Schiz.-linked gene effects on intrinsic single-neuron excitability (Maki-Marttunen et al. 2016)

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Accession:169457
Python scripts for running NEURON simulations that model a layer V pyramidal cell with certain genetic variants implemented. The genes included are obtained from genome-wide association studies of schizophrenia.
Reference:
1 . Mäki-Marttunen T, Halnes G, Devor A, Witoelar A, Bettella F, Djurovic S, Wang Y, Einevoll GT, Andreassen OA, Dale AM (2016) Functional Effects of Schizophrenia-Linked Genetic Variants on Intrinsic Single-Neuron Excitability: A Modeling Study. Biol Psychiatry Cogn Neurosci Neuroimaging 1:49-59 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I K; I K,leak; I M; I h; I K,Ca; I A, slow; Ca pump;
Gap Junctions:
Receptor(s):
Gene(s): Nav1.1 SCN1A; Nav1.7 SCN9A; Cav3.3 CACNA1I; Cav1.3 CACNA1D; Cav1.2 CACNA1C; Kv2.1 KCNB1; HCN1;
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Coincidence Detection; Active Dendrites; Detailed Neuronal Models; Schizophrenia;
Implementer(s): Maki-Marttunen, Tuomo [tuomo.maki-marttunen at tut.fi];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; I Na,p; I Na,t; I L high threshold; I T low threshold; I K; I K,leak; I M; I h; I K,Ca; I A, slow; Ca pump;
// Author: Etay Hay, 2011
//    Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of
//    Dendritic and Perisomatic Active Properties
//    (Hay et al., PLoS Computational Biology, 2011) 
//
// Model of L5 Pyramidal Cell, constrained both for BAC firing and Current Step Firing


begintemplate L5PCbiophys
public biophys

proc biophys() {
	forsec $o1.all {
	  insert pas
		cm = 1
		Ra = 100
		e_pas = -90
	}

  forsec $o1.somatic {
	  insert Ca_LVAst 
	  insert Ca_HVA 
	  insert SKv3_1 
	  insert SK_E2 
	  insert K_Tst 
	  insert K_Pst 
	  insert Nap_Et2 
	  insert NaTa_t
		insert CaDynamics_E2
		insert Ih
		ek = -85
		ena = 50
		gIhbar_Ih = 0.0002
    g_pas = 0.0000338 
  	decay_CaDynamics_E2 = 460.0 
  	gamma_CaDynamics_E2 = 0.000501 
  	gCa_LVAstbar_Ca_LVAst = 0.00343 
  	gCa_HVAbar_Ca_HVA = 0.000992 
  	gSKv3_1bar_SKv3_1 = 0.693 
  	gSK_E2bar_SK_E2 = 0.0441 
  	gK_Tstbar_K_Tst = 0.0812 
  	gK_Pstbar_K_Pst = 0.00223 
  	gNap_Et2bar_Nap_Et2 = 0.00172 
  	gNaTa_tbar_NaTa_t = 2.04 
  }

	forsec $o1.apical {
		cm = 2
		insert Ih
  	insert SK_E2 
  	insert Ca_LVAst 
  	insert Ca_HVA 
  	insert SKv3_1 
  	insert NaTa_t 
  	insert Im 
  	insert CaDynamics_E2
		ek = -85
		ena = 50
    decay_CaDynamics_E2 = 122 
    gamma_CaDynamics_E2 = 0.000509 
    gSK_E2bar_SK_E2 = 0.0012 
  	gSKv3_1bar_SKv3_1 = 0.000261 
  	gNaTa_tbar_NaTa_t = 0.0213 
  	gImbar_Im = 0.0000675 
  	g_pas = 0.0000589 
	}
	$o1.distribute_channels("apic","gIhbar_Ih",2,-0.8696,3.6161,0.0,2.0870,0.00020000000) 
	$o1.distribute_channels("apic","gCa_LVAstbar_Ca_LVAst",3,1.000000,0.010000,685.000000,885.000000,0.0187000000) 
	$o1.distribute_channels("apic","gCa_HVAbar_Ca_HVA",3,1.000000,0.100000,685.000000,885.000000,0.0005550000) 
	
  forsec $o1.basal {
		cm = 2
		insert Ih
		gIhbar_Ih = 0.0002
  	g_pas = 0.0000467 
	}

  forsec $o1.axonal {
  	g_pas = 0.0000325 
	}
}

endtemplate L5PCbiophys

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