initial after curation

Author: ramcdougal

AIY_simulation.py

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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
+
+# AIY neuron H-H MODEL
+# current and voltage clamp simulations shown in Figure 3
+
+import os
+from neuron import h,gui
+import numpy
+from matplotlib import pyplot
+from AIY_simulation_iclamp import AIY_simulation_iclamp
+from AIY_simulation_vclamp import AIY_simulation_vc
+from g_to_Scm2 import gScm2
+
+
+os.mkdir('AIY_SIMULATION')
+path='AIY_SIMULATION'
+
+v=numpy.linspace(start=-120, stop=50, num=18)
+ic=numpy.linspace(start=-15, stop=35, num=11)
+surf=65.89e-8# surface in cm^2 form neuromorpho AIYL
+
+#conductances: leak, slo1iso,kqt1,egl19,slo1egl19,nca,irk,eleak,cm
+g0=[0.14,1,0.2,0.1,0.92,0.06,0.5,-89.57,1.6]
+
+
+gbest=gScm2(g0,surf,6)
+
+
+best_results=AIY_simulation_vc(gbest,-120,50,18)
+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_WT=numpy.array(list(best_results[2]))
+
+fname4="AIY_simulated_current_WT.txt"
+fname5="AIY_simulated_time_WT.txt"
+fname6="AIY_simulated_IV_SS_WT.txt"
+fname7="AIY_IV_simulated_PEAK_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_WT, delimiter=", " , fmt="%s")
+
+
+best_cc=AIY_simulation_iclamp(gbest,-0.015,0.035,11)
+best_voltage=best_cc[0]
+best_time2=best_cc[1]
+best_VIss=best_cc[3]
+best_VIpeaks=best_cc[2]
+
+fname8='AIY_CC_simulated_voltage_WT.txt'
+fname9='AIY_CC_simulated_time_WT.txt'
+path8=os.path.join(path, fname8) 
+path9=os.path.join(path, fname9) 
+
+numpy.savetxt(path8, best_voltage, delimiter="," , fmt="%s")
+numpy.savetxt(path9, best_time2, delimiter=", " , fmt="%s")
+
+# plot
+
+fig=pyplot.figure(figsize=(8,4))
+iv_plot=pyplot.plot(v,best_iv,color='red',marker='+',markersize=15,label='optimized-ss')
+iv_plot=pyplot.plot(v,best_iv_WT,color='red',marker='o',markersize=15,label='optimized-peaks')
+pyplot.xlabel('V [mV]')
+pyplot.ylabel('I [pA]')
+pyplot.xlim(-130,60)
+fig.legend(loc=5)
+pyplot.title('IV-CURVES')
+pyplot.show()
+
+
+
+fig3=pyplot.figure(figsize=(8,4))
+for i in range(0,18):
+ 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()
+
+
+
+
+fig4=pyplot.figure(figsize=(8,4))
+for i in range(0,10):
+ volt_plot=pyplot.plot(best_time2[i],best_voltage[i],color='red',linestyle='solid')
+pyplot.xlabel('Time [ms]')
+pyplot.ylabel('V [mV]')
+#pyplot.xlim(0,0.7)
+pyplot.title('Current_Clamp')
+pyplot.show()
+             
+

AIY_simulation_iclamp.py

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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
+
+def AIY_simulation_iclamp(gAIY_scaled,s1,s2,ns):
+    
+ 
+    from neuron import h,gui
+    import numpy
+    import math
+    from operator import add
+
+    surf=65.89e-8 # surface in cm^2 form neuromorpho AIYL
+    vol=7.42e-12 # total volume
+    L=math.sqrt(surf/math.pi)
+    rsoma=L*1e4
+    cm_uFcm2=gAIY_scaled[8]
+ 
+    
+    
+    soma=h.Section(name="soma")
+    soma.L=rsoma
+    soma.diam=rsoma
+    soma.cm=cm_uFcm2
+    soma.Ra=100
+    h.psection(sec=soma)
+    
+    soma.insert('egl19')
+    
+
+    
+    soma.insert('slo1egl19')
+    
+    soma.insert('nca')
+    soma.insert('leak')
+    soma.insert('slo1iso')
+    soma.insert('kqt1')
+    soma.insert('shl1')
+
+    
+    
+    
+    for seg in soma:
+        
+        seg.leak.gbar =  gAIY_scaled[0]
+        seg.slo1iso.gbar =  gAIY_scaled[1]
+        seg.kqt1.gbar=gAIY_scaled[2]                
+        seg.egl19.gbar=gAIY_scaled[3]
+        seg.slo1egl19.gbar = gAIY_scaled[4]        
+        seg.nca.gbar =  gAIY_scaled[5]
+        seg.shl1.gbar =  gAIY_scaled[6]        
+        seg.leak.e=gAIY_scaled[7]
+        
+    
+        seg.eca=60
+        seg.ek=-80
+
+    
+    stim=h.IClamp(soma(0.5))
+    dir(stim)
+    
+    stim.delay=1000
+    stim.amp=10
+    stim.dur=5000
+    
+    v_vec = h.Vector()   
+    t_vec = h.Vector()        # Time stamp vector
+    v_vec.record(soma(0.5)._ref_v)
+    t_vec.record(h._ref_t)
+
+    simdur =11000
+
+    ref_v=[]
+    ref_t=[]
+
+  
+    
+    for i in numpy.linspace(start=s1, stop=s2, num=ns):
+        
+         stim.amp=i
+         h.tstop=simdur
+         h.dt=0.4
+         h.finitialize(-60)
+         h.run()
+            
+         ref_t_vec=numpy.zeros_like(t_vec)
+         t_vec.to_python(ref_t_vec)
+         ref_t.append(ref_t_vec)
+            
+        
+         ref_v_vec=numpy.zeros_like(v_vec)
+         v_vec.to_python(ref_v_vec)
+         ref_v.append(ref_v_vec)
+            
+            
+    v=[]
+    v=numpy.array(list(ref_v))
+    time1=numpy.array(ref_t)
+    
+   
+    
+    ## SS VOLTAGE-CURRENT RELATION
+    ind=numpy.where(numpy.logical_and(time1[0]>=5990, time1[0]<=6000))
+    ind_max=numpy.amax(ind)
+    ind_min=numpy.amin(ind)
+    vi=numpy.mean(v[:,ind_min:ind_max],axis=1)
+	
+	 # PEAK VOLTAGE-CURRENT RELATION
+    ind2=numpy.where(numpy.logical_and(time1[0]>=1000, time1[0]<=1300))
+    ind2_max=numpy.amax(ind2)
+    ind2_min=numpy.amin(ind2)
+    vi_peak=numpy.amax(v[:,ind2_min:ind2_max])
+    vi_peak=[]
+    
+
+    
+    for j in range(ns):
+        if j<=2:
+            peak=numpy.amin(v[j,ind2_min:ind2_max])
+        else:
+            peak=numpy.amax(v[j,ind2_min:ind2_max])
+        vi_peak.append(peak)
+
+    return v, time1, vi_peak, vi    
+    
+    
+    
+        
+
+