{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2d0a9f7d",
   "metadata": {},
   "source": [
    "# Radial diffusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90b61454",
   "metadata": {},
   "source": [
    "<p>Radial diffusion can be implemented in rxd using multicompartment reactions. By creating a series of shells and borders with reactions between them dependent the diffusion coefficient.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b6fc941c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:37:00.082167Z",
     "iopub.status.busy": "2025-08-18T03:37:00.082016Z",
     "iopub.status.idle": "2025-08-18T03:37:01.202456Z",
     "shell.execute_reply": "2025-08-18T03:37:01.202028Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuron import n, rxd\n",
    "from neuron.units import nM, uM\n",
    "from matplotlib import pyplot\n",
    "import numpy\n",
    "\n",
    "n.load_file(\"stdrun.hoc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e87c7adb",
   "metadata": {},
   "source": [
    "Create the NEURON, in this case a simply dendrite connected to a soma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "832bcfce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:37:01.204367Z",
     "iopub.status.busy": "2025-08-18T03:37:01.204145Z",
     "iopub.status.idle": "2025-08-18T03:37:01.212174Z",
     "shell.execute_reply": "2025-08-18T03:37:01.211810Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dend"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soma = n.Section(\"soma\")\n",
    "soma.L = soma.diam = 25\n",
    "soma.nseg = 1\n",
    "\n",
    "dend = n.Section(\"dend\")\n",
    "dend.L = 20\n",
    "dend.diam = 5\n",
    "dend.nseg = 101\n",
    "dend.connect(soma)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "462ac70c",
   "metadata": {},
   "source": [
    "<p>Following the rxd Where/Who/What framework start by defining the where the diffusion takes place. Here we use 5 shells each with thickness 1μM in the dendrite and 5μM in the soma.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "099f4b58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:37:01.214938Z",
     "iopub.status.busy": "2025-08-18T03:37:01.214787Z",
     "iopub.status.idle": "2025-08-18T03:37:01.222792Z",
     "shell.execute_reply": "2025-08-18T03:37:01.222412Z"
    }
   },
   "outputs": [],
   "source": [
    "# Where -- define the shells and borders\n",
    "N = 5  # number of shells -- must be >= 2\n",
    "\n",
    "# Where -- shells and border between them\n",
    "shells = []\n",
    "border = []\n",
    "for i in range(N - 1):\n",
    "    shells.append(\n",
    "        rxd.Region(\n",
    "            n.allsec(),\n",
    "            name=f\"shell{i}\",\n",
    "            geometry=rxd.Shell(i / N, (1.0 + i) / N),\n",
    "        )\n",
    "    )\n",
    "    border.append(\n",
    "        rxd.Region(\n",
    "            n.allsec(),\n",
    "            name=f\"border{i}\",\n",
    "            geometry=rxd.FixedPerimeter(2.0 * n.PI * (1.0 + i)),\n",
    "        )\n",
    "    )\n",
    "\n",
    "# the outer shell corresponds NEURON section concentration e.g. dend(0.5).cai\n",
    "shells.append(\n",
    "    rxd.Region(\n",
    "        n.allsec(),\n",
    "        nrn_region=\"i\",\n",
    "        name=f\"shell{N}\",\n",
    "        geometry=rxd.Shell((N - 1.0) / N, 1.0),\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34222335",
   "metadata": {},
   "source": [
    "<p>Next we define 'Who' the calcium that is diffusing. Because multicompartment reactions are specified in moleques/μ\n",
    "m2\n",
    "/ms we need to scale the diffusion coefficient used in the reactions.\n",
    "We start the simulation with the a high concentration (1μ\n",
    "M) in the middle of the dendrite on the outer shell.<p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "08248974",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:37:01.225965Z",
     "iopub.status.busy": "2025-08-18T03:37:01.224203Z",
     "iopub.status.idle": "2025-08-18T03:37:01.232983Z",
     "shell.execute_reply": "2025-08-18T03:37:01.232598Z"
    }
   },
   "outputs": [],
   "source": [
    "# Who -- calcium with an inhomogeneous initial condition\n",
    "Dca = 0.6  # um^2/ms\n",
    "\n",
    "# scale factor so the flux (Dca/dr)*Ca has units molecules/um^2/ms (where dr is the thickness of the shell)\n",
    "mM_to_mol_per_um = 6.0221409e23 * 1e-18\n",
    "\n",
    "ca = rxd.Species(\n",
    "    shells,\n",
    "    d=Dca,\n",
    "    name=\"ca\",\n",
    "    charge=2,\n",
    "    initial=lambda nd: 1.0 * uM\n",
    "    if 9.5 < nd.x3d - soma.L < 10.5 and nd.region == shells[-1]\n",
    "    else 60 * nM,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "320330ce",
   "metadata": {},
   "source": [
    "<p>Finally we specify 'What' the reactions that perform the radial diffusion.</p>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "48de464c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:37:01.234917Z",
     "iopub.status.busy": "2025-08-18T03:37:01.234595Z",
     "iopub.status.idle": "2025-08-18T03:37:01.241633Z",
     "shell.execute_reply": "2025-08-18T03:37:01.239777Z"
    }
   },
   "outputs": [],
   "source": [
    "# What -- use reactions to setup diffusion between the shells\n",
    "cas = [ca[reg] for reg in shells]  # calcium on the shells\n",
    "\n",
    "# create the multi-compartment reactions between the pairs of shells\n",
    "diffusions = []\n",
    "for i in range(N - 1):\n",
    "    diffusions.append(\n",
    "        rxd.MultiCompartmentReaction(\n",
    "            cas[i],\n",
    "            cas[i + 1],\n",
    "            mM_to_mol_per_um * Dca,\n",
    "            mM_to_mol_per_um * Dca,\n",
    "            border=border[i],\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c61517bc",
   "metadata": {},
   "source": [
    "<p>Now run the simulation and plot the results. The concentrations rapidly equilibrate between the shells, the speed is dependent on the diffusion coefficient, which could be reduced to account for tortuosity of macromolecular crowding in the cytosol. Additionally calcium buffering and uptake could be included with additional reactions and would also reduce this speed.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "919703c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:37:01.243804Z",
     "iopub.status.busy": "2025-08-18T03:37:01.243101Z",
     "iopub.status.idle": "2025-08-18T03:37:05.354807Z",
     "shell.execute_reply": "2025-08-18T03:37:05.354376Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:15: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:16: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:29: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:15: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:16: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:29: SyntaxWarning: invalid escape sequence '\\m'\n",
      "/tmp/ipykernel_8131/3075494742.py:15: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  pyplot.xlabel(\"x ($\\mu$m)\")\n",
      "/tmp/ipykernel_8131/3075494742.py:16: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  pyplot.ylabel(\"r ($\\mu$m)\")\n",
      "/tmp/ipykernel_8131/3075494742.py:29: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  pyplot.xlabel(\"x ($\\mu$m)\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Ca$^{+2}$ (nM)')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run for 2.5ms and plot the results\n",
    "n.finitialize(-65)\n",
    "n.continuerun(2.5)\n",
    "\n",
    "# plot the calcium concentration (in nM) in the dendrite\n",
    "pyplot.figure()\n",
    "pyplot.title(\"Calcium (nM) in the dendrite\")\n",
    "pyplot.imshow(\n",
    "    numpy.array(ca.nodes(dend).concentration).reshape(N, dend.nseg) / nM,\n",
    "    origin=\"lower\",\n",
    "    extent=(0, dend.L, 0, dend.diam / 2.0),\n",
    "    aspect=\"auto\",\n",
    ")\n",
    "pyplot.colorbar()\n",
    "pyplot.xlabel(\"x ($\\mu$m)\")\n",
    "pyplot.ylabel(\"r ($\\mu$m)\")\n",
    "\n",
    "\n",
    "# plot the calcium concentration in the neuron in each of the shells\n",
    "pyplot.figure()\n",
    "pyplot.title(\"Concentration in each shell of the neuron\")\n",
    "for i, shell in enumerate(shells):\n",
    "    pyplot.plot(\n",
    "        [nd.x3d - soma.L for nd in ca.nodes(shell)],\n",
    "        [nd.concentration / nM for nd in ca.nodes(shell)],\n",
    "        label=f\"shell$_{i}$\",\n",
    "    )\n",
    "pyplot.legend(frameon=False)\n",
    "pyplot.xlabel(\"x ($\\mu$m)\")\n",
    "pyplot.ylabel(\"Ca$^{+2}$ (nM)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "286f6403",
   "metadata": {},
   "source": [
    "The heatmap shows the concentration of calcium (in nM) along the length of the dendrite (x-axis) and across the radial shells (y-axis) after 2.5ms. The second plot shows the concentration in each of the shells over the whole length of the neuron (the soma connects to the dendrite at x=0)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.10"
  },
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}