E. coli pathway occupancy, Extended Data Fig 8 a and d.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f42f9fa2",
   "metadata": {},
   "source": [
    "## *E. coli* pathway occupancy, Extended Data Fig 8 a and d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fecd201a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import packages\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import scipy as scipy\n",
    "import math \n",
    "from tqdm.auto import tqdm\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a03a5748",
   "metadata": {},
   "outputs": [],
   "source": [
    "#don't print warning messages\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "af845db0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in proteomics datasets \n",
    "ecoli_proteomics = pd.read_csv('./data/2024_01_04_ecoli_proteomics_full.csv')\n",
    "\n",
    "#read in gene inclusion list for fermentation and respiration\n",
    "ecoli_inclusion = pd.read_csv('./data/ecoli_inclusion.csv')\n",
    "\n",
    "#read in ecoli accession number df\n",
    "ecoli_gene_accession = pd.read_csv('./data/ecoli_gene_accession.csv')\n",
    "ecoli_gene_accession = ecoli_gene_accession[['Gene Name', 'Accession']]\n",
    "ecoli_gene_accession.rename(columns = {'Gene Name':'Gene name'}, inplace = True)\n",
    "\n",
    "#read in metadata for ecolidatasets \n",
    "ecoli_meta = pd.read_csv('./data/2023_01_03_ecoli_metadata.csv')\n",
    "ecoli_meta.rename(columns = {'column_title':'variable'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab142cd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get the dataset names for melt function\n",
    "dataset_name = [col for col in ecoli_proteomics.columns if col not in ['Accession', 'Unnamed: 0']]\n",
    "#use melt funtion to put data table into correct format \n",
    "ecoli_proteomics_melt = pd.melt(ecoli_proteomics, id_vars=['Accession'],\n",
    "                                 value_vars=dataset_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f947d0e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge together the proteomics datasets with the metadata and accession number\n",
    "ecoli_proteomics_melt = pd.merge(pd.merge(ecoli_proteomics_melt, ecoli_meta, on='variable', how='left'), ecoli_gene_accession, on='Accession', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ff0663f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove non-mass spec datasets\n",
    "ecoli_proteomics_melt = ecoli_proteomics_melt.loc[~(ecoli_proteomics_melt['author'] == 'li')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e67c87c",
   "metadata": {},
   "source": [
    "### ETC Correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "57d1d4cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#subsetting the inclusion list that needs to be corrected\n",
    "total_inclusion = pd.merge(ecoli_inclusion[['Gene name', 'pathway', 'MW']], ecoli_proteomics_melt, on = 'Gene name', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a0136ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#selecting the ETC genes\n",
    "total_inclusion_etc = total_inclusion.loc[total_inclusion['Gene name'].str.contains('nuo|cyo|cyd|atp')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b790d026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd9e8e00490>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1008 with 28 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting histograms of the occupancy of etc genes (large bars at 0 are genes that will be corrected)\n",
    "sns.set_theme(style=\"white\")\n",
    "sns.displot(\n",
    "    total_inclusion_etc, x=\"value\", col=\"Gene name\",\n",
    "    height=2, facet_kws=dict(margin_titles=True),\n",
    "    col_wrap=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c86bfabe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the fraction of 'value' <= 0.00001 for each 'Gene name'\n",
    "fractions = total_inclusion_etc.groupby('Gene name')['value'].apply(lambda x: (x <= 0.00001).mean())\n",
    "\n",
    "#create a df from the fractions\n",
    "df_zero = fractions.reset_index(name='Fraction Zero')\n",
    "\n",
    "#determine which genes meet the criteria (less than 20 percent 0 occupancy)\n",
    "geneCorrect = fractions[fractions >= 0.2].index.tolist()\n",
    "geneTruth = fractions[fractions < 0.2].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "985d2125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7fd98879fb90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hist\n",
    "sns.set_theme(style=\"white\")\n",
    "sns.displot(\n",
    "    df_zero, x=\"Fraction Zero\", bins = 20)\n",
    "plt.axvline(0.2, color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c9c85b0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#find geneTruth to filter dataset \n",
    "dataset_filtered = total_inclusion_etc[total_inclusion_etc['Gene name'].isin(geneTruth)]\n",
    "\n",
    "#calculate correction value \n",
    "dataset_filtered['correction_value'] = dataset_filtered['value'] / dataset_filtered['MW']\n",
    "\n",
    "#create the df with correction values\n",
    "df = dataset_filtered[['variable', 'Gene name', 'correction_value']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "08a31025",
   "metadata": {},
   "outputs": [],
   "source": [
    "#gene groups that will be corrected (etc complexes)\n",
    "gene_list = ['nuo', 'cyo', 'cyd', 'atp']\n",
    "\n",
    "#create a new column to identify the gene group based on gene_list\n",
    "df['Gene group'] = df['Gene name'].apply(lambda x: next((gene for gene in gene_list if gene in x), None))\n",
    "\n",
    "#filter out nas\n",
    "df_filtered = df.dropna(subset=['Gene group'])\n",
    "\n",
    "#groupby variable and gene group', then calculate the mean correction value\n",
    "df_correction = df_filtered.groupby(['variable', 'Gene group'])['correction_value'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fbcba14f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace missing etc values \n",
    "\n",
    "#create a mapping of genes in geneCorrect that match any in gene_list to their simplified gene name\n",
    "gene_mapping = {gene: next((g for g in gene_list if g in gene), None) for gene in geneCorrect}\n",
    "\n",
    "#add a simplified gene name column to dataset for matching\n",
    "total_inclusion_etc['Simplified Gene Name'] = total_inclusion_etc['Gene name'].map(gene_mapping)\n",
    "\n",
    "#merge dataset with df_correction \n",
    "merged_dataset = pd.merge(total_inclusion_etc, df_correction, left_on=['variable', 'Simplified Gene Name'], right_on=['variable', 'Gene group'], how='left')\n",
    "\n",
    "#calculate corrected_value\n",
    "merged_dataset['corrected_value'] = merged_dataset['correction_value'] * merged_dataset['MW']\n",
    "\n",
    "#make a copy of merged_dataset\n",
    "df_final = merged_dataset.copy()\n",
    "\n",
    "#drop na in corrected_value\n",
    "df_final = df_final.dropna(subset=['corrected_value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dfd211d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd9d98dcc50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot corrected genes\n",
    "sns.set_theme(style=\"white\")\n",
    "sns.displot(\n",
    "    df_final.loc[~(df_final['Gene name'].str.contains('atp'))], x=\"corrected_value\", col=\"Gene name\",\n",
    "    height=2, facet_kws=dict(margin_titles=True),\n",
    "    col_wrap=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ec398b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace orginal values with corrected etc values\n",
    "for accession in list(df_final['Accession'].unique()):\n",
    "    for variable in list(df_final['variable'].unique()):\n",
    "        df_temp = df_final.loc[(df_final['variable'] == variable) & (df_final['Accession'] == accession)]\n",
    "        #print(df_temp)\n",
    "        #print(ecoli_proteomics.loc[ecoli_proteomics['Accession'] == accession,variable])\n",
    "        ecoli_proteomics.loc[ecoli_proteomics['Accession'] == accession,variable] = df_temp['corrected_value'].values\n",
    "    \n",
    "ecoli_proteomics_accession = ecoli_proteomics['Accession']\n",
    "    \n",
    "ecoli_proteomics_test = ecoli_proteomics.drop(['Accession'], axis=1)\n",
    "    \n",
    "replacement_etc = (ecoli_proteomics_test / ecoli_proteomics_test.sum())\n",
    "    \n",
    "ecoli_proteomics = pd.concat([replacement_etc, ecoli_proteomics_accession], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90b0b51e",
   "metadata": {},
   "source": [
    "## Pathway Occupancy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26dbfa98",
   "metadata": {},
   "source": [
    "### Fermentation and Pta-Ack pathway"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ebed8c4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in gene inclusion list for fermentation and respiration\n",
    "ecoli_inclusion = pd.read_csv('./data/ecoli_inclusion.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e41641bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#subset to ferm genes\n",
    "ecoli_inclusion_ferm_anaerobic = ecoli_inclusion.loc[ecoli_inclusion['meta_pathway'] == 'fermentation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "33cff6f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#subset to ferm genes (will eventually add the appropriate etc genes)\n",
    "ecoli_inclusion_ferm = ecoli_inclusion.loc[ecoli_inclusion['meta_pathway'] == 'fermentation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "944c966b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#subset to resp genes\n",
    "ecoli_inclusion_resp = ecoli_inclusion.loc[ecoli_inclusion['meta_pathway'] == 'respiration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ff34de98",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add ETC proteins to the Pta-Ack pathway\n",
    "#Note the high-yielding pathway is used here\n",
    "\n",
    "#High yielding\n",
    "ferm_etc_inclusion_list = ['nuo', 'cyo', 'aceE', 'aceF', 'atpB', 'atpF', 'atpE', 'atpD', 'atpG', 'atpA', 'atpH', 'atpC']\n",
    "#Low yielding \n",
    "#ferm_etc_inclusion_list = ['ndh', 'cyo']\n",
    "#No ETC\n",
    "#ferm_etc_inclusion_list = []\n",
    "\n",
    "fermentation_etc = pd.DataFrame()\n",
    "for gene in ferm_etc_inclusion_list: \n",
    "    output = ecoli_inclusion_resp.loc[ecoli_inclusion_resp['Gene name'].str.contains(gene)]\n",
    "    fermentation_etc['meta_pathway'] = 'fermentation'\n",
    "    fermentation_etc = pd.concat([fermentation_etc, output])\n",
    "\n",
    "ecoli_inclusion_ferm = pd.concat([ecoli_inclusion_ferm, fermentation_etc])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c5e9b836",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge the proteomics data with the inclusion list \n",
    "ecoli_proteomics_ferm_inclusion_final = pd.merge(ecoli_inclusion_ferm, ecoli_proteomics, on = 'Accession', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "72b53db1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#use melt funtion to put data table into correct format \n",
    "column_list = np.array([col for col in ecoli_proteomics.columns if col not in ['Accession', 'Unnamed: 0']])\n",
    "\n",
    "#note that only the fermentable carbon sources were used\n",
    "ecoli_proteomics_ferm_melt = pd.melt(ecoli_proteomics_ferm_inclusion_final, id_vars=['Gene name', 'pathway'],\n",
    "                                 value_vars=column_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0e06f18c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge proteomics data with the meta data\n",
    "ecoli_proteomics_ferm_melt = pd.merge(ecoli_proteomics_ferm_melt, ecoli_meta, on = 'variable', how = 'left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8abe0c3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#use only the fermentable carbon sources from batch culture for fermentation specific activity\n",
    "ecoli_proteomics_ferm_melt_sa = ecoli_proteomics_ferm_melt.loc[(ecoli_proteomics_ferm_melt['author'] == 'schmidt') \n",
    "                                                             & (ecoli_proteomics_ferm_melt['carbon_source_cat'] == 'fermentable') \n",
    "                                                             & (ecoli_proteomics_ferm_melt['method'] == 'batch')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9ffea65a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#make a copy that will not get biomass corrected for comparision\n",
    "ecoli_proteomics_ferm_melt_sa_no_biomass_correction = ecoli_proteomics_ferm_melt_sa.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a55e6ee",
   "metadata": {},
   "source": [
    "#### Fermentaion biomass producing fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "50576f04",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in the fraction to biomass data\n",
    "ecoli_biomass = pd.read_csv('./data/2022_11_06_ecoli_biomass.csv')\n",
    "\n",
    "#fraction of carbon dedicated to producing biomass\n",
    "ecoli_biomass_ferm_mean = ecoli_biomass[ecoli_biomass['Condition'] == 'Fermentation']['Fraction '].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f8fa109c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#correction applies to only the enzymes in glycolysis\n",
    "ecoli_proteomics_ferm_melt_sa.loc[(ecoli_proteomics_ferm_melt_sa['media'] == 'minimal') & \n",
    "                                  (ecoli_proteomics_ferm_melt_sa['pathway'] == 'glycolysis'), \n",
    "                                  'value'] = ecoli_proteomics_ferm_melt_sa['value'] * (1-ecoli_biomass_ferm_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f52acd11",
   "metadata": {},
   "source": [
    "#### Fermentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3574b2c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#select only the genes in for anaerobic fermentation\n",
    "ecoli_proteomics_fermentation_no_ETC = ecoli_proteomics_ferm_melt_sa.loc[\n",
    "    ecoli_proteomics_ferm_melt_sa['pathway'].isin(['glycolysis', 'pyruvate', 'fermentation'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e5eedf9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop duplicates\n",
    "ecoli_proteomics_fermentation_no_ETC = ecoli_proteomics_fermentation_no_ETC.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b184c412",
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby dataset variable and sum the entire set of genes for final sum\n",
    "ecoli_proteomics_ferm_no_ETC_sum = ecoli_proteomics_fermentation_no_ETC.groupby('variable').sum().reset_index().assign(\n",
    "    estimate='fermentation', organism='ecoli')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d3d7cc36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05648406405552262"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#average occupancy\n",
    "ecoli_proteomics_ferm_no_ETC_sum['value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ccc46030",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save df\n",
    "ecoli_proteomics_ferm_no_ETC_sum.to_csv('Data/2024_02_22_ecoli_specific_activity_fermentation_no_ETC_occupancy.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1caf68a",
   "metadata": {},
   "source": [
    "#### Pta-ack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ec05cfd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop duplicates\n",
    "ecoli_proteomics_ferm_melt_sa = ecoli_proteomics_ferm_melt_sa.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7ee5b481",
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby dataset variable and sum the entire set of genes for final sum\n",
    "ecoli_proteomics_ferm_inclusion_final_gene_sum = ecoli_proteomics_ferm_melt_sa.groupby('variable').sum().reset_index().assign(estimate = 'fermentation', organism='ecoli')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6fc34052",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08070300291611603"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#average occupancy\n",
    "ecoli_proteomics_ferm_inclusion_final_gene_sum['value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d97c2d4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save df\n",
    "ecoli_proteomics_ferm_inclusion_final_gene_sum.to_csv('Data/2024_02_22_ecoli_specific_activity_fermentation.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "35dc2f3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10896412845366836"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate occupancy for df that was not biomass corrected\n",
    "ecoli_proteomics_ferm_melt_sa_no_biomass_correction.drop_duplicates(inplace = True)\n",
    "ecoli_proteomics_ferm_melt_sa_no_biomass_correction.groupby('variable').sum().reset_index().assign(estimate = 'fermentation', organism='ecoli')['value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "764021d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Percent decrease = 25.94%'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate mean values\n",
    "mean_initial = ecoli_proteomics_ferm_melt_sa_no_biomass_correction.groupby('variable').sum().reset_index().assign(estimate = 'fermentation', organism='ecoli')['value'].mean()\n",
    "mean_final = ecoli_proteomics_ferm_inclusion_final_gene_sum['value'].mean()\n",
    "\n",
    "#calculate percent decrease\n",
    "percent_decrease = (mean_initial - mean_final) / mean_initial * 100\n",
    "\n",
    "#print proteome occupancy decrease from biomass correction\n",
    "message = \"Percent decrease = {:.2f}%\".format(percent_decrease)\n",
    "message"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85b0627a",
   "metadata": {},
   "source": [
    "### Respiration pathway"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "50000252",
   "metadata": {},
   "outputs": [],
   "source": [
    "#subset to resp genes\n",
    "ecoli_inclusion_resp = ecoli_inclusion.loc[ecoli_inclusion['meta_pathway'] == 'respiration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7bebaf71",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge the proteomics data with the inclusion list \n",
    "ecoli_proteomics_resp_inclusion_final = pd.merge(ecoli_inclusion_resp, ecoli_proteomics, on = 'Accession', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "011bd500",
   "metadata": {},
   "outputs": [],
   "source": [
    "#melt df\n",
    "ecoli_proteomics_resp_melt = pd.melt(ecoli_proteomics_resp_inclusion_final, id_vars=['Gene name', 'pathway'],\n",
    "                                 value_vars=column_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "efea21eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge proteomics data with the meta data\n",
    "ecoli_proteomics_resp_melt = pd.merge(ecoli_proteomics_resp_melt, ecoli_meta, on = 'variable', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "daba4b6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#use only the nonfermentable carbon sources from batch culture for fermentation specific activity\n",
    "ecoli_proteomics_resp_melt_sa = ecoli_proteomics_resp_melt.loc[(ecoli_proteomics_resp_melt['author'] == 'schmidt') \n",
    "                               & (ecoli_proteomics_resp_melt['carbon_source_cat'] == 'nonfermentable') \n",
    "                               & (ecoli_proteomics_resp_melt['method'] == 'batch')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1da98738",
   "metadata": {},
   "outputs": [],
   "source": [
    "#make a copy that will not get biomass corrected for comparision\n",
    "ecoli_proteomics_resp_melt_sa_no_biomass_correction = ecoli_proteomics_resp_melt_sa.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99b9a63d",
   "metadata": {},
   "source": [
    "#### Respiration Biomass producing fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "10d6f470",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in the fraction to biomass spreadsheet\n",
    "ecoli_biomass = pd.read_csv('./data/2022_11_06_ecoli_biomass.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "dd9787e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#subset to resp genes\n",
    "ecoli_biomass_resp = ecoli_biomass.loc[ecoli_biomass['Condition'] == 'Respiration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "dec5f8ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40945241093333334"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecoli_biomass_resp_mean = ecoli_biomass_resp['Fraction '].mean()\n",
    "ecoli_biomass_resp_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4152f073",
   "metadata": {},
   "outputs": [],
   "source": [
    "ecoli_pathway_list = ecoli_inclusion[['pathway', 'Gene name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "632b7cf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#correction applies to only the enzymes in the tca cycle\n",
    "ecoli_proteomics_resp_melt_sa.loc[(ecoli_proteomics_resp_melt_sa['media'] == 'minimal') & \n",
    "                                  (ecoli_proteomics_resp_melt_sa['pathway'] == 'tca') , \n",
    "                                  'value'] = ecoli_proteomics_resp_melt_sa['value'] * (1-ecoli_biomass_resp_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad696ab1",
   "metadata": {},
   "source": [
    "#### Respiration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7f58ca1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop duplicates\n",
    "ecoli_proteomics_resp_melt_sa = ecoli_proteomics_resp_melt_sa.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0b24ef54",
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby dataset variable and sum the entire set of genes for final sum\n",
    "ecoli_proteomics_resp_inclusion_final_gene_sum = ecoli_proteomics_resp_melt_sa.groupby('variable').sum().reset_index().assign(estimate = 'respiration', organism = 'ecoli')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0cab82dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11203320246301977"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean occupancy\n",
    "ecoli_proteomics_resp_inclusion_final_gene_sum['value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2cdb409c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save df\n",
    "ecoli_proteomics_resp_inclusion_final_gene_sum.to_csv('./data/2024_02_22_ecoli_specific_activity_respiration.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d33c9ec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16129742367788766"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate occupancy for df that was not biomass corrected\n",
    "ecoli_proteomics_resp_melt_sa_no_biomass_correction.drop_duplicates(inplace = True)\n",
    "ecoli_proteomics_resp_melt_sa_no_biomass_correction.groupby('variable').sum().reset_index().assign(estimate = 'respiration', organism = 'ecoli')['value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7e5285c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent decrease = 30.54%\n"
     ]
    }
   ],
   "source": [
    "#calculate mean values\n",
    "mean_initial = ecoli_proteomics_resp_melt_sa_no_biomass_correction.groupby('variable').sum().reset_index().assign(estimate = 'respiration', organism = 'ecoli')['value'].mean()\n",
    "mean_final = ecoli_proteomics_resp_inclusion_final_gene_sum['value'].mean()\n",
    "\n",
    "#calculate percent decrease\n",
    "percent_decrease = (mean_initial - mean_final) / mean_initial * 100\n",
    "\n",
    "#print proteome occupancy decrease from biomass correction\n",
    "message = \"Percent decrease = {:.2f}%\".format(percent_decrease)\n",
    "print(message)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20e6e1cb",
   "metadata": {},
   "source": [
    "### ATP-producing pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9cba7d32",
   "metadata": {},
   "outputs": [],
   "source": [
    "#inclusion list for all atp-producing genes\n",
    "ecoli_inclusion = ecoli_inclusion[['Gene name', 'pathway', 'Accession']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5db91884",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge with proteomics data\n",
    "ecoli_proteomics_full_pathway = pd.merge(ecoli_inclusion, ecoli_proteomics, on = 'Accession', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6fe5fa34",
   "metadata": {},
   "outputs": [],
   "source": [
    "#use melt funtion to put data table into correct format \n",
    "ecoli_proteomics_full_melt = pd.melt(ecoli_proteomics_full_pathway, id_vars=['Gene name', 'pathway'],\n",
    "                                 value_vars=column_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fa8a6af2",
   "metadata": {},
   "outputs": [],
   "source": [
    "ecoli_proteomics_full_melt = pd.merge(ecoli_proteomics_full_melt, ecoli_meta, on = 'variable', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a881336a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#biomass correction: correction applies to only the enzymes in glycolysis and tca cycle\n",
    "ecoli_proteomics_full_melt.loc[(ecoli_proteomics_full_melt['media'] == 'minimal') & \n",
    "                                  (ecoli_proteomics_full_melt['carbon_source_cat'] == 'fermentable') &\n",
    "                                  (ecoli_proteomics_full_melt['pathway'] == 'tca'), \n",
    "                                  'value'] = ecoli_proteomics_full_melt['value'] * (1-ecoli_biomass_ferm_mean)\n",
    "\n",
    "ecoli_proteomics_full_melt.loc[(ecoli_proteomics_full_melt['media'] == 'minimal') & \n",
    "                                  (ecoli_proteomics_full_melt['carbon_source_cat'] == 'fermentable') &\n",
    "                                  (ecoli_proteomics_full_melt['pathway'] == 'glycolysis'), \n",
    "                                  'value'] = ecoli_proteomics_full_melt['value'] * (1-ecoli_biomass_ferm_mean)\n",
    "\n",
    "ecoli_proteomics_full_melt.loc[(ecoli_proteomics_full_melt['media'] == 'minimal') & \n",
    "                                  (ecoli_proteomics_full_melt['carbon_source_cat'] == 'nonfermentable') &\n",
    "                                  (ecoli_proteomics_full_melt['pathway'] == 'tca'), \n",
    "                                  'value'] = ecoli_proteomics_full_melt['value'] * (1-ecoli_biomass_resp_mean)\n",
    "\n",
    "ecoli_proteomics_full_melt.loc[(ecoli_proteomics_full_melt['media'] == 'minimal') & \n",
    "                                  (ecoli_proteomics_full_melt['carbon_source_cat'] == 'nonfermentable') &\n",
    "                                  (ecoli_proteomics_full_melt['pathway'] == 'glycolysis'), \n",
    "                                  'value'] = ecoli_proteomics_full_melt['value'] * (1-ecoli_biomass_resp_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "48efa687",
   "metadata": {},
   "outputs": [],
   "source": [
    "#copy to another name\n",
    "ecoli_all = ecoli_proteomics_full_melt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bde8f47f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop duplicates\n",
    "ecoli_all = ecoli_all.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e8a86fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby dataset variable and sum the entire set of genes for final sum\n",
    "ecoli_proteomics_sum_total = ecoli_all.groupby('variable').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b8099165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12440515231088653"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean occupancy\n",
    "ecoli_proteomics_sum_total['value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "49fde59f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save sf\n",
    "ecoli_proteomics_sum_total.to_csv('./data/2024_02_22_ecoli_atp_total.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b86949d",
   "metadata": {},
   "source": [
    "### Translation Occupancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "21c82562",
   "metadata": {},
   "outputs": [],
   "source": [
    "#translation inclusion list\n",
    "ecoli_translation_inclusion = pd.read_csv('./data/2024_01_27_ecoli_translation.csv')\n",
    "ecoli_translation_inclusion.rename(columns = {'Gene': 'Gene name'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8c906710",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge the inclusion list with the gene accession\n",
    "ecoli_translation_inclusion = pd.merge(ecoli_translation_inclusion, ecoli_gene_accession, on='Gene name', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bf8d73d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ensuring that everything merged\n",
    "ecoli_translation_inclusion.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "cc139f4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge the proteomics data with the inclusion list \n",
    "ecoli_proteomics_translation = pd.merge(ecoli_translation_inclusion, ecoli_proteomics, on = 'Accession', how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "92949af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#melt df \n",
    "ecoli_proteomics_translation_melt = pd.melt(ecoli_proteomics_translation, id_vars=['Gene name'],\n",
    "                                 value_vars=column_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "92c9d7d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop duplicates\n",
    "ecoli_proteomics_translation_melt = ecoli_proteomics_translation_melt.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "cf95a0b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby dataset variable and sum the entire set of genes for final sum\n",
    "ecoli_proteomics_translation_sum = ecoli_proteomics_translation_melt.groupby('variable').sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "bb6b102c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ecoli_proteomics_translation_sum.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e252b096",
   "metadata": {},
   "outputs": [],
   "source": [
    "#subsetting for mori\n",
    "mori_sum = ecoli_proteomics_translation_sum.loc[ecoli_proteomics_translation_sum['variable'].str.contains('mori')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "65f07ea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#subsetting for C-limitation\n",
    "mori_carbon = mori_sum.loc[(mori_sum['variable'].str.contains('C-limitation')) | (mori_sum['variable'].str.contains('calibration'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "12abc77d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract the number after \"μ=\" and create a new column\n",
    "mori_carbon['growth_rate'] = mori_carbon['variable'].str.extract(r'μ=([0-9.]+)')\n",
    "\n",
    "#convert the extracted column to a numeric type if necessary\n",
    "mori_carbon['growth_rate'] = pd.to_numeric(mori_carbon['growth_rate'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "acac3eb1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot translation occupancy vs. growth rate\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "ax.patch.set_facecolor('white')\n",
    "sns.set_context(\"talk\")\n",
    "\n",
    "sns.scatterplot(mori_carbon['growth_rate'], mori_carbon['value'], color = 'black', alpha=.9, linewidth = 0, s=200, zorder = 3)\n",
    "\n",
    "ax.set_ylim(-0.01, 0.27)\n",
    "ax.set_xlim(.26, 0.96)\n",
    "#axis format\n",
    "ax.set_xlabel(\"Growth Rate \" + r'($h^{-1}$)', fontsize=23)\n",
    "ax.set_ylabel(r\"$\\phi_{\\mathrm{Total}}^{\\mathrm{Translation}}$\", fontsize=23)\n",
    "ax.tick_params(axis='both', which='major', labelsize=20, length=0)\n",
    "\n",
    "#save figure\n",
    "fig.savefig('./Figures/ecoli_total_translation.svg',format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "919b59cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#resetting the index\n",
    "ecoli_proteomics_sum_total_reset = ecoli_proteomics_sum_total.reset_index()\n",
    "\n",
    "#filtering rows based on c-limitation of control (calibration)\n",
    "mori_carbon_atp = ecoli_proteomics_sum_total_reset[\n",
    "    (ecoli_proteomics_sum_total_reset['variable'].str.contains('C-limitation')) |\n",
    "    (ecoli_proteomics_sum_total_reset['variable'].str.contains('calibration'))\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1faa92f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract the number after \"μ=\" and create a new column\n",
    "mori_carbon_atp['growth_rate'] = mori_carbon_atp['variable'].str.extract(r'μ=([0-9.]+)')\n",
    "\n",
    "#convert the extracted column to numeric\n",
    "mori_carbon_atp['growth_rate'] = pd.to_numeric(mori_carbon_atp['growth_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "823c62ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot atp-producing occupancy vs. growth rate\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "ax.patch.set_facecolor('white')\n",
    "sns.set_context(\"talk\")\n",
    "\n",
    "sns.scatterplot(mori_carbon_atp['growth_rate'], mori_carbon_atp['value'], color = 'black', alpha=.9, linewidth = 0, s=200, zorder = 3)\n",
    "\n",
    "ax.set_ylim(-0.01, 0.27)\n",
    "ax.set_xlim(.26, 0.96)\n",
    "#axis format\n",
    "ax.set_xlabel(\"Growth Rate \" + r'($h^{-1}$)', fontsize=23)\n",
    "ax.set_ylabel(r\"$\\phi_{\\mathrm{Total}}^{\\mathrm{ATP}}$\", fontsize=23)\n",
    "ax.tick_params(axis='both', which='major', labelsize=20, length=0)\n",
    "\n",
    "#save figure\n",
    "fig.savefig('./Figures/ecoli_total_atp_growth.svg',format='svg', dpi=1200)"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}