NicolettiEtAl2024_MN_IN/AVAL_simulations.py at main · openworm/NicolettiEtAl2024_MN_IN · GitHub

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# "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


# AVAL neuron H-H MODEL
# current and voltage clamp simulations shown in Figure 1 panels A, C, D, and F

import numpy
from numpy import loadtxt
from matplotlib import pyplot
import os
from AVAL_simulation_iclamp import AVA_simulation_iclamp
from AVAL_simulation_vclamp import AVA_simulation_vc
from g_to_Scm2 import gScm2

path='AVAL_SIMULATION'
if not os.path.isdir(path):
    os.mkdir(path)


v=numpy.linspace(start=-110, stop=50, num=17)
surf=1123.84e-8#


# coductances: egl19, leak, irk, nca, eleak, cm
g0=[0.104385,0.150164,0.1,0,-39,0.859551]

gbest=gScm2(g0,surf,3)


best_cc=AVA_simulation_iclamp(gbest,-0.03,0.03,7)
best_voltage=best_cc[0]
best_time2=best_cc[1]
best_vipeaks=best_cc[2]
best_viss=best_cc[3]


fname8="AVAL_simulated_VOLTAGE_WT.txt"
fname9="AVAL_simulated_timeCC_WT.txt"
fname10="AVAL_simulated_VI_SS_WT.txt"
fname11="AVAL_IV_simulated_VI_PEAKS_WT.txt"


path8=os.path.join(path, fname8)
path9=os.path.join(path, fname9)
path10=os.path.join(path, fname10)
path11=os.path.join(path, fname11)


numpy.savetxt(path8, best_voltage, delimiter="," , fmt="%s")
numpy.savetxt(path9, best_time2, delimiter=", " , fmt="%s")
numpy.savetxt(path10, best_viss, delimiter="," , fmt="%s")
numpy.savetxt(path11, best_vipeaks, delimiter=", " , fmt="%s")


best_results=AVA_simulation_vc(gbest,-110,50,17)
best_current=numpy.array(list(best_results[0]))
best_iv=numpy.array(list(best_results[3]))
best_time=numpy.array(list(best_results[1]))
best_iv_peak=numpy.array(list(best_results[2]))


fname4="AVAL_simulated_current_WT.txt"
fname5="AVAL_simulated_timeVC_WT.txt"
fname6="AVAL_simulated_IV_SS_WT.txt"
fname7="AVAL_IV_simulated_PEAKS_WT.txt"


path4=os.path.join(path, fname4)
path5=os.path.join(path, fname5)
path6=os.path.join(path, fname6)
path7=os.path.join(path, fname7)

numpy.savetxt(path4, best_current, delimiter="," , fmt="%s")
numpy.savetxt(path5, best_time, delimiter="," , fmt="%s")
numpy.savetxt(path6, best_iv, delimiter=", " , fmt="%s")
numpy.savetxt(path7, best_iv_peak, delimiter=", " , fmt="%s")


# Save current clamp traces in a format to allow tests and comparison to NeuroML data
with open(os.path.join(path, 'CurrentClamp.dat'),'w') as f:
    t = best_time2[0]
    for i in range(len(t)):
        f.write(f'{t[i]}')
        for j in range(len(best_voltage)):
            f.write(f' \t{best_voltage[j][i]}')
        f.write('\n')

import sys
if not '-nogui' in sys.argv:
    fig4=pyplot.figure(figsize=(8,4))
    for i in range(0,7):
        volt_plot=pyplot.plot(best_time2[i],best_voltage[i],color='red',linestyle='solid')
    pyplot.xlabel('Time [ms]')
    pyplot.ylabel('V [mV]')
    pyplot.title('Current_Clamp')
    #pyplot.show()


    fig3=pyplot.figure(figsize=(8,4))
    for i in range(0,17):
        curr_plot=pyplot.plot(best_time[i],best_current[i],color='red',linestyle='solid')
    pyplot.xlabel('Time [ms]')
    pyplot.ylabel('I [pA]')
    pyplot.title('Voltage clamp')
    #pyplot.show()


    # plot

    fig=pyplot.figure(figsize=(8,4))
    iv_plot=pyplot.plot(v,best_iv,color='red',marker='+',markersize=15)
    pyplot.xlabel('V [mV]')
    pyplot.ylabel('I [pA]')
    pyplot.xlim(-130,60)
    pyplot.title('IV steady-state')
    #pyplot.show()


    fig2=pyplot.figure(figsize=(8,4))
    iv2_plot=pyplot.plot(v,best_iv_peak,color='red',marker='+',markersize=15)
    pyplot.xlabel('V [mV]')
    pyplot.ylabel('I [pA]')
    pyplot.xlim(-130,60)
    pyplot.title('IV PEAKS')
    pyplot.show()