def main():
"""Simulate temperature dependent aggregation and disaggregation."""
# Rates and Temperatures
k1 = 0.01 # disaggregation rate
k2 = 0.1 # chaperone degradation rate
k3 = 0.0001 # dimerization rate
k4 = 0.05 # heat induced chaperone production rate
k5 = 0.001 # heat inactivation rate of assembler
# temp_fxn = [(20,315), (80,298)]
temp_fxn = [(1, 298)]
# Species
A = Species("A", 100)
AA = Species("AA", 0)
iAA = Species("iAA", 0)
C = Species("C", 10)
# Temperature independent reactions
disagg = MonomerReactivation("Disagg", [iAA, C], [A, C], None, k1)
deg = UniDeg("C Degredation", [C], [None], None, k2)
# Simulation and Temperature-Dependent Reaction
nuc = Temp_Dimerization("Dimerization", [A], [AA], None, k3)
hip = HeatInducedProduction("Heat Induced Production", [iAA], [C], None, k4)
inactivation = HeatInducedInactivation("Inactivation", [AA], [iAA], None, k5)
sp_list = [A, AA, iAA, C]
rxn_list = [inactivation, disagg, hip, deg, nuc]
system = Network(sp_list, rxn_list)
x = system.simulate(0, 70, temp_fxn, "None")
# x2 = [i[-1,0] for i in [x, y, z]]
# y2 = [T1, T2, T1]
colors = ["b", "g", "r", "c"]
fig, axis = plt.subplots(figsize=(7, 7))
for i in range(2, len(sp_list) + 2):
axis.step(x[:, 0], x[:, i], label=sp_list[i - 2].name, c=colors[i - 2])
plt.legend(loc=0)
plt.xlim(0, x[-1, 0])
plt.xlabel("Time")
plt.ylabel("Molecular species count")
# plt.ylim(0,500)
# axis2 = axis.twinx()
# axis2.step(x2,y2, c='darkgray')
# plt.ylim(T1,T2)
# plt.fill_between([x2[0], x2[1]], [220,220], alpha=0.5, color='darkgray')
# plt.ylabel("Temperature (T)")
# plt.savefig("reactivation_p1(wsqrt).pdf")
plt.show()
print np.mean(x[:, 1])
def main():
"""Simulate temperature dependent aggregation and disaggregation."""
# Rates and Temperatures
k1 = 0.01 #disaggregation rate
k2 = 0.1 #chaperone degradation rate
k3 = 0.0001 #dimerization rate
k4 = 0.05 #heat induced chaperone production rate
k5 = 0.001 #heat inactivation rate of assembler
#temp_fxn = [(20,315), (80,298)]
temp_fxn = [(1,298)]
# Species
A = Species("A", 100)
AA = Species("AA", 0)
iAA = Species("iAA", 0)
C = Species("C", 10)
# Temperature independent reactions
disagg = MonomerReactivation("Disagg", [iAA, C], [A, C], None, k1)
deg = UniDeg("C Degredation", [C], [None], None, k2)
# Simulation and Temperature-Dependent Reaction
nuc = Temp_Dimerization("Dimerization", [A], [AA], None, k3)
hip = HeatInducedProduction("Heat Induced Production", [iAA], [C], None, k4)
inactivation = HeatInducedInactivation("Inactivation", [AA], [iAA], None, k5)
sp_list = [A, AA, iAA, C]
rxn_list= [inactivation, disagg, hip, deg, nuc]
system = Network(sp_list, rxn_list)
x = system.simulate(0, 70, temp_fxn, "None")
#x2 = [i[-1,0] for i in [x, y, z]]
#y2 = [T1, T2, T1]
colors = ['b','g','r','c']
fig, axis = plt.subplots(figsize=(7,7))
for i in range(2,len(sp_list)+2):
axis.step(x[:,0], x[:,i], label=sp_list[i-2].name, c=colors[i-2])
plt.legend(loc=0)
plt.xlim(0,x[-1,0])
plt.xlabel("Time")
plt.ylabel("Molecular species count")
#plt.ylim(0,500)
#axis2 = axis.twinx()
#axis2.step(x2,y2, c='darkgray')
#plt.ylim(T1,T2)
#plt.fill_between([x2[0], x2[1]], [220,220], alpha=0.5, color='darkgray')
#plt.ylabel("Temperature (T)")
#plt.savefig("reactivation_p1(wsqrt).pdf")
plt.show()
print np.mean(x[:,1])
def main():
"""Generate and simulate a model of lemmings approaching and jumping off
a cliff.
Now with adjustable temperature!
"""
# Input species and reactions
L = Species("Lemming", 0)
arrival = TConstInduction("Induction", None, [L], -1e-22, 0.1)
jump = UniDeg("Degredation", [L], None, -1e-22, 0.1)
temp_fxn = [(100,350), (150,298)]
#temp_fxn = [(1,298)]
cliff = Network([L], [arrival, jump])
# Simulate and parse data
x = cliff.simulate(0, 200, temp_fxn, "dummy_file2.dat")
y = x[:,2]
y1 = [x[i,2] for i in range(len(x)) if x[i,0] > 50 and x[i,0] < 100]
y2 = [x[i,2] for i in range(len(x)) if x[i,0] > 100 and x[i,0] < 150]
y3 = [x[i,2] for i in range(len(x)) if x[i,0] > 150]
#print len(y1), len(y2), len(y3), len(x)
#print "Mean1: %f, Mean1/Var1: %f" % (np.mean(y1), np.mean(y1)/np.var(y1))
#print "Mean2: %f, Mean2/Var2: %f" % (np.mean(y2), np.mean(y2)/np.var(y2))
#print "Mean3: %f, Mean3/Var3: %f" % (np.mean(y3), np.mean(y3)/np.var(y3))
#plotting function -> plots temperature over timecourse w/ shared x axis
# Generate plots
fig = plt.figure(figsize=(8, 8))
gs = gridspec.GridSpec(2, 1, height_ratios=(1,3))
axis1 = plt.subplot(gs[1])
axis1.step(x[:,0], y)
plt.xlabel("Time (s)")
axis1.set_ylabel("Lemmings")
axis0 = plt.subplot(gs[0], sharex=axis1)
axis0.step(x[:,0],x[:,1], c='r')
axis0.set_ylabel("Temperature")
plt.setp(axis0.get_xticklabels(), visible=False)
plt.tight_layout()
plt.show()
def main():
"""Generate and simulate a model of lemmings approaching and jumping off
a cliff.
Now with adjustable temperature!
"""
# Input species and reactions
L = Species("Lemming", 0)
arrival = TConstInduction("Induction", None, [L], -1e-22, 0.1)
jump = UniDeg("Degredation", [L], None, -1e-22, 0.1)
temp_fxn = [(100, 350), (150, 298)]
#temp_fxn = [(1,298)]
cliff = Network([L], [arrival, jump])
# Simulate and parse data
x = cliff.simulate(0, 200, temp_fxn, "dummy_file2.dat")
y = x[:, 2]
y1 = [x[i, 2] for i in range(len(x)) if x[i, 0] > 50 and x[i, 0] < 100]
y2 = [x[i, 2] for i in range(len(x)) if x[i, 0] > 100 and x[i, 0] < 150]
y3 = [x[i, 2] for i in range(len(x)) if x[i, 0] > 150]
#print len(y1), len(y2), len(y3), len(x)
#print "Mean1: %f, Mean1/Var1: %f" % (np.mean(y1), np.mean(y1)/np.var(y1))
#print "Mean2: %f, Mean2/Var2: %f" % (np.mean(y2), np.mean(y2)/np.var(y2))
#print "Mean3: %f, Mean3/Var3: %f" % (np.mean(y3), np.mean(y3)/np.var(y3))
#plotting function -> plots temperature over timecourse w/ shared x axis
# Generate plots
fig = plt.figure(figsize=(8, 8))
gs = gridspec.GridSpec(2, 1, height_ratios=(1, 3))
axis1 = plt.subplot(gs[1])
axis1.step(x[:, 0], y)
plt.xlabel("Time (s)")
axis1.set_ylabel("Lemmings")
axis0 = plt.subplot(gs[0], sharex=axis1)
axis0.step(x[:, 0], x[:, 1], c='r')
axis0.set_ylabel("Temperature")
plt.setp(axis0.get_xticklabels(), visible=False)
plt.tight_layout()
plt.show()
def main():
# Rates
k1 = .1 # Deactivation
k2 = .1 # Reactivation
k3 = 1 # Protein synthesis
k4 = .1 # Protein degradation
#temp_fxn = [(0,5), (200, 50), (600, 5)]
temp_fxn = [(0, 1)]
# Species
Pab1 = Species("Pab1", 100)
C = Species("Chaperone", 50)
iPab1 = Species("iPab1", 0)
# Reactions
aggregation = Pab1Deactivation("agg", [Pab1], [iPab1], None, k1)
disaggregation = Pab1Reactivation("disagg", [iPab1, C], [Pab1, C], None, k2)
Ctranslation = CProduction("C Translation", [Pab1], [C], None, k3)
Cdeg = UniDeg("C Degradation", [C], [], None, k4)
# System
species_list = [Pab1, C, iPab1]
rxn_list = [aggregation, disaggregation, Ctranslation, Cdeg]
system = Network(species_list, rxn_list)
# Simulation
x = system.simulate(0, 100, temp_fxn, None)
# Plots
colors = ['royalblue', 'gold', 'firebrick']
fig, (axis2, axis) = plt.subplots(2, 1, figsize=(7,7))
for i in range(2,len(species_list)+2):
axis.plot(x[:,0], x[:,i], label=species_list[i-2].name, c=colors[i-2], linewidth=3)
axis.legend(loc=0)
axis.set_xlim(0,x[-1,0]+5)
axis.set_xlabel("Time")
axis.set_ylabel("Molecular species count")
axis2.step(x[:, 0], x[:, 1], linewidth=3)
axis2.set_xlabel("Time (s)")
axis2.set_ylabel("Temperature")
plt.tight_layout()
plt.show()
