Phenomenological models of NaV1.5: Hodgkin-Huxley and kinetic formalisms (Andreozzi et al 2019)

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"Computational models of ion channels represent the building blocks of conductance-based, biologically inspired models of neurons and neural networks. Ion channels are still widely modelled by means of the formalism developed by the seminal work of Hodgkin and Huxley (HH), although the electrophysiological features of the channels are currently known to be better fitted by means of kinetic Markov-type models. The present study is aimed at showing why simplified Markov-type kinetic models are more suitable for ion channels modelling as compared to HH ones, and how a manual optimization process can be rationally carried out for both. ..."
1 . Andreozzi E, Carannante I, D'Addio G, Cesarelli M, Balbi P (2019) Phenomenological models of NaV1.5. A side by side, procedural, hands-on comparison between Hodgkin-Huxley and kinetic formalisms Scientific Reports 9:17493 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Channel/Receptor;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s): I Sodium;
Gap Junctions:
Gene(s): Nav1.5 SCN5A;
Simulation Environment: NEURON; Python;
Model Concept(s): Markov-type model;
Implementer(s): Carannante, Ilaria [ilariac at]; Balbi, Pietro [piero.balbi at]; Andreozzi, Emilio [emilio.andreozzi at];
Search NeuronDB for information about:  I Sodium;
from neuron import h, gui
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import as cmx
import matplotlib.animation as animation
from matplotlib.animation import FuncAnimation  

dtype = np.float64

# one-compartment cell (soma)
soma        = h.Section(name='soma')
soma.diam   = 50         # micron
soma.L      = 63.66198   # micron, so that area = 10000 micron2
soma.nseg   = 1          # adimensional     = 1          # uF/cm2
soma.Ra     = 70         # ohm-cm

soma.nseg   = 1
soma.insert('na15')      # insert mechanism
soma.ena    = 65
h.celsius   = 24         # temperature in celsius
v_init      = -120       # holding potential
h.dt        = 0.01       # ms - value of the fundamental integration time step, dt, used by fadvance().

# clamping parameters
dur         = 500        # clamp duration, ms
step        = 3          # voltage clamp increment
st_cl       = -120       # clamp start, mV
end_cl      = 1          # clamp end, mV
v_cl        = -120       # actual voltage clamp, mV

#number of elements of the vector containing the values from st_cl to end_cl with the fixed step
L=len(np.arange(st_cl, end_cl, step))

# vectors for data handling
t_vec       = h.Vector()     # vector for time
v_vec       = h.Vector()     # vector for voltage
v_vec_t     = h.Vector()     # vector for voltage as function of time
i_vec       = h.Vector()     # vector for current 
ipeak_vec   = h.Vector()     # vector for peak current

# a two-electrodes voltage clamp
f3cl = h.VClamp(soma(0.5))
f3cl.dur[0] = 40	     # ms
f3cl.amp[0] = -120	     # mV
f3cl.dur[1] = dur        # ms
f3cl.amp[1] = v_cl       # mV
f3cl.dur[2] = 20         # ms
f3cl.amp[2] = -10        # mV

# finding the "initial state variables values"
from state_variables import finding_state_variables
initial_values = [x for x in finding_state_variables(v_init,h.celsius)]

print('Initial values [C1, C2, O1, I1, I2]=  ', initial_values)

for seg in soma:

### definizione figure

fig, ax = plt.subplots(1,3,figsize=(18,4))  
ln0, = ax[0].plot([], [], '-')
ln1, = ax[1].plot([], [], '-')
ln2, = ax[2].plot([], [], '-')
fig.suptitle('2. Fast inactivation availability', fontsize=15, fontweight='bold')

def init():

    ax[0].set_xlabel('Time $(ms)$')
    ax[0].set_ylabel('Voltage $(mV)$')
    ax[0].set_title('Time/Voltage relation')

    ax[1].set_xlabel('Time $(ms)$')
    ax[1].set_ylabel('Current density $(mA/cm^2)$')
    ax[1].set_title('Time/Current density relation')

    ax[2].set_xlabel('Voltage $(mV)$')
    ax[2].set_ylabel('Current density $(mA/cm^2)$')
    ax[2].set_title('Current density/Voltage relation')

    return ln0, ln1, ln2, 

#to plot in rainbow colors
values    =range(L)
rbw       = cm = plt.get_cmap('rainbow') 
cNorm     = colors.Normalize(vmin=0, vmax=values[-1])
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=rbw)

# animation definition
def animate(frame):
        ran=np.arange(st_cl, end_cl, step)

        #resizing vectors


        colorVal1 = scalarMap.to_rgba(values[int(frame)])

        colorVal2 = scalarMap.to_rgba(values[0:int(frame)+2])
        ln0,=ax[0].plot(t_vec, v_vec_t,color=colorVal1)
        ln1,=ax[1].plot(t_vec, i_vec,color=colorVal1)

        ln2=ax[2].scatter(v_vec, ipeak_vec, c=colorVal2)
        return ln0, ln1, ln2

# clamping definition
def clamp(v_cl):

    f3cl.dur[1]=dur     # ms
    f3cl.amp[1]=v_cl    # mV
    peak_curr = 0
    dens = 0
    t_peak = 0

    while (h.t<h.tstop): # runs a single trace, calculates peak current
        dens = f3cl.i/soma(0.5).area()*100.0-soma(0.5).i_cap # clamping current in mA/cm2, for each dt

        t_vec.append(h.t)               # code for store the current
        v_vec_t.append(soma.v)          # trace to be plotted
        i_vec.append(dens)              # trace to be plotted
        if ((h.t>=540)and(h.t<=542)):       # evaluate the peak
                peak_curr = dens            # updates the peak current
                t_peak = h.t

    if len(v_vec) > L-1:    #resizing v_vec and ipeak_vec when the protocol is completed (it is needed for looping the animation)

    v_vec.append(v_cl)              # updates the vectors at the end of the run

def start():
    h.tstop = 40 + dur + 20     # time stop


    ani = animation.FuncAnimation(fig, animate, frames=L,
                     init_func=init, blit=True, interval=500, repeat=True)