{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: Circadian rhythm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A version of this notebook may be run online via Google Colab at https://tinyurl.com/rxd-circadian (make a copy or open in playground mode)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:36:24.919693Z",
     "iopub.status.busy": "2025-08-18T03:36:24.919543Z",
     "iopub.status.idle": "2025-08-18T03:36:25.276895Z",
     "shell.execute_reply": "2025-08-18T03:36:25.276419Z"
    }
   },
   "outputs": [],
   "source": [
    "import neuron"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we develop a NEURON implementation of the Leloup-Goldbeter model for circadian rhythms in Drosophila, using the version of the model specified in:\n",
    "\n",
    "Leloup, J. C., Gonze, D., & Goldbeter, A. (1999). Limit cycle models for\n",
    "circadian rhythms based on transcriptional regulation in Drosophila and\n",
    "Neurospora. Journal of biological rhythms, 14(6), 433-448.\n",
    "https://doi.org/10.1177/074873099129000948\n",
    "\n",
    "We'll use the initial conditions from the BioModels version:\n",
    "https://www.ebi.ac.uk/biomodels/BIOMD0000000298"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load libraries\n",
    "\n",
    "As usual, we'll want NEURON's h library and its standard run system as well as rxd. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:36:25.286542Z",
     "iopub.status.busy": "2025-08-18T03:36:25.285076Z",
     "iopub.status.idle": "2025-08-18T03:36:25.327554Z",
     "shell.execute_reply": "2025-08-18T03:36:25.325792Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuron import n, rxd\n",
    "\n",
    "n.load_file(\"stdrun.hoc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll also use <tt>matplotlib</tt> to plot concentrations of circadian proteins over time. We could of course use NEURON's graphics, bokeh, or any other plotting library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:36:25.424855Z",
     "iopub.status.busy": "2025-08-18T03:36:25.424116Z",
     "iopub.status.idle": "2025-08-18T03:36:26.012651Z",
     "shell.execute_reply": "2025-08-18T03:36:26.010675Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Units\n",
    "\n",
    "Recall NEURON measures concentrations in mM and time in ms. Leloup et al., 1999, however expresses concentration in nM and time in hours. We could ignore the unit inconsistency and NEURON would still be able to run the simulation, however if we did so, this could cause problems for interacting with other NEURON mechanisms. To avoid this problem, we use the `nM` and `hour` units from `neuron.units` in addition to our usual `mV` for initializing the membrane potential:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:36:26.020990Z",
     "iopub.status.busy": "2025-08-18T03:36:26.020754Z",
     "iopub.status.idle": "2025-08-18T03:36:26.025493Z",
     "shell.execute_reply": "2025-08-18T03:36:26.025110Z"
    }
   },
   "outputs": [],
   "source": [
    "from neuron.units import nM, hour, mV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions for clarity\n",
    "\n",
    "For clarity, we prefer to define parameters and species by just specifying their names and values instead of repeteadly writing <tt>rxd.Parameter</tt> or <tt>rxd.Species</tt>. Fortunately, since Python uses keyword arguments and allows functions to modify the global variable dictionary, we can define helper functions that let us do exactly that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:36:26.029533Z",
     "iopub.status.busy": "2025-08-18T03:36:26.029367Z",
     "iopub.status.idle": "2025-08-18T03:36:26.034489Z",
     "shell.execute_reply": "2025-08-18T03:36:26.034125Z"
    }
   },
   "outputs": [],
   "source": [
    "def declare_parameters(**kwargs):\n",
    "    \"\"\"enables clean declaration of parameters in top namespace\"\"\"\n",
    "    for key, value in kwargs.items():\n",
    "        globals()[key] = rxd.Parameter(r, name=key, initial=value)\n",
    "\n",
    "\n",
    "def declare_species(**kwargs):\n",
    "    \"\"\"enables clean declaration of species in top namespace\"\"\"\n",
    "    for key, value in kwargs.items():\n",
    "        globals()[key] = rxd.Species(r, name=key, initial=value, atolscale=1e-3 * nM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notify NEURON's variable step solver that we want an absolute error tolerance on the order of 1e-3 nM since Leloup et al's concentrations are on the order of 1 nM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You don't need to fully understand the code for these functions; their usage will be clear below, and they can be reused verbatim in many similar models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feel free to reuse these functions in your own code if you find them helpful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the morphology\n",
    "\n",
    "Even though this simulation does not use any electrophysiology, we define the <tt>nrn_region</tt> to allow the option of easily connecting to electrophysiology kinetics in the future:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:36:26.036883Z",
     "iopub.status.busy": "2025-08-18T03:36:26.036736Z",
     "iopub.status.idle": "2025-08-18T03:36:26.039333Z",
     "shell.execute_reply": "2025-08-18T03:36:26.039004Z"
    }
   },
   "outputs": [],
   "source": [
    "cell = n.Section(name=\"cell\")\n",
    "cell.diam = cell.L = 5\n",
    "r = rxd.Region([cell], nrn_region=\"i\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters\n",
    "\n",
    "Using Parameters instead of numbers makes it easier to rerun simulations. Beginning in NEURON 7.7, the rxd parameters panel allows quickly changing parameter values and rerunning simulations from the graphical user interface.\n",
    "\n",
    "Here we explicitly specify units (defined above) as the units in Leloup et al do not match NEURON's default units. We use the helper function defined above instead of repeatedly writing rxd.Parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:36:26.043801Z",
     "iopub.status.busy": "2025-08-18T03:36:26.043652Z",
     "iopub.status.idle": "2025-08-18T03:36:26.065689Z",
     "shell.execute_reply": "2025-08-18T03:36:26.065299Z"
    }
   },
   "outputs": [],
   "source": [
    "declare_parameters(\n",
    "    vsP=1.1 * nM / hour,\n",
    "    vmP=1.0 * nM / hour,\n",
    "    KmP=0.2 * nM,\n",
    "    KIP=1.0 * nM,\n",
    "    ksP=0.9 / hour,\n",
    "    vdP=2.2 * nM / hour,\n",
    "    KdP=0.2 * nM,\n",
    "    vsT=1.0 * nM / hour,\n",
    "    vmT=0.7 * nM / hour,\n",
    "    KmT=0.2 * nM,\n",
    "    KIT=1.0 * nM,\n",
    "    ksT=0.9 / hour,\n",
    "    vdT=3.0 * nM / hour,\n",
    "    KdT=0.2 * nM,\n",
    "    kdC=0.01 * nM / hour,\n",
    "    kdN=0.01 * nM / hour,\n",
    "    k1=0.8 / hour,\n",
    "    k2=0.2 / hour,\n",
    "    k3=1.2 / (nM * hour),\n",
    "    k4=0.6 / hour,\n",
    "    kd=0.01 * nM / hour,\n",
    "    V1P=8.0 * nM / hour,\n",
    "    V1T=8.0 * nM / hour,\n",
    "    V2P=1.0 * nM / hour,\n",
    "    V2T=1.0 * nM / hour,\n",
    "    V3P=8.0 * nM / hour,\n",
    "    V3T=8.0 * nM / hour,\n",
    "    V4P=1.0 * nM / hour,\n",
    "    V4T=1.0 * nM / hour,\n",
    "    K1P=2.0 * nM,\n",
    "    K1T=2.0 * nM,\n",
    "    K2P=2.0 * nM,\n",
    "    K2T=2.0 * nM,\n",
    "    K3P=2.0 * nM,\n",
    "    K3T=2.0 * nM,\n",
    "    K4P=2.0 * nM,\n",
    "    K4T=2.0 * nM,\n",
    "    hill_coefficient=4,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Declaring proteins\n",
    "\n",
    "As with the parameters, we declare all the protiens (species), their initial values, and units:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:36:26.068554Z",
     "iopub.status.busy": "2025-08-18T03:36:26.068398Z",
     "iopub.status.idle": "2025-08-18T03:36:26.077566Z",
     "shell.execute_reply": "2025-08-18T03:36:26.076184Z"
    }
   },
   "outputs": [],
   "source": [
    "declare_species(\n",
    "    MP=0.0614368 * nM,\n",
    "    P0=0.0169928 * nM,\n",
    "    P1=0.0141356 * nM,\n",
    "    P2=0.0614368 * nM,\n",
    "    MT=0.0860342 * nM,\n",
    "    T0=0.0217261 * nM,\n",
    "    T1=0.0213384 * nM,\n",
    "    T2=0.0145428 * nM,\n",
    "    C=0.207614 * nM,\n",
    "    CN=1.34728 * nM,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reactions\n",
    "\n",
    "For the explanation of the model, see Leloup et al., 1999 or Leloup and Goldbeter 1998. Here we define all the reactions for NEURON. For those reactions that are not governed by mass-action kinetics, we specify <tt>custom_dynamics=True</tt>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:36:26.079877Z",
     "iopub.status.busy": "2025-08-18T03:36:26.079729Z",
     "iopub.status.idle": "2025-08-18T03:36:26.091526Z",
     "shell.execute_reply": "2025-08-18T03:36:26.091161Z"
    }
   },
   "outputs": [],
   "source": [
    "MTtranscription = rxd.Rate(\n",
    "    MT,\n",
    "    vsT * KIT**hill_coefficient / (KIT**hill_coefficient + CN**hill_coefficient),\n",
    ")\n",
    "MPtranscription = rxd.Rate(\n",
    "    MP,\n",
    "    vsP * KIP**hill_coefficient / (KIP**hill_coefficient + CN**hill_coefficient),\n",
    ")\n",
    "MTdegradation = rxd.Rate(MT, -(vmT * MT / (KmT + MT) + kd * MT))\n",
    "MPdegradation = rxd.Rate(MP, -(vmP * MP / (KmP + MP) + kd * MP))\n",
    "T0production = rxd.Rate(T0, ksT * MT)\n",
    "T0degradation = rxd.Rate(T0, -kd * T0)\n",
    "T1degradation = rxd.Rate(T1, -kd * T1)\n",
    "T2degradation = rxd.Rate(T2, -kd * T2)\n",
    "T2degradation_due_to_light = rxd.Rate(T2, -vdT * T2 / (KdT + T2))\n",
    "T0toT1 = rxd.Reaction(\n",
    "    T0, T1, V1T * T0 / (K1T + T0), V2T * T1 / (K2T + T1), custom_dynamics=True\n",
    ")\n",
    "T1toT2 = rxd.Reaction(\n",
    "    T1, T2, V3T * T1 / (K3T + T1), V4T * T2 / (K4T + T2), custom_dynamics=True\n",
    ")\n",
    "P0production = rxd.Rate(P0, ksP * MP)\n",
    "P0degradation = rxd.Rate(P0, -kd * P0)\n",
    "P1degradation = rxd.Rate(P1, -kd * P1)\n",
    "P2degradation = rxd.Rate(P2, -kd * P2 - vdP * P2 / (KdP + P2))\n",
    "P0toP1 = rxd.Reaction(\n",
    "    P0, P1, V1P * P0 / (K1P + P0), V2P * P1 / (K2P + P1), custom_dynamics=True\n",
    ")\n",
    "P1toP2 = rxd.Reaction(\n",
    "    P1, P2, V3P * P1 / (K3P + P1), V4P * P2 / (K4P + P2), custom_dynamics=True\n",
    ")\n",
    "P2T2toC = rxd.Reaction(P2 + T2, C, k3, k4)\n",
    "CtoCN = rxd.Reaction(C, CN, k1, k2)\n",
    "Cdegradation = rxd.Rate(C, -kdC * C)\n",
    "CNdegradation = rxd.Rate(CN, -kdN * CN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Record states\n",
    "\n",
    "We define a number of Vectors to record variables of interest. For convenience we use the <tt>recorder</tt> function defined above:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, and always, we'll record time. Otherwise we won't know how to interpret the remaining variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:36:26.093020Z",
     "iopub.status.busy": "2025-08-18T03:36:26.092870Z",
     "iopub.status.idle": "2025-08-18T03:36:26.095083Z",
     "shell.execute_reply": "2025-08-18T03:36:26.094732Z"
    }
   },
   "outputs": [],
   "source": [
    "t = n.Vector().record(n._ref_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the concentrations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:36:26.099901Z",
     "iopub.status.busy": "2025-08-18T03:36:26.099754Z",
     "iopub.status.idle": "2025-08-18T03:36:26.416754Z",
     "shell.execute_reply": "2025-08-18T03:36:26.416321Z"
    }
   },
   "outputs": [],
   "source": [
    "mpvec = n.Vector().record(MP.nodes[0]._ref_concentration)\n",
    "cnvec = n.Vector().record(CN.nodes[0]._ref_concentration)\n",
    "p0vec = n.Vector().record(P0.nodes[0]._ref_concentration)\n",
    "p1vec = n.Vector().record(P1.nodes[0]._ref_concentration)\n",
    "p2vec = n.Vector().record(P2.nodes[0]._ref_concentration)\n",
    "cvec = n.Vector().record(C.nodes[0]._ref_concentration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we're running for hours rather than ms, we'll use the variable step solver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:36:26.421078Z",
     "iopub.status.busy": "2025-08-18T03:36:26.420913Z",
     "iopub.status.idle": "2025-08-18T03:36:27.208309Z",
     "shell.execute_reply": "2025-08-18T03:36:27.207932Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.finitialize(-65 * mV)\n",
    "n.CVode().active(True)\n",
    "n.continuerun(72 * hour)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot\n",
    "\n",
    "We begin by converting the units back from NEURON's units to nM and hours to have more intuitive values to display. We also define the variable <tt>pt</tt> which is the total concentration of PER protein, in any of its forms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:36:27.213803Z",
     "iopub.status.busy": "2025-08-18T03:36:27.213641Z",
     "iopub.status.idle": "2025-08-18T03:36:27.218580Z",
     "shell.execute_reply": "2025-08-18T03:36:27.217107Z"
    }
   },
   "outputs": [],
   "source": [
    "pt = (p0vec + p1vec + p2vec + cvec + cnvec) / nM\n",
    "mp = mpvec / nM\n",
    "cn = cnvec / nM\n",
    "t_in_hours = t / hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally the actual plot, which you can compare to Figure 2A of Leloup et al., 1999."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-08-18T03:36:27.220832Z",
     "iopub.status.busy": "2025-08-18T03:36:27.220689Z",
     "iopub.status.idle": "2025-08-18T03:36:28.030100Z",
     "shell.execute_reply": "2025-08-18T03:36:28.029747Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(t_in_hours, mp, label=\"MP\")\n",
    "plt.plot(t_in_hours, cn, label=\"CN\")\n",
    "plt.plot(t_in_hours, pt, label=\"PT\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"t (hours)\")\n",
    "plt.ylabel(\"concentration (nM)\")\n",
    "plt.savefig(\"circadian.pdf\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}