Intrinsic sensory neurons of the gut (Chambers et al. 2014)

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Accession:155796
A conductance base model of intrinsic neurons neurons in the gastrointestinal tract. The model contains all the major voltage-gated and calcium-gated currents observed in these neurons. This model can reproduce physiological observations such as the response to multiple brief depolarizing currents, prolonged depolarizing currents and hyperpolarizing currents. This model can be used to predict how different currents influence the excitability of intrinsic sensory neurons in the gut.
Reference:
1 . Chambers JD, Bornstein JC, Gwynne RM, Koussoulas K, Thomas EA (2014) A detailed, conductance-based computer model of intrinsic sensory neurons of the gastrointestinal tract. Am J Physiol Gastrointest Liver Physiol 307:G517-32 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Channel/Receptor;
Brain Region(s)/Organism:
Cell Type(s): Gastrointestinal tract intrinsic sensory neuron;
Channel(s): I Na,p; I Na,t; I K,leak; I K,Ca; I CAN; I Mixed; I Na, leak; Ca pump;
Gap Junctions:
Receptor(s):
Gene(s): Nav1.3 SCN3A; Nav1.7 SCN9A; Nav1.9 SCN11A SCN12A;
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Detailed Neuronal Models; Action Potentials; Calcium dynamics;
Implementer(s): Chambers, Jordan [jordandchambers at gmail.com];
Search NeuronDB for information about:  I Na,p; I Na,t; I K,leak; I K,Ca; I CAN; I Mixed; I Na, leak; Ca pump;
TITLE Ca-dependent potassium current
: Taken from Reuveni et al (1993) -- neocortical cells
: Written by Zach Mainen, Salk Institute, 1995, zach@salk.edu
: 26 Ago 2002 Modification of original channel to allow variable time step and to correct an initialization error. Done by Michael Hines(michael.hines@yale.e) and Ruggero Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational Neuroscience. Obidos, Portugal
: Adapted calcium dependence and time course for intrinsic sensory neuron in the gastrointestinal tract by Jordan Chambers (jordandchambers@gmail.com)

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
	SUFFIX kca_slow
	USEION k READ ek WRITE ik
	USEION ca READ cai
	RANGE n, gk, gbar, jikcas
	RANGE ninf, ntau
	GLOBAL Ra, Rb, caix
	GLOBAL q10, temp, tadj, vmin, vmax
	THREADSAFE Ra, Rb, caix, q10, temp, tadj, vmin, vmax
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(pS) = (picosiemens)
	(um) = (micron)
} 

PARAMETER {
	gbar = 4e-1   	(pS/um2)	: 0.03 mho/cm2
	v 		(mV)
	cai  		(mM)
	caix = 1
	cac = 2e-4
	cas = 1e-5
									
	Ra   = 3e-3	(/ms)		: max act rate  
	Rb   = 5e-5	(/ms)		: max deact rate 

	dt		(ms)
	celsius		(degC)
	temp = 23	(degC)		: original temp 	
	q10  = 2.3			: temperature sensitivity

	vmin = -120	(mV)
	vmax = 100	(mV)
	eK=  -95 (mV)
	jikcas 		(mA/cm2)
} 


ASSIGNED {
	a		(/ms)
	b		(/ms)
	ik 		(mA/cm2)
	gk		(pS/um2)
	ek		(mV)
	ninf
	ntau 		(ms)	
	tadj
}
 

STATE { n }

INITIAL { 
	rates(cai)
	n = ninf
}

BREAKPOINT {
        SOLVE states METHOD cnexp
	gk = tadj*gbar*n
	ik = (1e-4) * gk * (v - eK)
	jikcas = ik
} 

LOCAL nexp

DERIVATIVE states {
        rates(cai)
        n' =  (ninf-n)/ntau
}

PROCEDURE rates(cai(mM)) {  
    a = Ra/(1+exp((cac-cai)/cas))
    b = Rb
    
    tadj = q10^((celsius - temp)/10)
    
    ntau = 1/tadj/(a+b)
    ninf = a/(a+b)
}




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