KineticModelingScenario2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "overall-reputation",
   "metadata": {},
   "source": [
    "# Scenario 2 - kinetic Modeling with ODE Solver"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unsigned-bolivia",
   "metadata": {},
   "source": [
    "Description"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weighted-burner",
   "metadata": {},
   "source": [
    "### Imports\n",
    "Import packages and set global varibales used in this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "compliant-grill",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os # operating system to work with directories and files\n",
    "import matplotlib.pyplot as plt # plot data and results\n",
    "import seaborn as sns # prettier visualization\n",
    "import pandas as pd # convert excel to dataframe\n",
    "import numpy as np # convert dataframe to nparray for solver\n",
    "from scipy.integrate import odeint # solve ode\n",
    "from lmfit import minimize, Parameters, Parameter, report_fit # fitting\n",
    "from pyenzyme.enzymeml.tools import EnzymeMLReader # EnzymeML document functionalities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "careful-strengthening",
   "metadata": {},
   "source": [
    "## Select EnzymeML document\n",
    "Select the EnzymeML document created with BioCatHub, by changing the path vriable accodingly. <br>\n",
    "The whole EnzymeML document is stored in the enzmldoc varible. <br>\n",
    "Prints a short overview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "automatic-collins",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> Units\n",
      "\tID: u0 \t Name: ml\n",
      "\tID: u1 \t Name: K\n",
      "\tID: u2 \t Name: mole / l\n",
      "\tID: u3 \t Name: g\n",
      "\tID: u4 \t Name: min\n",
      ">>> Reactants\n",
      "\tID: s0 \t Name: pyruvate\n",
      "\tID: s1 \t Name: acetolactate\n",
      "\tID: s2 \t Name: Flavin dunucleotide\n",
      "\tID: s3 \t Name: Magnesium sulfide\n",
      ">>> Proteins\n",
      "\tID: p0 \t Name: HcAHAS_C_unit\n",
      ">>> Reactions\n",
      "\tID: r0 \t Name: pH measuremnt\n"
     ]
    }
   ],
   "source": [
    "path = 'datasets/pH optimum 7.omex'\n",
    "\n",
    "# check for correct file path and file extension:\n",
    "if os.path.isfile(path) and os.path.basename(path).lower().endswith('.omex'):\n",
    "    enzmldoc = EnzymeMLReader().readFromFile(path)\n",
    "    print(enzmldoc)\n",
    "else:\n",
    "    print('Incorrect file path.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dependent-integrity",
   "metadata": {},
   "source": [
    "## Visualization of timecourse data\n",
    "A short visualisation to get a first impression of the data. <br>\n",
    "First select the reaction to visualize by changing the reaction_id accordingly, see overview above for selction options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "respected-liver",
   "metadata": {},
   "outputs": [],
   "source": [
    "#basic/general settings\n",
    "sns.set_theme(style=\"whitegrid\", palette ='bright',color_codes=True, context = 'notebook')\n",
    "\n",
    "# set reaction id to 'r0' or 'r1'\n",
    "reaction_id = 'r0'\n",
    "reaction = enzmldoc.getReaction(reaction_id)\n",
    "reaction_name = reaction.getName()\n",
    "educts = reaction.getEducts() # list of tuples: (Reactant ID, stoichiometry, Constant, Replicate, Initial-Concentration)\n",
    "products = reaction.getProducts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "humanitarian-vegetation",
   "metadata": {},
   "source": [
    "Visualize educts, if the EnzymeML document contains time course data for educts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "passing-accommodation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x252 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Educts:\n",
    "for reactant_id, stoich, _, replicates, init_conc in educts:\n",
    "        \n",
    "    if len(replicates) > 0:\n",
    "            \n",
    "        df = reaction.exportReplicates(reactant_id)\n",
    "        time_val = df.index.tolist()\n",
    "        time = df.index.name.split('/')[0]\n",
    "        time_unit_name = df.index.name.split('/')[1]\n",
    "        time_unit = enzmldoc.getUnitDict()[time_unit_name].getName()\n",
    "            \n",
    "        f, ax = plt.subplots(figsize=(7,3.5))\n",
    "            \n",
    "        # Visualization\n",
    "        for col in df.columns:\n",
    "                \n",
    "            name = enzmldoc.getReactant(col.split('/')[1]).getName()\n",
    "            unit_name = enzmldoc.getReactant(col.split('/')[1]).getSubstanceUnits()\n",
    "            unit = enzmldoc.getUnitDict()[unit_name].getName()\n",
    "                \n",
    "            sns.lineplot( x=time_val, y=df[col], label = col.split('/')[0] )\n",
    "            \n",
    "        #set graph title, legend, axes \n",
    "        ax.set_title(reaction_name, fontsize = 12)\n",
    "\n",
    "        ax.legend(fontsize = 10, \\\n",
    "                    bbox_to_anchor= (1, 0.75), \\\n",
    "                    title= name, \\\n",
    "                    title_fontsize = 10, \\\n",
    "                    shadow = True, \\\n",
    "                    facecolor = 'white');\n",
    "\n",
    "        xlabel = f\"{time} [{time_unit}]\"\n",
    "        ylabel = f\"{'concentration'} [{unit}]\"\n",
    "\n",
    "        ax.set_xlabel(xlabel , fontsize=10)\n",
    "        ax.set_ylabel(ylabel, fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "greater-philippines",
   "metadata": {},
   "source": [
    "Visualize products, if the EnzymeML document contains time course data for products <br>\n",
    "The example data does not contain measurments of products."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "developmental-public",
   "metadata": {},
   "outputs": [],
   "source": [
    "for reactant_id, stoich, _, replicates, init_conc in products:\n",
    "        \n",
    "    if len(replicates) > 0:\n",
    "            \n",
    "        df = reaction.exportReplicates(reactant_id)\n",
    "        time_val = df.index.tolist()\n",
    "        time = df.index.name.split('/')[0]\n",
    "        time_unit_name = df.index.name.split('/')[1]\n",
    "        time_unit = enzmldoc.getUnitDict()[time_unit_name].getName()\n",
    "            \n",
    "        f, ax = plt.subplots(figsize=(7,3.5))\n",
    "            \n",
    "        # Visualization\n",
    "        for col in df.columns:\n",
    "                \n",
    "            name = enzmldoc.getReactant(col.split('/')[1]).getName()\n",
    "            unit_name = enzmldoc.getReactant(col.split('/')[1]).getSubstanceUnits()\n",
    "            unit = enzmldoc.getUnitDict()[unit_name].getName()\n",
    "                \n",
    "            sns.lineplot( x=time_val, y=df[col], label = col.split('/')[0] )\n",
    "            \n",
    "        #set graph title, legend, axes \n",
    "        ax.set_title(reaction_name, fontsize = 12)\n",
    "\n",
    "        ax.legend(fontsize = 10, \\\n",
    "                    bbox_to_anchor= (1, 0.75), \\\n",
    "                    title= name, \\\n",
    "                    title_fontsize = 10, \\\n",
    "                    shadow = True, \\\n",
    "                    facecolor = 'white');\n",
    "\n",
    "        xlabel = f\"{time} [{time_unit}]\"\n",
    "        ylabel = f\"{'concentration'} [{unit}]\"\n",
    "\n",
    "        ax.set_xlabel(xlabel , fontsize=10)\n",
    "        ax.set_ylabel(ylabel, fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "engaged-filename",
   "metadata": {},
   "source": [
    "## Parameter Estimation and Modeling\n",
    "#### Data preparation\n",
    "Convert pandas dataframe from EnzymeML data to numpy arrays. <br>\n",
    "First select the reactant to model by changing the reactant_id accordingly, see overview above for selction options.<br>\n",
    "In this example substrate 's0' will be modeled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "absolute-discharge",
   "metadata": {},
   "outputs": [],
   "source": [
    "reactant_id = 's0'\n",
    "replicates = reaction.exportReplicates(reactant_id)\n",
    "# time:\n",
    "data_time = replicates.index.values # numpy array shape (60,)\n",
    "# substrate data (absorption):\n",
    "data_s = np.transpose(replicates.iloc[:,:].to_numpy(np.float64)) # shape: (3, 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "norman-landscape",
   "metadata": {},
   "source": [
    "### Fit data to a system of ODEs\n",
    "#### Define the ODE functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "middle-bottle",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(w, t, params):\n",
    "    '''\n",
    "    System of differential equations\n",
    "    Arguments:\n",
    "        w: vector of state variables: w = [v,S]\n",
    "        t: time\n",
    "        params: parameters\n",
    "    '''\n",
    "    v, s = w\n",
    "    \n",
    "    a = params['a'].value\n",
    "    vmax = params['vmax'].value\n",
    "    km = params['Km'].value\n",
    "    \n",
    "    # f(v',s'):\n",
    "    f0 = a*(vmax-v) # v'\n",
    "    f1 = -v*s/(km+s) # S'\n",
    "    return [f0,f1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compact-diary",
   "metadata": {},
   "source": [
    "#### Solve ODE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "nominated-profile",
   "metadata": {},
   "outputs": [],
   "source": [
    "def g(t, w0, params):\n",
    "    '''\n",
    "    Solution to the ODE w'(t)=f(t,w,p) with initial condition w(0)= w0 (= [v0, S0])\n",
    "    '''\n",
    "    w = odeint(f, w0, t, args=(params,))\n",
    "    return w"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "functional-establishment",
   "metadata": {},
   "source": [
    "#### Compute residual between actual data (S) and fitted data\n",
    "In this model we assume that the data contains a bias on the y-axis. <br>\n",
    "Therfore we compute the distance between the modeled substrate + bias and the actual mesuared substrate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "binding-privilege",
   "metadata": {},
   "outputs": [],
   "source": [
    "def residual(params, t, data_s):\n",
    "    ndata, nt = data_s.shape # get dimensions of data (here we fit against 3 measurments => ndata = 3)\n",
    "    resid = 0.0*data_s[:] # initialize the residual vector\n",
    "    # compute residual per data set\n",
    "    for i in range(ndata):\n",
    "        w0 = params['v0'].value, params['S0'].value\n",
    "        model = g(t, w0, params) # solve the ODE with the given parameters\n",
    "        # get modeled substrate\n",
    "        s_model = model[:,1]\n",
    "        s_model_b = s_model + params['b'].value # adding bias\n",
    "        resid[i,:]=data_s[i,:]-s_model_b # compute distance to measured data\n",
    "    return resid.flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intense-detective",
   "metadata": {},
   "source": [
    "#### Functions to compute initial value for vmax and Km\n",
    "To get a good guess for vmax, v is computed for each time step. <br>\n",
    "For Km the mean of s values at aproximatly vmax/2 is taken."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "specified-framework",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_v(time, data_s):\n",
    "    v_all = 0.0*data_s[:] # initialize velocity vector\n",
    "    for i in range(data_s.shape[0]):\n",
    "        prev_value = data_s[i,0]\n",
    "        prev_time = 0.0\n",
    "        for j in range(data_s.shape[1]):\n",
    "            if time[j] == 0:\n",
    "                delta = prev_value - data_s[i,j]\n",
    "            else:\n",
    "                delta = abs( (prev_value - data_s[i,j])/(time[j]-prev_time))\n",
    "            v_all[i,j] = delta\n",
    "            prev_value = data_s[i,j]\n",
    "            prev_time = time[j]\n",
    "    v = np.mean(v_all, axis=0)\n",
    "    return v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "acquired-confirmation",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_initial_vmax(time, data_s):\n",
    "    v = get_v(time,data_s)\n",
    "    return np.max(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "integrated-merit",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_initial_Km(time, data_s):\n",
    "    v = get_v(time,data_s)\n",
    "    idx_max = np.where(v == np.max(v))[0][0]\n",
    "    idx_Km = (np.abs(v[idx_max:]-np.max(v)/2)).argmin()\n",
    "    km = np.mean(data_s,axis=0)[idx_max+idx_Km]\n",
    "    return km"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dynamic-sudan",
   "metadata": {},
   "source": [
    "#### Bringing everything together\n",
    "Initialize parameters:\n",
    "- $v_0$ is fixed on 0.\n",
    "- bias is estimated by taking the mean of the last data point for all measured data.\n",
    "- for $S_0$ the mean of first data point for all measured data is taken and substracted by the estimated bias.\n",
    "- functions to get initial values for $v_{max}$ and $K_m$ are called.\n",
    "- initial value for a is set to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "catholic-pontiac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# time\n",
    "t_measured = data_time\n",
    "\n",
    "# initial conditions:\n",
    "v0 = 0\n",
    "s0 = np.mean(data_s,axis=0)[0]\n",
    "\n",
    "# Set parameters including bounds\n",
    "bias = np.mean(data_s,axis=0)[-1]\n",
    "vmax = get_initial_vmax(t_measured, data_s)\n",
    "km = get_initial_Km(t_measured, data_s)\n",
    "if km > bias:\n",
    "    km = km-bias\n",
    "\n",
    "params = Parameters()\n",
    "params.add('v0', value=v0, vary=False)\n",
    "params.add('S0', value=s0-bias, min=0.1, max=s0)\n",
    "params.add('a', value=1., min=0.0001, max=2.)\n",
    "params.add('vmax', value=vmax, min=0.0001, max=1.)\n",
    "params.add('Km', value=km, min=0.0001, max=1.)\n",
    "params.add('b', value=bias, min=0.01, max=s0*0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rural-eleven",
   "metadata": {},
   "source": [
    "#### Fit model and visualize results\n",
    "Statistics for the Fit and the parameters a printed. <br>\n",
    "In the graph the red line shows the result of the model. <br>\n",
    "The dotted curves are the measured data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "coordinate-delaware",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 64\n",
      "    # data points      = 180\n",
      "    # variables        = 5\n",
      "    chi-square         = 0.04123983\n",
      "    reduced chi-square = 2.3566e-04\n",
      "    Akaike info crit   = -1498.63538\n",
      "    Bayesian info crit = -1482.67059\n",
      "[[Variables]]\n",
      "    v0:    0 (fixed)\n",
      "    S0:    0.86085389 +/- 0.00534287 (0.62%) (init = 0.8834)\n",
      "    a:     1.69790718 +/- 0.15515626 (9.14%) (init = 1)\n",
      "    vmax:  0.28159300 +/- 0.01082297 (3.84%) (init = 0.2668)\n",
      "    Km:    0.04761829 +/- 0.01094641 (22.99%) (init = 0.04726667)\n",
      "    b:     0.09725101 +/- 0.00436843 (4.49%) (init = 0.09466667)\n",
      "[[Correlations]] (unreported correlations are < 0.100)\n",
      "    C(vmax, Km) =  0.947\n",
      "    C(a, vmax)  = -0.895\n",
      "    C(a, Km)    = -0.743\n",
      "    C(S0, b)    = -0.736\n",
      "    C(Km, b)    = -0.638\n",
      "    C(vmax, b)  = -0.504\n",
      "    C(a, b)     =  0.333\n",
      "    C(S0, Km)   =  0.312\n",
      "    C(S0, a)    =  0.211\n",
      "    C(S0, vmax) =  0.122\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fit model\n",
    "result = minimize(residual , params, args=(t_measured, data_s), method='leastsq')\n",
    "report_fit(result) # access values of fitted parameters: result.params['Km'].value\n",
    "# plot the data sets and fits\n",
    "w0 = params['v0'].value, params['S0'].value\n",
    "data_fitted = g(t_measured, w0, result.params)\n",
    "plt.figure()\n",
    "\n",
    "for i in range(data_s.shape[0]):\n",
    "    plt.plot(t_measured, data_s[i, :], 'o')\n",
    "plt.plot(t_measured, data_fitted[:, 1]+params['b'].value, '-', linewidth=4, color='red', label='fitted data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "moved-tolerance",
   "metadata": {},
   "source": [
    "Add equeations to EnzymeML document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "alone-celebrity",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dying-qatar",
   "metadata": {},
   "source": [
    "Add parameters to EnzymeML document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "honey-farmer",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "apparent-jesus",
   "metadata": {},
   "source": [
    "Dataverse Entry?"
   ]
  }
 ],
 "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}