Notebooks/GroExpSim_Nr2.ipynb

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    "# Analysis of growth rate, substrate uptake and yield\n",
    "\n",
    "## Background\n",
    "The second lecture in our biotechnology course delves into the critical aspect of data analysis in microbial growth dynamics experiments. Through hands-on exercises, students will gain proficiency in fitting dynamic growth data to mathematical models and extracting key parameters such as growth rate, substrate uptake rate, and yield. Three fundamental equations—linear, exponential, and logistic—will serve as the cornerstone for data fitting, each offering unique insights into microbial growth kinetics.\n",
    "\n",
    "Linear equations, particularly when applied to the logarithm of biomass, provide a simplistic yet robust approach to characterizing microbial growth dynamics. By transforming the biomass data logarithmically, linear relationships can emerge, facilitating easier interpretation and calculation of growth parameters. However, linear equations may not adequately capture complex growth patterns exhibited by certain microorganisms under varying conditions.\n",
    "\n",
    "> **_Linear equations_**<br>\n",
    ">  $\\rm{ln}(\\it{N}(t)) = \\mu t + \\rm{ln(\\it{N}_0)}$\n",
    ">  - $N_0$: initial biomass, gDW/L \n",
    ">  - $\\mu$: growth rate, /h\n",
    ">  - $\\rm{t}$: duration, h\n",
    "\n",
    "Exponential equations offer a more nuanced representation of microbial growth, allowing for the depiction of exponential increases in cell number over time. This model is particularly suitable for describing rapid growth phases in microbial cultures with abundant nutrients. While exponential equations excel in capturing rapid growth kinetics, they may fall short in accounting for factors such as nutrient limitation or inhibitory effects.\n",
    "\n",
    "> **_Exponential law_**<br>\n",
    ">  $N(t) = N_0e^{\\mu t}$\n",
    ">  - $N_0$: initial biomass, gDW/L \n",
    ">  - $\\mu$: growth rate, /h\n",
    ">  - $t$: duration, h\n",
    "\n",
    "Logistic equations provide a sophisticated framework for modeling microbial growth, incorporating a carrying capacity parameter that represents the maximum population size achievable in a given environment. This model is invaluable for elucidating growth dynamics under conditions of nutrient limitation or space constraints. However, logistic equations require more parameters to be fitted, increasing the complexity of data analysis and potentially introducing greater uncertainty.\n",
    "\n",
    "> **_Logistic growth law (Verhulst)_**<br>\n",
    ">  $N(t) = \\frac{K}{1+\\left( \\frac{K-N_0}{N_0}e^{-\\mu t} \\right)}$\n",
    ">  - $N_0$: initial biomass, gDW/L\n",
    ">  - $\\mu$: growth rate, /h\n",
    ">  - $t$: duration, h\n",
    ">  - $K$: carrying capacity, max biomass, gDW/L\n",
    "\n",
    "Alternative growth laws like Gompertz and Baranyi models offer effective alternatives to traditional growth equations, especially in scenarios with complex growth patterns. Gompertz model captures sigmoidal growth, including lag phases, while Baranyi model considers initial microbial concentration and environmental conditions. They find applicability in food microbiology for predicting growth dynamics influenced by temperature, pH, and food composition, aiding in food safety assessment and shelf-life prediction.\n",
    "\n",
    "> **_Gompertz function_** <br>\n",
    ">  $N(t) = N_0e^{\\left(\\textrm{ln} \\left(\\frac{K}{N_0} \\right)\\left(1 - e^{-\\mu t} \\right) \\right)}$\n",
    ">  - $N_0$: initial biomass, gDW/L\n",
    ">  - $\\mu$: growth rate, /h\n",
    ">  - $t$: duration, h\n",
    ">  - $K$: carrying capacity, max biomass, gDW/L\n",
    "\n",
    "---\n",
    "Through practical exercises, students will gain insights into the advantages and disadvantages of each equation, learning to discern the most appropriate model for a given set of experimental data. Moreover, they will explore the significance of statistical measurements such as standard deviations and correlation coefficients in assessing the confidence of their results and guiding the design of future experiments.\n",
    "\n",
    "The relevance of this topic to biotechnology is immense, as accurate characterization of microbial growth dynamics is essential for optimizing bioprocesses and enhancing product yields. By mastering data analysis techniques, students will be equipped to tackle real-world challenges in biotechnological research and industry, from designing efficient fermentation processes to developing novel bioproducts.\n",
    "\n",
    "## Running simulations\n",
    "The code cells below contain the commands to simulate the experiments. User input is required for variables that are by default set to `None`.\n",
    "\n",
    "The results for each growth experiment should be stored and entered in the appropriate variables in section 2. final outcome of this sheet is an Excel file with simulated growth experiments containing biomass and substrate dynamics. The analysis of the Excel sheet to calculate growth rate, substrate uptake rate and yields is performed with the subsequent notebook [GroExpSim_Nr2](./GroExpSim_Nr2.ipynb).\n",
    "\n",
    "## Learning Objectives:\n",
    "1. Understand the principles underlying microbial growth dynamics and the importance of data analysis in biotechnology processes. (Knowledge)\n",
    "2. Apply mathematical models such as linear, exponential, and logistic equations to analyze dynamic growth data and extract key parameters. (Application)\n",
    "3. Evaluate the advantages and disadvantages of different growth models in capturing various growth patterns exhibited by microorganisms. (Analysis)\n",
    "4. Interpret statistical measurements such as standard deviations and correlation coefficients to assess the confidence of experimental results. (Evaluation)\n",
    "5. Propose strategies for optimizing experimental design to improve confidence intervals and enhance the reliability of growth kinetics measurements. (Synthesis)\n",
    "\n",
    "### Additional information\n",
    "- Verduyn et al., A theoretical evaluation of growth yields of yeasts, Antoie van Leeuwenhoek, 1991. doi [10.1007/BF00582119](https://doi.org/10.1007/BF00582119)\n",
    "- Pirt, The maintenance energy of bacteria in growing cultures, 1965. doi [10.1098/rspb.1965.0069](https://doi.org/10.1098/rspb.1965.0069)\n",
    "- Grijspeerdt & Vanrolleghem, Estimating the parameters of the Baranyi model for bacterial growth, Food Microbiology, 1999, doi [10.1006/fmic.1999.0285](https://doi.org/10.1006/fmic.1999.0285)\n",
    "- A template for he data analysis with Excel sheet is provided by Tian and Reed in the Article [Sato et al.](https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006372) in Appendices 1+2.\n",
    "\n",
    "## Workflow\n",
    "\n",
    "**0 Set-up of computation environment**\n",
    " * *0.1 Loading Python libraries*\n",
    " * *0.2 Definition of functions*\n",
    "\n",
    "**1 Data import**\n",
    " * *1.1 File definition*\n",
    " * *1.2 Sheet definition*\n",
    "\n",
    "**2 Data plotting**\n",
    "\n",
    "---\n",
    "\n",
    "## 0 Set-up of simulation environment\n",
    "### 0.1 Loading libraries\n",
    "Loading libraries and fixing visualization. No user input necessary."
   ]
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      "Environment ready.\n"
     ]
    }
   ],
   "source": [
    "# Adapting environment to GitHub or Google Colab\n",
    "# In GitHub, the full repository is available with all directories, and libraries can be installed with pip on requirements.txt\n",
    "# In Google Colab only the selected notebook is loaded and the requirements file must be downloaded from GitHub before installation.\n",
    "# \n",
    "\n",
    "# file system and path operations\n",
    "import os\n",
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "    IN_COLAB = True\n",
    "    # Download the requirements file\n",
    "    os.system('wget https://raw.githubusercontent.com/biolabsim/BioLabSim/refs/heads/master/requirements.txt')\n",
    "    os.system('pip install -r requirements.txt')\n",
    "else:\n",
    "    IN_COLAB = False\n",
    "    os.system('pip install -r ../requirements.txt')\n",
    "\n",
    "print('Environment ready.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libraries loaded.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "# # import glob\n",
    "# # import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import date\n",
    "from scipy import stats\n",
    "from scipy.optimize import curve_fit\n",
    "# # import ipywidgets as widgets\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "mol2g = {'glc':1/.18}\n",
    "# All files are attached with the current date\n",
    "Today = date.today().strftime('%y%m%d')\n",
    "# print plus minus sign\n",
    "pm = '\\u00B1'\n",
    "\n",
    "from silvio.catalog.GroExpSim import GrowthExperiment\n",
    "\n",
    "Par_Bud = 5000\n",
    "\n",
    "\n",
    "print('Libraries loaded.')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0.2 Definition of functions\n",
    "\n",
    "Below a function is defined that extracts a time vector that lies between two input times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SubVector(StartEnd, Vector):\n",
    "    '''\n",
    "    Extract a sub-vector based on start and end values. The output are the indices in the input vector.\n",
    "    '''\n",
    "    Above = Vector >= StartEnd[0]\n",
    "    Below = Vector <= StartEnd[1]\n",
    "    Select = np.logical_and(Above, Below)\n",
    "    return np.arange(len(Vector))[Select]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data loading\n",
    "\n",
    "### 1.1 File definition\n",
    "Here, the data generated in the notebook [GroExpSim_Nr1](GroExpSim_Nr1.ipynb) and stored in the `Data`-directory in file `GrowthExperiment_StandardFormat.xlsx`. However, before this file can be used, the metadata needs to be updated. In particular, the following analysis can only be performed if the metadata-field about the OD to cell dry weight conversion (OD2CDW) is completed. \n",
    "\n",
    " - **Input:**\n",
    "   - **`File`: String, Growth rate Excel file, e.g. 'GrowthExperiment_StandardFormat.xlsx'**\n",
    " - **Output:**\n",
    "   - **Direct response of the available sheets in the Excel file.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excel sheets:\n",
      "['Reference']\n"
     ]
    }
   ],
   "source": [
    "# General Working Directory\n",
    "# For automatic reading of directory, e.g. in cloud use:\n",
    "Root_Dir = os.getcwd()\n",
    "# remove the last directory fro Root_Dir\n",
    "Root_Dir = os.path.dirname(Root_Dir)\n",
    "Target_Dir = os.path.join(Root_Dir, 'Data')\n",
    "\n",
    "File = 'GrowthExperiment_StandardFormat.xlsx' # Add Excel file with growth data, e.g. GrowthExperiment_StandardFormat.xlsx\n",
    "FileAddress = os.path.join(Target_Dir, File)\n",
    "Xls_df = pd.ExcelFile(FileAddress)\n",
    "print('Excel sheets:\\n{}'.format(Xls_df.sheet_names))\n",
    "\n",
    "# Output Files\n",
    "SaveFig = False # set to True to save figures\n",
    "# All files are attached with the current date\n",
    "Today = date.today().strftime('%y%m%d')\n",
    "# Choose image file type, e.g. svg, png, pdf, eps\n",
    "ImageType = 'png'\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Sheet definition\n",
    "Select in the following cell the sheet with which an analysis should be performed. The commands in the cell read the metadata and extract the relevant information.\n",
    "\n",
    "- **Input**\n",
    "  - **`Sheet`: String, name of the sheet in the Excel file with the relevant data**\n",
    "- **Output**\n",
    "  - **Direct response of the data columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data columns: ['time', 'OD', 'Substrate']\n"
     ]
    }
   ],
   "source": [
    "# Choosing Excel sheet\n",
    "Sheet = 'Reference'\n",
    "# Define conversion factor, e.g. yeast: 0.62 (BNID 111182 in Bionumbers)\n",
    "\n",
    "\n",
    "# Loading data from sheet into python, ignoring Metadata columns\n",
    "df_orig = pd.read_excel(FileAddress, sheet_name=Sheet, usecols=lambda x: 'Metadata' not in x,)\n",
    "df_orig = df_orig.dropna(how = 'all')\n",
    "DelHeader = df_orig.columns[['Unnamed' in header for header in df_orig.columns]].values\n",
    "df_orig = df_orig.drop(DelHeader, axis=1)\n",
    "\n",
    "myMeta = pd.read_excel(FileAddress, sheet_name=Sheet, usecols=lambda x: 'Metadata' in x,)\n",
    "TimeUnit = ''.join(myMeta.loc[myMeta['Metadata: Key']=='Time unit']['Metadata: Value'].values)\n",
    "# add a biomass correction factor, e.g. from OD to gCDW, if none set to 1\n",
    "BiomassUnit = ''.join(myMeta.loc[myMeta['Metadata: Key']=='Biomass unit']['Metadata: Value'].values)\n",
    "# read or define existing conversion factor for OD to gCDW/L and assert that it is a float\n",
    "OD2DW = myMeta.loc[myMeta['Metadata: Key']=='OD2CDW']['Metadata: Value'].values[0]\n",
    "# assert that OD2DW is not nan\n",
    "OD2DW = OD2DW if not np.isnan(OD2DW) else float(input('give OD2DW:'))\n",
    "BiomassUnit = 'gDW/L'\n",
    "\n",
    "SubstrateUnit = ''.join(myMeta.loc[myMeta['Metadata: Key']=='Substrate unit']['Metadata: Value'].values)# 'mM'\n",
    "# add a substrate correction factor, e.g. to convert from g/l to mM\n",
    "SubstrateID = ''.join(myMeta.loc[myMeta['Metadata: Key']=='Substrate']['Metadata: Value'].values).lower()\n",
    "# conversion of g/L concentration to mmol/L\n",
    "SubstrateCorr = mol2g[SubstrateID]\n",
    "YieldCorr = 1/mol2g[SubstrateID]\n",
    "\n",
    "print('Data columns: {}'.format(list(df_orig.columns)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Data plotting\n",
    "\n",
    "The following cells plots the data of biomass and substrate over time. To do so, Python has to know into which columns to look and the right column Ids for the time, OD, and substrate measurements have to be supplied.\n",
    "\n",
    " - **Input**  \n",
    "   - **`TimeID`: String, ID of column for time, e.g. 'time'\n",
    "   - **`BiomassID`: String, ID of column for biomass, e.g. 'OD'\n",
    "   - **`SubstrateID`: String, ID of column for substrate concentration, e.g. 'Substrate'\n",
    " - **Output**\n",
    "   - **Plot with biomass and substrate dynamics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the column names of biomass and substrate\n",
    "TimeID = 'time'\n",
    "BiomassID =  'OD'\n",
    "SubstrateID = 'Substrate'\n",
    "\n",
    "df=df_orig.copy()\n",
    "# Convert OD to gCDW\n",
    "df[BiomassID] = df_orig[BiomassID]*OD2DW\n",
    "# Convert g/L to mM\n",
    "df[SubstrateID] = df_orig[SubstrateID]*SubstrateCorr\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "#### Biomass plot\n",
    "color = 'tab:green'\n",
    "ax1.set_xlabel(f'time in {TimeUnit}')\n",
    "ax1.set_ylabel(f'Biomass in {BiomassUnit}', color=color)\n",
    "ax1.scatter(df[TimeID], df[BiomassID], color=color)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "#### Substrate plot\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('Substrate in mM', color=color)  # we already handled the x-label with ax1\n",
    "ax2.scatter(df[TimeID], df[SubstrateID], color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "\n",
    "# Export plot to the Target_Dir if SaveFig is True\n",
    "if SaveFig:\n",
    "    ImageName = f'Growth_{Sheet}_{Today}.{ImageType}'\n",
    "    ImageAddress = os.path.join(Target_Dir, ImageName)\n",
    "    plt.savefig(ImageAddress)\n",
    "    print(f'Image saved to {ImageAddress}')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Growth law\n",
    "More parameters mean that more properties of the growth curve can be reproduced, the curve can get more complex e.g. curved (+1 parameter in exponential law), or sigmoidal (+2 parameters in logistic law)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pDGsyctxuJ53KADId+azJyKm8okTOglpkqqinnnqKO++8k+bNm1NQUIDx/5+KSk9Px+Fw+Pzz//3vfxMSEsKgQYMoKCigV69evPrqq27HnFzLX3/9xaJFiwBKDCj39ddf07VrVyIjI/n444+ZMGEChw8fJj4+nt69e/PYY48RHh5eZi0DBw4kLS2tzP1NmjRxjStjt9v54osvGDVqFO3ataN+/fo88cQTDB8+3HW8w+EgPT3d7Ry3336722dceOGFAGRkZNC0adNynVekMmTnlR1iKnKciNVshlHO534DVG5uLna7HYfDUaK/RX5+PhkZGSQmJhIREWFRhSKl0++n+MKqbfsZ/Ob3Zzzu/TsuJrX5mW/vivjK6b6/T6ZbSyIiQaRjYj3i7RGU1fvFhvn0UsfEepVZlkiFKciIiASR0BAbE/omA5QIM8XrE/omq6OvBAwFGRGRINM7JZ7pQy4izu5+yzLOHqFHryXgqLOviEgQ6p0ST8/kOI3sKwFPQQao4v2dJUDp91J8LTTEpg69EvCC+tZS8cBkR44csbgSkZKKfy81hYKISNmCukUmNDSUOnXqkJ2dDUBkZGSZ8/WIVBbDMDhy5AjZ2dnUqVOH0NBQq0sSEfFbQR1k4MQ8N8VhRsRf1KlTR/MwiYicQdAHGZvNRnx8PDExMRQWFlpdjghg3k5SS4yIyJkFfZApFhoaqi8OERGRAKMgIyISQIqchh6ZFjmJgoyISIBYuimTiYs3u81eHW+PYELfZA1iJ0ErqB+/FhEJFEs3ZTJy1nq3EAOQ5chn5Kz1LN2UaVFlItZSkBER8XNFToOJizdT2hCJxdsmLt5MkVODKErwUZAREfFzazJySrTEnMwAMh35rMnIqbyiRPyEgoyIiJ/Lzis7xFTkOJGqREFGRMTPxURFnPkgD44TqUoUZERE/FzHxHrE2yMo6yFrG+bTSx0T61VmWSJ+QUFGRMTPhYbYmNA3GaBEmClen9A3WePJSFBSkBERCQC9U+KZPuQi4uzut4/i7BFMH3KRxpGRoKUB8UREAkTvlHh6JsdpZF+RkyjIiIgEkNAQG6nNo60uQ8Rv6NaSiIiIBCwFGREREQlYCjIiIiISsBRkREREJGApyIiIiEjAUpARERGRgGVpkJk0aRIdOnQgKiqKmJgY+vfvT3p6utsx+fn5jBo1iujoaGrVqsWgQYPYs2ePRRWLiIiIP7E0yKSlpTFq1Ci+//57li1bRmFhIVdeeSWHDx92HXPvvfeyePFi5s+fT1paGrt372bgwIEWVi0iIiL+wmYYhmF1EcX27t1LTEwMaWlpXHbZZTgcDho0aMCcOXO45pprAPjtt98477zzWLVqFRdffPEZz5mbm4vdbsfhcFC7dm1f/wgiIiLiBeX9/varPjIOhwOAevXMGVzXrVtHYWEhPXr0cB2TlJRE48aNWbVqVannKCgoIDc31+0lIiIiVZPfTFHgdDoZO3Ysl156KSkpKQBkZWURFhZGnTp13I6NjY0lKyur1PNMmjSJiRMn+rpcEREJBsU3LU6+eVHWckX3nenGiK/3e+M94eEQFub553iB3wSZUaNGsWnTJr799tuzOs/DDz/MuHHjXOu5ubkkJCScbXkiIv7JMOD4cSgoMF/5+aUvl2dfYSEUFZ14HT/u3XWn06y3rH/Pdl/x9Tg5fJS1XJ79Un6vvw7Dh1vy0X4RZEaPHs2SJUtYuXIljRo1cm2Pi4vj2LFjHDx40K1VZs+ePcTFxZV6rvDwcMLDw31dsohIxRmGGRwOHICDB0u+Stt+4ADk5ZUeSPTFK0HM0iBjGAZjxoxhwYIFrFixgsTERLf97dq1o3r16ixfvpxBgwYBkJ6ezs6dO0lNTbWiZBGRknJyYOtW2L7dDBxlBZGT148d800t1aqZzfwREea/xa8zrRffGggNNV/Vqp1YLm29PMecuh4SYr5stpL/lrbN0302m3kNyrtckf3Fylqu6L5TjzuVr/efrerVfXv+07A0yIwaNYo5c+awcOFCoqKiXP1e7HY7NWrUwG63c9tttzFu3Djq1atH7dq1GTNmDKmpqeV6YklExCsMA/bsgW3bzMBS/CpeP3CgYucNDYU6dUq+6tYtfT0qyj2AnLpcHEREgoilj1/bykiI77zzDjfffDNgDoh333338f7771NQUECvXr149dVXy7y1dCo9fi0i5eJ0wl9/lR5Utm6Fk8a3KlXDhtCsGdSvX3YgOXVbrVq+/y9lkQBV3u9vvxpHxhcUZETE5fhx2LGjZEjZuhX++MPsc1KWkBBo3BiaN4cWLU68mjc3A0zNmpX3c4gEgfJ+f/tFZ18REZ/JyoIlS2DxYvjySzhypOxjq1UzQ0lpYaVpU/MWjoj4FQUZEalaDAM2bjSDy+LFsGaN+/6IiNKDSosWkJBghhkRCRj6f6yIBL6CAlix4kR42bnTfX+HDtC3r/lq3dq8TSQiVYKCjIgEpr174dNPzeDy+edw6NCJfTVqQI8eZnD5+98hPt66OkXEpxRkRCQwGAb8+qsZXBYtglWr3AeCi483Q0u/fnDFFRAZaV2tIlJpFGRExH8VFsI335jBZfFi88mik7VtawaXvn3hoot0y0gkCCnIiIh/ycmBzz4zg8vSpeBwnNgXFgbdu5+4ZaR51ESCnoKMiPiHP/6AcePMR6WLik5sb9DADC19+0LPnuYgciIi/5+CjIhY69gx+Oc/4amnzAkQAVJSTjxl1LGjht0XkTIpyIiIdb79FkaMgF9+MdevuAJeegmSk62tS0QChnrGiUjly8mBO+6ALl3MEFO/PvznP+bIuwoxIuIBtciISOUxDJg92+wLs3evue322+H556FePWtrE5GApCAjIpVjyxYYORKWLzfXk5PhtdfMVhmRUhQ5DdZk5JCdl09MVAQdE+sRGqLZwsWdgoyI+FZBAUyZAs8+ay5HRMATT8B995mPU4uUYummTCYu3kymI9+1Ld4ewYS+yfRO0UjNcoL6yIiI76SlQZs2ZnApKIBevWDTJnj4YYUYKdPSTZmMnLXeLcQAZDnyGTlrPUs3ZVpUmfgjBRkR8b59++CWW6BrV0hPh9hYeP99c6C75s2trk78WJHTYOLizRil7CveNnHxZoqcpR0hwUhBRkS8xzBg5kxISjL/tdnMx6t/+w3+8Q9zXeQ01mTklGiJOZkBZDryWZORU3lFiV9THxkR8Y7ffjNDS1qaud66Nbz+Olx8sbV1SUDJzis7xFTkOKn61CIjImcnP9/sA9O6tRliIiPNzr1r1yrEiMdioiK8epxUfWqREZGK+/JL85HqrVvN9auugpdfhqZNLS1LAlfHxHrE2yPIcuSX2k/GBsTZzUexRUAtMiJSEdnZMGSIOYnj1q3QsCF8+KE5Y7VCjJyF0BAbE/qaozuf2qOqeH1C32SNJyMuCjIiUn5OJ7z5JrRqZY7Qa7PBmDHw668waJA684pX9E6JZ/qQi4izu98+irNHMH3IRRpHRtzo1pKIlI/TCYMHwwcfmOsXXmh25u3Qwdq6pErqnRJPz+Q4jewrZ6QgIyLl88gjZoipXt2cG2nMGKimPyHiO6EhNlKbR1tdhvg5/RUSkTObMcMMLwBvv232jxER8QPqIyMip7d8uflkEpiPWSvEiIgfUZARkbIVd+I9ftzsH/Pkk1ZXJCLiRkFGREqXnW2OC+NwwKWXmreU9FSSiPgZBRkRKSk/H/r3h4wMaNYMFiyACI2kKiL+R0FGRNw5nebM1atWQZ068N//QoMGVlclIlIqBRkRcTdhAsydaz5a/dFH5kzWIiJ+SkFGRE74z3/gmWfM5TfegCuusLYeEZEzUJAREVNaGtx+u7n80EPm7SURET+nICMi8PvvMGAAFBbCtdfCs89aXZGISLkoyIgEu/37zcesDxyATp3g3XchRH8aRCQw6K+VSDArKDBbYrZuhSZNYOFCqFHD6qpERMpNQUYkWBkG3HEHfPMN1K5tPmYdG2t1VSIiHlGQEQlWzzwD770HoaEwfz6cf77VFYmIeExBRiQYvf++OQEkwCuvwJVXWluPiEgFeS3IHDx4kDlz5njrdCLiK999d+LR6vvugzvvtLYeEZGz4LUgs2PHDm666SZvnU5EfGHbNrj6arOT79VXw/PPW12RiMhZ0a0lkWBx4AD8/e+wbx+0awezZ5v9Y0REApiCjEgwOHYMrrkGfvsNGjWCRYugZk2rqxIROWsKMiJVnWHAXXfBV19BrVqwZAk0bGh1VSIiXlGtvAdOmzbttPv/+uuvsy5GRHxgyhR46y1ztN65c6FNG6srEhHxmnIHmX//+99nPKZx48ZnVYyIeNmHH5oTQAJMnWpORSAiUoWUO8hkZGT4sg4R8bY1a6D4ScIxY8yXiEgVU+4+MkOHDuWjjz7i8OHDvqxHRLxhxw7o1w/y881WmHK0qIqIBKJyB5kWLVrw3HPPUb9+ffr06cP06dPVL0bEHzkcZnjZs8fsD/P++3rMWkSqLJthGIYnb/jzzz9ZtGgRCxcuJC0tjfPPP5+rr76afv360bZtWx+VWXG5ubnY7XYcDge1a9e2uhwR3zp+3AwxX3wB8fHm7aVGjayuSkTEY+X9/vb48etGjRpx11138fnnn7N3717Gjx9Peno6V1xxBU2aNGH06NH88ssvZ1W8iFTQCy+YISYyEhYvVogRkSrvrMaRiYqK4rrrrmP27Nns3buXt99+m9DQUFatWuWt+kSkvPbtg8mTzeVXXzVH7xURqeLK/dTShAkT6N69OxdffDFhYWEl9oeGhtK9e3e6d+/u1QJFpJyeew5yc6Ft2xNPK4mIVHHl7iOTmJjIjh07iIiIIDU1lW7dutGtWzc6depEtWrlzkOVTn1kJCjs2AEtW5pTEXz+OVx5pdUViYicFa/3kcnIyOCPP/7glVdeoVGjRsyYMYMuXbpQt25devfuzfPPP8+aNWu8UryIeOiJJ8wQc8UV0LOn264ip8GqbftZuOEvVm3bT5HTo/79IiJ+zeOnlk6WkZHB119/zYoVK1i4cCGHDx/m+PHj3qzvrKlFRqq8jRvN20mGYT6l1KGDa9fSTZlMXLyZTEe+a1u8PYIJfZPpnRJvQbEiIuXjs6eWiu3YsYOVK1eSlpbGypUrKSws5LLLLqvo6USkoh55xAwx115bIsSMnLXeLcQAZDnyGTlrPUs3ZVZ2pSIiXlfuzi07d+5kxYoVrhaYffv2cckll3D55Zdzxx130LFjx1I7AYuID6WlwX//aw549+yzrs1FToOJizdTWnOrAdiAiYs30zM5jtAQW2VVKyLideVukWnatClPPPEESUlJzJs3j4MHD/L555/zyCOP0Llz5wqFmJUrV9K3b18aNmyIzWbjk08+cdt/8803Y7PZ3F69e/f2+HNEqiTDgPHjzeXhw+Hcc1271mTklGiJcXsrkOnIZ01Gjo+LFBHxrXIHmeuuu46CggKef/55nnnmGaZOncr69es5iy42HD58mDZt2vDKK6+UeUzv3r3JzMx0vd5///0Kf55IlfLJJ7B6tTn43RNPuO3Kzis7xFTkOBERf1XuW0tz584F4LfffnPdXnrhhRfIz8+nc+fOXH755XTt2pUOJ92jP5M+ffrQp0+f0x4THh5OXFxcuc8pEhSOH4eHHzaXx42DU/4/EhMVUa7TlPc4ERF/5XFn36SkJEaOHMm8efPIysriu+++o23btjzzzDOkpqZ6vcAVK1YQExNDq1atGDlyJPv37z/t8QUFBeTm5rq9RKqcmTMhPR2io+GBB0rs7phYj3h7BGX1frFhPr3UMbGeL6sUEfG5Co1kt2fPHlasWOHq/Pv7778THh5Oly5dvFpc7969GThwIImJiWzbto1HHnmEPn36sGrVKkLLmM130qRJTJw40at1iPiVI0dgwgRz+bHHoJTHEkNDbEzom8zIWeuxgVun3+JwM6Fvsjr6ikjAK/c4Mh988IErvKSnp1O9enU6dOjgGuH3kksuITw8vOKF2GwsWLCA/v37l3nMH3/8QfPmzfnyyy/LnAqhoKCAgoIC13pubi4JCQkaR0aqjsmTzdtKTZqYrTKn+f+dxpERkUBV3nFkyt0iM2TIENq3b8+AAQPo1q0bl156KTVq1PBKseXVrFkz6tevz9atW8sMMuHh4WcVqET8Wk7OiYkhn376tCEGoHdKPD2T41iTkUN2Xj4xUebtJLXEiEhVUe4gc+DAAWrWrOnLWs7ozz//ZP/+/cTH678kJUhNmgQOB7RuDTfcUK63hIbYSG0e7ePCRESsUe4gUxxiHA4Hy5YtY/v27dhsNhITE+nRo0eFbtscOnSIrVu3utYzMjLYsGED9erVo169ekycOJFBgwYRFxfHtm3bePDBB2nRogW9evXy+LNEAt6uXfDSS+bypEnmIHgiIkHOo86+s2bNYvTo0SWeBLLb7bz22mtcf/31Hn342rVr6datm2t93LhxAAwbNozp06ezceNG3n33XQ4ePEjDhg258sorefrpp3XrSILThAlQUACXXw5nGLZARCRYlLuz7/r16+nUqRM33ngj9957L0lJSRiGwebNm5k6dSpz587lhx9+oE2bNr6u2SOaNFKqhF9+MW8nOZ3w/ffQqZPVFYmI+FR5v7/LHWRuueUWDh06xPz580vdf80111C7dm3efvvtilXsIwoyUiVcfTUsWgQDB8JHH1ldjYiIz3l99uv//e9/3HnnnWXuHzFiBN9++61nVYoEqSKnwapt+1m44S9WbdtPkfM0/z3x7bewaBFGaCg/jnigfO8REQkS5e4js3v3blq2bFnm/pYtW/LXX395pSiRqsyjsV1Omhhy4UW9Gbt8P7D/9O8REQki5W6ROXLkCBERZc/LEh4eTn6+JqATOZ2lmzIZOWt9iZmpsxz5jJy1nqWbMt3fsHgxfPcdR6uF81yH68r3HhGRIOLRU0uff/45dru91H0HDx70Rj0iVVaR02Di4s2UdkPIwJw6YOLizfRMjjMHrCsqwnj4YWzA2+37kR0Vfeb3iIgEGY+CzLBhw06732bTH1KRsqzJyCnREnMyA8h05LMmI8ccwO4//8G2eTMHI2rxeqdB5XuPiEiQKXeQcTqdvqxDpMrLzivfrdfsvHw4ehSeeAKAl1OvIzeillfOLSJS1ZS7j4yInJ2YqLL7mJU47uWX4c8/KYg/h/cu+rvXzi0iUtV4dGsJYNGiRaVut9lsRERE0KJFCxITE8+6MJGqpmNiPeLtEWQ58kvtJ2MD4uwRdKxjM6cgAKo/8zT19tQ+83sS6/mwchER/+VxkOnfvz82m41Tx9Er3maz2ejcuTOffPIJdevW9VqhIoEuNMTGhL7JjJy1Hhu4BZPi3mUT+iYT+sIUOHAAzj+fkGFDmfBr9pnfo46+IhKkPL61tGzZMjp06MCyZctwOByuSSQ7derEkiVLWLlyJfv37+f+++/3Rb0iAa13SjzTh1xEnN39VlCcPYLpQy6id10nvPiiufH/Twx5xvdoHBkRsYBHA3v6ULmnKCiWkpLCG2+8wSWXXOK2/X//+x/Dhw/nl19+4csvv+TWW29l586dXi22IjRFgfijIqfBmowcsvPyiYkybw2Fhtjgjjtgxgzo3BlWroSTngQs8z0iIpXMo4E9K6i8398e31ratm1bqSesXbs2f/zxBwDnnnsu+/bt8/TUIkEjNMRW8nHpX3+F4rnKnn/eLcSU+R4RkUpWPLDnqa0gxYN0VnZLsce3ltq1a8cDDzzA3r17Xdv27t3Lgw8+SIcOHQDYsmULCQkJ3qtSJBg8+qg5u/XVV8MpLZ4iIv7gTAN7gjlIZ2XeZvI4yLz11ltkZGTQqFEjWrRoQYsWLWjUqBHbt29nxowZABw6dIjHHnvM68WKVFmrVsGCBRASAs89Z3U1IiKl8mRgz8ri8a2lVq1asXnzZr744gt+//1317aePXsSEmLmov79+3u1SJEq7aSJIbn5ZkhOtrQcEZGyeDSwZyXxOMgAhISE0Lt3b3r37u3tekSCz6efwjffQEQEPPmk1dWIiJTJo4E9K4lG9hWxUlERPPSQuXz33aC+ZSLix4oH9izreUkb5tNLlTlIp4KMiJVmz4ZNm6BOnROBRkTETxUP7AmUCDNWDdKpICNilfx8ePxxc/nhh0EjYYtIAPC3QTor1EdGRLzg1Vdh50445xwYM8bqakREyq13Sjw9k+P8YpDOCgUZp9PJ1q1byc7Oxul0uu277LLLvFKYSJXmcMCzz5rLEydCjRrW1iMi4iF/GaTT4yDz/fffc8MNN7Bjx45SJ44sKiryWnEiVdaUKZCTA+edB8OGWV2NiEjA8jjIjBgxgvbt2/Pf//6X+Ph4bDbN9SLikd274d//Npefew6q6Q6viEhFefwXdMuWLXz44Ye0aNHCF/WIVH1PPQVHj0JqqjkdgYiIVJjHTy116tSJrVu3+qIWkarvjz/M2a2h1IkhRUTEMx63yIwZM4b77ruPrKwsLrjgAqpXr+62v3Xr1l4rTqTKmTbNHATvyiuhSxerqxERCXg249Qeu2dQPJ+S20lsNgzD8MvOvrm5udjtdhwOB7Vr17a6HAlmubnQqBHk5cHSpdCrl9UViYj4rfJ+f3vcIpORkXFWhYkErbffNkPMeeeZLTIiInLWPA4yTZo08UUdIlVbURG89JK5fM896hsjIuIl5QoyixYtok+fPlSvXp1Fixad9th+/fp5pTCRKmXJErOjb926cNNNVlcjIlJllCvI9O/fn6ysLGJiYujfv3+Zx/ljHxkRvzB1qvnvnXdCZKSlpYiIVCXlCjInT0Nw6pQEInIGGzbAihUQGgqjRlldjYhIlaLZr0V87cUXzX+vucZ8aklERLxGQUbEl/bsgTlzzOWxYy0tRUSkKlKQEfGl11+HY8egUye4+GKrqxERqXIUZER8paAAXn3VXFZrjIiITyjIiPjKvHnmraVzzoFBg6yuRkSkSvI4yKxfv56ff/7Ztb5w4UL69+/PI488wrFjx7xanEjAMowTj1yPGgWnzEkmIiLe4XGQufPOO/n9998B+OOPP/jHP/5BZGQk8+fP58EHH/R6gSIB6Ztv4McfISIChg+3uhoRkSrL4yDz+++/07ZtWwDmz5/PZZddxpw5c5g5cyYfffSRt+sTCUzFrTFDh0J0tKWliIhUZR4HGcMwXIPiffnll/ztb38DICEhgX379nm3OpFAlJEBCxeay/fcY20tIiJVnMdBpn379jzzzDO89957pKWlcdVVVwHmrNixsbFeL1Ak4Lz8Mjid5gzXyclWVyMiUqV5HGSmTp3K+vXrGT16NI8++igtWrQA4MMPP+SSSy7xeoEiASUvD2bMMJf1yLWIiM/ZDMMwvHGi/Px8QkNDqe5nT2fk5uZit9txOBzUrl3b6nKkqnvpJbj7bmjZEn79FUI0woGISEWU9/u7XJNGnmzXrl3YbDYa/f85Y9asWcOcOXNITk5muJ7OkCBT5DRYk5FDdl4+MTXDuPjFF7GB2TdGIUZExOc8DjI33HADw4cP56abbiIrK4uePXty/vnnM3v2bLKysnjiiSd8UaeI31m6KZOJizeT6cgHoPvW1aRu20ZhlJ3qQ4daXJ2ISHDw+D8ZN23aRMeOHQH44IMPSElJ4bvvvmP27NnMnDnT2/WJ+KWlmzIZOWu9K8QA3LJ2EQBvn9edpdvzrCpNRCSoeBxkCgsLCQ8PB8zHr/v16wdAUlISmZmZ3q1OxA8VOQ0mLt7MyZ3LWu3dTucdP3HcFsJ/2v2diYs3U+T0SvczERE5DY+DzPnnn89rr73GN998w7Jly+jduzcAu3fvJloDf0kQWJOR49YSAydaY5a2vIS/aseQ6chnTUaOFeWJiAQVj4PM888/z+uvv07Xrl0ZPHgwbdq0AWDRokWuW04iVVl2nnuIqXfEwYBfvgbgnfb9yjxORES8z+POvl27dmXfvn3k5uZSt25d1/bhw4cTGRnp1eJE/FFMVITb+g0bPiO8qJCf4s5l3TnnlXmciIh4n8dBBiA0NNQtxAA0bdrUG/WI+L2OifWIt0eQ5cinWlEhN/34KQBvt+8HNhs2IM4eQcfEetYWKiISBCoUZD788EM++OADdu7cybFjx9z2rV+/3iuFifir0BAbE/omM3LWeq767VtiD+Wwp1Y9Pk3qbI4hA0zom0xoiO205xERkbPncR+ZadOmccsttxAbG8uPP/5Ix44diY6O5o8//qBPnz6+qFHE7/ROiWf6jRcy/MfFAPznwqsoDK1OnD2C6UMuondKvMUViogEB4+nKEhKSmLChAkMHjyYqKgofvrpJ5o1a8YTTzxBTk4OL7/8sq9qrRBNUSA+87//QefOOMPC+fyzNdRpcg4dE+upJUZExAvK+/3tcYvMzp07XZND1qhRg7w8c+Cvm266iffff7+C5YoEoKlTAQi5aQh9rmhNavNohRgRkUrmcZCJi4sjJ8ccH6Nx48Z8//33AGRkZOCl+SdF/N+OHfDxx+byPfdYW4uISBDzOMhcccUVLFpkDv51yy23cO+999KzZ0+uv/56BgwY4NG5Vq5cSd++fWnYsCE2m41PPvnEbb9hGDzxxBPEx8dTo0YNevTowZYtWzwtWcT7XnkFnE7o3h0uuMDqakREgpbHTy298cYbOJ1OAEaNGkV0dDTfffcd/fr148477/ToXIcPH6ZNmzbceuutDBw4sMT+KVOmMG3aNN59910SExN5/PHH6dWrF5s3byYiQmN0iPe4zWIdFXH6vi6HDsGbb5rLY8dWWo0iIlKSx519fcVms7FgwQL69+8PmK0xDRs25L777uP+++8HwOFwEBsby8yZM/nHP/5R6nkKCgooKChwrefm5pKQkKDOvlKmU2exBoi3RzChb3LpTx+9+iqMGgUtWkB6OoR43LApIiJnUN7OvhUaRyY/P5+NGzeSnZ3tap0pVjyJ5NnKyMggKyuLHj16uLbZ7XY6derEqlWrygwykyZNYuLEiV6pQaq+4lmsT03zWY58Rs5aX/JRaqcTXnzRXL77boUYERGLeRxkli5dytChQ9m3b1+JfTabjaKiIq8UlpWVBUBsbKzb9tjYWNe+0jz88MOMGzfOtV7cIiNyqtJmsS5mADZg4uLN9EyOO3GbaelS+P13qF0bbr658ooVEZFSefyfk2PGjOHaa68lMzMTp9Pp9vJWiDkb4eHh1K5d2+0lUprSZrE+mQElZ7H+/49cc/vtEBXl0/pEROTMPA4ye/bsYdy4cSVaSrwtLi7O9Xmnfn7xPpGzUd7ZqV3H/fILLFtm3k4aPdqHlYmISHl5HGSuueYaVqxY4YNS3CUmJhIXF8fy5ctd23Jzc1m9ejWpqak+/3yp+so7O7XruGnTzH/794fERN8UJSIiHvG4j8zLL7/MtddeyzfffMMFF1xA9erV3fbffffd5T7XoUOH2Lp1q2s9IyODDRs2UK9ePRo3bszYsWN55plnOPfcc12PXzds2ND1ZJPI2Th5FuvS+sm4zWK9fz/85z/mDj1yLSLiNzwOMu+//z5ffPEFERERrFixApvtxFgbNpvNoyCzdu1aunXr5lov7qQ7bNgwZs6cyYMPPsjhw4cZPnw4Bw8epHPnzixdulRjyIhXnDyLtQ3cwkyJWazfeAPy8+HCC6FzZwuqFRGR0ng8jkxcXBx33303Dz30ECEB8OipJo2UMznjODKFhdC0KezeDe++C0OHWlesiEiQ8Nk4MseOHeP6668PiBAjUh69U+LpmRxX9si+H35ohpjYWLj+emuLFRERNx6nkWHDhjFv3jxf1CJimdAQG6nNo7m67TklZ7EuHgDvrrsgPNyaAkVEpFQet8gUFRUxZcoUPv/8c1q3bl2is++//vUvrxUnYrnvv4fVqyEsDEaMsLoaERE5hcdB5ueff+bCCy8EYNOmTW77Tu74K1IlFA+Ad8MNEBNjaSkiIlKSx0Hm66+/9kUdIv5n1y6zfwzAPfdYW4uIiJTqrHrs/vnnn/z555/eqkXEv7zyChQVQdeu0Lat1dWIiEgpPA4yTqeTp556CrvdTpMmTWjSpAl16tTh6aefLjETtkjAOnzYHDsGNACeiIgf8/jW0qOPPspbb73F5MmTufTSSwH49ttvefLJJ8nPz+fZZ5/1epEilW7WLDhwAJo1g7//3epqRESkDB4HmXfffZcZM2bQr18/17bWrVtzzjnncNdddynISOBzOk908r37bggNtbQcEREpm8e3lnJyckhKSiqxPSkpiZycHK8UJWKpZcvgt98gKgpuucXqakRE5DQ8DjJt2rTh5ZdfLrH95Zdfpk2bNl4pSsRSxa0xt94KmtZCRMSveXxracqUKVx11VV8+eWXpKamArBq1Sp27drFp59+6vUCRSrVhg2wdCmEhMCYMVZXIyIiZ+Bxi8zll1/O77//zoABAzh48CAHDx5k4MCBpKen06VLF1/UKFJ5nnvO/Pe666B5c2trERGRM/J49utAo9mvpdx+/RXOPx8MAzZuhAsusLoiEZGg5dXZrzdu3EhKSgohISFs3LjxtMe2bt3as0pF/MXkyWaIufpqhRgRkQBRrhaZkJAQsrKyiImJISQkBJvNRmlvs9lsFBUV+aTQilKLjJTLH39Ay5bmSL5r1kCHDlZXJCIS1LzaIpORkUGDBg1cyyJVzpQpZoi58kqFGBGRAFKuINOkSZNSl0WqhL/+gnfeMZcfe8zaWkRExCMeP369f/9+oqOjAdi1axdvvvkmR48epV+/fnpqSQLT//0fHDsGXbqYLxERCRjlfvz6559/pmnTpsTExJCUlMSGDRvo0KED//73v3njjTfo1q0bn3zyiQ9LFfGBvXvh9dfN5UcftbYWERHxWLmDzIMPPsgFF1zAypUr6dq1K3//+9+56qqrcDgcHDhwgDvvvJPJkyf7slYR7/v3v+HoUWjf3uwfIyIiAaXc48jUr1+fr776itatW3Po0CFq167NDz/8QLt27QD47bffuPjiizl48KAv6/WYnlqSMh04AE2aQF4eLFgA/ftbXZGIiPx/5f3+LneLTE5ODnFxcQDUqlWLmjVrUrduXdf+unXrkpeXdxYli1Syl182Q0xKCpw0m7uIiAQOj6YosNlsp10XCRiHDp2YHPKRR8y5lUREJOB49NTSzTffTHh4OAD5+fmMGDGCmjVrAlBQUOD96kR85bXXICcHzj3XnFdJREQCUrmDzLBhw9zWhwwZUuKYoUOHnn1FIr6Wnw///Ke5/NBDEBpqbT0iIlJh5Q4y7xQPGCYS6N5+G7KyICEBSgnkIiISONQxQIJLYSE8/7y5PH48hIVZW4+IiJwVBRkJLrNmwc6dEBsLt95qdTUiInKWFGQkeBQVwaRJ5vL990ONGtbWIyIiZ01BRoLH/PmwZQvUqwcjRlhdjYiIeIGCjAQHpxOefdZcHjsWatWytBwREfEOBRkJDosXw6ZNEBUFo0dbXY2IiHiJgoxUfYYBzzxjLo8aBSdNrSEiIoFNQUaqvmXLYO1as3PvvfdaXY2IiHiRgoxUfcV9Y4YPh5gYa2sRERGvUpCRqu2bb2DlSnPgu/vvt7oaERHxMgUZqdqKW2NuvhkaNbK0FBER8T4FGam6fvgBPv/cnBRy/HirqxERER9QkJGq67nnzH9vuAGaNbO2FhER8QkFGamafv4ZPvkEbDZ4+GGrqxERER9RkJGqqXhOpUGD4LzzrK1FRER8RkFGqp4tW2DePHP5kUesrUVERHxKQUaqnsmTzbmVrroKLrzQ6mpERMSHFGSkatm5E/7zH3P50UetrUVERHxOQUaqlilT4PhxuOIKSE21uhoREfExBRmpOrKyYMYMc1mtMSIiQUFBRqqOf/4TCgrg4ouhWzerqxERkUqgICNVw/79MH26ufzYY+b4MSIiUuUpyEjVMG0aHD4MbdvC3/5mdTUiIlJJFGQk8OXmmkEGzL4xao0REQkaCjIS+F59FQ4ehKQkGDjQ6mpERKQSKchIYDtyBP71L3P5kUcgRL/SIiLBRH/1JbC9+Sbs3QuJiTB4sNXViIhIJVOQkcBVUGAOgAcwfjxUq2ZtPSIiUukUZCRwvfsu7N4NDRvCzTdbXY2IiFhAQUYC0/Hj5uSQAA88AOHh1tYjIiKWUJCRwPT++5CRAQ0awB13WF2NiIhYxK+DzJNPPonNZnN7JSUlWV2WWM3phOeeM5fvvRdq1ixxSJHTYNW2/Szc8Bertu2nyGlUcpEiIlIZ/L535Pnnn8+XX37pWq+mDp3y8cfw229Qpw6MGlVi99JNmUxcvJlMR75rW7w9ggl9k+mdEl+JhYqIiK/5fSqoVq0acXFx5T6+oKCAgoIC13pubq4vyhKrHDwI48aZy2PGQO3abruXbspk5Kz1nNr+kuXIZ+Ss9UwfcpHCjIhIFeLXt5YAtmzZQsOGDWnWrBk33ngjO3fuPO3xkyZNwm63u14JCQmVVKlUilGjYNcuaN7c7OR7kiKnwcTFm0uEGMC1beLizbrNJCJShfh1kOnUqRMzZ85k6dKlTJ8+nYyMDLp06UJeXl6Z73n44YdxOByu165duyqxYvGpOXPMV2gozJoFUVFuu9dk5LjdTjqVAWQ68lmTkePjQkVEpLL49a2lPn36uJZbt25Np06daNKkCR988AG33XZbqe8JDw8nXI/iVj07dsBdd5nLjz8OF19c4pDsvLJDTEWOExER/+fXLTKnqlOnDi1btmTr1q1WlyKVqagIhg4Fh8MMMI8+WuphMVER5TpdeY8TERH/F1BB5tChQ2zbto34eHXWDCr/93+wciXUqmXeUirjybWOifWIt0dgK+M0Nsynlzom1vNZqSIiUrn8Osjcf//9pKWlsX37dr777jsGDBhAaGgogzU5YPBYv968lQQwbZrZybcMoSE2JvRNBigRZorXJ/RNJjSkrKgjIiKBxq+DzJ9//sngwYNp1aoV1113HdHR0Xz//fc0aNDA6tKkMhw5AjfeCIWFMHBgueZT6p0Sz/QhFxFnd799FGeP0KPXIiJVkM0wjCr9LGpubi52ux2Hw0HtU8YcET83ahS8+irEx8PPP0N0dLnfWuQ0WJORQ3ZePjFR5u0ktcSIiASO8n5/+/VTSxLEPv3UDDFgznLtQYgB8zZTanPP3iMiIoHHr28tSZDKzoZbbjGXx46Fnj0tLUdERPyXgoz4F8OA2283w0xKCkyaZHVFIiLixxRkxL+88QYsXgxhYTB7NkRozBcRESmbgoz4j/R0uPdec3nSJGjd2tp6RETE7ynIiH8oLIQhQ+DoUeje3ewbIyIicgYKMuIfJk6EtWuhbl2YORNC9KspIiJnpm8Lsd63357o1PvGG9CokbX1iIhIwFCQEWs5HOYtJacThg2Da66xuiIREQkgCjJirTFjYMcOSEw051ISERHxgIKMWGfePHjvPbM/zHvvgaaQEBERDynIiDV27YIRI8zlRx+FSy+1th4REQlICjJS+Yr7wxw8CB07wuOPW12RiIgEKAUZqXz/+hd8/TVERsKsWVC9utUViYhIgFKQkcr100/wyCPm8tSpcO65lpYjIiKBTUFGKs/Ro3DDDeYovldfbU4OKSIichYUZKTyPPQQbN4McXHw5ptgs1ldkYiIBDgFGakcn39+YpyYd96BBg2srUdERKoEBRnxvb174eabzeXRo6F3b0vLERGRqkNBRnzLMGD4cMjKgvPOgylTrK5IRESqEAUZ8a233oJPPjEfsZ4zB2rUsLoiERGpQhRkxHe2bIF77jGXn30W2ra1tBwREal6FGTENwoLzVmtjxyBrl1h3DirKxIRkSpIQUZ845lnYM0asNvhP/+B0FCrKxIRkSpIQUa8b/58M8gAvPYaJCRYW4+IiFRZCjLiPUVF5qB3111nTgw5dCj84x9WVyUiIlVYNasLkCpi/34YPBiWLTPX778fJk2ytiYREanyFGTk7G3YAAMGwPbt5ozWb72llhgREakUurUkZ2f2bLjkEjPENGsGq1YpxIiISKVRkJGKKSyEsWPNR6yPHoU+fWDtWmjd2urKREQkiCjIiOf27IGePeHFF831Rx+FxYuhbl1r6xIRkaCjPjLimTVrYOBA+OsviIoyx4jp39/qqkREJEipRUbK7623oEsXM8S0amWGGoUYERGxkIKMnFlBAYwYAbffDseOmeFlzRpISrK6MhERCXK6tSSnt3s3DBoE338PNhs8/TQ8/DCEKAOLiIj1FGSkbN9+C9dcY3burVMH5swxn04SERHxE/rPainJMOCVV6BbNzPEXHCB+Wi1QoyIiPgZBRlxd/Qo3HILjB4Nx4/D9debg9w1b251ZSIiIiXo1pKcsGOH2R9m3TqzD8yUKTBunNk3RkRExA8pyIjpq6/M1pd9+yA6GubNg+7dra5KRETktHRrKdgZBvzzn+ZIvfv2Qbt2ZouMQoyIiAQABZlgdvgw3HAD3H8/OJ0wbBh88w00aWJ1ZSIiIuWiW0vBats2GDAAfv4ZqlWDqVPhrrvUH0ZERAKKgkwwMQz47jt480344APzCaXYWPjwQ+jc2erqREREPKYgEwz27zcnd3zzTfj11xPbO3eGuXPhnHOsq01EROQsKMhUVU4nrFhhhpePPzbnSAKIjDSfTho+HDp10q0kEREJaAoyVc2ePTBzphlgtm07sf2ii+COO8zOvbVrW1aeiIiINynIVAVFRbBsmRleFi0yR+QFiIqCG280A8xFF1lbo4iIiA8oyASyP/+Et9+Gt96CnTtPbE9NNcPLdddBzZrW1SciIuJjCjKB5vhx+PRTs/Xl00/NvjAAdevCTTeZASYlxdoaRUREKomCTKDIyDBbXt55B3bvPrH98svN8DJoEEREWFefiIiIBRRk/NmxY7Bwodn68uWX5jgwAA0awM03w+23Q8uWlpYoIiJiJQUZf3LgAPz0E2zYYP773//C3r0n9vfsaT423a8fhIVZVqaIiIi/UJCxgtMJf/zhHlo2bIBdu0oeGx8Pt94Kt90GiYmVXamIiIhfU5DxtSNHzPmMTg4tGzfCoUOlH5+YCG3aQNu25oB1PXqYcyGJiIhICfqG9BbDMDvh/vSTe2j5/fcTfVtOFhFhPl1UHFratIHWrcFur+zKRUREApaCTAUUOQ02Ll+Nc/Vq4rf/Tvz2dGw//QT79pX+htjYE2Gl+N+WLdXSIiIicpb0TeqhpZsymbh4Mw+/9xT9fl3pvjM0FFq1KhlaYmOtKFVERKTKC4gg88orr/DCCy+QlZVFmzZteOmll+jYsWOl17F0UyYjZ63HANaecx6xefvZHNuM3xoksjm2GWPG9OPKduqQKyIiUllshlFaBw7/MW/ePIYOHcprr71Gp06dmDp1KvPnzyc9PZ2YmJgzvj83Nxe73Y7D4aD2WUyWWOQ06Pz8V2Q68kvdbwPi7BF8O/4KQkM0o7SIiMjZKO/3d0gl1lQh//rXv7jjjju45ZZbSE5O5rXXXiMyMpK333671OMLCgrIzc11e3nDmoycMkMMgAFkOvJZk5Hjlc8TERGRM/PrIHPs2DHWrVtHjx49XNtCQkLo0aMHq1atKvU9kyZNwm63u14JCQleqSU7r+wQU5HjRERE5Oz5dZDZt28fRUVFxJ7SWTY2NpasrKxS3/Pwww/jcDhcr12lDTJXATFR5ZvHqLzHiYiIyNkLiM6+nggPDyc8PNzr5+2YWI94ewRZjnxK61RU3EemY2I9r3+2iIiIlM6vW2Tq169PaGgoe/bscdu+Z88e4uLiKrWW0BAbE/omA2ZoOVnx+oS+yeroKyIiUon8OsiEhYXRrl07li9f7trmdDpZvnw5qamplV5P75R4pg+5iDi7++2jOHsE04dcRO+U+EqvSUREJJj5/a2lcePGMWzYMNq3b0/Hjh2ZOnUqhw8f5pZbbrGknt4p8fRMjmNNRg7ZefnERJm3k9QSIyIiUvn8Pshcf/317N27lyeeeIKsrCzatm3L0qVLS3QArkyhITZSm0db9vkiIiJi8vsB8c6WtwbEExERkcpTZQbEExERESmLgoyIiIgELAUZERERCVgKMiIiIhKwFGREREQkYCnIiIiISMBSkBEREZGApSAjIiIiAcvvR/Y9W8Xj/eXm5lpciYiIiJRX8ff2mcbtrfJBJi8vD4CEhASLKxERERFP5eXlYbfby9xf5acocDqd7N69m6ioKGw2703smJubS0JCArt27dLUB+h6nErXw52uhztdj5J0TdzpepgtMXl5eTRs2JCQkLJ7wlT5FpmQkBAaNWrks/PXrl07aH/JSqPr4U7Xw52uhztdj5J0TdwF+/U4XUtMMXX2FRERkYClICMiIiIBS0GmgsLDw5kwYQLh4eFWl+IXdD3c6Xq40/Vwp+tRkq6JO12P8qvynX1FRESk6lKLjIiIiAQsBRkREREJWAoyIiIiErAUZERERCRgKchUwCuvvELTpk2JiIigU6dOrFmzxuqSKs3KlSvp27cvDRs2xGaz8cknn7jtNwyDJ554gvj4eGrUqEGPHj3YsmWLNcX62KRJk+jQoQNRUVHExMTQv39/0tPT3Y7Jz89n1KhRREdHU6tWLQYNGsSePXssqtj3pk+fTuvWrV2DeKWmpvLZZ5+59gfb9TjZ5MmTsdlsjB071rUt2K7Hk08+ic1mc3slJSW59gfb9QD466+/GDJkCNHR0dSoUYMLLriAtWvXuvYH09/UilKQ8dC8efMYN24cEyZMYP369bRp04ZevXqRnZ1tdWmV4vDhw7Rp04ZXXnml1P1Tpkxh2rRpvPbaa6xevZqaNWvSq1cv8vPzK7lS30tLS2PUqFF8//33LFu2jMLCQq688koOHz7sOubee+9l8eLFzJ8/n7S0NHbv3s3AgQMtrNq3GjVqxOTJk1m3bh1r167liiuu4Oqrr+aXX34Bgu96FPvhhx94/fXXad26tdv2YLwe559/PpmZma7Xt99+69oXbNfjwIEDXHrppVSvXp3PPvuMzZs3889//pO6deu6jgmmv6kVZohHOnbsaIwaNcq1XlRUZDRs2NCYNGmShVVZAzAWLFjgWnc6nUZcXJzxwgsvuLYdPHjQCA8PN95//30LKqxc2dnZBmCkpaUZhmH+7NWrVzfmz5/vOubXX381AGPVqlVWlVnp6tata8yYMSNor0deXp5x7rnnGsuWLTMuv/xy45577jEMIzh/PyZMmGC0adOm1H3BeD3Gjx9vdO7cucz9wf43tbzUIuOBY8eOsW7dOnr06OHaFhISQo8ePVi1apWFlfmHjIwMsrKy3K6P3W6nU6dOQXF9HA4HAPXq1QNg3bp1FBYWul2PpKQkGjduHBTXo6ioiLlz53L48GFSU1OD9nqMGjWKq666yu3nhuD9/diyZQsNGzakWbNm3HjjjezcuRMIzuuxaNEi2rdvz7XXXktMTAwXXnghb775pmt/sP9NLS8FGQ/s27ePoqIiYmNj3bbHxsaSlZVlUVX+o/gaBOP1cTqdjB07lksvvZSUlBTAvB5hYWHUqVPH7diqfj1+/vlnatWqRXh4OCNGjGDBggUkJycH5fWYO3cu69evZ9KkSSX2BeP16NSpEzNnzmTp0qVMnz6djIwMunTpQl5eXlBejz/++IPp06dz7rnn8vnnnzNy5Ejuvvtu3n33XSC4/6Z6osrPfi1SGUaNGsWmTZvc7vcHq1atWrFhwwYcDgcffvghw4YNIy0tzeqyKt2uXbu45557WLZsGREREVaX4xf69OnjWm7dujWdOnWiSZMmfPDBB9SoUcPCyqzhdDpp3749zz33HAAXXnghmzZt4rXXXmPYsGEWVxc41CLjgfr16xMaGlqiF/2ePXuIi4uzqCr/UXwNgu36jB49miVLlvD111/TqFEj1/a4uDiOHTvGwYMH3Y6v6tcjLCyMFi1a0K5dOyZNmkSbNm148cUXg+56rFu3juzsbC666CKqVatGtWrVSEtLY9q0aVSrVo3Y2Niguh6lqVOnDi1btmTr1q1B9/sBEB8fT3Jystu28847z3W7LVj/pnpKQcYDYWFhtGvXjuXLl7u2OZ1Oli9fTmpqqoWV+YfExETi4uLcrk9ubi6rV6+uktfHMAxGjx7NggUL+Oqrr0hMTHTb365dO6pXr+52PdLT09m5c2eVvB5lcTqdFBQUBN316N69Oz///DMbNmxwvdq3b8+NN97oWg6m61GaQ4cOsW3bNuLj44Pu9wPg0ksvLTFkw++//06TJk2A4PubWmFW9zYONHPnzjXCw8ONmTNnGps3bzaGDx9u1KlTx8jKyrK6tEqRl5dn/Pjjj8aPP/5oAMa//vUv48cffzR27NhhGIZhTJ482ahTp46xcOFCY+PGjcbVV19tJCYmGkePHrW4cu8bOXKkYbfbjRUrVhiZmZmu15EjR1zHjBgxwmjcuLHx1VdfGWvXrjVSU1ON1NRUC6v2rYceeshIS0szMjIyjI0bNxoPPfSQYbPZjC+++MIwjOC7Hqc6+aklwwi+63HfffcZK1asMDIyMoz//e9/Ro8ePYz69esb2dnZhmEE3/VYs2aNUa1aNePZZ581tmzZYsyePduIjIw0Zs2a5TommP6mVpSCTAW89NJLRuPGjY2wsDCjY8eOxvfff291SZXm66+/NoASr2HDhhmGYT4u+PjjjxuxsbFGeHi40b17dyM9Pd3aon2ktOsAGO+8847rmKNHjxp33XWXUbduXSMyMtIYMGCAkZmZaV3RPnbrrbcaTZo0McLCwowGDRoY3bt3d4UYwwi+63GqU4NMsF2P66+/3oiPjzfCwsKMc845x7j++uuNrVu3uvYH2/UwDMNYvHixkZKSYoSHhxtJSUnGG2+84bY/mP6mVpTNMAzDmrYgERERkbOjPjIiIiISsBRkREREJGApyIiIiEjAUpARERGRgKUgIyIiIgFLQUZEREQCloKMiIiIBCwFGREREQlYCjIi4jMrVqzAZrOVmAiwMthsNj755BPLzyEivlXN6gJEpGro2rUrbdu2ZerUqa5tl1xyCZmZmdjt9kqvJzMzk7p161b654pI5VKQERGfCQsLIy4uzpLPtupzRaRy6daSiJy1m2++mbS0NF588UVsNhs2m43t27eXuLU0c+ZM6tSpw5IlS2jVqhWRkZFcc801HDlyhHfffZemTZtSt25d7r77boqKilznLygo4P777+ecc86hZs2adOrUiRUrVpy2ppNvC23fvh2bzcbHH39Mt27diIyMpE2bNqxateqMP9u+ffsYMGAAkZGRnHvuuSxatKiil0lEfEBBRkTO2osvvkhqaip33HEHmZmZZGZmkpCQUOqxR44cYdq0acydO5elS5eyYsUKBgwYwKeffsqnn37Ke++9x+uvv86HH37oes/o0aNZtWoVc+fOZePGjVx77bX07t2bLVu2eFTno48+yv3338+GDRto2bIlgwcP5vjx46d9z8SJE7nuuuvYuHEjf/vb37jxxhvJycnx6HNFxIesnn5bRKqGyy+/3Ljnnnvctn399dcGYBw4cMAwDMN45513DMDYunWr65g777zTiIyMNPLy8lzbevXqZdx5552GYRjGjh07jNDQUOOvv/5yO3f37t2Nhx9+uMx6AGPBggWGYRhGRkaGARgzZsxw7f/ll18MwPj1119Pe47HHnvMtX7o0CEDMD777LMy3yMilUt9ZESkUkVGRtK8eXPXemxsLE2bNqVWrVpu27KzswH4+eefKSoqomXLlm7nKSgoIDo62qPPbt26tWs5Pj4egOzsbJKSksr1npo1a1K7dm1XbSJiPQUZEalU1atXd1u32WylbnM6nQAcOnSI0NBQ1q1bR2hoqNtxJ4cfTz/bZrMBuD7Hk3rP9B4RqTwKMiLiFWFhYW4ddL3lwgsvpKioiOzsbLp06eL184tIYFNnXxHxiqZNm7J69Wq2b9/Ovn37vNZq0bJlS2688UaGDh3Kxx9/TEZGBmvWrGHSpEn897//9cpniEjgUpAREa+4//77CQ0NJTk5mQYNGrBz506vnfudd95h6NCh3HfffbRq1Yr+/fvzww8/0LhxY699hogEJpthGIbVRYiIiIhUhFpkREREJGApyIiIiEjAUpARERGRgKUgIyIiIgFLQUZEREQCloKMiIiIBCwFGREREQlYCjIiIiISsBRkREREJGApyIiIiEjAUpARERGRgPX/AFfeDcsdenwfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# linear function:          lambda t, m, b: m*t + b\n",
    "# exponential function:     lambda t, r, y0: y0*np.exp(r*t)\n",
    "# sigmidal function:        lambda t, r, y0, K: y0*K / (y0 + (K-y0)*np.exp(-r*t))\n",
    "# Replace the function with the one you want to use\n",
    "GrowthLaw = lambda t, r, y0, K: y0*K / (y0 + (K-y0)*np.exp(-r*t))\n",
    "\n",
    "# Defining the time span for the fit\n",
    "TimeSpan = [0,75]\n",
    "# Extracting the time span\n",
    "FitIndex = SubVector(TimeSpan, df[TimeID].values.reshape(-1))\n",
    "# Extracting the time span from the dataframe using index and converting to numpy array\n",
    "TimeFit = np.array(df[TimeID].iloc[FitIndex], dtype=float).reshape(-1)\n",
    "# Extracting the biomass span from the dataframe using index and converting to numpy array\n",
    "# If you are using the linear growth law, you have to use the log of the biomass, np.log()\n",
    "BiomassFit = np.array(df[BiomassID].iloc[FitIndex], dtype=float).reshape(-1)\n",
    "# Substrate span\n",
    "SubstrateFit = np.array(df[SubstrateID].iloc[FitIndex], dtype=float).reshape(-1)\n",
    "\n",
    "# Fitting the growth law to the biomass data\n",
    "popt, pcov = curve_fit(GrowthLaw, TimeFit, BiomassFit)\n",
    "rate, y_init = popt[0:2]\n",
    "rate_sterr = np.sqrt(np.diag(pcov))[0]\n",
    "\n",
    "# Plotting the fit\n",
    "plt.scatter(TimeFit, BiomassFit, label='data')\n",
    "plt.plot(np.linspace(TimeFit[0], TimeFit[-1],20), GrowthLaw(np.linspace(TimeFit[0], TimeFit[-1],20), *popt), 'r-', label=f'fit: r={rate:.2f} {pm}{rate_sterr:.2f}') #np.linspace(TimeFit[0], TimeFit[-1],20)\n",
    "plt.xlabel(f'time in {TimeUnit}')\n",
    "plt.ylabel(f'Biomass in {BiomassUnit}')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Substrate uptake rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating substrate uptake rate\n",
    "DeltaBm = np.array(BiomassFit-BiomassFit[0], dtype=float)\n",
    "Sub_sl, Sub_ic, Sub_r, _, Sub_sl_sterr = stats.linregress(DeltaBm, SubstrateFit)\n",
    "SubUp = round(-Sub_sl*rate,2)\n",
    "SubUp_sterr = round(SubUp*np.sqrt( (Sub_sl_sterr/Sub_sl)**2 + (rate_sterr/rate)**2 ), 2)\n",
    "\n",
    "# plot the linear regression\n",
    "plt.scatter(TimeFit, SubstrateFit, label='data')\n",
    "plt.plot(TimeFit, Sub_ic + Sub_sl*DeltaBm, 'r-', label=f'fit: rate={SubUp:.1f} {pm}{SubUp_sterr:.1f} mmol/gDW/h')\n",
    "plt.xlabel(f'Change in Biomass in {BiomassUnit}')\n",
    "plt.ylabel(f'Substrate in mmol/L')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Yield calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yield: 0.29 ± 0.04 gDW/mmol\n"
     ]
    }
   ],
   "source": [
    "# calculation of the yield\n",
    "Yield = round(rate/SubUp/.18,2)\n",
    "Yield_sterr = round(Yield*np.sqrt( (SubUp_sterr/SubUp)**2 + (rate_sterr/rate)**2 ), 2)\n",
    "print(f'Yield: {Yield} {pm} {Yield_sterr} gDW/mmol')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of multiple experiments\n",
    "\n",
    "The initial substrate concentration and the measured growth rate can be effectively combined using the Monod equation, which relates the specific growth rate of microorganisms to the substrate concentration. By fitting experimental data to the Monod equation, biotechnologists can determine the substrate affinity of the microorganism, providing valuable insights into its metabolic characteristics and substrate utilization kinetics. This analysis allows for the quantification of substrate uptake rates and the prediction of growth behavior under varying nutrient conditions, enabling informed decisions in bioprocess optimization. Ultimately, leveraging the Monod equation enhances the efficiency and productivity of biotechnological processes by guiding substrate feeding strategies and maximizing microbial growth yields.\n",
    "\n",
    "The growth rate $\\mu$ is a function of the substrate level and can be calculated via the Monod equation:\n",
    "- $\\mu = \\frac{\\mu_{max}S}{K_S + S}$\n",
    "  - $\\mu$: growth rate, /h\n",
    "  - $\\mu_{max}$: maximum growth rate, /h\n",
    "  - $S$: Substrate concentration, mmol/L\n",
    "  - $K_S$: half velocity constant, the value of S when $\\frac{\\mu}{\\mu_{max}} = 0.5$, mmol/L\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "InitialConc = [10,20,40,2,1]\n",
    "GrowthRates = [.32,.28,.4,.12,.09]\n",
    "GrowthRates_sterr = [.03,.03,.03,.01,.01]\n",
    "SubUptake = [6,6,7.1,2.5,1.7]\n",
    "SubUptake_sterr = [.7,.9,.8,.3,.2]\n",
    "Yields = [.3,.25,.31,.27,.29]\n",
    "Yields_sterr = [.04,.04,.04,.04,.04]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yield: 0.3 ±0.03 gCDW/g and R2: 0.985833776552065\n",
      "NGAM glucose requirement: 0.23 ±0.02 mmol/gCDW/h\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Yield, S0, r2, _, Yield_sterr = stats.linregress(SubUptake, GrowthRates)\n",
    "Yield_gg = round(Yield/.18,2)\n",
    "Yield_sterr_gg = round(Yield_sterr/.18,2)\n",
    "# calculation of NGAM via the substrate uptake at zero growth, \n",
    "# Growth = Yield*SubUp + Int -> Growth=0, then SubUp(Growth=0) = -Int / Yield\n",
    "NGAM = round(-S0/Yield,2)\n",
    "NGAM_std = round((-S0/(Yield-Yield_sterr) - -S0/(Yield+Yield_sterr))/2,2)\n",
    "print(f'Yield: {Yield_gg} {pm}{Yield_sterr_gg} gCDW/g and R2: {r2}')\n",
    "print(f'NGAM glucose requirement: {NGAM} {pm}{NGAM_std} mmol/gCDW/h')\n",
    "# Growth rate VS Substrate uptake plot\n",
    "plt.errorbar(SubUptake, GrowthRates, xerr=SubUptake_sterr, yerr=GrowthRates_sterr, fmt='ok') #, 'xk', label='Substrate uptake'\n",
    "plt.plot(SubUptake, S0+Yield*np.array(SubUptake), '-k', label=f'fit, Y = {Yield_gg} {pm}{Yield_sterr_gg} gCDW/g, R0 = {S0:.2f} /h')\n",
    "plt.title('Growth rate VS substrate uptake')\n",
    "plt.xlabel('Substrate uptake, mmol/gCDW/h')\n",
    "plt.ylabel('Growth rate, /h')\n",
    "plt.legend()\n",
    "# plt.savefig(FigureAccelPath)\n",
    "plt.show()\n",
    "# print('Growth rate versus substrate uptake figure saved as {}'.format(FigureAccelPath))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###############################\n",
    "# Plot of initial substrate concentration versus substrate uptake rate\n",
    "###############################\n",
    "\n",
    "# define michaelis menten equation with lambda\n",
    "MiMe = lambda x, Vmax, Ks: Vmax * x / (Ks + x)\n",
    "\n",
    "# curve fitting\n",
    "popt, pcov = curve_fit(MiMe, InitialConc, GrowthRates, sigma=GrowthRates_sterr, absolute_sigma=True)\n",
    "perr = np.sqrt(np.diag(pcov))\n",
    "\n",
    "# Estimate the parameters of the hyperbolic curve\n",
    "Vmax = popt[0]\n",
    "# standard deviation of the Vmax\n",
    "Vmax_std = perr[0]\n",
    "\n",
    "Ks = popt[1]\n",
    "# standard deviation of the Km\n",
    "Ks_std = perr[1]\n",
    "\n",
    "# generating sample concentration values for the plot\n",
    "x = np.linspace(0, np.max(InitialConc), 50)\n",
    "# Plot the transformed data and the fitted curve\n",
    "plt.plot(x, MiMe(x, Vmax, Ks), ':r', label=f'$\\mu$max = {Vmax:.2f}±{Vmax_std:.2f} /h, Ks = {Ks:.2f}±{Ks_std:.2f} g/L')\n",
    "\n",
    "\n",
    "# Initial substrate concentration VS substrate uptake plot\n",
    "plt.errorbar(InitialConc, GrowthRates, yerr=GrowthRates_sterr, fmt='ok') #, 'xk', label='Substrate uptake'\n",
    "plt.xlabel('Initial substrate concentration, g/L')\n",
    "plt.ylabel('Growth rate in /h')\n",
    "plt.legend()\n",
    "# plt.savefig(InhibitCorrPath)\n",
    "plt.show()\n",
    "# print('Growth rate versus substrate uptake figure saved as {}'.format(InhibitCorrPath))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check your results\n",
    "Testing values of:\n",
    "- Optimal temperature\n",
    "- OD to dry weight correlation\n",
    "- Growth rate\n",
    "- Maximum biomass\n",
    "- Growth yield\n",
    "- Glucose concentration with half maximum uptake rate\n",
    "- Glucose maximum uptake rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "mySeed = 161710  # Your student ID\n",
    "myInvest = 2000 # Your lab expenditure, choose anything between 1000-2500\n",
    "\n",
    "exp = GrowthExperiment(mySeed, myInvest, Par_Bud)\n",
    "\n",
    "Organism = 'ecol'\n",
    "host = exp.create_host(Organism)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GlcRateMax: 6±1 mmol/gDW/h ✅\n",
      "GrowthRate: 0.3±0.03 /h ❌ Value is 1.1x standard deviations from the reference value\n",
      "GrowthYield: 0.3±0.03 g/g ✅\n",
      "Ks: 4±1 g/L ❌ Value is 1.4x standard deviations from the reference value\n",
      "MaxBiomass: 19 gDW/L ❌\n",
      "OD2X: 0.459 a.u. ✅\n",
      "Temperature: 28 °C ✅\n"
     ]
    }
   ],
   "source": [
    "# Enter your determined optimal values for the parameters here. Delete parameters that are not calculated.\n",
    "Temperature = 28\n",
    "MaxBiomass = 19\n",
    "OD2X = .459\n",
    "GrowthYield_Avg = .3\n",
    "GrowthYield_Std = .03\n",
    "GrowthRate_Avg = .3\n",
    "GrowthRate_Std = .03\n",
    "GlcRateMax_Avg = 6\n",
    "GlcRateMax_Std = 1\n",
    "Ks_Avg = 4\n",
    "Ks_Std = 1\n",
    "\n",
    "Results = {'Temperature': Temperature,\n",
    "           'MaxBiomass': MaxBiomass,\n",
    "           'OD2X': OD2X,\n",
    "           'GrowthYield_Avg': GrowthYield_Avg, 'GrowthYield_Std': GrowthYield_Std,\n",
    "           'GrowthRate_Avg':GrowthRate_Avg, 'GrowthRate_Std':GrowthRate_Std, \n",
    "           'GlcRateMax_Avg': GlcRateMax_Avg, 'GlcRateMax_Std': GlcRateMax_Std,\n",
    "           'Ks_Avg': Ks_Avg, 'Ks_Std': Ks_Std,\n",
    "           } #\n",
    "exp.check_Results(Organism, Results)"
   ]
  }
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