Repass_Kinetic_Model.ipynb

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   "cell_type": "markdown",
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   "source": [
    "## Kinetic Modeling of Repass Pathways for Prenol/Isoprenol production\n",
    "\n",
    "This work describes a reaction network in which two phosphatases, an isomerase, and a kinase generate C5 alcohols from either IP or IPP. The reaction network has 16 rate constants (Vm/Km) for 8 reactions, 4 substrates, and 2 products.\n",
    "\n",
    "### Inputs:\n",
    "- The user will be able to define whether they want to model the OriMVA-Repass or the IPP-Repass\n",
    "    - The difference is the initial condition of 10 arb. units of IPP or IP, respectively\n",
    "\n",
    "### Outputs:\n",
    "- Two .csvs of the pvalues and Spearman rank correlation coefficients of each paramter\n",
    "- Heatmaps of the pvalues and coefficients\n",
    "- An example plot of the model given input parameters provided"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b54c4ae-0551-47c0-b092-b87207619007",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c50add50-0d88-4847-9dd5-22b9f11b8ae2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scipy.stats import qmc\n",
    "from scipy.integrate import odeint\n",
    "from scipy.stats import spearmanr\n",
    "import matplotlib.pylab as plt\n",
    "import chart_studio.plotly as py\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import os\n",
    "import plotly.graph_objs as go\n",
    "from plotly import tools\n",
    "import array\n",
    "import warnings\n",
    "from textwrap import wrap\n",
    "from matplotlib.font_manager import FontProperties"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2be4d92-f88f-49db-8e18-022e69795790",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Define Variables \n",
    "- Define upper and lower bounds for kinetic parameters\n",
    "- Define number of LHS iterations\n",
    "- Make readable for kinetic model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "881432de-13b6-4da7-aeb2-a32b2c76d8f4",
   "metadata": {
    "tags": []
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   "outputs": [],
   "source": [
    "sampler = qmc.LatinHypercube(d=16)\n",
    "sample = sampler.random(n=10000)\n",
    "results_list=[]\n",
    "\n",
    "lb_kcat=0.1       #[s^-1]\n",
    "ub_kcat=100       #[s^-1]\n",
    "\n",
    "lb_km=0.1         #[M]\n",
    "ub_km=100         #[M]\n",
    "\n",
    "l_bounds=[lb_kcat]*8\n",
    "u_bounds=[ub_kcat]*8\n",
    "l_bounds_km=[lb_km]*8\n",
    "u_bounds_km=[ub_km]*8\n",
    "\n",
    "l_bounds.extend(l_bounds_km)\n",
    "u_bounds.extend(u_bounds_km)\n",
    "\n",
    "A=qmc.discrepancy(sample)\n",
    "B=qmc.scale(sample, l_bounds, u_bounds)\n",
    "LHS=np.transpose(B)\n",
    "num_iterations=LHS.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8073a177-d1f5-4315-9f4a-e614c97d565f",
   "metadata": {
    "tags": []
   },
   "source": [
    "### First-order kinetic model\n",
    "\n",
    "- Here the initial condition is set to 1 arb. unit of either IPP or IP depending on the selected pathy\n",
    "- Time set to 150 arb. unit with a sampling of n = 100\n",
    "    - Time and sampling may be changed, these were set because most simulations reach steady-state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c447395d-efe9-4069-a2ca-0b542993e43c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please choose a Repass pathway configuration:\n",
      "1: Original MVA Repass Pathway\n",
      "2: IPP-Repass Pathway\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter 1 or 2:  2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You chose IPP-Repass Pathway\n"
     ]
    }
   ],
   "source": [
    "def get_user_choice():\n",
    "    print(\"Please choose a Repass pathway configuration:\")\n",
    "    print(\"1: Original MVA Repass Pathway\")\n",
    "    print(\"2: IPP-Repass Pathway\")\n",
    "\n",
    "    while True:\n",
    "        choice = input(\"Enter 1 or 2: \")\n",
    "        if choice == '1':\n",
    "            pathway = 1\n",
    "            return pathway, \"You chose Original MVA Repass Pathway\"\n",
    "        elif choice == '2':\n",
    "            pathway = 2\n",
    "            return pathway, \"You chose IPP-Repass Pathway\"\n",
    "        else:\n",
    "            print(\"Invalid input. Please enter 1 or 2.\")\n",
    "\n",
    "# Call the function and print the result\n",
    "pathway, result = get_user_choice()\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8fee55d0-6e38-491e-b79f-4d51a670399a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running simulation 10000 times\n",
      "0.0  %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Carruthers\\AppData\\Local\\Temp\\ipykernel_28052\\960277054.py:86: ODEintWarning:\n",
      "\n",
      "Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0  %\n",
      "20.0  %\n",
      "30.0  %\n",
      "40.0  %\n",
      "50.0  %\n",
      "60.0  %\n",
      "70.0  %\n",
      "80.0  %\n",
      "90.0  %\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "print(\"Running simulation {} times\".format(num_iterations))\n",
    "for j in range (0,num_iterations):\n",
    "    def model(y,t):\n",
    "        x1 = y[0]\n",
    "        x2 = y[1]\n",
    "        x3 = y[2]\n",
    "        x4 = y[3]\n",
    "        x5 = y[4]\n",
    "        x6 = y[5]\n",
    "        \n",
    "        x7=LHS[0,j]\n",
    "        x8=LHS[1,j]\n",
    "        x9=LHS[2,j]\n",
    "        x10=LHS[3,j]\n",
    "        x11=LHS[4,j]\n",
    "        x12=LHS[5,j]\n",
    "        x13=LHS[6,j]\n",
    "        x14=LHS[7,j]\n",
    "        \n",
    "        x15=LHS[8,j]\n",
    "        x16=LHS[9,j]\n",
    "        x17=LHS[10,j]\n",
    "        x18=LHS[11,j]\n",
    "        x19=LHS[12,j]\n",
    "        x20=LHS[13,j]\n",
    "        x21=LHS[14,j]\n",
    "        x22=LHS[15,j]\n",
    "        \n",
    "        # k3/k4 magnitude favors DMAPP>IPP by isomerase\n",
    "        # k7/k2 magntiude favors DMAP>IP by phosphatase\n",
    "        # k6/k1 magnitude favors DMAPP>IPP by kinase\n",
    "        # k8/K4 magnitude favors prenol>isoprenol\n",
    "        \n",
    "        k1 = x7/(x15+x1)  # IP -> IPP\n",
    "        k2 = x8/(x16+x3)  # IPP -> IP\n",
    "        k3 = x9/(x17+x4)  # DMAPP -> IPP\n",
    "        k4 = x10/(x18+x3) # IPP -> DMAPP\n",
    "\n",
    "        k5 = x11/(x19+x1) # IP -> Isoprenol\n",
    "        k6 = x12/(x20+x5) # DMAP -> DMAPP\n",
    "        k7 = x13/(x21+x4) # DMAPP -> DMAP\n",
    "        k8 = x14/(x22+x5) # DMAP -> Prenol\n",
    "\n",
    "        dy1dt=-k1*x1-k5*x1+k2*x3 #IP\n",
    "        dy2dt=k5*x1 #Isoprenol\n",
    "        dy3dt=k1*x1+k3*x4-k4*x3-k2*x3 #IPP\n",
    "        dy4dt=k4*x3+k6*x5-k3*x4-k7*x4 #DMAPP\n",
    "        dy5dt=k7*x4-k6*x5-k8*x5 #DMAP\n",
    "        dy6dt=k8*x5 #Prenol\n",
    "\n",
    "        dydt = [dy1dt,dy2dt,dy3dt,dy4dt,dy5dt,dy6dt]\n",
    "        return dydt\n",
    "    \n",
    "    if pathway ==1:\n",
    "        y0 = [0,0,10,0,0,0]\n",
    "    else:\n",
    "        y0 = [10,0,0,0,0,0]\n",
    "\n",
    "    n = 100\n",
    "    culture_time=150\n",
    "\n",
    "    # time points\n",
    "    t = np.linspace(0,culture_time,n)\n",
    "\n",
    "    # store solution\n",
    "    x1 = np.empty_like(t)\n",
    "    x2 = np.empty_like(t)\n",
    "    x3 = np.empty_like(t)\n",
    "    x4 = np.empty_like(t)\n",
    "    x5 = np.empty_like(t)\n",
    "    x6 = np.empty_like(t)\n",
    "\n",
    "    # record initial conditions\n",
    "    x1[0] = y0[0]\n",
    "    x2[0] = y0[1]\n",
    "    x3[0] = y0[2]\n",
    "    x4[0] = y0[3]\n",
    "    x5[0] = y0[4]\n",
    "    x6[0] = y0[5]\n",
    "\n",
    "    # solve ODE\n",
    "    for i in range(1,n):\n",
    "        # span for next time step\n",
    "        tspan = [t[i-1],t[i]]\n",
    "        # solve for next step\n",
    "        y = odeint(model,y0,tspan)\n",
    "        # store solution for plotting\n",
    "        x1[i] = y[1][0]\n",
    "        x2[i] = y[1][1]\n",
    "        x3[i] = y[1][2]\n",
    "        x4[i] = y[1][3]\n",
    "        x5[i] = y[1][4]\n",
    "        x6[i] = y[1][5]\n",
    "        \n",
    "        # next initial condition\n",
    "        y0 = y[1]\n",
    "        \n",
    "        ##enter in other results of interest\n",
    "        #results=[x2[n-1],x6[n-1], max(x1),np.mean(x3),max(x5),max(x4)]\n",
    "        results=[x2[n-1],x6[n-1]]\n",
    "\n",
    "    # plot results\n",
    "    results_list.append(results)\n",
    "    if j % (0.1*num_iterations) == 0:\n",
    "        percentj=100*(j/num_iterations)\n",
    "        print(percentj, \" %\")\n",
    "        \n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c074c8b8-84f0-45bc-ae43-8fe1989d2fe2",
   "metadata": {},
   "source": [
    "##### Bifurcation of alcohols as a ratio of prenol to isoprenol (arb. units)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e68c27b6-b09f-4d32-9271-ddd7731d0be1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ratio of prenol to isoprenol across all LHS conditions: 0.3202063146859164\n",
      "Prenol as a fraction of total alcohols: 0.24254263225675834\n"
     ]
    }
   ],
   "source": [
    "column_index = 0  \n",
    "column_index2 = 1 \n",
    "\n",
    "column_values = [row[column_index] for row in results_list]\n",
    "column_values2 = [row[column_index2] for row in results_list]\n",
    "\n",
    "mean_value = sum(column_values) / len(column_values)\n",
    "mean_value2 = sum(column_values2) / len(column_values2)\n",
    "prenol_frac= mean_value2/(mean_value+mean_value2)\n",
    "ratio = mean_value2/mean_value\n",
    "print(f\"Ratio of prenol to isoprenol across all LHS conditions: {ratio}\")\n",
    "print(f\"Prenol as a fraction of total alcohols: {prenol_frac}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fa8175c-11ef-4562-a767-d4e0b9d4983d",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Repass Sensitivity\n",
    "\n",
    "- Provides the Spearmans rank correlation coefficients and their associated p-values\n",
    "- Plots heatmaps and saving as .csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "df2e0a3a-c7c4-45a8-b29a-5e650c9f807f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "if pathway ==1:\n",
    "    fig='OriMVA-Repass_Sensitivity'\n",
    "else:\n",
    "    fig='IPP-Repass_Sensitivity'\n",
    "\n",
    "outputs_names=['Isoprenol','Prenol']\n",
    "inputs_names=['$k_{cat_{1}}^{*}$','$k_{cat_{2}}^{*}$','$k_{cat_{3}}^{*}$','$k_{cat_{4}}^{*}$',\n",
    "              '$k_{cat_{5}}^{*}$','$k_{cat_{6}}^{*}$','$k_{cat_{7}}^{*}$','$k_{cat_{8}}^{*}$',\n",
    "              '$K_{m_{1}}$','$K_{m_{2}}$','$K_{m_{3}}$','$K_{m_{4}}$','$K_{m_{5}}$','$K_{m_{6}}$','$K_{m_{7}}$','$K_{m_{8}}$']\n",
    "\n",
    "results_list_Repass=np.array(results_list)\n",
    "LHS=np.transpose(np.array(LHS))\n",
    "\n",
    "inputs=16\n",
    "outputs=2\n",
    "rho_list = []\n",
    "rho_list=np.zeros((inputs,outputs))\n",
    "pval_list=[]\n",
    "pval_list=np.zeros((inputs,outputs))\n",
    "\n",
    "for m in range(outputs):\n",
    "    for k in range(inputs):\n",
    "        rho_list[k,m],pval_list[k,m]=spearmanr(LHS[:,k],results_list_Repass[:,m], axis=1) \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "af2b6fe9-9149-474d-80bb-8d31e44c2b1e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_rho = pd.DataFrame(rho_list)\n",
    "df_pval = pd.DataFrame(pval_list)\n",
    "\n",
    "rho_path=f'{fig}_Rhos.csv'\n",
    "pval_path=f'{fig}_Pvals.csv'\n",
    "\n",
    "df_rho.to_csv(f'Figure_Export/{rho_path}', index=False, header=False)\n",
    "df_pval.to_csv(f'Figure_Export/{pval_path}', index=False, header=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a31b55de-69cd-4f79-a231-7b3691d15f8c",
   "metadata": {},
   "source": [
    "#### Plotting a heatmap of Spearman rank correlation coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "90ecfc62-e13a-4840-8d57-941261c4bd79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 150x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(1.5,5))\n",
    "vmin_value = -0.45  # Minimum value\n",
    "vmax_value = 0.45  # Maximum value\n",
    "\n",
    "p1 = sns.heatmap(rho_list, cmap='viridis',vmin=vmin_value, vmax=vmax_value,\n",
    "                 xticklabels=outputs_names, yticklabels=inputs_names, annot_kws={\"size\": 8,\"fontfamily\": \"Arial\"})\n",
    "# Annotate all values\n",
    "for i in range(len(inputs_names)):\n",
    "    for j in range(len(outputs_names)):\n",
    "        p1.text(j + 0.5, i + 0.5, f'{rho_list[i, j]:.2f}', ha='center', va='center', color='white',fontsize=8, fontfamily=\"Arial\")\n",
    "\n",
    "plt.yticks(rotation='horizontal')\n",
    "plt.title('Ori-Repass Pathway',pad=10)        \n",
    "        \n",
    "plt.tick_params(axis='x', which='both', bottom=False, top=False)\n",
    "plt.tick_params(axis='y', which='both', left=False, right=False)\n",
    "\n",
    "plt.savefig(f'Figure_Export/{fig}_Rhos.svg',dpi=1200, bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b86ada9-4fd6-4297-901e-f6335af14cfb",
   "metadata": {},
   "source": [
    "#### Plotting a heatmap of p-values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c056efea-c3f1-4c3b-b001-7a5c21915254",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 150x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(1.5,5))\n",
    "p2 = sns.heatmap(pval_list, cmap='viridis', xticklabels=outputs_names, yticklabels=inputs_names, \n",
    "                 annot_kws={\"size\": 8,\"fontfamily\": \"Arial\"})\n",
    "for i in range(len(inputs_names)):\n",
    "    for j in range(len(outputs_names)):\n",
    "        p2.text(j + 0.5, i + 0.5, f'{pval_list[i, j]:.1e}', ha='center', va='center', color='white',fontsize=8, fontfamily=\"Arial\")\n",
    "plt.yticks(rotation='horizontal')\n",
    "plt.title('Ori-Repass Pathway',pad=10)        \n",
    "        \n",
    "plt.tick_params(axis='x', which='both', bottom=False, top=False)\n",
    "plt.tick_params(axis='y', which='both', left=False, right=False)\n",
    "\n",
    "plt.savefig(f'Figure_Export/{fig}_Pvals.svg',dpi=1200, bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1120a8a8-66a8-4827-ac06-01f6ff8f2276",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Example plot of a selected pathway\n",
    "\n",
    "Here, an example pathway depicts profiles of given reaction productions. The pathway can be adjusted by manually defining the Vmx/Kmx values as well as changing the model to the IPP-Repass as follows:\n",
    "\n",
    "- Original MVA-Repass:\n",
    "    - fig='OriMVA'\n",
    "    - y0 = [0,0,10,0,0,0]\n",
    "- To change to IPP-Repass:\n",
    "    - fig='IPPRe'\n",
    "    - y0 = [10,0,0,0,0,0]\n",
    "- Parameters may be changed at will\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "83a2c1d6-eab1-4c97-b7ad-1d92b015ffed",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "if pathway ==1:\n",
    "    fig='OriMVA-Repass_Example'\n",
    "    y0 = [0,0,10,0,0,0]\n",
    "else:\n",
    "    fig='IPP-Repass_Example'\n",
    "    y0 = [10,0,0,0,0,0]\n",
    "    \n",
    "#Define variables as x1, x2, x3, x4, x5, x6\n",
    "def model(y,t):\n",
    "    x1 = y[0]\n",
    "    x2 = y[1]\n",
    "    x3 = y[2]\n",
    "    x4 = y[3]\n",
    "    x5 = y[4]\n",
    "    x6 = y[5]\n",
    "    \n",
    "    k1 = Vm1/(Km1+x1) #IP to IPP\n",
    "    k2 = Vm2/(Km2+x3) #IPP to IP\n",
    "    k3 = Vm3/(Km3+x4) #IPP to DMAPP\n",
    "    k4 = Vm4/(Km4+x3) #DMAPP to IPP\n",
    "    k5 = Vm5/(Km5+x1) #IP to isoprenol\n",
    "    k6 = Vm6/(Km6+x5) #DMAP to DMAPP\n",
    "    k7 = Vm7/(Km7+x4) #DMAPP to DMAP\n",
    "    k8 = Vm8/(Km8+x5) #DMAP to prenol\n",
    "\n",
    "    dy1dt=-k1*x1-k5*x1+k2*x3 #IP\n",
    "    dy2dt=k5*x1 #Isoprenol\n",
    "    dy3dt=k1*x1+k3*x4-k4*x3-k2*x3 #IPP\n",
    "    dy4dt=k4*x3+k6*x5-k3*x4-k7*x4 #DMAPP\n",
    "    dy5dt=k7*x4-k6*x5-k8*x5 #DMAP\n",
    "    dy6dt=k8*x5 #Prenol\n",
    "\n",
    "    dydt = [dy1dt,dy2dt,dy3dt,dy4dt,dy5dt,dy6dt]\n",
    "    return dydt\n",
    "\n",
    "# initial condition\n",
    "   \n",
    "Vm1=10\n",
    "Vm2=10\n",
    "Vm3=20\n",
    "Vm4=20\n",
    "Vm5=10\n",
    "Vm6=10\n",
    "Vm7=10\n",
    "Vm8=10\n",
    "\n",
    "Km1=20\n",
    "Km2=20\n",
    "Km3=40\n",
    "Km4=40\n",
    "Km5=20\n",
    "Km6=200\n",
    "Km7=20\n",
    "Km8=200\n",
    "\n",
    "# number of time points\n",
    "n = 100\n",
    "\n",
    "# time points\n",
    "t = np.linspace(0,72,n)\n",
    "\n",
    "# store solution\n",
    "x1 = np.empty_like(t)\n",
    "x2 = np.empty_like(t)\n",
    "x3 = np.empty_like(t)\n",
    "x4 = np.empty_like(t)\n",
    "x5 = np.empty_like(t)\n",
    "x6 = np.empty_like(t)\n",
    "\n",
    "# record initial conditions\n",
    "x1[0] = y0[0]\n",
    "x2[0] = y0[1]\n",
    "x3[0] = y0[2]\n",
    "x4[0] = y0[3]\n",
    "x5[0] = y0[4]\n",
    "x6[0] = y0[5]\n",
    "\n",
    "# solve ODE\n",
    "for i in range(1,n):\n",
    "    # span for next time step\n",
    "    tspan = [t[i-1],t[i]]\n",
    "    # solve for next step\n",
    "    y = odeint(model,y0,tspan)\n",
    "    # store solution for plotting\n",
    "    x1[i] = y[1][0]\n",
    "    x2[i] = y[1][1]\n",
    "    x3[i] = y[1][2]\n",
    "    x4[i] = y[1][3]\n",
    "    x5[i] = y[1][4]\n",
    "    x6[i] = y[1][5]\n",
    "    # next initial condition\n",
    "    y0 = y[1]\n",
    "results=[x1[n-1],x2[n-1],x3[n-1],x4[n-1],x5[n-1],x6[n-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e81d5bfe-31af-4edb-bcb0-a7318bb430f1",
   "metadata": {},
   "source": [
    "#### Plotting and saving the figure\n",
    "\n",
    "- Parameters customized for publication quality figure\n",
    "- Color values set according to (https://davidmathlogic.com/colorblind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7683431a-cfbb-4992-b721-fc79e385f356",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3,2))\n",
    "color_values = ['#648FFF', '#785EF0', '#DC267F', '#FE6100', '#FFB000'] \n",
    "\n",
    "line_size=0.5\n",
    "custom_params = {\n",
    "    'axes.facecolor': 'white',\n",
    "    'axes.edgecolor': 'black',\n",
    "    'axes.grid': False,\n",
    "    'axes.axisbelow': 'line',\n",
    "    'axes.labelcolor': 'black',\n",
    "    'figure.facecolor': 'white',\n",
    "    \"axes.spines.right\": True, \n",
    "    \"axes.spines.bottom\": True,\n",
    "    'xtick.direction': 'inout',\n",
    "    'ytick.direction': 'in',\n",
    "    'xtick.bottom': True,\n",
    "    'xtick.minor.visible': False,\n",
    "    'xtick.major.bottom':  True,\n",
    "    'xtick.minor.bottom':  True,\n",
    "    'xtick.major.pad':     7,     # distance to major tick label in points#\n",
    "    'xtick.minor.pad':     3.4,     # distance to the minor tick label in points#\n",
    "    'xtick.major.size':    10,\n",
    "    'ytick.major.size':    4,\n",
    "    'xtick.major.width':   line_size,\n",
    "    'ytick.major.width':   line_size,\n",
    "    'axes.linewidth': line_size,\n",
    "    'xtick.minor.size':    2,\n",
    "    'pdf.fonttype': 42}\n",
    "\n",
    "sns.set_theme(style=\"ticks\", rc=custom_params, font=\"Arial\",font_scale=0.7)\n",
    "\n",
    "# plot results\n",
    "plt.plot(t,x1,color=color_values[0], linestyle=':',label='IP',linewidth=1.5)\n",
    "plt.plot(t,x2,color=color_values[0], linestyle='-',label='Isoprenol',linewidth=1.5)\n",
    "plt.plot(t,x3,color=color_values[0], linestyle='--',label='IPP',linewidth=1.5)\n",
    "plt.plot(t,x4,color=color_values[3], linestyle='--',label='DMAPP',linewidth=1.5)\n",
    "plt.plot(t,x5,color=color_values[3], linestyle=':',label='DMAP',linewidth=1.5)\n",
    "plt.plot(t,x6,color=color_values[3], linestyle='-',label='Prenol',linewidth=1.5)\n",
    "plt.ylabel('values')\n",
    "plt.xlabel('time')\n",
    "plt.legend(loc='upper left',bbox_to_anchor=(0.05, 0.99),fontsize=\"7\")\n",
    "plt.ylim(0,None)\n",
    "plt.xlim(0,72)\n",
    "#plt.title('IPK Recursion Kinetic Model')\n",
    "plt.xlabel('Time (arb. unit)', labelpad=10)\n",
    "plt.ylabel('Titer (arb. unit)', labelpad=10)\n",
    "\n",
    "plt.tick_params(axis='x', which='both', length=6) \n",
    "plt.xticks(range(0,80,12))\n",
    "              \n",
    "plt.savefig(f'Figure_Export/{fig}_Kinetic_Model.svg',dpi=1200, bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  }
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