing
CRN.A[5] = forcing
CRN.A[6] = 5.0
CRN.A[7] = 5.0
CRN.A[8] = forcing
# Clamp concentration of species 4 to low concentration
CRN.clamp_concentration(4, 5.0)
# Clamp concentration of species 0 to high concentration
CRN.clamp_concentration(0, forcing)
A, fluxes, delta_Gs, clamped_fluxes, clamped_potentials, potentials, AG = CRN.run(steps, log_fluxes = True)
eprs = -(fluxes*delta_Gs)
epr_total = eprs.sum(axis = 1)
print('Efficiency of state %d:' % state)
efficiency = (1.0 - (eprs[-1,0] + eprs[-1,1] + eprs[-1,2] + eprs[-1,3] + eprs[-1,4] + eprs[-1,5])/epr_total[-1])
print(efficiency)
stable = A[-1, state + 1] > 0.5*forcing
print('Stable:')
print(stable)
if stable:
efficiencies[i,state] = efficiency
else:
efficiencies[i,state] = 0.0
np.save('./results/efficiencies.npy', efficiencies)
np.save('./results/forcings_for_efficiencies.npy',np.array(forcings))
### Plot graph for figure 4b
efficiencies = np.load('./results/efficiencies.npy')
forcings = np.load('./results/forcings_for_efficiencies.npy')
colors = ['tab:orange','tab:green','tab:red']
cm = 1/2.54 # centimeters in inches
plt.figure(figsize = (30*cm, 20*cm))
for i in range(3):
eff = efficiencies[:,i]
idx = np.where( eff > 1e-12 )[0]
f = forcings[idx]
e = eff[idx]
plt.plot(f, e, color = colors[i], lw = 2.0)
plt.xlim([500, 3000])
plt.savefig("./figures/Fig_4b_efficiency_plot.svg")