def initial_values():
y0 = [0] * V.len_f_vars
x = f_params()
y0[V.TGFb] = 0.05
y0[V.Rec] = 1.84
y0[V.TGFb_pRec] = 0
y0[V.S2] = x[C.S2tot]
y0[V.S3] = x[C.S3tot]
y0[V.S4] = x[C.S4tot]
y0[V.ppS2_ppS2_ppS2] = 0
y0[V.ppS3_ppS3_ppS3] = 0
y0[V.S4_S4_S4] = 0
y0[V.pS2] = 0
y0[V.pS3] = 0
y0[V.ppS2] = 0
y0[V.ppS3] = 0
y0[V.ppS2_ppS2_S4] = 0
y0[V.ppS2_ppS2_ppS3] = 0
y0[V.ppS2_ppS3_ppS3] = 0
y0[V.ppS3_ppS3_S4] = 0
y0[V.ppS2_ppS3_S4] = 0
y0[V.ppS3_S4_S4] = 0
y0[V.ppS2_S4_S4] = 0
y0[V.gene] = 1
return y0
class Simulation(object):
x = f_params()
y0 = initial_values()
tspan = np.arange(0, 72, 0.01)
t = np.array(tspan)
Y = odeint(diffeq, y0, tspan, args=tuple(x))
def get_steady_state():
x = f_params()
y0 = initial_values()
ss_time = range(2401) # 2400 min
y0[V.Ins] = 0.01 # 0.01 nM of insulin during starvation
Y = odeint(diffeq, y0, ss_time, args=tuple(x))
return Y[-1,:]
class Simulation(object):
tspan = [i / 10 for i in range(120 * 10 + 1)]
t = np.array(tspan)
condition = 9
Gwl = np.empty((len(t), condition))
Cdc25 = np.empty((len(t), condition))
ENSAP = np.empty((len(t), condition))
Y15 = np.empty((len(t), condition))
B55 = np.empty((len(t), condition))
x = f_params()
y0 = initial_values()
for i in range(condition):
#OA insensitive phosphatase
if i in [0, 3, 6]:
x[C.kigwl] = 0
x[C.kigwl_d] = 2
x[C.kigwl_dd] = 0
#OA sensitive phosphatase
if i in [1, 4, 7]:
x[C.kigwl] = 0
x[C.kigwl_d] = 0.02
x[C.kigwl_dd] = 2
#PP2A-B55
if i in [2, 5, 8]:
x[C.kigwl] = 2
x[C.kigwl_d] = 0.02
x[C.kigwl_dd] = 0
#Cdk1 Inhibition +OA
if i in [3, 4, 5]:
x[C.OA] = 100
x[C.RO] = 25
#Mitotic block
if i in [6, 7, 8]:
y0[V.MPF] = 0.96
y0[V.Cdc25] = 0.97
y0[V.Wee1] = 0.03
y0[V.Gwl] = 0.9
y0[V.ENSAPt] = 0.75
y0[V.PP2] = 0.027
x[C.OA] = 0
x[C.RO] = 100
Y = odeint(diffeq, y0, tspan, args=tuple(x))
Gwl[:, i] = Y[:, V.Gwl]
Cdc25[:, i] = Y[:, V.Cdc25]
ENSAP[:, i] = Y[:, V.ENSAPt]
B55[:, i] = Y[:, V.PP2]
Y15[:, i] = x[C.CycT] - Y[:, V.MPF]
class Simulation(object):
tspan = range(121)
t = np.array(tspan)
condition = 4
IL13stimulation = np.empty((len(t), condition))
Rec = np.empty((len(t), condition))
Rec_i = np.empty((len(t), condition))
IL13_Rec = np.empty((len(t), condition))
p_IL13_Rec = np.empty((len(t), condition))
p_IL13_Rec_i = np.empty((len(t), condition))
JAK2 = np.empty((len(t), condition))
pJAK2 = np.empty((len(t), condition))
SHP1 = np.empty((len(t), condition))
STAT5 = np.empty((len(t), condition))
pSTAT5 = np.empty((len(t), condition))
SOCS3mRNA = np.empty((len(t), condition))
DecoyR = np.empty((len(t), condition))
IL13_DecoyR = np.empty((len(t), condition))
SOCS3 = np.empty((len(t), condition))
CD274mRNA = np.empty((len(t), condition))
x = f_params()
y0 = initial_values()
for i in range(condition):
if i == 0:
y0[V.IL13stimulation] = 80.0
elif i == 1:
y0[V.IL13stimulation] = 20.0
elif i == 2:
y0[V.IL13stimulation] = 4.0
elif i == 3:
y0[V.IL13stimulation] = 0.0
Y = odeint(diffeq, y0, tspan, args=tuple(x))
IL13stimulation[:, i] = Y[:, V.IL13stimulation]
Rec[:, i] = Y[:, V.Rec]
Rec_i[:, i] = Y[:, V.Rec_i]
IL13_Rec[:, i] = Y[:, V.IL13_Rec]
p_IL13_Rec[:, i] = Y[:, V.p_IL13_Rec]
p_IL13_Rec_i[:, i] = Y[:, V.p_IL13_Rec_i]
JAK2[:, i] = Y[:, V.JAK2]
pJAK2[:, i] = Y[:, V.pJAK2]
SHP1[:, i] = Y[:, V.SHP1]
STAT5[:, i] = Y[:, V.STAT5]
pSTAT5[:, i] = Y[:, V.pSTAT5]
SOCS3mRNA[:, i] = Y[:, V.SOCS3mRNA]
DecoyR[:, i] = Y[:, V.DecoyR]
IL13_DecoyR[:, i] = Y[:, V.IL13_DecoyR]
SOCS3[:, i] = Y[:, V.SOCS3]
CD274mRNA[:, i] = Y[:, V.CD274mRNA]
class Simulation(object):
x = f_params()
y0 = initial_values()
tspan = range(901)
t = np.array(tspan)
Y = odeint(diffeq, y0, tspan, args=tuple(x))
CyclinA = Y[:, V.CycAT] - Y[:, V.CycAp27]
CyclinE = Y[:, V.CycET] - Y[:, V.CycEp27]
p27_tot = Y[:, V.p27T]
class Simulation(object):
x = f_params()
y0 = initial_values()
tspan = range(1200)
t = np.array(tspan) / 60
Y = odeint(diffeq, y0, tspan, args=tuple(x))
CycA = Y[:, V.tCa]
CycE = Y[:, V.tCe]
active_RC = Y[:, V.aRc]
P21_tot = Y[:, V.tP21]
class Simulation(object):
t = range(121)
condition = 3
totalShc = np.empty((len(t), condition))
totalGrb2 = np.empty((len(t), condition))
RSh = np.empty((len(t), condition))
RGrb2 = np.empty((len(t), condition))
totalSOS = np.empty((len(t), condition))
ShGS = np.empty((len(t), condition))
PLCg = np.empty((len(t), condition))
x = f_params()
y0 = initial_values()
for i in range(condition):
if i == 0: # 20nM
pass
elif i == 1: # 2nM
y0[V.EGF] = 68.
elif i == 2: # Absence of the PLCγP translocation step
y0[V.EGF] = 680.
x[C.k25f] = 0.
x[C.k25b] = 0.
Y = odeint(diffeq, y0, t, args=tuple(x))
totalShc[:,
i] = Y[:, V.
R_ShP] + Y[:, V.
R_Sh_G] + Y[:, V.
R_Sh_G_S] + Y[:, V.
ShP] + Y[:, V.
Sh_G] + Y[:,
V
.
Sh_G_S]
totalGrb2[:,
i] = Y[:, V.R_Sh_G] + Y[:,
V.Sh_G] + Y[:,
V.R_Sh_G_S] + Y[:,
V.Sh_G_S]
RSh[:, i] = Y[:, V.R_ShP] + Y[:, V.R_Sh_G] + Y[:, V.R_Sh_G_S]
RGrb2[:,
i] = Y[:, V.R_G] + Y[:, V.R_G_S] + Y[:, V.R_Sh_G] + Y[:,
V.R_Sh_G_S]
totalSOS[:, i] = Y[:, V.R_G_S] + Y[:, V.R_Sh_G_S]
ShGS[:, i] = Y[:, V.Sh_G_S]
PLCg[:, i] = Y[:, V.R_PLP] + Y[:, V.PLCgP]
class Simulation(object):
def get_steady_state():
x = f_params()
y0 = initial_values()
ss_time = range(2401) # 2400 min
y0[V.Ins] = 0.01 # 0.01 nM of insulin during starvation
Y = odeint(diffeq, y0, ss_time, args=tuple(x))
return Y[-1,:]
tspan = range(481)
t = np.array(tspan)
condition = 5
pAKT = np.empty((len(t), condition))
pS6K = np.empty((len(t), condition))
pGSK3B = np.empty((len(t), condition))
G6Pase = np.empty((len(t), condition))
x = f_params()
y0 = get_steady_state()
for i in range(condition):
if i == 0:
y0[V.Ins] = 0.01
elif i == 1:
y0[V.Ins] = 0.03
elif i == 2:
y0[V.Ins] = 0.1
elif i == 3:
y0[V.Ins] = 0.3
elif i == 4:
y0[V.Ins] = 1.0
Y = odeint(diffeq, y0, tspan, args=tuple(x))
pAKT[:, i] = Y[:, V.pAKT]
pS6K[:, i] = Y[:, V.pS6K] * 83.8672192461257
pGSK3B[:, i] = Y[:, V.pGSK3B] * 0.111097316860158
G6Pase[:, i] = Y[:, V.G6Pase] * 0.0363622452066626
class Simulation(object):
tspan = np.linspace(0, 480, 4801)
t = np.array(tspan) / 60 # min -> hour
Ton = np.linspace(0, 0.5, 6) # 30 s pulse
Toff = np.linspace(0, 479.5, 4796)
x = f_params()
y0 = initial_values()
Y = odeint(diffeq, y0, tspan, args=tuple(x))
totalNumPSmad2_sustained = (Y[:,V.PSmad2c] + 2*Y[:,V.PSmad2_PSmad2_c] + Y[:,V.PSmad2_PSmad4_c])*2.3*602 \
+ (Y[:,V.PSmad2n] + 2*Y[:,V.PSmad2_PSmad2_n] + Y[:,V.PSmad2_Smad4_n])*602
pulse = odeint(diffeq, y0, Ton, args=tuple(x))
Y0 = pulse[-1, :]
# washout
Y0[V.TGF_beta_ex] = 0
washout = odeint(diffeq, Y0, Toff, args=tuple(x))
Y = np.vstack((np.delete(pulse, -1, axis=0), washout))
totalNumPSmad2_singlePulse = (Y[:,V.PSmad2c] + 2*Y[:,V.PSmad2_PSmad2_c] + Y[:,V.PSmad2_PSmad4_c])*2.3*602 \
+ (Y[:,V.PSmad2n] + 2*Y[:,V.PSmad2_PSmad2_n] + Y[:,V.PSmad2_Smad4_n])*602
class Simulation(object):
x = f_params()
y0 = initial_values()
t = range(481)
condition = 4
p38_activity = np.empty((len(t),condition))
for i in range(condition):
# k8: the rate constant for MKP-1 protein degradation
if i==0:
x[C.k8] = 0.024
elif i==1:
x[C.k8] = 0.012
elif i==2:
x[C.k8] = 0.008
elif i==3:
x[C.k8] = 0.004
Y = odeint(diffeq,y0,t,args=tuple(x))
p38_activity[:,i] = Y[:,V.FRET]
class Simulation(object):
tspan_ss = range(3600 * 100 + 1)
tspan_a = range(3601)
tspan_b = range(3600 * 6 + 1)
t = np.arange(3600 * 7 + 1) / 3600.
x = f_params()
y0 = initial_values()
# t < 0
Yss = odeint(diffeq, y0, tspan_ss, args=tuple(x))
y0 = Yss[-1, :]
# 0 <= t < 1
Ya = odeint(diffeq, y0, tspan_a, args=tuple(x))
y0 = Ya[-1, :]
x[C.TR] = 1
# 1 <= t <= 7
Yb = odeint(diffeq, y0, tspan_b, args=tuple(x))
Y = np.vstack((np.delete(Ya, -1, axis=0), Yb))
Neutral_IKK = Y[:, V.IKKn]
Active_IKK = Y[:, V.IKKa]
Inactive_IKK = Y[:, V.IKKi]
Free_cyt_IkBa = Y[:, V.IkBa]
Cyt = Y[:, V.IkBaNFkB]
Free_nuclear_IkBa = Y[:, V.IkBan]
Free_nuclear_NFkB = Y[:, V.NFkBn]
IkBa_mRNA = Y[:, V.IkBat]
A20_mRNA = Y[:, V.A20t]
A20_protein = Y[:, V.A20]
cgen_mRNA = Y[:, V.cgent]
class Simulation(object):
t_start = 0
t_end = 600
h = 10000
t = np.linspace(t_start, t_end, h)
conditions = 12
x = f_params()
y0 = initial_values()
for i in range(conditions):
if i == 0:
x[C.gene_turn] = x[C.Ski_turn]
x[C.gene_act1] = x[C.Ski_act1]
x[C.gene_act2] = x[C.Ski_act2]
x[C.gene_act3] = x[C.Ski_act3]
x[C.gene_inh1] = x[C.Ski_inh1]
x[C.gene_inh2] = x[C.Ski_inh2]
x[C.gene_inh3] = x[C.Ski_inh3]
Y = odeint(diffeq, y0, t, args=tuple(x))
Ski_treated = np.log2(Y[:, V.gene])
if i == 1:
x[C.gene_turn] = x[C.Skil_turn]
x[C.gene_act1] = x[C.Skil_act1]
x[C.gene_act2] = x[C.Skil_act2]
x[C.gene_act3] = x[C.Skil_act3]
x[C.gene_inh1] = x[C.Skil_inh1]
x[C.gene_inh2] = x[C.Skil_inh2]
x[C.gene_inh3] = x[C.Skil_inh3]
Y = odeint(diffeq, y0, t, args=tuple(x))
Skil_treated = np.log2(Y[:, V.gene])
if i == 2:
x[C.gene_turn] = x[C.Dnmt3a_turn]
x[C.gene_act1] = x[C.Dnmt3a_act1]
x[C.gene_act2] = x[C.Dnmt3a_act2]
x[C.gene_act3] = x[C.Dnmt3a_act3]
x[C.gene_inh1] = x[C.Dnmt3a_inh1]
x[C.gene_inh2] = x[C.Dnmt3a_inh2]
x[C.gene_inh3] = x[C.Dnmt3a_inh3]
Y = odeint(diffeq, y0, t, args=tuple(x))
Dnmt3a_treated = np.log2(Y[:, V.gene])
if i == 3:
x[C.gene_turn] = x[C.Sox4_turn]
x[C.gene_act1] = x[C.Sox4_act1]
x[C.gene_act2] = x[C.Sox4_act2]
x[C.gene_act3] = x[C.Sox4_act3]
x[C.gene_inh1] = x[C.Sox4_inh1]
x[C.gene_inh2] = x[C.Sox4_inh2]
x[C.gene_inh3] = x[C.Sox4_inh3]
Y = odeint(diffeq, y0, t, args=tuple(x))
Sox4_treated = np.log2(Y[:, V.gene])
if i == 4:
x[C.gene_turn] = x[C.Jun_turn]
x[C.gene_act1] = x[C.Jun_act1]
x[C.gene_act2] = x[C.Jun_act2]
x[C.gene_act3] = x[C.Jun_act3]
x[C.gene_inh1] = x[C.Jun_inh1]
x[C.gene_inh2] = x[C.Jun_inh2]
x[C.gene_inh3] = x[C.Jun_inh3]
Y = odeint(diffeq, y0, t, args=tuple(x))
Jun_treated = np.log2(Y[:, V.gene])
if i == 5:
x[C.gene_turn] = x[C.Smad7_turn]
x[C.gene_act1] = x[C.Smad7_act1]
x[C.gene_act2] = x[C.Smad7_act2]
x[C.gene_act3] = x[C.Smad7_act3]
x[C.gene_inh1] = x[C.Smad7_inh1]
x[C.gene_inh2] = x[C.Smad7_inh2]
x[C.gene_inh3] = x[C.Smad7_inh3]
Y = odeint(diffeq, y0, t, args=tuple(x))
Smad7_treated = np.log2(Y[:, V.gene])
if i == 6:
x[C.gene_turn] = x[C.Klf10_turn]
x[C.gene_act1] = x[C.Klf10_act1]
x[C.gene_act2] = x[C.Klf10_act2]
x[C.gene_act3] = x[C.Klf10_act3]
x[C.gene_inh1] = x[C.Klf10_inh1]
x[C.gene_inh2] = x[C.Klf10_inh2]
x[C.gene_inh3] = x[C.Klf10_inh3]
Y = odeint(diffeq, y0, t, args=tuple(x))
Klf10_treated = np.log2(Y[:, V.gene])
if i == 7:
x[C.gene_turn] = x[C.Bmp4_turn]
x[C.gene_act1] = x[C.Bmp4_act1]
x[C.gene_act2] = x[C.Bmp4_act2]
x[C.gene_act3] = x[C.Bmp4_act3]
x[C.gene_inh1] = x[C.Bmp4_inh1]
x[C.gene_inh2] = x[C.Bmp4_inh2]
x[C.gene_inh3] = x[C.Bmp4_inh3]
Y = odeint(diffeq, y0, t, args=tuple(x))
Bmp4_treated = np.log2(Y[:, V.gene])
if i == 8:
x[C.gene_turn] = x[C.Cxcl15_turn]
x[C.gene_act1] = x[C.Cxcl15_act1]
x[C.gene_act2] = x[C.Cxcl15_act2]
x[C.gene_act3] = x[C.Cxcl15_act3]
x[C.gene_inh1] = x[C.Cxcl15_inh1]
x[C.gene_inh2] = x[C.Cxcl15_inh2]
x[C.gene_inh3] = x[C.Cxcl15_inh3]
Y = odeint(diffeq, y0, t, args=tuple(x))
Cxcl15_treated = np.log2(Y[:, V.gene])
if i == 9:
x[C.gene_turn] = x[C.Dusp5_turn]
x[C.gene_act1] = x[C.Dusp5_act1]
x[C.gene_act2] = x[C.Dusp5_act2]
x[C.gene_act3] = x[C.Dusp5_act3]
x[C.gene_inh1] = x[C.Dusp5_inh1]
x[C.gene_inh2] = x[C.Dusp5_inh2]
x[C.gene_inh3] = x[C.Dusp5_inh3]
Y = odeint(diffeq, y0, t, args=tuple(x))
Dusp5_treated = np.log2(Y[:, V.gene])
if i == 10:
x[C.gene_turn] = x[C.Tgfa_turn]
x[C.gene_act1] = x[C.Tgfa_act1]
x[C.gene_act2] = x[C.Tgfa_act2]
x[C.gene_act3] = x[C.Tgfa_act3]
x[C.gene_inh1] = x[C.Tgfa_inh1]
x[C.gene_inh2] = x[C.Tgfa_inh2]
x[C.gene_inh3] = x[C.Tgfa_inh3]
Y = odeint(diffeq, y0, t, args=tuple(x))
Tgfa_treated = np.log2(Y[:, V.gene])
if i == 11:
x[C.gene_turn] = x[C.Pdk4_turn]
x[C.gene_act1] = x[C.Pdk4_act1]
x[C.gene_act2] = x[C.Pdk4_act2]
x[C.gene_act3] = x[C.Pdk4_act3]
x[C.gene_inh1] = x[C.Pdk4_inh1]
x[C.gene_inh2] = x[C.Pdk4_inh2]
x[C.gene_inh3] = x[C.Pdk4_inh3]
Y = odeint(diffeq, y0, t, args=tuple(x))
Pdk4_treated = np.log2(Y[:, V.gene])
def timecourse(sim):
x = f_params()
yticks = [np.arange(-0.4, 0.6, 0.2)]
plt.rcParams['font.size'] = 6
plt.rcParams['font.family'] = 'Arial'
#plt.rcParams['axes.linewidth'] = 1
plt.rcParams['lines.linewidth'] = 2
plt.subplots_adjust(wspace=0.3, hspace=1.0)
for i in range(12):
plt.subplot(4, 3, i + 1)
plt.gca().spines['right'].set_visible(False)
plt.gca().spines['top'].set_visible(False)
if i == 0:
plt.plot(sim.t, sim.Ski_treated, 'r')
plt.fill_between(sim.t,
sim.Ski_treated - x[C.sd_Ski],
sim.Ski_treated + x[C.sd_Ski],
facecolor='r',
lw=0,
alpha=0.1)
plt.title('Ski (Cluster 1)', fontweight="bold")
plt.ylim([-0.5, 0.7])
plt.yticks([-0.4, -0.2, 0, 0.2, 0.4, 0.6])
elif i == 1:
plt.plot(sim.t, sim.Skil_treated, 'r')
plt.fill_between(sim.t,
sim.Skil_treated - x[C.sd_Skil],
sim.Skil_treated + x[C.sd_Skil],
facecolor='r',
lw=0,
alpha=0.1)
plt.title('Skil (Cluster 2)', fontweight="bold")
plt.ylim([-0.2, 1.2])
plt.yticks([0, 0.5, 1.0])
elif i == 2:
plt.plot(sim.t, sim.Dnmt3a_treated, 'r')
plt.fill_between(sim.t,
sim.Dnmt3a_treated - x[C.sd_Dnmt3a],
sim.Dnmt3a_treated + x[C.sd_Dnmt3a],
facecolor='r',
lw=0,
alpha=0.1)
plt.title('Dnmt3a (Cluster 3)', fontweight="bold")
plt.ylim([-0.2, 0.5])
plt.yticks([-0.2, 0, 0.2, 0.4])
elif i == 3:
plt.plot(sim.t, sim.Sox4_treated, 'r')
plt.fill_between(sim.t,
sim.Sox4_treated - x[C.sd_Sox4],
sim.Sox4_treated + x[C.sd_Sox4],
facecolor='r',
lw=0,
alpha=0.1)
plt.title('Sox4 (Cluster 4)', fontweight="bold")
plt.ylim([-0.6, 1.0])
plt.yticks([-0.5, 0.0, 0.5])
elif i == 4:
plt.plot(sim.t, sim.Jun_treated, 'r')
plt.fill_between(sim.t,
sim.Jun_treated - x[C.sd_Jun],
sim.Jun_treated + x[C.sd_Jun],
facecolor='r',
lw=0,
alpha=0.1)
plt.title('Jun (Cluster 5)', fontweight="bold")
plt.ylim([-0.8, 1.0])
plt.yticks([-0.5, 0, 0.5, 1.0])
elif i == 5:
plt.plot(sim.t, sim.Smad7_treated, 'r')
plt.fill_between(sim.t,
sim.Smad7_treated - x[C.sd_Smad7],
sim.Smad7_treated + x[C.sd_Smad7],
facecolor='r',
lw=0,
alpha=0.1)
plt.title('Smad7 (Cluster 6)', fontweight="bold")
plt.ylim([-0.5, 1.1])
plt.yticks([0, 0.5, 1.0])
elif i == 6:
plt.plot(sim.t, sim.Klf10_treated, 'r')
plt.fill_between(sim.t,
sim.Klf10_treated - x[C.sd_Klf10],
sim.Klf10_treated + x[C.sd_Klf10],
facecolor='r',
lw=0,
alpha=0.1)
plt.title('Klf10 (Cluster 7)', fontweight="bold")
plt.ylim([-0.3, 1.5])
plt.yticks([0, 0.5, 1.0, 1.5])
elif i == 7:
plt.plot(sim.t, sim.Bmp4_treated, 'r')
plt.fill_between(sim.t,
sim.Bmp4_treated - x[C.sd_Bmp4],
sim.Bmp4_treated + x[C.sd_Bmp4],
facecolor='r',
lw=0,
alpha=0.1)
plt.title('Bmp4 (Cluster 8)', fontweight="bold")
plt.ylim([-0.5, 0.6])
plt.yticks([-0.4, -0.2, 0, 0.2, 0.4])
elif i == 8:
plt.plot(sim.t, sim.Cxcl15_treated, 'r')
plt.fill_between(sim.t,
sim.Cxcl15_treated - x[C.sd_Cxcl15],
sim.Cxcl15_treated + x[C.sd_Cxcl15],
facecolor='r',
lw=0,
alpha=0.1)
plt.title('Cxcl15 (Cluster 9)', fontweight="bold")
plt.ylim([-0.5, 0.4])
plt.yticks([-0.4, -0.2, 0, 0.2])
elif i == 9:
plt.plot(sim.t, sim.Dusp5_treated, 'r')
plt.fill_between(sim.t,
sim.Dusp5_treated - x[C.sd_Dusp5],
sim.Dusp5_treated + x[C.sd_Dusp5],
facecolor='r',
lw=0,
alpha=0.1)
plt.title('Dusp5 (Cluster 10)', fontweight="bold")
plt.ylim([-0.4, 0.3])
plt.yticks([-0.2, 0, 0.2])
elif i == 10:
plt.plot(sim.t, sim.Tgfa_treated, 'r')
plt.fill_between(sim.t,
sim.Tgfa_treated - x[C.sd_Tgfa],
sim.Tgfa_treated + x[C.sd_Tgfa],
facecolor='r',
lw=0,
alpha=0.1)
plt.title('Tgfa (Cluster 11)', fontweight="bold")
plt.ylim([-0.6, 0.2])
plt.yticks([-0.4, -0.2, 0, 0.2])
elif i == 11:
plt.plot(sim.t, sim.Pdk4_treated, 'r')
plt.fill_between(sim.t,
sim.Pdk4_treated - x[C.sd_Pdk4],
sim.Pdk4_treated + x[C.sd_Pdk4],
facecolor='r',
lw=0,
alpha=0.1)
plt.title('Pdk4 (Cluster 12)', fontweight="bold")
plt.ylim([-0.6, 0.1])
plt.yticks([-0.4, -0.2, 0])
if i >= 9:
plt.xlabel('time (min)')
plt.xticks([0, 120, 240, 360, 480, 600])
if i % 3 == 0:
plt.ylabel('gene expression(log2)')
plt.show()
class Simulation(object):
t_start = 0
t_end = 300
h = 1000
t = np.linspace(t_start, t_end, h)
conditions = 5
pEGFR_au = np.empty((len(t), conditions))
pErbB2_au = np.empty((len(t), conditions))
pErbB3_au = np.empty((len(t), conditions))
pIGF1R_au = np.empty((len(t), conditions))
pERK_au = np.empty((len(t), conditions))
pAKT_au = np.empty((len(t), conditions))
pS6_au = np.empty((len(t), conditions))
x = f_params()
y0 = initial_values()
# Cell line H322M
for i in range(conditions):
if i == 0:
y0[V.dose_EGF] = 0
y0[V.dose_HGF] = 0
y0[V.dose_IGF1] = 0
y0[V.dose_HRG] = 0
elif i == 1:
y0[V.dose_EGF] = 0.156 * x[C.scale_Ligand]
y0[V.dose_IGF1] = 0
y0[V.dose_HRG] = 0
elif i == 2:
y0[V.dose_EGF] = 0.625 * x[C.scale_Ligand]
y0[V.dose_HGF] = 0
y0[V.dose_IGF1] = 0
y0[V.dose_HRG] = 0
elif i == 3:
y0[V.dose_EGF] = 2.5 * x[C.scale_Ligand]
y0[V.dose_HGF] = 0
y0[V.dose_IGF1] = 0
y0[V.dose_HRG] = 0
elif i == 4:
y0[V.dose_EGF] = 10 * x[C.scale_Ligand]
y0[V.dose_HGF] = 0
y0[V.dose_IGF1] = 0
y0[V.dose_HRG] = 0
Y = odeint(diffeq, y0, t, args=tuple(x))
pEGFR = 2*Y[:,V.pEGFRd] + 2*Y[:,V.pEGFRi] + 2*Y[:,V.pEGFRi_ph] + Y[:,V.pErbB12] \
+ Y[:,V.pErbB12i] + Y[:,V.pErbB12i_ph] + Y[:,V.pErbB13] + Y[:,V.pErbB13i] \
+ Y[:,V.pErbB13i_ph] + Y[:,V.pMetEGFR] + Y[:,V.pMetEGFRi] + Y[:,V.pMetEGFRi_ph]
pErbB2 = Y[:,V.pErbB12] + Y[:,V.pErbB12i] + Y[:,V.pErbB12i_ph] + 2*Y[:,V.pErbB2] + 2*Y[:,V.pErbB2i] \
+ 2*Y[:,V.pErbB2i_ph] + Y[:,V.pErbB32] + Y[:,V.pErbB32i] + Y[:,V.pErbB32i_ph]
pErbB3 = Y[:,V.pErbB13] + Y[:,V.pErbB13i] + Y[:,V.pErbB13i_ph] + Y[:,V.pErbB32] \
+ Y[:,V.pErbB32i] + Y[:,V.pErbB32i_ph] + 2*Y[:,V.pErbB3d] + 2*Y[:,V.pErbB3i] \
+ 2*Y[:,V.pErbB3i_ph] + Y[:,V.pMetErbB3] + Y[:,V.pMetErbB3i] + Y[:,V.pMetErbB3i_ph]
pERK = Y[:, V.pERK]
pAKT = Y[:, V.pAKT]
pS6 = Y[:, V.pS6]
pEGFR_au[:, i] = np.log10(x[C.offset_pEGFR_CelllineH322M] +
x[C.scale_pEGFR_CelllineH322M] * pEGFR)
pErbB2_au[:, i] = np.log10(x[C.offset_pErbB2_CelllineH322M] +
x[C.scale_pErbB2_CelllineH322M] * pErbB2)
pErbB3_au[:, i] = np.log10(x[C.offset_pErbB3_CelllineH322M] +
x[C.scale_pErbB3_CelllineH322M] * pErbB3)
pERK_au[:, i] = np.log10(x[C.offset_pERK_CelllineH322M] +
x[C.scale_pERK_CelllineH322M] * pERK)
pAKT_au[:, i] = np.log10(x[C.offset_pAKT_CelllineH322M] +
x[C.scale_pAKT_CelllineH322M] * pAKT)
pS6_au[:, i] = np.log10(x[C.offset_pS6_CelllineH322M] +
x[C.scale_pS6_CelllineH322M] * pS6)
