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

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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.
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:
Gene(s): Nav1.3 SCN3A; Nav1.7 SCN9A; Nav1.9 SCN11A SCN12A;
Simulation Environment: NEURON;
Model Concept(s): Detailed Neuronal Models; Action Potentials; Calcium dynamics;
Implementer(s): Chambers, Jordan [jordandchambers at];
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;
access soma

gbar_kca_slow = 0.5e0
gbar_cansc = 3e-4
beta_cansc = 7.5e-4
gbar_ih = 0.5e-4

///// Conductance changes for panel D2 /////
///// Using Ih only /////
//gbar_ih = 5e-4

///// Using Ih, cansc, kca_slow and ka /////
//gbar_kca_slow = 0.25e0
//gbar_cansc = 0.3e-4
//gbar_ih = 1e-4
//gbar_ka = 0.5e-2

///// Using BK, Ih, ka, kca_slow /////
//gbar_kca_slow = 0.5e0
//gbar_cansc = 1e-4
//gbar_ih = 1e-4
//gbar_ka = 0.5e-2
//gbar_kca_fast = 3e-2

//inserting current clamp at soma
///// comment out the relevent stim to produce panels A2 and B2 /////
access soma
objectvar stim1
stim1 = new IClamp(0.5)
stim1.del = 100
stim1.dur = 10
stim1.amp = 0.25

objectvar stim2
stim2 = new IClamp(0.5)
stim2.del = 120
stim2.dur = 10
stim2.amp = 0.25

objectvar stim3
stim3 = new IClamp(0.5)
stim3.del = 140
stim3.dur = 10
stim3.amp = 0.25

objectvar stim4
stim4 = new IClamp(0.5)
stim4.del = 160
stim4.dur = 10
stim4.amp = 0.25

objectvar stim5
stim5 = new IClamp(0.5)
stim5.del = 180
stim5.dur = 10
stim5.amp = 0.25

//integration time step
dt = 0.01
tstop = 1000

//for writing result files
objref vrec[1]
vrec[0] = new Vector()
{vrec[0].record(&soma.v(0.5), dt)}
objref tvec
tvec = new Vector()
{tvec.record(&t, dt)}
objref all
all = new File()
strdef datei

objref gmem
gmem = new Graph()
gmem.size(0, tstop, -100, 50)
gmem.addvar("Membrane Potential", &soma.v(0.5), 2, 1)

//defining some functions
proc run_sn() {
    sprint(datei, "./fig2currents.txt")
    soma v = -55
    while (t < tstop) {
	all.printf("%g %g %g %g %g %g %g %g %g %g %g\n", t, cai, jina13_nav13, jina17_nav17, ica, jikdr_kdr, jika_ka, jikcaf_kca_fast, iother2, jikcas_kca_slow, jiih_ih)

proc write_file() {
    sprint(datei, "./fig2mp.txt")
    for i=0, vrec[0].size()-1 {
        all.printf("%g %g\n", tvec.x(i), vrec[0].x(i))


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