{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "***Note:***\n", "\n", "- This example is discussed in detail by \n", " Gawthrop and Pan (2020) \n", " available [here](https://arxiv.org/abs/2009.02217).\n", "\n", "- This is the ElectroChemical.ipynb notebook. The\n", "PDF version\n", "is available [here](https://github.com/gawthrop/GawPan20/blob/main/ElectroChemical.pdf).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction\n", "## Faraday equivalent potential\n", "The bond graph approach uses the notion of energy covariables: a pair\n", "of variables whose product is power. Thus, for example, electrical\n", "systems have voltage (with units V) and current (with units\n", "A) as covariables and the product has units of power\n", "(W or Js$^{-1}$). Chemical system covariables\n", "are chemical potential $\\mu$ (with units JC$^{-1}$) and\n", "molar flow $f$ (with units\n", "mol.s$^{-1}$)\\citep{OstPerKat71,OstPerKat73,GawCra14}; again\n", "the product has units of power (W or Js$^{-1}$).\n", "\n", "The commonality of power over different physical domains makes the\n", "bond graph approach particularly appropriate to model multi-domain\n", "systems, in particular chemoelectrical systems\n", "\\citep{GawSieKam17}. Noting that the conversion factor relating the\n", "electrical and chemical domains is *Faraday's constant*\n", "$F\\approx 96485{C.mol^{-1}}$. As discussed by \\citet{Kar90}\n", "and \\citet{GawSieKam17}, this conversion can be represented by the\n", "bond graph transformer (**TF**) component. An alternative approach\n", "introduced by \\citet{Gaw17a} is to divide the covariables $\\mu$ and\n", "$f$ by $F$ to give the pair of covariables $\\phi$ and $f$\n", "where:\n", "\\begin{align}\n", " &\\text{Faraday-equivalent chemical potential}& \\phi &= \\frac{\\mu}{F} \\text{V} \\label{eq:phi}\\\\ &\\text{Faraday-equivalent flow}& f &= F v \\text{A} \\label{eq:f} \n", "\\end{align}\n", " \n", "## Chemical properties\n", "The **Ce** components representing chemical species generate Faraday-equivalent potential (FEP) $\\phi$ (measured in Volts) in terms of the amount of species $x$ as:\n", "\\begin{align}\n", "\\phi &= \\phi^\\ominus + \\phi_N \\ln \\frac{x}{x^\\ominus}\\\\\n", "&= \\phi_N \\ln K x\\\\\n", "\\text{where }\n", "K &= \\frac{K^\\ominus}{x^\\ominus}\\\\\n", "V_N &= \\frac{RT}{F} \\approx 26 mV\\\\\n", "\\text{and }\n", "K^\\ominus &= \\ln\\frac{\\phi^\\ominus}{\\phi_N}\n", "\\end{align}\n", "$\\phi^\\ominus$ in the standard potential at the standard amount $x^\\ominus$.\n", "$R$ is the universal gas constant and $F$ Faraday's constant.\n", "\n", "The amount of species $x$ is the integral of the species flow $f$:\n", "\\begin{equation}\n", "x = \\int^t f(\\tau)d\\tau\n", "\\end{equation}\n", "\n", "The formula can also be expressed in terms of concentration $c$ as:\n", "\\begin{align}\n", "\\phi &= \\phi_N \\ln K_C^\\prime c\\\\\n", "\\text{where }\n", "K_c^\\prime &= \\frac{K^\\ominus}{c^\\ominus}\\\\\n", "\\end{align}\n", "$c^\\ominus$ is the concentration at standard conditions.\n", "## Electrical properties\n", "The **C** components representing electrical capacitance generate electrical potential $\\phi$ (measured in Volts) in terms of the amount of positively charges $x$ and electrical capacitance $C$ as:\n", "\\begin{align}\n", "\\phi &= \\frac{x}{C}\\\\\n", "&= \\phi_N K_E x_E\\\\\n", "\\text{where }\n", "K_E &= \\frac{1}{x_N}\\\\\n", "\\text{and }\n", "x_N &= C\\phi_N \n", "\\end{align}\n", "The amount of charge $x_E$ is the integral of the charge flow (current) $f_E$:\n", "\\begin{equation}\n", "x_E = \\int^t f_E(\\tau)d\\tau\n", "\\end{equation}" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/peterg/WEB/Github/Papers/GawPan20/stoich.py:38: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n", " if name[0] is 'x':\n", "/home/peterg/WEB/Github/Papers/GawPan20/stoich.py:40: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n", " elif name[0] is 'u':\n", "/home/peterg/WEB/Github/Papers/GawPan20/stoich.py:49: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n", " if (name[0] is 'u') and (comp.name[0] is 'r'):\n", "/home/peterg/WEB/Github/Papers/GawPan20/stoich.py:49: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n", " if (name[0] is 'u') and (comp.name[0] is 'r'):\n", "/home/peterg/WEB/Github/Papers/GawPan20/stoich.py:53: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n", " if comp.metamodel is 'BG':\n", "/home/peterg/WEB/Github/Papers/GawPan20/stoich.py:1469: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n", " if invar is 'phi':\n" ] } ], "source": [ "## Some useful imports\n", "import BondGraphTools as bgt\n", "import numpy as np\n", "import sympy as sp\n", "import matplotlib.pyplot as plt\n", "\n", "## Stoichiometric analysis\n", "import stoich as st\n", "\n", "## SVG\n", "import svgBondGraph as sbg\n", "\n", "## Display (eg disp.SVG(), disp.\n", "import IPython.display as disp\n", "\n", "quiet = True\n", "\n", "## Fix the concentrations via chemostats\n", "Fix_conc = False" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "## Concentrations in nM for Na and K in Giant Squid Axon\n", "## From Keener & Sneyd Table 2.1\n", "conc_e = {'Na':437, 'K':20}\n", "conc_i = {'Na':50, 'K':397}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Electrodiffusion\n", "Cellular membranes have pores though which chemical species can diffuse. If the species are charged, the diffusion both depends on and creates an electrical potential.\n", "This section looks at a single ionic species with generic name I$^+$; this can be thought of as Na$^{+}$ or K$^{+}$.\n", "\n", "The bond graph representation of a charged ion has three components: a **Ce** component to represent the *chemical* properties of the ion, a **C** component to repesent the *electrical* properties of the ion and a **1** junction to make the flow into the two components identical.\n", "\n", "The resultant potential is then the sum of the chemical and electrical components:\n", "\\begin{align}\n", "\\phi &= \\phi_C + \\phi_E\\\\\n", "\\text{where }\n", "\\phi_C &= \\phi_N \\ln K x\\\\\n", "\\text{and }\n", "\\phi_E = \\phi_N K_E x_E\n", "\\end{align}\n", "\n", "If the ion has *two* charges (I$^{++}$) the bold bonds in the diagram would each be replaced by *two* bonds; alternatively, if the ion had a *negative* charge (I$^{-}$) the bold bonds in the diagram would each be replaced by a bond with *reversed* direction.\n", "\n", "The bond graph of the pore itself has two pools of charged ions: internal and external connected by a reaction (**Re**) component. As the ion in each pool is the same, the property $K^\\prime$ is the same for each pool. Thus the reaction potential $\\Phi$ is the difference of the potentials of the internal and external ion pools:\n", "\\begin{equation}\n", "\\Phi = \\phi_N \\left ( \\ln K^\\prime c_i - \\ln K^\\prime c_e \\right )\n", "+ \\left ( \\phi_{Ei} - \\phi_{Ee} \\right ) \n", "\\end{equation}\n", "Defining $\\Delta E = \\phi_{Ei} - \\phi_{Ee}$ and noting that at equilibrium $\\Phi=0$:\n", "\\begin{equation}\n", "\\Delta E = \\phi_N \\ln\\frac{c_e}{c_i}\n", "\\end{equation}\n", "This is the expresion for the *Nernst potential* for a species with a single positive charge." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "External\n", "\n", "Chemical\n", "\n", "Electrical\n", "\n", "Internal\n", "\n", "Re:r\n", "\n", "1\n", "\n", "C:Ie\n", "\n", "C:Ee\n", "\n", "1\n", "\n", "C:Ei\n", "\n", "C:Ii\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Electrodiffusion\n", "sbg.model('Electrodiffusion_abg.svg')\n", "import Electrodiffusion_abg\n", "disp.SVG('Electrodiffusion_abg.svg')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "## Stoichiometry: linear Re\n", "s = st.stoich(Electrodiffusion_abg.model(),linear=['Ei','Ee','r'],quiet=quiet)\n", "\n", "if Fix_conc:\n", " chemostats = ['Ii','Ie']\n", "else:\n", " chemostats = []\n", " \n", "sc = st.statify(s,chemostats=chemostats)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{align}\n", "N &=\n", "\\left(\\begin{matrix}1\\\\-1\\\\1\\\\-1\\end{matrix}\\right)\n", "\\end{align}\n" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Stoichiometric matrix\n", "disp.Latex(st.sprintl(s,'N'))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{align}\n", "\\ch{Ei + Ii &<>[ r ] Ee + Ie }\n", "\\end{align}\n" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Reactions\n", "disp.Latex(st.sprintrl(s,chemformula=True,all=True))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{align}\n", "v_{r} &= \\kappa_{r} \\left(- K_{Ee} x_{Ee} + K_{Ei} x_{Ei} - V_{N} \\left(\\log{\\left(K_{Ie} x_{Ie} \\right)} - \\log{\\left(K_{Ii} x_{Ii} \\right)}\\right)\\right)\n", "\\end{align}\n" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Flows\n", "disp.Latex(st.sprintvl(s))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "## Stoichiometry: nonlinear Re\n", "s = st.stoich(Electrodiffusion_abg.model(),linear=['Ei','Ee'],quiet=quiet)\n", "\n", "if Fix_conc:\n", " chemostats = ['Ii','Ie']\n", "else:\n", " chemostats = []\n", " \n", "sc = st.statify(s,chemostats=chemostats)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{align}\n", "\\ch{Ei + Ii &<>[ r ] Ee + Ie }\n", "\\end{align}\n" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Reactions\n", "disp.Latex(st.sprintrl(s,chemformula=True,all=True))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\\begin{align}\n", "v_{r} &= \\kappa_{r} \\left(- K_{Ie} x_{Ie} e^{\\frac{K_{Ee} x_{Ee}}{V_{N}}} + K_{Ii} x_{Ii} e^{\\frac{K_{Ei} x_{Ei}}{V_{N}}}\\right)\n", "\\end{align}\n", "\n" ] }, { "data": { "text/latex": [ "\\begin{align}\n", "v_{r} &= \\kappa_{r} \\left(- K_{Ie} x_{Ie} e^{\\frac{K_{Ee} x_{Ee}}{V_{N}}} + K_{Ii} x_{Ii} e^{\\frac{K_{Ei} x_{Ei}}{V_{N}}}\\right)\n", "\\end{align}\n" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Flows\n", "print(st.sprintvl(s))\n", "disp.Latex(st.sprintvl(s))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "## Set non-unit parameters\n", "K_Ii = 1e-3\n", "K_Ie = 1e-3\n", "C = 1\n", "def setPar(s,C=1,conc_i=1,conc_e=1,prefix=['']):\n", " K_E = 1/C\n", "\n", " ## Parameters\n", " parameter = {}\n", " parameter['K_Ei'] = 0\n", " parameter['K_Ee'] = K_E\n", " \n", " ## Initial state\n", " sp = s['species']\n", " re = s['reaction']\n", " X0 = np.ones(s['n_X'])\n", " X0[sp.index('Ei')] = 0\n", " X0[sp.index('Ee')] = 0\n", " for p in prefix:\n", " ## Parameters\n", " KK = 'K_'+p\n", " kk = 'kappa_'+p\n", " parameter[KK+'Ii'] = K_Ii\n", " parameter[KK+'Ie'] = K_Ie\n", " \n", " ## States and kappa\n", " if len(p) == 0:\n", " Ion = 'Na'\n", " else:\n", " Ion = p[0:len(p)-1]\n", " #X0[sp.index('Ee')] = 0.077/K_E\n", " \n", " print(Ion) \n", " X0[sp.index(p+'Ii')] = conc_i[Ion]/K_Ii\n", " X0[sp.index(p+'Ie')] = conc_e[Ion]/K_Ie\n", " parameter[kk+'r'] = 1/conc_i[Ion]\n", " \n", " return parameter,X0" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def CheckTheory(dat):\n", " \n", " if 'Ii' in s['species']:\n", " ## Check Nernst potential\n", " t = dat['t']\n", " phi_Ei = dat['phi'][:,s['species'].index('Ei')]\n", " phi_Ee = dat['phi'][:,s['species'].index('Ee')]\n", " x_Ii = dat['X'][:,s['species'].index('Ii')]\n", " x_Ie = dat['X'][:,s['species'].index('Ie')]\n", " # v = dat['V'][:,s['reaction'].index('r')]\n", " V_N = st.V_N()\n", "\n", " # v_ss = v[-1]\n", " dV = (phi_Ei[-1]-phi_Ee[-1] )\n", " dV_theory = V_N*np.log(x_Ie[-1]/x_Ii[-1])\n", " # print(f'Steady-state flow is {v_ss:0.2}')\n", " print(f'dV = {dV*1000:4.1f}mV')\n", " print(f'dV_Theory = {dV_theory*1000:4.1f}mV')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def Simulate(s,sc,T=1,X_chemo=None,prefix=['']):\n", " ## Time\n", " t = np.linspace(0,T,500)\n", "\n", " ## Parameters and initial state\n", " parameter,X0 = setPar(s,C=C,conc_i=conc_i,conc_e=conc_e,prefix=prefix)\n", " \n", " ## Simulate\n", " dat = st.sim(s,sc=sc,t=t,parameter=parameter,X0=X0,X_chemo=X_chemo,quiet=True)\n", "\n", " CheckTheory(dat)\n", "\n", " return dat" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Na\n", "dV = 57.9mV\n", "dV_Theory = 57.9mV\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dat = Simulate(s,sc)\n", "st.plot(s,dat,plotPhi=True,species=['Ei','Ee'])\n", "st.plot(s,dat,plotPhi=True,species=['Ei','Ee']) ##,filename='Figs/electrodiffusion.pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Voltage clamp\n", "The voltage agross the membrane is clamped by setting C:Ei and C_Ee as chemostats. This allows the voltage-current relationship to be plotted. It is compared with the Hodgkin-Huxley (linear) model and the Goldman-Huxley-Katz model.\n", "The bond graph model can be modified to reflect the other two models \\cite{GawSieKam17}." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "## Stoichiometry\n", "chemostats = chemostats + ['Ei','Ee']\n", "scc = st.statify(s,chemostats=chemostats)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Na\n", "dV = 57.9mV\n", "dV_Theory = 57.8mV\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X_chemo = {}\n", "V_Nernst = st.V_N()*np.log(conc_e['Na']/conc_i['Na'])\n", "#print(f'V_Nernst = {1000*V_Nernst:4.1f} mV')\n", "T = 100\n", "\n", "CV = C*V_Nernst\n", "# x_chemo = f'{CV}*(np.sin({2*np.pi/T}*t))'\n", "# #X_chemo['Ee'] = f'{-CV/2}-'+x_chemo\n", "# X_chemo['E'] = x_chemo\n", "x_chemo = f'-{CV}*(1+1.0*np.sin({2*np.pi/T}*t))'\n", "X_chemo['Ee'] = x_chemo\n", "\n", "\n", "#print(X_chemo)\n", "\n", "dat = Simulate(s,scc,T=T,X_chemo=X_chemo)\n", "#st.plot(s,dat)\n", "st.plot(s,dat,species=['Ei','Ee'])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def PlotClamp():\n", " t = dat['t']\n", " phi_Ei = dat['phi'][:,s['species'].index('Ei')]\n", " phi_Ee = dat['phi'][:,s['species'].index('Ee')]\n", " x_Ii = dat['X'][:,s['species'].index('Ii')]\n", " x_Ie = dat['X'][:,s['species'].index('Ie')]\n", " v = dat['V'][:,s['reaction'].index('r')]\n", " V_N = st.V_N()\n", "\n", " dV = phi_Ei-phi_Ee\n", "\n", " ## BG\n", " v_BG = (1/x_Ii)*(x_Ii - x_Ie*np.exp(-dV/V_N))\n", "\n", " ## GHK\n", " v_GHK = 0.5*v_BG*(dV/V_N)/(1-np.exp(-dV/V_N))\n", "\n", " ## HH\n", " v_HH = (np.exp(-V_Nernst))*(dV - V_Nernst)/V_N\n", "\n", " plt.plot(dV*1000,v,label='Clamp',lw = 6)\n", " plt.plot(dV*1000,v_BG,label='BG')\n", " plt.plot(dV*1000,v_GHK,label='GHK')\n", " plt.plot(dV*1000,v_HH,label='HH')\n", " plt.vlines(1000*V_Nernst,min(v),max(v),linestyle='dashed')\n", " plt.grid()\n", " plt.legend()\n", " plt.xlabel('$\\Delta E$ mV')\n", " plt.ylabel('$v/\\kappa$')\n", " ##plt.savefig('Figs/clamp.pdf')\n", " \n", "PlotClamp()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gated ion channel\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "External\n", "\n", "Chemical\n", "\n", "Electrical\n", "\n", "Internal\n", "\n", "Re:r\n", "\n", "0\n", "\n", "1\n", "\n", "C:Ie\n", "\n", "C:Ee\n", "\n", "1\n", "\n", "C:Ei\n", "\n", "C:Ii\n", "\n", "C:G\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Ion Channel\n", "sbg.model('IonChannel_abg.svg')\n", "import IonChannel_abg\n", "disp.SVG('IonChannel_abg.svg')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Ee', 'Ei', 'G', 'Ie', 'Ii']\n" ] } ], "source": [ "## Stoichiometry\n", "s = st.stoich(IonChannel_abg.model(),linear=['Ei','Ee'],quiet=quiet)\n", "if Fix_conc:\n", " chemostats = ['Ii','Ie','G']\n", "else:\n", " chemostats = ['G']\n", "sc = st.statify(s,chemostats=chemostats)\n", "print(s['species'])\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{align}\n", "\\ch{Ei + G + Ii &<>[ r ] Ee + G + Ie }\n", "\\end{align}\n" ], "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Reactions\n", "disp.Latex(st.sprintrl(s,chemformula=True,all=True))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/latex": [ "\\begin{align}\n", "v_{r} &= K_{G} \\kappa_{r} x_{G} \\left(- K_{Ie} x_{Ie} e^{\\frac{K_{Ee} x_{Ee}}{V_{N}}} + K_{Ii} x_{Ii} e^{\\frac{K_{Ei} x_{Ei}}{V_{N}}}\\right)\n", "\\end{align}\n" ], "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Flows\n", "disp.Latex(st.sprintvl(s))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Na\n", "dV = 57.4mV\n", "dV_Theory = 57.9mV\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X_chemo = {'G':'0.1'}\n", "dat = Simulate(s,sc,X_chemo=X_chemo)\n", "#st.plot(s,dat)\n", "st.plot(s,dat,plotPhi=True,species=['Ei','Ee'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Interacting ion channels\n", "\n", "Two instances of the ion channel module are combined; one corresponds to Na$^+$ and one to K$^+$. The species concentations are encapsulated in the individual modules, but the electrical capaciter are shared. This is a simplified version of the Hodgkin-Huxley model of the squid giant axon and the correponding Na$^+$ and K$^+$ concentrations are used.\n", "\n", "The simulations use piecewise constant gating variables $G_{Na}$ and $G_{K}$:\n", "\\begin{align}\n", "G_K &= \n", "\\begin{cases}\n", "10^{-6} & \\text{for $0.30.35$: $\\Delta E$ \n", "returns to the resting potential.\n", "\n", "In this simple example the gating variables $G_{Na}$ and $G_{K}$ are independent variables, in reality, and in the HH model, the gating variables are modulated by the membrane potential $\\Delta E$. This is discussed in a bond graph context by \\citet{GawSieKam17}.\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating subsystem: IonChannel:K\n", "Creating subsystem: IonChannel:Na\n" ] }, { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "0\n", "\n", "C:Ee\n", "\n", "C:Ei\n", "\n", "0\n", "\n", "IonChannel:Na\n", "\n", "IonChannel:K\n", "\n", "[Ei]\n", "\n", "[Ee]\n", "\n", "[Ee]\n", "\n", "[Ei]\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Ion Channels\n", "sbg.model('IonChannels_abg.svg')\n", "import IonChannels_abg\n", "disp.SVG('IonChannels_abg.svg')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Ee', 'Ei', 'K_G', 'K_Ie', 'K_Ii', 'Na_G', 'Na_Ie', 'Na_Ii']\n" ] } ], "source": [ "## Stoichiometry\n", "s = st.stoich(IonChannels_abg.model(),linear=['Ei','Ee'],quiet=quiet)\n", "if Fix_conc:\n", " chemostats = ['Na_Ii','Na_Ie','Na_G', 'K_Ii','K_Ie','K_G']\n", "else:\n", " chemostats = ['Na_G','K_G']\n", "sc = st.statify(s,chemostats=chemostats)\n", "print(s['species'])\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "\\begin{align}\n", "\\ch{Ei + K_G + K_Ii &<>[ K_r ] Ee + K_G + K_Ie }\\\\\n", "\\ch{Ei + Na_G + Na_Ii &<>[ Na_r ] Ee + Na_G + Na_Ie }\n", "\\end{align}\n" ], "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Reactions\n", "disp.Latex(st.sprintrl(s,chemformula=True,all=True))" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/latex": [ "\\begin{align}\n", "v_{K r} &= K_{K G} \\kappa_{K r} x_{K G} \\left(- K_{K Ie} x_{K Ie} e^{\\frac{K_{Ee} x_{Ee}}{V_{N}}} + K_{K Ii} x_{K Ii} e^{\\frac{K_{Ei} x_{Ei}}{V_{N}}}\\right)\\\\\n", "v_{Na r} &= K_{Na G} \\kappa_{Na r} x_{Na G} \\left(- K_{Na Ie} x_{Na Ie} e^{\\frac{K_{Ee} x_{Ee}}{V_{N}}} + K_{Na Ii} x_{Na Ii} e^{\\frac{K_{Ei} x_{Ei}}{V_{N}}}\\right)\n", "\\end{align}\n" ], "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Flows\n", "disp.Latex(st.sprintvl(s))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Na\n", "K\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "t0_Na = 0.3\n", "t1_Na = 0.35\n", "t0_K = 0.35\n", "t1_K = 1.0\n", "\n", "G_K_0 = 1e-6\n", "G_Na_0 = 4.3e-3\n", "\n", "G_K = f'{G_K_0}+np.heaviside(t,1)-np.heaviside(t-{t0_Na},1)+np.heaviside(t-{t0_K},1)-np.heaviside(t-{t1_K},1)'\n", "G_Na = f'{G_Na_0}+np.heaviside(t-{t0_Na},1)-np.heaviside(t-{t1_Na},1)'\n", "X_chemo = {'Na_G':G_Na,'K_G':G_K}\n", "dat = Simulate(s,sc,X_chemo=X_chemo,prefix=['Na_','K_'],T=0.6)\n", "st.plot(s,dat,plotPhi=True,species=['Ei','Ee'])" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Resting potential = -64.90 mV\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def PlotAction():\n", " t = dat['t']\n", " phi_Ei = dat['phi'][:,s['species'].index('Ei')]\n", " phi_Ee = dat['phi'][:,s['species'].index('Ee')]\n", " dE = phi_Ei-phi_Ee\n", " \n", " print(f'Resting potential = {1000*dE[-1]:.2f} mV')\n", " \n", " X_G_K = dat['X'][:,s['species'].index('K_G')]\n", " X_G_Na = dat['X'][:,s['species'].index('Na_G')]\n", "\n", " v_Na = dat['V'][:,s['reaction'].index('Na_r')]\n", " v_K = dat['V'][:,s['reaction'].index('K_r')]\n", " \n", " \n", " conc_Na_e = K_Ie*dat['X'][:,s['species'].index('Na_Ie')]\n", " conc_Na_i = K_Ii*dat['X'][:,s['species'].index('Na_Ii')]\n", " conc_Na_e_0 = conc_Na_e[0]\n", " conc_Na_i_0 = conc_Na_i[0]\n", "\n", " conc_K_e = K_Ie*dat['X'][:,s['species'].index('K_Ie')]\n", " conc_K_i = K_Ii*dat['X'][:,s['species'].index('K_Ii')]\n", " conc_K_e_0 = conc_K_e[0]\n", " conc_K_i_0 = conc_K_i[0]\n", "\n", " plt.plot(t,1000*dE)\n", " plt.grid()\n", " plt.ylabel('$\\Delta E$ mV')\n", " plt.xlabel('$t$')\n", " #plt.savefig('Figs/action.pdf')\n", " plt.show()\n", " \n", " plt.plot(t,v_Na,label='Na')\n", " plt.plot(t,v_K,label='K')\n", " plt.grid()\n", " plt.legend()\n", " plt.ylabel('$i$ mA')\n", " plt.xlabel('$t$')\n", " #plt.savefig('Figs/action_current.pdf')\n", " plt.show()\n", " \n", " plt.plot(t,100*(conc_Na_e-conc_Na_e_0)/conc_Na_e_0,label='Na_e')\n", " plt.plot(t,100*(conc_Na_i-conc_Na_i_0)/conc_Na_i_0,label='Na_i')\n", " plt.plot(t,100*(conc_K_e-conc_K_e_0)/conc_K_e_0,label='K_e')\n", " plt.plot(t,100*(conc_K_i-conc_K_i_0)/conc_K_i_0,label='K_i')\n", "\n", " plt.legend()\n", " plt.grid()\n", " plt.ylabel(r'$\\Delta c (\\%)$')\n", " plt.xlabel('$t$')\n", " ##plt.savefig('Figs/action_conc.pdf')\n", " plt.show()\n", " \n", " plt.plot(t,X_G_K,label='G_K')\n", " plt.plot(t,X_G_Na,label='G_Na')\n", " plt.legend()\n", " plt.grid()\n", " plt.ylabel('Gating')\n", " plt.xlabel('$t$')\n", " ##plt.savefig('Figs/action_gating.pdf')\n", " plt.show()\n", " \n", " \n", "\n", "PlotAction()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }