Disentangling astroglial physiology with a realistic cell model in silico (Savtchenko et al 2018)

 Download zip file   Auto-launch 
Help downloading and running models
"Electrically non-excitable astroglia take up neurotransmitters, buffer extracellular K+ and generate Ca2+ signals that release molecular regulators of neural circuitry. The underlying machinery remains enigmatic, mainly because the sponge-like astrocyte morphology has been difficult to access experimentally or explore theoretically. Here, we systematically incorporate multi-scale, tri-dimensional astroglial architecture into a realistic multi-compartmental cell model, which we constrain by empirical tests and integrate into the NEURON computational biophysical environment. This approach is implemented as a flexible astrocyte-model builder ASTRO. As a proof-of-concept, we explore an in silico astrocyte to evaluate basic cell physiology features inaccessible experimentally. ..."
1 . Savtchenko LP, Bard L, Jensen TP, Reynolds JP, Kraev I, Medvedev N, Stewart MG, Henneberger C, Rusakov DA (2018) Disentangling astroglial physiology with a realistic cell model in silico. Nat Commun 9:3554 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Glia;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Astrocyte;
Channel(s): I Calcium; I Potassium; Kir;
Gap Junctions: Gap junctions;
Transmitter(s): Glutamate;
Simulation Environment: NEURON; MATLAB; C or C++ program;
Model Concept(s): Calcium waves; Calcium dynamics; Potassium buffering; Volume transmission; Membrane Properties;
Implementer(s): Savtchenko, Leonid P [leonid.savtchenko at ucl.ac.uk];
Search NeuronDB for information about:  I Calcium; I Potassium; Kir; Glutamate;
TITLE inward rectifier potassium (Kir) channel


Mod File by A. Hanuschkin <AH, 2011> for:
Yim MY, Hanuschkin A, Wolfart J (2015) Hippocampus 25:297-308.

Channel description and parameters from:
Stegen M, Kirchheim F, Hanuschkin A, Staszewski O, Veh R, and Wolfart J. Cerebral Cortex, 22:9, 2087-2101, 2012.

Mod File history:
- tau(V), linf(V) fitted to experimental values of human dentate gyrus granual cells
- ModelDB file adapted from 
  Wolf JA, Moyer JT, Lazarewicz MT, Contreras D, Benoit-Marand M, O'Donnell P, Finkel LH (2005) J Neurosci 25:9080-95
- file modified to uses nomoclature of 
  Li X, Ascoli GA (2006) J of Comput Neurosci 21(2):191-209 
  Li X, Ascoli GA (2008) Neural Comput 20:1717-31

A. Hanuschkin(c) 2011,2012


    (mA) = (milliamp)
    (mV) = (millivolt)
    (S)  = (siemens)

    v 		(mV)
    gkbar  = 0 (S/cm2) :1.44e-05	 	: to be fitted     	

    : Boltzman steady state curve	
        vhalfl = -98.92  (mV)    		: fitted to patch data, Stegen et al. 2012
        kl = 10.89       (mV)    		: Stegen et al. 2012

    : tau_infty 
        vhalft=67.0828	 (mV)    		: fitted #100 \muM sens curr 350a,  Stegen et al. 2012
        at=0.00610779	 (/ms)   		: Stegen et al. 2012
    bt=0.0817741	 (/ms)	 		: Note: typo in Stegen et al. 2012

    : Temperature dependence
        celsius         (degC)  		: unused if q10 == 1.
        q10 = 1.                              	: temperature scaling

    SUFFIX kir 			
    USEION k READ ek WRITE ik	
        RANGE  ik, gkbar, vhalfl, kl, vhalft, at, bt, q10 
        GLOBAL linf,taul


    ik      (mA/cm2)
    gk      (S/cm2)
    ek      (mV)


    SOLVE states METHOD cnexp	: solve differential equations in states with method 'cnexp'
    gk = gkbar*l			: use state l to calulate gk
        ik = gk * ( v - ek )		: calculate ik 

DERIVATIVE states {     
    l' =  (linf - l)/taul		: differential equation 

PROCEDURE rate(v (mV)) { :callable from hoc
        LOCAL qt
        linf = 1/(1 + exp((v-vhalfl)/kl))			: l_steadystate
    taul = 1/(qt *(at*exp(-v/vhalft) + bt*exp(v/vhalft) ))

Loading data, please wait...