ExampleModelTester.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the necessary packages\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy as sp\n",
    "from scipy.integrate import odeint\n",
    "from types import *\n",
    "import matplotlib.animation as animation\n",
    "from matplotlib import rc\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "rc('animation', html='jshtml')\n",
    "figSize=600\n",
    "\n",
    "# Let's define a simple function that computes the time of the next reaction given our propensity functions:\n",
    "def next_time(x, t, W, pars=None):\n",
    "    return -np.log(np.random.rand()) / np.sum(W(x, t, pars=pars))\n",
    "\n",
    "# Let's define a simple function that computes the index of the next reaction given our propensity functions:\n",
    "def next_reaction(x, t, W, pars, di, ti):\n",
    "    W = W(x, t, pars=pars)\n",
    "    W0 = np.sum(W)\n",
    "    r = np.random.rand()\n",
    "    i = 0\n",
    "    W_sum = W[0]\n",
    "    while W_sum / W0 < r:\n",
    "        i += 1\n",
    "        W_sum += W[i]\n",
    "    return i\n",
    "\n",
    "def gillespie_drugs(x0, t0, tmax, S, W, pars, di, ti, returnFullTraj = False):\n",
    "    # Initialize the time and the state\n",
    "    t = t0\n",
    "    x = np.array(x0)\n",
    "    S = np.array(S)\n",
    "    \n",
    "    # Initialize the output (if requested)\n",
    "    if returnFullTraj:\n",
    "        times = [t0]\n",
    "        states = [x0]\n",
    "    \n",
    "    # Run the simulation\n",
    "    while t < tmax:\n",
    "\n",
    "        # Compute the time of the next reaction\n",
    "        tau = next_time(x, t, W, pars, di, ti)\n",
    "        t += tau\n",
    "        \n",
    "        # Exit the loop if the next reaction is beyond tmax\n",
    "        if t > tmax:\n",
    "            break\n",
    "        \n",
    "        # Compute the index of the next reaction\n",
    "        i = next_reaction(x, t, W, pars, di, ti)\n",
    "        # Update the state\n",
    "        x = x + S[:, i]\n",
    "        \n",
    "        # Append the results (if requested)\n",
    "        if returnFullTraj:\n",
    "            times.append(t)\n",
    "            states.append(x)\n",
    "\n",
    "    # Return the results\n",
    "    if returnFullTraj:\n",
    "        # Return the full trajectory\n",
    "        return np.array(times), np.array(states)\n",
    "    else:\n",
    "        # Return only the final state\n",
    "        return x\n",
    "    \n",
    "def dxdt(x, t, pars, di, ti):\n",
    "    x = np.atleast_2d(x).T\n",
    "    y = (S @ W(x, t, pars, di, ti)).flatten()\n",
    "    return y\n",
    "\n",
    "\n",
    "def solve(S, W, x0, t, pars, di, ti, model='ODE', ntraj=1):\n",
    "\n",
    "    if model == 'ODE':\n",
    "        trajectory = odeint(dxdt,np.array(x0),t, args=(pars, di, ti))\n",
    "\n",
    "    if model == 'SSA':\n",
    "        trajectory = np.zeros([ntraj, len(t), len(x0)])\n",
    "        t0 = t[0]\n",
    "        tmax = t[-1]\n",
    "        for i in range(ntraj):\n",
    "            times,states = gillespie_drugs(x0, t0, tmax, S, W, pars, di, ti, returnFullTraj=True)\n",
    "            \n",
    "            k = 0\n",
    "            for j in range(len(times)):\n",
    "                while times[j] > t[k]:\n",
    "                    trajectory[i,k,:] = states[j]\n",
    "                    k+=1\n",
    "\n",
    "        trajectory = np.mean(trajectory, axis=0)\n",
    "    return trajectory\n",
    "\n",
    "\n",
    "def compare_model_to_data(pars,di,ti, x0, drug_number, speciesDict, drug_conc=1000, path='.', model='ODE', ntraj=10):\n",
    "    #\n",
    "    # example_df = pd.read_csv(r'C:\\Users\\wsraymon\\Desktop\\uqbio2024_challenge_solns\\Data\\Drug1_3000nM_DesignResults.csv')\n",
    "    if drug_number == 0:\n",
    "        example_df = pd.read_csv(path + '/NoDrug_DesignResults.csv' )\n",
    "    else:\n",
    "        example_df = pd.read_csv(path + '/Drug%i_%inM_DesignResults.csv'%(drug_number,drug_conc) )\n",
    "    t = np.unique(example_df['Time'])\n",
    "    t_app = example_df['drug_time'].iloc[0]\n",
    "    data = np.zeros([5,len(t)])\n",
    "    data_std = np.zeros([5,len(t)])\n",
    "    k = 0\n",
    "    for tp in t:\n",
    "        subdf = example_df[example_df['Time'] == tp]\n",
    "        subdf = subdf[subdf['is_cell_vital'] == 1]\n",
    "        data[1,k] = np.mean(subdf['num_rna_nascent'])\n",
    "        data[2,k] = np.mean(subdf['num_rna_nuc'])\n",
    "        data[3,k] = np.mean(subdf['num_rna_cyto'])\n",
    "        data[4,k] = np.mean(subdf['Cyto_Intensity_1'])\n",
    "\n",
    "        data_std[1,k] = np.std(subdf['num_rna_nascent'])/np.sqrt(len(subdf['num_rna_nascent']))\n",
    "        data_std[2,k] = np.std(subdf['num_rna_nuc'])/np.sqrt(len(subdf['num_rna_nuc']))\n",
    "        data_std[3,k] = np.std(subdf['num_rna_cyto'])/np.sqrt(len(subdf['num_rna_cyto']))\n",
    "        data_std[4,k] = np.std(subdf['Cyto_Intensity_1'])/np.sqrt(len(subdf['Cyto_Intensity_1']))\n",
    "        k+=1\n",
    "\n",
    "    fig,ax = plt.subplots(1,4, tight_layout=True, figsize=(10,4))\n",
    "    ax[0].errorbar(t, data[1], yerr=data_std[1])\n",
    "    ax[1].errorbar(t, data[2], yerr=data_std[2])\n",
    "    ax[2].errorbar(t, data[3], yerr=data_std[3])\n",
    "    ax[3].errorbar(t, data[4], yerr=data_std[4])\n",
    "\n",
    "    ax[0].plot([t_app, t_app], [0, max(data[1])], 'r--', label='_nolegend_')\n",
    "    ax[1].plot([t_app, t_app], [0, max(data[2])], 'r--', label='_nolegend_')\n",
    "    ax[2].plot([t_app, t_app], [0, max(data[3])], 'r--', label='_nolegend_')\n",
    "    ax[3].plot([t_app, t_app], [0, max(data[4])], 'r--', label='_nolegend_')\n",
    "\n",
    "    ax[0].set_title('Nascent RNA')\n",
    "    ax[1].set_title('Nuclear RNA')\n",
    "    ax[2].set_title('Cytosolic RNA')\n",
    "    ax[3].set_title('Protein (intensity)')\n",
    "\n",
    "    s = solve(S, W, x0, np.linspace(0,2000,2001),pars, di, ti, model=model, ntraj=ntraj )\n",
    "\n",
    "    ax[0].plot(np.linspace(0,2000,2001), s.T[speciesDict['Nascent']], )\n",
    "    ax[1].plot(np.linspace(0,2000,2001), s.T[speciesDict['Nuclear']],)\n",
    "    ax[2].plot(np.linspace(0,2000,2001), s.T[speciesDict['Cytoplasmic']],)\n",
    "    ax[3].plot(np.linspace(0,2000,2001), s.T[speciesDict['Protein']],)\n",
    "\n",
    "    ax[0].set_xlim([1000,2000])\n",
    "    ax[1].set_xlim([1000,2000])\n",
    "    ax[2].set_xlim([1000,2000])\n",
    "    ax[3].set_xlim([1000,2000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model variables\n",
    "\n",
    "#x1 = gene (DNA) ON\n",
    "#x2 = gene (DNA) OFF\n",
    "#x3 = nascent mRNA\n",
    "#x4 = mature mRNA nuclei\n",
    "#x5 = mature mRNA cytoplasm\n",
    "#x6 = protein\n",
    "\n",
    "#Reactions\n",
    "#R1 = Gene Activation\n",
    "#R2 = Gene deactivation\n",
    "#R3 = Gene Transcription  --> drug\n",
    "#R4 = nascent mRNA maturation\n",
    "#R5 = mature mRNA Nuclear Export\n",
    "#R6 = mature mRNA Nuclear degradation\n",
    "#R7 = mature mRNA Cytoplasmic degradation\n",
    "#R8 = mature mRNA Cytoplasmic Translation\n",
    "#R9 = protein Degradation\n",
    "\n",
    "K = 1000\n",
    "n = 3\n",
    "\n",
    "# Hill functions\n",
    "def hillPositive(x, K=K, n=n):\n",
    "    return x**n / (K**n + x**n)\n",
    "\n",
    "def hillNegative(x, K=K, n=n):\n",
    "    return K**n / (K**n + x**n)\n",
    "\n",
    "#initial conditions\n",
    "x1 = 1\n",
    "x2 = 0\n",
    "x3 = 4 #??\n",
    "x4 = 5\n",
    "x5 = 8\n",
    "x6 = 760\n",
    "\n",
    "k1 = 0.1\n",
    "k2 = 0.1\n",
    "k3 = 0.1\n",
    "k4 = 0.1\n",
    "k5 = 0.1\n",
    "k6 = 0.1\n",
    "k7 = 0.1\n",
    "k8 = 0.1\n",
    "k9 = 0.1\n",
    "\n",
    "S1 = np.array([1,-1,0,0,0,0])  #R1 = Gene Activation\n",
    "S2 = np.array([-1,1,0,0,0,0])  #R2 = Gene Deactivation\n",
    "S3 = np.array([0,0,1,0,0,0])   #R3 = Gene Transcription  --> drug\n",
    "S4 = np.array([0,0,-1,1,0,0])  #R4 = nascent mRNA maturation\n",
    "S5 = np.array([0,0,0,-1,1,0])  #R5 = mature mRNA Nuclear Export\n",
    "S6 = np.array([0,0,0,-1,0,0])  #R6 = mature mRNA Nuclear degradation\n",
    "S7 = np.array([0,0,0,0,-1,0])  #R7 = mature mRNA Cytoplasmic degradation\n",
    "S8 = np.array([0,0,0,0,0,1])   #R8 = mature mRNA Cytoplasmic Translation\n",
    "S9 = np.array([0,0,0,0,0,-1])  #R9 = protein Degradation\n",
    "\n",
    "\n",
    "r1 = k1*x1 - k2*x2\n",
    "r2 = k2*x2 - k1*x1\n",
    "r3 = k3*x1\n",
    "r4 = -k4*x3 + k4*x4\n",
    "r5 = -k5*x3\n",
    "r6 = -k6*x4\n",
    "r7 = k7*x4\n",
    "r8 = -k8*x5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 4 is different from 6)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[24], line 22\u001b[0m\n\u001b[0;32m     20\u001b[0m pathName \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTrueData\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     21\u001b[0m drug_number \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;66;03m# use 0 for no drug!\u001b[39;00m\n\u001b[1;32m---> 22\u001b[0m \u001b[43mcompare_model_to_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mti\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdrug_number\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mspeciesDict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdrug_conc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpathName\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     23\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mODE\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mntraj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[1;32mIn[22], line 130\u001b[0m, in \u001b[0;36mcompare_model_to_data\u001b[1;34m(pars, di, ti, x0, drug_number, speciesDict, drug_conc, path, model, ntraj)\u001b[0m\n\u001b[0;32m    127\u001b[0m ax[\u001b[38;5;241m2\u001b[39m]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCytosolic RNA\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m    128\u001b[0m ax[\u001b[38;5;241m3\u001b[39m]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mProtein (intensity)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m--> 130\u001b[0m s \u001b[38;5;241m=\u001b[39m \u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mS\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mW\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinspace\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2001\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43mpars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mti\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mntraj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mntraj\u001b[49m\u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    132\u001b[0m ax[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mplot(np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m2000\u001b[39m,\u001b[38;5;241m2001\u001b[39m), s\u001b[38;5;241m.\u001b[39mT[speciesDict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNascent\u001b[39m\u001b[38;5;124m'\u001b[39m]], )\n\u001b[0;32m    133\u001b[0m ax[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mplot(np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m2000\u001b[39m,\u001b[38;5;241m2001\u001b[39m), s\u001b[38;5;241m.\u001b[39mT[speciesDict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNuclear\u001b[39m\u001b[38;5;124m'\u001b[39m]],)\n",
      "Cell \u001b[1;32mIn[22], line 69\u001b[0m, in \u001b[0;36msolve\u001b[1;34m(S, W, x0, t, pars, di, ti, model, ntraj)\u001b[0m\n\u001b[0;32m     66\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msolve\u001b[39m(S, W, x0, t, pars, di, ti, model\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mODE\u001b[39m\u001b[38;5;124m'\u001b[39m, ntraj\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[0;32m     68\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m model \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mODE\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m---> 69\u001b[0m         trajectory \u001b[38;5;241m=\u001b[39m \u001b[43modeint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdxdt\u001b[49m\u001b[43m,\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx0\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mpars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mti\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     71\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m model \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSSA\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m     72\u001b[0m         trajectory \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros([ntraj, \u001b[38;5;28mlen\u001b[39m(t), \u001b[38;5;28mlen\u001b[39m(x0)])\n",
      "File \u001b[1;32mc:\\Users\\wikto\\anaconda3\\envs\\uqbio2024_py310\\lib\\site-packages\\scipy\\integrate\\_odepack_py.py:243\u001b[0m, in \u001b[0;36modeint\u001b[1;34m(func, y0, t, args, Dfun, col_deriv, full_output, ml, mu, rtol, atol, tcrit, h0, hmax, hmin, ixpr, mxstep, mxhnil, mxordn, mxords, printmessg, tfirst)\u001b[0m\n\u001b[0;32m    241\u001b[0m t \u001b[38;5;241m=\u001b[39m copy(t)\n\u001b[0;32m    242\u001b[0m y0 \u001b[38;5;241m=\u001b[39m copy(y0)\n\u001b[1;32m--> 243\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43m_odepack\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43modeint\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mDfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcol_deriv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mml\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmu\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    244\u001b[0m \u001b[43m                         \u001b[49m\u001b[43mfull_output\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrtol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43matol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtcrit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhmax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhmin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    245\u001b[0m \u001b[43m                         \u001b[49m\u001b[43mixpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmxstep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmxhnil\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmxordn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmxords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    246\u001b[0m \u001b[43m                         \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mbool\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtfirst\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    247\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    248\u001b[0m     warning_msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m_msgs[output[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Run with full_output = 1 to \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    249\u001b[0m                    \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mget quantitative information.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[1;32mIn[22], line 62\u001b[0m, in \u001b[0;36mdxdt\u001b[1;34m(x, t, pars, di, ti)\u001b[0m\n\u001b[0;32m     60\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdxdt\u001b[39m(x, t, pars, di, ti):\n\u001b[0;32m     61\u001b[0m     x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39matleast_2d(x)\u001b[38;5;241m.\u001b[39mT\n\u001b[1;32m---> 62\u001b[0m     y \u001b[38;5;241m=\u001b[39m (\u001b[43mS\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m@\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mW\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mti\u001b[49m\u001b[43m)\u001b[49m)\u001b[38;5;241m.\u001b[39mflatten()\n\u001b[0;32m     63\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m y\n",
      "\u001b[1;31mValueError\u001b[0m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 4 is different from 6)"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#stechiometry matrix\n",
    "S = np.array([\n",
    "    [1, -1, 0, 0, 0, 0, 0, 0, 0],   # R1: Gene Activation\n",
    "    [-1, 1, 0, 0, 0, 0, 0, 0, 0],   # R2: Gene Deactivation\n",
    "    [0, 0, 1, 0, 0, 0, 0, 0, 0],    # R3: Gene Transcription\n",
    "    [0, 0, -1, 1, 0, 0, 0, 0, 0],   # R4: nascent mRNA maturation\n",
    "    [0, 0, 0, -1, 1, 0, 0, 0, 0],   # R5: mature mRNA Nuclear Export\n",
    "    [0, 0, 0, -1, 0, 0, 0, 0, 0],   # R6: mature mRNA Nuclear degradation\n",
    "    [0, 0, 0, 0, -1, 0, 0, 0, 0],   # R7: mature mRNA Cytoplasmic degradation\n",
    "    [0, 0, 0, 0, 0, 1, 0, 0, 0],    # R8: mature mRNA Cytoplasmic Translation\n",
    "    [0, 0, 0, 0, 0, 0, 0, 0, -1]    # R9: protein Degradation\n",
    "])\n",
    "\n",
    "\n",
    "di = [0,1000,0] #drug concentration\n",
    "ti = [np.inf,1200,np.inf] #time of drug addition\n",
    "\n",
    "x0 = np.array([x3,x4,x5,x6]) #beginning state\n",
    "\n",
    "pars = [10,0.3,0.5,0.2,0.4,0.6,0.7,0.8,0.01] #k1,k2,k3,k4,k5,k6,k7,k8,k9\n",
    "\n",
    "speciesDict = {\n",
    "    'Nascent': 2,\n",
    "    'Nuclear': 3,\n",
    "    'Cytoplasmic': 4,\n",
    "    'Protein': 5\n",
    "}\n",
    "\n",
    "def W(x, t, pars, di, ti):\n",
    "    W = [pars[0] + pars[4]*di[1]*(t>ti[1]),  # Note -- you may want to add Hill Functions here\n",
    "        pars[1] + pars[5]*(t>ti[1]),\n",
    "        pars[2] + pars[6]*(t>ti[1]),\n",
    "        pars[3] + pars[7]*(t>ti[1])]\n",
    "    return np.array(W)\n",
    "\n",
    "pathName = \"TrueData\"\n",
    "drug_number = 0 # use 0 for no drug!\n",
    "compare_model_to_data(pars, di, ti, x0, drug_number, speciesDict, drug_conc=1000, path=pathName,\n",
    "                    model='ODE',ntraj=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
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