Ca2+ oscillations in single astrocytes (Lavrentovich and Hemkin 2008) (python) (Manninen et al 2017)

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Accession:223144
We tested the reproducibility and comparability of four astrocyte models (Manninen, Havela, Linne, 2017). Model by Lavrentovich and Hemkin (2008) was one of them. We implemented and ran the model by Lavrentovich and Hemkin (2008) using Jupyter Notebook. Model code produces results of Figure 1 in Manninen, Havela, Linne (2017).
References:
1 . Lavrentovich M, Hemkin S (2008) A mathematical model of spontaneous calcium(II) oscillations in astrocytes. J Theor Biol 251:553-60 [PubMed]
2 . Lavrentovich M, Hemkin S (2009) Corrigendum to “A mathematical model of spontaneous calcium(II) oscillations in astrocytes” [J. Theor. Biol. 251 (2008) 553–560] Journal of Theoretical Biology 260(2):332
3 . Manninen T, Havela R, Linne ML (2017) Reproducibility and comparability of computational models for astrocyte calcium excitability Front. Neuroinform.
Model Information (Click on a link to find other models with that property)
Model Type: Glia;
Brain Region(s)/Organism: Generic;
Cell Type(s): Astrocyte;
Channel(s):
Gap Junctions:
Receptor(s): IP3;
Gene(s):
Transmitter(s):
Simulation Environment: Python;
Model Concept(s): Calcium dynamics; Oscillations; Signaling pathways;
Implementer(s): Manninen, Tiina [tiina.h.manninen at gmail.com];
Search NeuronDB for information about:  IP3;
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LavrentovichHemkin2008python
.ipynb_checkpoints
Lavrentovich-checkpoint.ipynb
                            
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Tiina Manninen\n",
    "# Implementation of astrocyte model by Lavrentovich and Hemkin (2008)\n",
    "# Lavrentovich, M. and Hemkin, S. (2008). A mathematical model of spontaneous \n",
    "# calcium (II) oscillations in astrocytes. J. Theor. Biol. 251, 553–560.\n",
    "# Corrigendum available: Lavrentovich, M. and Hemkin, S. (2009). J. Theor. Biol., 260, 332.\n",
    "\n",
    "# Model implemented and ran with using Jupyter Notebook.\n",
    "\n",
    "# Model code used in publication: Manninen, T., Havela, R., and Linne, M.-L. (2017). \n",
    "# Reproducibility and comparability of computational models for astrocyte calcium excitability.\n",
    "# Front. Neuroinform.\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "class ModelSystem:\n",
    "    def __init__(self, params):\n",
    "        self.params = params\n",
    "    \n",
    "    def computeDeriv(self, state, t):\n",
    "        Ca, Ca_ER, IP3 = state\n",
    "        modelPar = self.params\n",
    "        \n",
    "        # Intermediate variables\n",
    "        v_SERCA = modelPar.v_M2 * Ca ** 2 / (Ca ** 2 + modelPar.k_2 ** 2)\n",
    "        v_PLC = modelPar.v_p * Ca ** 2 / (Ca ** 2 + modelPar.k_p ** 2)\n",
    "        v_CICR = 4 * modelPar.v_M3 * modelPar.k_CaA ** modelPar.n * Ca ** modelPar.n \\\n",
    "                    / ( (Ca ** modelPar.n + modelPar.k_CaA ** modelPar.n) \\\n",
    "                    * (Ca ** modelPar.n + modelPar.k_CaI ** modelPar.n) )\\\n",
    "                * IP3 ** modelPar.m \\\n",
    "                    / ( (IP3 ** modelPar.m + modelPar.k_IP3 ** modelPar.m) )\\\n",
    "                * (Ca_ER - Ca)\n",
    "                \n",
    "        # dx/dt \n",
    "        dCa_per_dt = modelPar.v_in - modelPar.k_out * Ca + v_CICR - v_SERCA \\\n",
    "                    + modelPar.k_f * (Ca_ER - Ca)\n",
    "        dCa_ER_per_dt = v_SERCA - v_CICR - modelPar.k_f * (Ca_ER - Ca)\n",
    "        dIP3_per_dt = v_PLC - modelPar.k_deg * IP3\n",
    "        \n",
    "        deriv = [dCa_per_dt, dCa_ER_per_dt, dIP3_per_dt]\n",
    "        return deriv   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class ModelParameters:\n",
    "    k_2 = 1e-1     # uM\n",
    "    k_CaA = 1.5e-1 # uM \n",
    "    k_CaI = 1.5e-1 # uM\n",
    "    k_deg = 0.08   # 1/s\n",
    "    k_f = 0.5      # 1/s\n",
    "    k_IP3 = 1e-1   # uM\n",
    "    k_out = 0.5    # 1/s\n",
    "    k_p = 3e-1     # uM\n",
    "    m = 2.2\n",
    "    n = 2.02\n",
    "    v_in = 5e-2    # uM/s \n",
    "    v_M2 = 15      # uM/s \n",
    "    v_M3 = 40      # 1/s\n",
    "    v_p = 5e-2     # uM/s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1\n"
     ]
    }
   ],
   "source": [
    "params = ModelParameters()\n",
    "mySys = ModelSystem(params)\n",
    "print params.k_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.integrate import odeint\n",
    "initial = [0.1, 1.5, 0.1] # uM\n",
    "\n",
    "Tmax = 600\n",
    "dt = 0.1\n",
    "t = np.arange(0,Tmax,dt)\n",
    "\n",
    "data = odeint(mySys.computeDeriv, initial, t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
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kvogsEJFh5ex3oojsKK+k0ZgopPMAtXaAJtek+wJj7QBNLknn/rV2gMllFACq6lRVHayq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b818a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(9, 6), dpi=200)\n",
    "plt.subplot(3,1,1)\n",
    "plt.plot(t, data[:, 0])\n",
    "plt.title('Calcium concentration', usetex=True)\n",
    "plt.ylabel('Ca (uM)', fontsize=11, usetex=True)\n",
    "\n",
    "plt.subplot(3,1,2)\n",
    "plt.plot(t, data[:, 2])\n",
    "plt.title('IP3 concentration', usetex=True)\n",
    "plt.ylabel('IP3 (uM)', fontsize=11, usetex=True)\n",
    "\n",
    "plt.subplot(3,1,3)\n",
    "plt.plot(t, data[:, 1])\n",
    "plt.title('Calcium concentration in the ER', usetex=True)\n",
    "plt.ylabel('CaER (uM)', fontsize=11, usetex=True)\n",
    "plt.xlabel('t (s)', fontsize=11, usetex=True)\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "np.savetxt('Lavrentovich2008.csv', (t, data[:,0], data[:,1], data[:,2]), delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}

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