-
Notifications
You must be signed in to change notification settings - Fork 2
/
VA5_simulation_vclamp.py
188 lines (131 loc) · 4.7 KB
/
VA5_simulation_vclamp.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
# "Biophysical modeling of the whole-cell dynamics of C. elegans motor and interneurons families"
# M. Nicoletti et al. PloS ONE, 19(3): e0298105.
# https://doi.org/10.1371/journal.pone.0298105
def VA5_simulation_vc(gVA5_scaled,vstart,vstop,ns):
# lista canali da cenGen tramite ricerca per geni
# calcio e kcnl fisstao con i valori di t15.ode
from neuron import h,gui
import numpy
import math
surf=389.3e-8 # surface in cm^2 form neuromorpho VA5L
vol=48.07e-12 # total volume
L=math.sqrt(surf/math.pi)
rsoma=L*1e4
cm_uFcm2=1
soma=h.Section(name="soma")
soma.L=rsoma
soma.diam=rsoma
soma.Ra=100
soma.cm=gVA5_scaled[9]
h.psection(sec=soma)
soma.insert('slo2egl19')
soma.insert('slo2iso')
soma.insert('egl19')
soma.insert('irk')
soma.insert('shk1')
soma.insert('leak')
soma.insert('nca')
soma.insert('cadiff')
for seg in soma:
seg.slo2egl19.gbar = gVA5_scaled[0]
seg.slo2iso.gbar=gVA5_scaled[1]
seg.egl19.gbar=gVA5_scaled[2]
seg.irk.gbar=gVA5_scaled[3]
seg.shk1.gbar=gVA5_scaled[4]
seg.nca.gbar=gVA5_scaled[5]
seg.leak.gbar=gVA5_scaled[6]
seg.leak.e=gVA5_scaled[7]
seg.slo2iso.c2=gVA5_scaled[8]
seg.eca=60
seg.ek=-80
stim=h.VClamp(soma(0.5))
dir(stim)
simdur = 1700
stim.amp[0]=-60
stim.amp[1]=-110
stim.amp[2]=-60
stim.dur[0]=1025
stim.dur[1]=600
stim.dur[2]=100
ik_vec = h.Vector()
ica_vec=h.Vector()
inca_vec=h.Vector()
ileakVA5_vec=h.Vector()
cai_vec=h.Vector()
t_vec = h.Vector()
#curr_shl1_vec=h.Vector()
# Time stamp vector
ik_vec.record(soma(0.5)._ref_ik)
ica_vec.record(soma(0.5)._ref_ica)
inca_vec.record(soma(0.5)._ref_i_nca)
cai_vec.record(soma(0.5)._ref_cai)
ileakVA5_vec.record(soma(0.5)._ref_i_leak)
t_vec.record(h._ref_t)
ref_ik=[]
ref_ica=[]
ref_t=[]
ref_inca=[]
ref_ileakVA5=[]
ref_cai=[]
for i in numpy.linspace(start=vstart, stop=vstop, num=ns):
stim.amp[1]=i
h.tstop=simdur
h.dt=0.01
h.finitialize(-60)
h.run()
#time
ref_t_vec=numpy.zeros_like(t_vec)
t_vec.to_python(ref_t_vec)
ref_t.append(ref_t_vec)
# potassium current
ref_ik_vec=numpy.zeros_like(ik_vec)
ik_vec.to_python(ref_ik_vec)
ref_ik.append(ref_ik_vec)
# shl1
ref_cai_vec=numpy.zeros_like(cai_vec)
cai_vec.to_python(ref_cai_vec)
ref_cai.append(ref_cai_vec)
#calcium currents
ref_ica_vec=numpy.zeros_like(ica_vec)
ica_vec.to_python(ref_ica_vec)
ref_ica.append(ref_ica_vec)
# NCA currents
ref_inca_vec=numpy.zeros_like(inca_vec)
inca_vec.to_python(ref_inca_vec)
ref_inca.append(ref_inca_vec)
# LEAKAGE current
ref_ileakVA5_vec=numpy.zeros_like(ileakVA5_vec)
ileakVA5_vec.to_python(ref_ileakVA5_vec)
ref_ileakVA5.append(ref_ileakVA5_vec)
# total current calculation
itot=[]
itot=map(sum, zip(ref_ik,ref_ica,ref_ileakVA5,ref_inca))
current=numpy.array(list(itot))
inorm=current*1e9*surf #total current in pA
#time array
time1=numpy.array(ref_t)
resc_ind=numpy.where(time1[1,:]>=1000)
resc_min=numpy.amin(resc_ind)
resc_max=numpy.amax(resc_ind)
itot_normalized=inorm[:,resc_min:resc_max]
cai=numpy.array(list(ref_cai))
cai_tot=cai[:,resc_min:resc_max]
time=time1[:,resc_min:resc_max]-1000
# I-V CURRENTS
ind=numpy.where(numpy.logical_and(time[0]>=600, time[0]<=625))
ind_max=numpy.amax(ind)
ind_min=numpy.amin(ind)
iv=numpy.mean(itot_normalized[:,ind_min:ind_max],axis=1)
# PEAKS I-V
ind2=numpy.where(numpy.logical_and(time[0]>=25, time[0]<=100))
ind2_max=numpy.amax(ind2)
ind2_min=numpy.amin(ind2)
iv_peak=numpy.amax(itot_normalized[:,ind2_min:ind2_max])
iv_peak=[]
for j in range(ns):
if j<=6:
peak=numpy.amin(itot_normalized[j,ind2_min:ind2_max])
else:
peak=numpy.amax(itot_normalized[j,ind2_min:ind2_max])
iv_peak.append(peak)
return itot_normalized, time, iv_peak, iv,cai_tot