In [1]:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import linalg
from scipy.integrate import odeint
sns.set()
%matplotlib inline
In [2]:
t = 0;dt = 0.003; s4 = 0;
chi=2; ci=chi; s5=0.7; factive=0.2; s7=0.01; s6=1-factive-s7-s4;
tnow =[]
ixnow = []
F1=[]
F2=[]
F3=[]
F4=[]
for i in range(1,150000+1):
t = t + dt
ni = 0
if t>30 and t<60:
ni = 100
ci = chi
if t>60 and t<90:
ni = 0
ci = 0
if t>90 and t<130:
ni = 100
ci = 0
if t>130 and t<160:
ni = 100
ci = chi
if t>160 and t<190:
ni = 100
ci = 0
if t>190 and t<220:
ni = 100
ci = chi
if t>220 and t<250:
ni = 100
ci = 0
if t>250 and t<280:
ni = 0
ci = chi
if t>280 and t<310:
ni = 100
ci = 0
if t>310 and t<340:
ni = 0
ci = 0
if t>340 and t<370:
ni = 100
ci = 0
if t>370 and t<400:
ni = 0
ci = 0
if t>400 and t<430:
ni = 100
ci = chi
if t>430 and t<460:
ni = 0
ci = 0
f3n=ni**2.5/(ni**2.5+17**2.5)
kcon1=0.1
kcoff1=0.05
kcon2=20
kcoff2=0.3
kinact=0.2
#kinact=1
#kinact=0.001
#kinact=1
#kinact=0.2
'''s5 = F1,
s7 = F3,
s6 = F4,
factive = F2'''
s5 = s5 + (s6*ci*kcon1 - s5*kcoff1 + factive*f3n*kinact - s5*0.3)*dt
factive = factive + (s7*ci*kcon2 - factive*kcoff2+s5*0.15 - factive*f3n*kinact)*dt
s7 = s7 + (factive*kcoff2 + s6*0.1 - s7*ci*kcon2 - s7*f3n*kinact*25)*dt
s6 = 1 - s5 - factive - s7
F1.append(s5)
F2.append(factive)
F3.append(s7)
F4.append(s6)
incx=factive*f3n
tnow.append(t)
ixnow.append(incx)
In [3]:
plt.figure(figsize = [10,6])
plt.plot(tnow,ixnow)
plt.xlabel("Time(t)",fontsize=15)
plt.ylabel("Current",fontsize=15)
plt.xticks([i*50 for i in range(10)])
plt.savefig("plot/current.png")
plt.savefig("plot/current.pdf")
plt.show()
In [4]:
[i*50 for i in range(10)]
Out[4]:
[0, 50, 100, 150, 200, 250, 300, 350, 400, 450]
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In [5]:
plt.figure(figsize = [10,3])
plt.plot(tnow,F1,label = "F1")
plt.plot(tnow,F2,label = "F2")
plt.plot(tnow,F3,label="F3")
plt.plot(tnow,F4,label="F4")
plt.xlabel("Time(t)",fontsize=15)
plt.ylabel("states",fontsize=15)
plt.xticks([i*50 for i in range(10)])
plt.legend(fontsize = 8)
plt.savefig("plot/states.png")
plt.savefig("plot/states.pdf")
plt.show()
In [6]:
plt.figure(figsize = [10,3])
plt.plot(tnow,F1,label = "F1",color='magenta',linewidth = 1)
plt.plot(tnow,F2,label = "F2",color='green',linewidth = 1)
plt.plot(tnow,F3,label = "F3",color='red',linewidth = 1)
plt.plot(tnow,F4,label = "F4",color='blue',linewidth = 1)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
plt.xticks([i*50 for i in range(10)])
plt.xlabel("Time(t)",fontsize=12)
plt.ylabel("states",fontsize=12)
plt.legend(fontsize=8)
#plt.savefig('./plot/states.png')
#plt.savefig("plot/states.pdf")
plt.show()
Na-dependent inactivation dictates activation by Ca¶
In [7]:
ni = 40
plt.figure(figsize = [10,6])
for kinact in [0, 0.03,0.1,0.3,1.0]:
cinow = []
ixnow = []
for i in range(1,1001):
logci = -(8-(i/300))
ci = (10**(logci))*1000000
f3n = (ni**2.5)/((ni**2.5)+(17**2.5))
kcon1 = 0.1
kcoff1 = 0.05
kcon2 = 20
kcoff2 = 0.3
k2 = ci*kcon1
k1 = kcoff1
k5 = ci*kcon2
k6 = kcoff2
k8 = 0.3
k7 = f3n*kinact
k3 = 0.1
k4 = f3n*kinact*25
x1 = k2*k4*(k7+k6)+k5*k7*(k2+k3)
x2 = k1*k7*(k4+k5)+k4*k6*(k1+k8)
x3 = k1*k3*(k7+k6)+k8*k6*(k2+k3)
x4 = k2*k8*(k4+k5)+k3*k5*(k1+k8)
d = x1+x2+x3+x4
e1 = x1/d
e2 = x2/d
e3 = x3/d
e4 = x4/d
incx = e4*f3n
cinow.append(logci)
ixnow.append(incx)
plt.plot(cinow,ixnow,label= "kinact="+str(kinact))
plt.xlabel("$pCa_{in}$")
plt.ylabel("$F_{max}$")
plt.legend()
plt.savefig("plot/inact.png")
plt.savefig("plot/inact.pdf")
plt.show()
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