{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "bMOOBVzP9ave", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bMOOBVzP9ave", "outputId": "d89d67c6-cfcd-4158-e43c-33fb6f556c46" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CobraMod version: 1.2.0\n", "COBRApy version: 0.29.0\n" ] } ], "source": [ "from IPython.display import display\n", "from cobramod import __version__\n", "from cobra import __version__ as cobra_version\n", "print(f'CobraMod version: {__version__}')\n", "print(f'COBRApy version: {cobra_version}')\n", "# From Escher:\n", "# This option turns off the warning message if you leave or refresh this page\n", "import escher\n", "escher.rc['never_ask_before_quit'] = True" ] }, { "cell_type": "markdown", "id": "SLOGRXdE8zOJ", "metadata": { "id": "SLOGRXdE8zOJ" }, "source": [ "\n", "# Analyising shikimate production using an *E. coli* core model" ] }, { "cell_type": "markdown", "id": "c25b3164", "metadata": { "id": "c25b3164" }, "source": [ "## Introduction\n", "\n", "In this tutorial we use a core model of [*E. coli*](\n", "http://bigg.ucsd.edu/models/e_coli_core\n", ") [1] and extend it with the [shikimate synthesis pathway](\n", " https://biocyc.org/ECOLI/NEW-IMAGE?type=PATHWAY&object=ARO-PWY\n", "). Shikimate is an important precursor for aromatic compounds such as\n", "phenylalanine, tyrosine, and tryptophan. In [this article](\n", "https://doi.org/10.1016/j.biortech.2014.05.035) the authors *in vivo*\n", "engineered five *E. coli* strains for shikimate overproduction by deactivating\n", "genes for the enzymatic regulation of the shikimate metabolism [2].\n", "In our test case, we *in silico* compare the control strain (`W3110`) with a strain (`SA3`) from the article. To this end, we use CobraMod to add the shikimate pathway from the BioCyc\n", "sub-database [EcoCyc](\n", " https://www.ecocyc.org/\n", ") to the *E. coli* model. We then simulate and\n", "visualize shikimate production for the two strains. The shikimate pathway is shown below and is represented by\n", "two sub-pathways with the EcoCyc identifiers [PWY-6164](\n", " https://biocyc.org/ECOLI/NEW-IMAGE?type=PATHWAY&object=PWY-6164\n", ") and [PWY-6163](\n", " https://biocyc.org/ECOLI/NEW-IMAGE?type=PATHWAY&object=PWY-6163\n", "). \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Pathway identifierPathway name
PWY-61643-dehydroquinate biosynthesis I
PWY-6163chorismate biosynthesis from 3-dehydroquinate
\n", "\n" ] }, { "cell_type": "markdown", "id": "GVfuxpKy9Twf", "metadata": { "id": "GVfuxpKy9Twf" }, "source": [ "\n", "## Loading CobraMod, the *E. coli* model and setting the growth medium for the simulations\n", "\n", "We use the *E. coli* core model from COBRApy as our model. This core model can be found under `cobramod.test.textbook`. It contains 72\n", "metabolites, 95 reactions and 137 genes. We load the function\n", "[cobramod.add_pathway()](\n", "module/cobramod/index.html#cobramod.add_pathway\n", ") and the Python module [pathlib](\n", "https://docs.python.org/3/library/pathlib.html\n", ") to represent the system path where CobraMod stores the metabolic pathway\n", "information. We define the directory for pathway information as `dir_data`. The\n", "pathways with the respective reaction, gene and metabolite information are \n", "saved here." ] }, { "cell_type": "code", "execution_count": 2, "id": "f2e21846", "metadata": { "execution": { "iopub.execute_input": "2021-06-04T09:23:30.608216Z", "iopub.status.busy": "2021-06-04T09:23:30.605802Z", "iopub.status.idle": "2021-06-04T09:23:33.249873Z", "shell.execute_reply": "2021-06-04T09:23:33.249480Z" }, "id": "f2e21846", "scrolled": true }, "outputs": [], "source": [ "from pathlib import Path\n", "\n", "from cobramod import add_pathway\n", "from cobramod.test import textbook\n", "\n", "# Defining system path for data\n", "dir_data = Path.cwd().joinpath(\"data\")\n", "# Defining model\n", "model = textbook.copy()" ] }, { "cell_type": "markdown", "id": "ecca1Ry_7ej0", "metadata": { "id": "ecca1Ry_7ej0" }, "source": [ "In the selected study, the authors used a culture medium including glucose and several other metabolites. Since only glucose is included in our core model and for the sake of simplicity we only constrain glucose and oxygen uptake." ] }, { "cell_type": "code", "execution_count": 3, "id": "0a93a132", "metadata": { "id": "0a93a132", "scrolled": true }, "outputs": [], "source": [ "# limiting glucose\n", "model.exchanges.EX_glc__D_e.bounds = ( -10, 0)\n", "# limiting oxygen\n", "model.exchanges.EX_o2_e.bounds = (0, 0)" ] }, { "cell_type": "markdown", "id": "4fb87bd2", "metadata": { "id": "4fb87bd2" }, "source": [ "## Adding the shikimate pathways\n", "\n", "To add a pathway to the model we use the function [add_pathway()](\n", "module/cobramod/index.html#cobramod.add_pathway\n", "). To do so, the first argument `model` is the respective model to extend. The\n", "argument `pathway` is the respective database-specific identifier for the pathway (`PWY-6164` and `PWY-6163`) followed by the database identifier `ECOLI`.\n", "We set cytosol (`c`) as the compartment for the reactions and metabolites\n", "of the pathways. Because the two added sub-pathways are part of a larger pathway, we use\n", "the argument `group` to create a single [cobramod.Pathway](\n", "module/cobramod/index.html#cobramod.Pathway\n", ") object.\n", "\n", "CobraMod tries to avoid duplicate reactions and metabolites. In our case, the\n", "`textbook` model uses BiGG identifiers and the downloaded data contain EcoCyc identifiers. When downloading pathway information, CobraMod checks the\n", "cross-references within these data and searches for corresponding objects in the model. In this example CobraMod warns the user that several metabolites, \n", "such as `Pi` (phosphate), `WATER` and `ATP`, are already in the model. Calling the pathway object shows a table summarising the pathway attributes." ] }, { "cell_type": "code", "execution_count": 4, "id": "7be52d6e", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "execution": { "iopub.execute_input": "2021-06-04T09:23:33.254331Z", "iopub.status.busy": "2021-06-04T09:23:33.253999Z", "iopub.status.idle": "2021-06-04T09:23:33.301626Z", "shell.execute_reply": "2021-06-04T09:23:33.301876Z" }, "id": "7be52d6e", "outputId": "bef9a1af-111b-47ec-f450-de4672773afe", "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mGene-reaction rule for reaction \"DAHPSYN-RXN\" set to \"OR\". Please modify it if necessary.\u001b[0m\n", "\u001b[33mGene-reaction rule for reaction \"3-DEHYDROQUINATE-SYNTHASE-RXN\" set to \"OR\". Please modify it if necessary.\u001b[0m\n", "\u001b[33mThe model cannot turnover the following metabolites ['3_DEOXY_D_ARABINO_HEPTULOSONATE_7_P_c']. To overcome this, sink reactions were created to simulate their synthesis.\u001b[0m\n", "\u001b[36mNon-zero flux test for reaction 'DAHPSYN_RXN_c' passed.\u001b[0m\n", "\u001b[36mReaction \"DAHPSYN_RXN_c\" added to group \"PWY-SHIKIMATE\".\u001b[0m\n", "\u001b[36mNon-zero flux test for reaction '3_DEHYDROQUINATE_SYNTHASE_RXN_c' passed.\u001b[0m\n", "\u001b[36mReaction \"3_DEHYDROQUINATE_SYNTHASE_RXN_c\" added to group \"PWY-SHIKIMATE\".\u001b[0m\n", "\u001b[33mAuxiliary sink reaction for \"SK_3_DEOXY_D_ARABINO_HEPTULOSONATE_7_P_c\" created. Consider removing it and adding the synthesis reactions for the metabolite.\u001b[0m\n", "\u001b[33mAuxiliary sink reaction for \"SK_DEHYDROQUINATE_c\" created. Consider removing it and adding the synthesis reactions for the metabolite.\u001b[0m\n", "\u001b[33mGene-reaction rule for reaction \"3-DEHYDROQUINATE-DEHYDRATASE-RXN\" set to \"OR\". Please modify it if necessary.\u001b[0m\n", "\u001b[33mGene-reaction rule for reaction \"SHIKIMATE-5-DEHYDROGENASE-RXN\" set to \"OR\". Please modify it if necessary.\u001b[0m\n", "\u001b[33mGene-reaction rule for reaction \"SHIKIMATE-KINASE-RXN\" set to \"OR\". Please modify it if necessary.\u001b[0m\n", "\u001b[33mGene-reaction rule for reaction \"2.5.1.19-RXN\" set to \"OR\". Please modify it if necessary.\u001b[0m\n", "\u001b[33mGene-reaction rule for reaction \"CHORISMATE-SYNTHASE-RXN\" set to \"OR\". Please modify it if necessary.\u001b[0m\n", "\u001b[33mThe model cannot turnover the following metabolites ['3_DEHYDRO_SHIKIMATE_c']. To overcome this, sink reactions were created to simulate their synthesis.\u001b[0m\n", "\u001b[36mNon-zero flux test for reaction '3_DEHYDROQUINATE_DEHYDRATASE_RXN_c' passed.\u001b[0m\n", "\u001b[36mReaction \"3_DEHYDROQUINATE_DEHYDRATASE_RXN_c\" added to group \"PWY-SHIKIMATE\".\u001b[0m\n", "\u001b[33mThe model cannot turnover the following metabolites ['SHIKIMATE_c']. To overcome this, sink reactions were created to simulate their synthesis.\u001b[0m\n", "\u001b[36mNon-zero flux test for reaction 'SHIKIMATE_5_DEHYDROGENASE_RXN_c' passed.\u001b[0m\n", "\u001b[36mReaction \"SHIKIMATE_5_DEHYDROGENASE_RXN_c\" added to group \"PWY-SHIKIMATE\".\u001b[0m\n", "\u001b[33mThe model cannot turnover the following metabolites ['SHIKIMATE_5P_c']. To overcome this, sink reactions were created to simulate their synthesis.\u001b[0m\n", "\u001b[36mNon-zero flux test for reaction 'SHIKIMATE_KINASE_RXN_c' passed.\u001b[0m\n", "\u001b[36mReaction \"SHIKIMATE_KINASE_RXN_c\" added to group \"PWY-SHIKIMATE\".\u001b[0m\n", "\u001b[33mThe model cannot turnover the following metabolites ['3_ENOLPYRUVYL_SHIKIMATE_5P_c']. To overcome this, sink reactions were created to simulate their synthesis.\u001b[0m\n", "\u001b[36mNon-zero flux test for reaction '2.5.1.19_RXN_c' passed.\u001b[0m\n", "\u001b[36mReaction \"2.5.1.19_RXN_c\" added to group \"PWY-SHIKIMATE\".\u001b[0m\n", "\u001b[36mNon-zero flux test for reaction 'CHORISMATE_SYNTHASE_RXN_c' passed.\u001b[0m\n", "\u001b[36mReaction \"CHORISMATE_SYNTHASE_RXN_c\" added to group \"PWY-SHIKIMATE\".\u001b[0m\n", "\u001b[33mAuxiliary sink reaction for \"SK_3_ENOLPYRUVYL_SHIKIMATE_5P_c\" created. Consider removing it and adding the synthesis reactions for the metabolite.\u001b[0m\n", "\u001b[33mAuxiliary sink reaction for \"SK_CHORISMATE_c\" created. Consider removing it and adding the synthesis reactions for the metabolite.\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of new | removed entities in\n", "*=====================|===================*\n", "Reactions 2 | 0 \n", "Metabolites 2 | 0 \n", "Exchange 0 | 0 \n", "Demand 0 | 0 \n", "Sinks 2 | 0 \n", "Genes 4 | 0 \n", "Groups 1 | 0 \n", "\n", "Number of new | removed entities in\n", "*=====================|===================*\n", "Reactions 5 | 0 \n", "Metabolites 5 | 0 \n", "Exchange 0 | 0 \n", "Demand 0 | 0 \n", "Sinks 2 | 1 \n", "Genes 6 | 0 \n", "Groups 0 | 0 \n", "\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Pathway identifierPWY-SHIKIMATE
Name
Memory address0x0128071484779088
Reactions involved

CHORISMATE_SYNTHASE_RXN_c, 3_DEHYDROQUINATE_SYNTHASE_RXN_c, SHIKIMATE_KINASE_RXN_c, SHIKIMATE_5_DEHYDROGENASE_RXN_c, DAHPSYN_RXN_c, 3_DEHYDROQUINATE_DEHYDRATASE_RXN_c, 2.5.1.19_RXN_c
Genes involved

EG10075, EG10074, EG10081, EG10082, EG10077, EG10080, EG10078, EG10079, EG10076, EG10073
Visualization attributes
  • vertical =\n", "False
  • color_negative = None
    • \n", "
  • color_positive = None
  • color_quantile =\n", "False

 

" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# first pathway\n", "add_pathway(\n", " model=model,\n", " pathway=\"PWY-6164\",\n", " database=\"ECOLI\",\n", " compartment=\"c\",\n", " group=\"PWY-SHIKIMATE\",\n", " directory=dir_data,\n", ")\n", "\n", "# second pathway\n", "add_pathway(\n", " model=model,\n", " pathway=\"PWY-6163\",\n", " database=\"ECOLI\",\n", " compartment=\"c\",\n", " group=\"PWY-SHIKIMATE\",\n", " directory=dir_data,\n", ")\n", "\n", "# Show attributes\n", "model.groups.get_by_id(\"PWY-SHIKIMATE\")" ] }, { "cell_type": "markdown", "id": "1jlXNBHmQQwZ", "metadata": { "id": "1jlXNBHmQQwZ" }, "source": [ "CobraMod does not automatically merge sub-pathways in the visualization. When we call\n", "the function [Pathway.visualize()](\n", "module/cobramod/core/pathway/index.html#cobramod.core.pathway.Pathway.visualize\n", "), CobraMod shows both sub-pathways one above the other, instead of a single linear pathway.\n", "To change this, we use the method [Pathway.modify_graph()](\n", "module/cobramod/core/pathway/index.html#cobramod.core.pathway.Pathway.modify_graph\n", "). This function merges the two sub-pathways by connecting two reactions in the \n", "visualization. Here, the argument `reaction` specifies the last reaction of the first sub-pathway and the argument `next_reaction` specifies the subsequent reaction of the second sub-pathway.\n", "\n", "In the first visualization, `3_DEHYDROQUINATE_SYNTHASE_RXN_c` and\n", "`3_DEHYDROQUINATE_DEHYDRATASE_RXN_c` are not connected. We use\n", "the `modify_graph` method to join both reactions. When we now\n", "visualize the pathway, we obtain one linear pathway." ] }, { "cell_type": "code", "execution_count": 5, "id": "cFSK-j5rQQai", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "d6240177b4e345ca83b70ed6b0e57a6f", "aaeee1e20c184e83beaa2124a2222306", "bc6b2507f417485293f7010ec0c99b36", "12f27afa71464a789ad428b3308050b2" ] }, "id": "cFSK-j5rQQai", "outputId": "6487c034-c2fa-47f1-b182-5dc9c9b1a576" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[36mVisualization saved in \"pathway.html\"\u001b[0m\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7cde89c380ab4d708ce6449e4355b1bf", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Builder(never_ask_before_quit=True, reaction_scale={}, reaction_styles=['color', 'text'])" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[36mThe reaction order of pathway \"PWY-SHIKIMATE\" was modified. Reaction \"3_DEHYDROQUINATE_DEHYDRATASE_RXN_c\" takes place after \"3_DEHYDROQUINATE_SYNTHASE_RXN_c\".\u001b[0m\n", "\u001b[36mVisualization saved in \"pathway.html\"\u001b[0m\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "761c0ffa28704ba6bf361d67bd8a1783", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Builder(never_ask_before_quit=True, reaction_scale={}, reaction_styles=['color', 'text'])" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Show pathway map\n", "display(model.groups.get_by_id(\"PWY-SHIKIMATE\").visualize())\n", "# Merge reactions of the sub-pathways\n", "model.groups.get_by_id(\"PWY-SHIKIMATE\").modify_graph(\n", " reaction=\"3_DEHYDROQUINATE_SYNTHASE_RXN_c\",\n", " next_reaction=\"3_DEHYDROQUINATE_DEHYDRATASE_RXN_c\"\n", ")\n", "# Show pathway map\n", "model.groups.get_by_id(\"PWY-SHIKIMATE\").visualize()" ] }, { "cell_type": "markdown", "id": "103e1713", "metadata": { "id": "103e1713" }, "source": [ "## Creating biomass reactions for the two *E. coli* strains\n", "\n", "Our reference [article](\n", "https://doi.org/10.1016/j.biortech.2014.05.035) lists the following values for biomass an shikimate for the two strains of interest:\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
StrainsBiomass (g/L)Shikimate (mg/L)
W31103.42 ± 0.261.31 ± 0.12
SA35.24 ± 0.34417.20 ± 50.01
\n", "\n", "We use this data to update the biomass equation of our core model by including the contribution of shikimate to the overall biomass composition. For the sake of our tutorial we make some simplifying assumptions (e.g. the ratios of the other biomass components do not change, shikimate productions is coupled to growth) and use the following relationship:\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "_RAbEPDAeaW4", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_RAbEPDAeaW4", "outputId": "555ba483-d279-4f58-daae-0ad2e15fbf6b" }, "outputs": [ { "data": { "text/plain": [ "{'W3110': 0.00038304093567251464, 'SA3': 0.07961832061068702}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculate ratios for the strains\n", "shikimate_ratio = {\n", " \"W3110\": (1.31 / 1000) / 3.42,\n", " \"SA3\": (417.20 / 1000) / 5.24\n", "}\n", "shikimate_ratio" ] }, { "cell_type": "markdown", "id": "tLg-hl9blOo1", "metadata": { "id": "tLg-hl9blOo1" }, "source": [ "\n", "We calculate the new coefficients for the biomass reactions which include\n", "shikimate. The detailed calculations are listed in the\n", "[supplementary information](#Supplementary-Information:-Approximation-of-biomass-compositions) at the\n", "bottom of this page. We obtain the following new biomass equations:" ] }, { "cell_type": "code", "execution_count": 10, "id": "g8tfsGuNodRM", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "g8tfsGuNodRM", "outputId": "595f70f3-fd1d-4b8d-ae9f-098e147af647", "tags": [ "=>A" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "New coefficients for W3110 strain:\n" ] }, { "data": { "text/html": [ "
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identifiernew_coefficient
03pg_c-1.495427
1accoa_c-3.746364
2atp_c-59.78709
3e4p_c-0.360862
4f6p_c-0.070873
5g3p_c-0.128951
6g6p_c-0.204921
7gln__L_c-0.255602
8glu__L_c-4.939507
9h2o_c-59.78709
10nad_c-3.545641
11nadph_c-13.02291
12oaa_c-1.786016
13pep_c-0.518901
14pyr_c-2.831715
15r5p_c-0.897356
16SHIKIMATE_c-0.106557
17adp_c59.78709
18akg_c4.1182
19coa_c3.746364
20h_c59.78709
21nadh_c3.545641
22nadp_c13.02291
23pi_c59.78709
\n", "
" ], "text/plain": [ " identifier new_coefficient\n", "0 3pg_c -1.495427\n", "1 accoa_c -3.746364\n", "2 atp_c -59.78709\n", "3 e4p_c -0.360862\n", "4 f6p_c -0.070873\n", "5 g3p_c -0.128951\n", "6 g6p_c -0.204921\n", "7 gln__L_c -0.255602\n", "8 glu__L_c -4.939507\n", "9 h2o_c -59.78709\n", "10 nad_c -3.545641\n", "11 nadph_c -13.02291\n", "12 oaa_c -1.786016\n", "13 pep_c -0.518901\n", "14 pyr_c -2.831715\n", "15 r5p_c -0.897356\n", "16 SHIKIMATE_c -0.106557\n", "17 adp_c 59.78709\n", "18 akg_c 4.1182\n", "19 coa_c 3.746364\n", "20 h_c 59.78709\n", "21 nadh_c 3.545641\n", "22 nadp_c 13.02291\n", "23 pi_c 59.78709" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "New coefficients for SA3 strain:\n" ] }, { "data": { "text/html": [ "
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identifiernew_coefficient
03pg_c-1.376891
1accoa_c-3.449406
2atp_c-55.048028
3e4p_c-0.332258
4f6p_c-0.065255
5g3p_c-0.118729
6g6p_c-0.188678
7gln__L_c-0.235342
8glu__L_c-4.547974
9h2o_c-55.048028
10nad_c-3.264594
11nadph_c-11.99064
12oaa_c-1.644446
13pep_c-0.47777
14pyr_c-2.607257
15r5p_c-0.826227
16SHIKIMATE_c-22.148743
17adp_c55.048028
18akg_c4.1182
19coa_c3.449406
20h_c55.048028
21nadh_c3.264594
22nadp_c11.99064
23pi_c55.048028
\n", "
" ], "text/plain": [ " identifier new_coefficient\n", "0 3pg_c -1.376891\n", "1 accoa_c -3.449406\n", "2 atp_c -55.048028\n", "3 e4p_c -0.332258\n", "4 f6p_c -0.065255\n", "5 g3p_c -0.118729\n", "6 g6p_c -0.188678\n", "7 gln__L_c -0.235342\n", "8 glu__L_c -4.547974\n", "9 h2o_c -55.048028\n", "10 nad_c -3.264594\n", "11 nadph_c -11.99064\n", "12 oaa_c -1.644446\n", "13 pep_c -0.47777\n", "14 pyr_c -2.607257\n", "15 r5p_c -0.826227\n", "16 SHIKIMATE_c -22.148743\n", "17 adp_c 55.048028\n", "18 akg_c 4.1182\n", "19 coa_c 3.449406\n", "20 h_c 55.048028\n", "21 nadh_c 3.264594\n", "22 nadp_c 11.99064\n", "23 pi_c 55.048028" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "W3110_df = pd.concat(\n", " [\n", " W3110_df_negative.loc[:, [\"identifier\", \"new_coefficient\"]],\n", " W3110_df_positive.loc[:, [\"identifier\", \"new_coefficient\"]],\n", " ]\n", ")\n", "W3110_df = W3110_df.reset_index(drop=True)\n", "print(\"New coefficients for W3110 strain:\")\n", "display(W3110_df)\n", "\n", "SA3_df = pd.concat(\n", " [\n", " SA3_df_negative.loc[:, [\"identifier\", \"new_coefficient\"]],\n", " SA3_df_positive.loc[:, [\"identifier\", \"new_coefficient\"]],\n", " ]\n", ")\n", "SA3_df = SA3_df.reset_index(drop=True)\n", "print(\"New coefficients for SA3 strain:\")\n", "display(SA3_df)" ] }, { "cell_type": "markdown", "id": "dCs4IkKTuO2l", "metadata": { "id": "dCs4IkKTuO2l" }, "source": [ "Finally, we create a function that uses these value to generate the updated strain-specific biomass equations and add it to the model." ] }, { "cell_type": "code", "execution_count": 11, "id": "67c6b272", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 460 }, "id": "67c6b272", "outputId": "70b1fef6-4948-4c6f-d5fc-f434e1aa346e" }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierW3110_biomass
Name
Memory address0x747af72fd9d0
Stoichiometry\n", "

1.495426970760234 3pg_c + 0.10655682044443099 SHIKIMATE_c + 3.746364439181286 accoa_c + 59.78709032163742 atp_c + 0.3608617222222222 e4p_c + 0.07087284239766083 f6p_c + 0.12895058771929824 g3p_c +...

\n", "

1.495426970760234 3-Phospho-D-glycerate + 0.10655682044443099 shikimate + 3.746364439181286 Acetyl-CoA + 59.78709032163742 ATP + 0.3608617222222222 D-Erythrose 4-phosphate + 0.07087284239766083...

\n", "
GPR
Lower bound0.0
Upper bound1000.0
\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierSA3_biomass
Name
Memory address0x747af72fc140
Stoichiometry\n", "

1.376890992366412 3pg_c + 22.148742610250682 SHIKIMATE_c + 3.4494064580152664 accoa_c + 55.0480282442748 atp_c + 0.33225778625954194 e4p_c + 0.0652550610687023 f6p_c + 0.11872923664122137 g3p_c +...

\n", "

1.376890992366412 3-Phospho-D-glycerate + 22.148742610250682 shikimate + 3.4494064580152664 Acetyl-CoA + 55.0480282442748 ATP + 0.33225778625954194 D-Erythrose 4-phosphate + 0.0652550610687023...

\n", "
GPR
Lower bound0.0
Upper bound1000.0
\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from cobra import Reaction\n", "\n", "\n", "def create_biomass(df: pd.DataFrame, identifier: str):\n", " \"\"\"\n", " Creates a Reaction from the reactant and product DataFrame with new\n", " coefficients.\n", " \"\"\"\n", " reaction = Reaction(id=identifier)\n", " # Iterate over dataframe\n", " for index, row in df.iterrows():\n", " reaction.add_metabolites(\n", " {\n", " model.metabolites.get_by_id(row[\"identifier\"]): row[\n", " \"new_coefficient\"\n", " ]\n", " }\n", " )\n", " return reaction\n", "\n", "\n", "biomass_W3110 = create_biomass(W3110_df, \"W3110_biomass\")\n", "\n", "biomass_SA3 = create_biomass(SA3_df, \"SA3_biomass\")\n", "# Show new reactions\n", "display(biomass_W3110)\n", "display(biomass_SA3)\n", "\n", "# Add to model\n", "model.add_reactions([biomass_W3110, biomass_SA3])" ] }, { "cell_type": "markdown", "id": "33db9895", "metadata": { "id": "33db9895" }, "source": [ "## Simulating shikimate production in the two *E. coli* strains\n", "\n", "We use COBRApy to run flux balance analysis for the two strains. Using the COBRApy method\n", "[cobra.model.optimize()](\n", " https://cobrapy.readthedocs.io/en/latest/autoapi/cobra/core/model/index.html#cobra.core.model.Model.optimize\n", ") we get two [solutions objects](\n", " https://cobrapy.readthedocs.io/en/latest/autoapi/cobra/core/solution/index.html#cobra.core.solution.Solution\n", "). \n", "We can see that the solution for the W3110 strain has a higher objective value\n", "(0.2105) than the SA3 strain objective value (0.0995) which is caused by the different shikimate ratios in the biomass equation.\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "2cf9f942", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 991 }, "id": "2cf9f942", "outputId": "70ec3db9-dc31-4d66-998e-664d451a7093", "scrolled": true }, "outputs": [ { "data": { "text/html": [ "

Objective

1.0 W3110_biomass = 1.3166970006837229

Uptake

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MetaboliteReactionFluxC-NumberC-Flux
co2_eEX_co2_e0.811410.01%
glc__D_eEX_glc__D_e1060.60%
nh4_eEX_nh4_e7.17700.00%
3_DEOXY_D_ARABINO_HEPTULOSONATE_7_P_cSK_3_DEOXY_D_ARABINO_HEPTULOSONATE_7_P_c0.140370.01%
3_ENOLPYRUVYL_SHIKIMATE_5P_cSK_3_ENOLPYRUVYL_SHIKIMATE_5P_c10001099.39%

Secretion

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MetaboliteReactionFluxC-NumberC-Flux
for_eEX_for_e-4.81110.05%
h2o_eEX_h2o_e-8.77200.00%
h_eEX_h_e-31.2100.00%
pi_eEX_pi_e-995.300.00%
CHORISMATE_cSK_CHORISMATE_c-10001099.95%
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

Objective

1.0 SA3_biomass = 1.2001394453983747

Uptake

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MetaboliteReactionFluxC-NumberC-Flux
glc__D_eEX_glc__D_e1060.60%
nh4_eEX_nh4_e6.02300.00%
3_DEOXY_D_ARABINO_HEPTULOSONATE_7_P_cSK_3_DEOXY_D_ARABINO_HEPTULOSONATE_7_P_c26.5871.86%
3_ENOLPYRUVYL_SHIKIMATE_5P_cSK_3_ENOLPYRUVYL_SHIKIMATE_5P_c977.51097.54%

Secretion

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MetaboliteReactionFluxC-NumberC-Flux
co2_eEX_co2_e-14.9610.15%
h2o_eEX_h2o_e-22.2200.00%
h_eEX_h_e-21.3700.00%
pi_eEX_pi_e-100000.00%
CHORISMATE_cSK_CHORISMATE_c-977.51099.85%
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# W3110 type\n", "model.objective = \"W3110_biomass\"\n", "\n", "solution_W3110 = model.optimize()\n", "display(model.summary(solution=solution_W3110))\n", "\n", "# SA3 strain\n", "model.objective = \"SA3_biomass\"\n", "solution_SA3 = model.optimize()\n", "display(model.summary(solution=solution_SA3))" ] }, { "cell_type": "markdown", "id": "d4db6f31", "metadata": { "id": "d4db6f31" }, "source": [ "## Visualization with Escher\n", "\n", "The object [cobramod.Pathway](\n", "module/cobramod/core/pathway/index.html#cobramod.core.pathway.Pathway\n", ") has a method for visualizing metabolic pathway using the pathway visualization tool [Escher](\n", "https://escher.readthedocs.io/en/latest/\n", ").\n", "The function\n", "[Pathway.visualize()](\n", "module/cobramod/core/pathway/index.html#cobramod.core.pathway.Pathway.visualize\n", ") generates pathway maps which can be\n", "easily customized with user-defined colors, flux ranges and gradient settings." ] }, { "cell_type": "code", "execution_count": 13, "id": "8f8282d4", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "a2fd8ee947bf4b08a72a5115e027ac59", "a9c0c8ac37f842fea13c14bb21c1b04f" ] }, "id": "8f8282d4", "outputId": "94a59895-68bf-4597-f765-da532214509a", "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[36mVisualization saved in \"pathway.html\"\u001b[0m\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8ac51b537bb44f3e81064328aa04e891", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Builder(never_ask_before_quit=True, reaction_scale={}, reaction_styles=['color', 'text'])" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model.groups.get_by_id(\"PWY-SHIKIMATE\").visualize()" ] }, { "cell_type": "markdown", "id": "tKeliuyeOZRS", "metadata": { "id": "tKeliuyeOZRS" }, "source": [ "To visually compare our results we set the colors for positive and\n", "negative fluxes to blue and red, respectively (By default, all color gradients use grey tones around zero, i.e. more saturated colors indicate larger absolute flux values). We set the map orientation to horizontal. To be able to compare the two flux simulations we set the bounds for the color gradient (`color_min_max`) to -3 and 3. The coloring of the arrows represents the values of the flux solution. To visualize the solution, we use the argument `solution_fluxes`.\n", "The first visualization displays the flux distributions for the\n", "W3110 strain. In this example, the flux values are close to 0 and thus the colors are pale. The second visualization displays the flux distributions for the SA3 strain which has higher fluxes and thus more saturated colors. Note that\n", "the reaction `SHIKIMATE_5_DEHYDROGENASE_RXN_c` has a\n", "negative flux because it is operating in the backwards direction ([reaction information](\n", " https://biocyc.org/ECOLI/NEW-IMAGE?type=REACTION&object=SHIKIMATE-5-DEHYDROGENASE-RXN\n", "))." ] }, { "cell_type": "code", "execution_count": 14, "id": "_Kv7Z89emppB", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17, "referenced_widgets": [ "1e5d73af432e465ca2cee79fc93305fd", "3c2ab75926ab40c3a5415b586cfc4458", "ed498c8ce30c4c36a0d3c2c7ae84fee7", "e494ff28cf924dd6afd140de4f49e7a1" ] }, "id": "_Kv7Z89emppB", "outputId": "b1d6d0b9-8487-4c80-c2be-df6daf09f564" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[36mVisualization saved in \"pathway.html\"\u001b[0m\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6e7829b09813490aafb23b337b8116d3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Builder(never_ask_before_quit=True, reaction_data={'ACALD': 0.0, 'ACALDt': 0.0, 'ACKr': 0.0, 'ACONTa': 1.41796…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[36mVisualization saved in \"pathway.html\"\u001b[0m\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1b18f0571c0d498194cd61e63273ca5a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Builder(never_ask_before_quit=True, reaction_data={'ACALD': 0.0, 'ACALDt': 0.0, 'ACKr': 0.0, 'ACONTa': 0.79823…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Customize pathway\n", "model.groups.get_by_id(\"PWY-SHIKIMATE\").vertical = False\n", "model.groups.get_by_id(\"PWY-SHIKIMATE\").color_positive = \"blue\"\n", "model.groups.get_by_id(\"PWY-SHIKIMATE\").color_negative = \"red\"\n", "model.groups.get_by_id(\"PWY-SHIKIMATE\").color_min_max = [-3, 3]\n", "\n", "# Visualize pathway with solutions\n", "display(model.groups.get_by_id(\"PWY-SHIKIMATE\").visualize(\n", " solution_fluxes=solution_W3110)\n", ")\n", "display(model.groups.get_by_id(\"PWY-SHIKIMATE\").visualize(\n", " solution_fluxes=solution_SA3)\n", ")" ] }, { "cell_type": "markdown", "id": "t0TnIORYOyDf", "metadata": { "id": "t0TnIORYOyDf" }, "source": [ "## Supplementary Information: Approximation of biomass compositions\n", "\n", "To add shikimate to the biomass reactions, we calculate the shikimate\n", "contribution and the contribution of the other metabolite to biomass." ] }, { "cell_type": "code", "execution_count": 18, "id": "c7dc2c63", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "c7dc2c63", "outputId": "b2a043c4-b44c-44c2-a5de-01186b1b36c4", "tags": [ "#C" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sum of contribution for negative coefficients: -48165.2120532678\n", "Sum of contribution for positive coefficients: 46624.4458868131\n" ] } ], "source": [ "import pandas as pd\n", "\n", "# Building dataframe from Biomass\n", "biomass_df = pd.DataFrame(\n", " [metabolite.id, metabolite.formula, metabolite.formula_weight, value]\n", " for metabolite, value in model.reactions.get_by_id(\n", " \"Biomass_Ecoli_core\"\n", " ).metabolites.items()\n", ")\n", "biomass_df.columns = [\"identifier\", \"formula\", \"mol_weight\", \"coefficient\"]\n", "biomass_df[\"mol_weight*coefficient\"] = (\n", " biomass_df[\"mol_weight\"] * biomass_df[\"coefficient\"]\n", ")\n", "# display(biomass_df)\n", "\n", "# Calculate Sum of coefficients multiplied by constants\n", "sum_mol_coefficient_negative = biomass_df[\"mol_weight*coefficient\"][\n", " biomass_df[\"mol_weight*coefficient\"] < 0\n", "].sum()\n", "sum_mol_coefficient_positive = biomass_df[\"mol_weight*coefficient\"][\n", " biomass_df[\"mol_weight*coefficient\"] > 0\n", "].sum()\n", "print(\n", " \"Sum of contribution for negative coefficients: \"\n", " + f\"{sum_mol_coefficient_negative}\"\n", ")\n", "print(\n", " \"Sum of contribution for positive coefficients: \"\n", " + f\"{sum_mol_coefficient_positive}\"\n", ")" ] }, { "cell_type": "markdown", "id": "VxBYL872QuIq", "metadata": { "id": "VxBYL872QuIq" }, "source": [ "We created a function that returns a table with the updated negative biomass coefficients. In the first part we calculate the contribution of\n", "shikimate (`shikimate_mol_weight_coefficient`) to the biomass followed by the calculations for the remaining metabolites. Finally, we divide the values by the molecular weight of the respective metabolite which gives us the new biomass coefficients.\n", "\n", "The two tables below show the new coefficients (`new_coefficient`) for the two \n", "strains." ] }, { "cell_type": "code", "execution_count": 19, "id": "yq_O6zL-mEsN", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "yq_O6zL-mEsN", "outputId": "98f9f7f8-49b3-47c3-c89a-457aec84d96a", "tags": [ "#B", "=>C" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Table with new negative coefficients for W3110 strain\n" ] }, { "data": { "text/html": [ "
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identifiernew_coefficient
03pg_c-1.495427
1accoa_c-3.746364
2atp_c-59.78709
3e4p_c-0.360862
4f6p_c-0.070873
5g3p_c-0.128951
6g6p_c-0.204921
7gln__L_c-0.255602
8glu__L_c-4.939507
9h2o_c-59.78709
10nad_c-3.545641
11nadph_c-13.02291
12oaa_c-1.786016
13pep_c-0.518901
14pyr_c-2.831715
15r5p_c-0.897356
16SHIKIMATE_c-0.106557
\n", "
" ], "text/plain": [ " identifier new_coefficient\n", "0 3pg_c -1.495427\n", "1 accoa_c -3.746364\n", "2 atp_c -59.78709\n", "3 e4p_c -0.360862\n", "4 f6p_c -0.070873\n", "5 g3p_c -0.128951\n", "6 g6p_c -0.204921\n", "7 gln__L_c -0.255602\n", "8 glu__L_c -4.939507\n", "9 h2o_c -59.78709\n", "10 nad_c -3.545641\n", "11 nadph_c -13.02291\n", "12 oaa_c -1.786016\n", "13 pep_c -0.518901\n", "14 pyr_c -2.831715\n", "15 r5p_c -0.897356\n", "16 SHIKIMATE_c -0.106557" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Table with new negative coefficients for SA3 strain\n" ] }, { "data": { "text/html": [ "
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identifiernew_coefficient
03pg_c-1.376891
1accoa_c-3.449406
2atp_c-55.048028
3e4p_c-0.332258
4f6p_c-0.065255
5g3p_c-0.118729
6g6p_c-0.188678
7gln__L_c-0.235342
8glu__L_c-4.547974
9h2o_c-55.048028
10nad_c-3.264594
11nadph_c-11.99064
12oaa_c-1.644446
13pep_c-0.47777
14pyr_c-2.607257
15r5p_c-0.826227
16SHIKIMATE_c-22.148743
\n", "
" ], "text/plain": [ " identifier new_coefficient\n", "0 3pg_c -1.376891\n", "1 accoa_c -3.449406\n", "2 atp_c -55.048028\n", "3 e4p_c -0.332258\n", "4 f6p_c -0.065255\n", "5 g3p_c -0.118729\n", "6 g6p_c -0.188678\n", "7 gln__L_c -0.235342\n", "8 glu__L_c -4.547974\n", "9 h2o_c -55.048028\n", "10 nad_c -3.264594\n", "11 nadph_c -11.99064\n", "12 oaa_c -1.644446\n", "13 pep_c -0.47777\n", "14 pyr_c -2.607257\n", "15 r5p_c -0.826227\n", "16 SHIKIMATE_c -22.148743" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def new_coefficient(ratio: float):\n", " \"\"\"\n", " Returns the a dataframe with the new coefficient for reactants of the biomass\n", " equation.\n", " \"\"\"\n", " SHIKIMATE_MOL_WEIGHT = 173.14\n", " shikimate_mol_weight_coefficient = sum_mol_coefficient_negative * ratio\n", " # Calculating new multiplication between the coefficients and constants\n", " new_mol_weight_coefficient = biomass_df[\"mol_weight*coefficient\"][\n", " biomass_df[\"mol_weight*coefficient\"] < 0\n", " ] * (1 - ratio)\n", " new_mol_weight_coefficient[\n", " len(new_mol_weight_coefficient)\n", " ] = shikimate_mol_weight_coefficient\n", " # Verify that the sums are the same\n", " assert round(new_mol_weight_coefficient.sum(), 4) == round(\n", " sum_mol_coefficient_negative, 4\n", " )\n", "\n", " # Creating series with names of metabolites\n", " identifiers = biomass_df[\"identifier\"][\n", " biomass_df[\"mol_weight*coefficient\"] < 0\n", " ]\n", " identifiers[len(identifiers)] = \"SHIKIMATE_c\"\n", "\n", " # Creating series with molecular weight of metabolites\n", " mol_weight = biomass_df[\"mol_weight\"][\n", " biomass_df[\"mol_weight*coefficient\"] < 0\n", " ]\n", " mol_weight[len(mol_weight)] = SHIKIMATE_MOL_WEIGHT\n", "\n", " # Building new dataframe\n", " new_coefficient_df = pd.DataFrame(\n", " [identifiers, new_mol_weight_coefficient]\n", " ).transpose()\n", " new_coefficient_df[\"new_coefficient\"] = new_coefficient_df[\n", " \"mol_weight*coefficient\"\n", " ].divide(mol_weight)\n", "\n", " # Appending old coefficients\n", " new_coefficient_df[\"old_coefficient\"] = biomass_df[\"coefficient\"][\n", " biomass_df[\"mol_weight*coefficient\"] < 0\n", " ]\n", " return new_coefficient_df\n", "\n", "W3110_df_negative = new_coefficient(ratio=shikimate_ratio[\"W3110\"])\n", "SA3_df_negative = new_coefficient(ratio=shikimate_ratio[\"SA3\"])\n", "\n", "# Display and format only relevant information\n", "print(\"Table with new negative coefficients for W3110 strain\")\n", "display(W3110_df_negative.loc[:,[\"identifier\",\"new_coefficient\"]])\n", "print(\"Table with new negative coefficients for SA3 strain\")\n", "SA3_df_negative.loc[:,[\"identifier\",\"new_coefficient\"]]" ] }, { "cell_type": "markdown", "id": "AlsGM2Y3k8oU", "metadata": { "id": "AlsGM2Y3k8oU" }, "source": [ "Next, we calculate the updated positive coefficients for the biomass equations.\n", "In the biomass equation are maintenance related metabolites such as `adp_c` and `atp_c` for which we assign the absolute value of their respective counterpart. The only exception is `akg_c` and for the sake of simplicity for this tutorial, we use the same coefficient for this metabolite.\n", "\n", "We create a function that returns a data frame with the new positive coefficient.\n", "The tables below shows the updated positive coefficients for both strains." ] }, { "cell_type": "code", "execution_count": 20, "id": "-ME-2_JOhI2q", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 562 }, "id": "-ME-2_JOhI2q", "outputId": "2cbee37c-db57-4c85-96ac-84b4dba0d1ad", "tags": [ "#A", "=>B" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Table with new positive coefficients for W3110 strain\n" ] }, { "data": { "text/html": [ "
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identifiernew_coefficient
0adp_c59.787090
1akg_c4.118200
2coa_c3.746364
3h_c59.787090
4nadh_c3.545641
5nadp_c13.022910
6pi_c59.787090
\n", "
" ], "text/plain": [ " identifier new_coefficient\n", "0 adp_c 59.787090\n", "1 akg_c 4.118200\n", "2 coa_c 3.746364\n", "3 h_c 59.787090\n", "4 nadh_c 3.545641\n", "5 nadp_c 13.022910\n", "6 pi_c 59.787090" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Table with new positive coefficients for SA3 strain\n" ] }, { "data": { "text/html": [ "
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identifiernew_coefficient
0adp_c55.048028
1akg_c4.118200
2coa_c3.449406
3h_c55.048028
4nadh_c3.264594
5nadp_c11.990640
6pi_c55.048028
\n", "
" ], "text/plain": [ " identifier new_coefficient\n", "0 adp_c 55.048028\n", "1 akg_c 4.118200\n", "2 coa_c 3.449406\n", "3 h_c 55.048028\n", "4 nadh_c 3.264594\n", "5 nadp_c 11.990640\n", "6 pi_c 55.048028" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def new_positive_coefficient(df: pd.DataFrame):\n", " \"\"\"\n", " Returns a DataFrame with the new coefficients for the products of the\n", " biomass reaction. The input dataframe is the dataframe with the new\n", " coefficients for the reactants.\n", " \"\"\"\n", " # Copy original biomass df and remove columns\n", " products = biomass_df[biomass_df[\"mol_weight*coefficient\"] > 0].copy()\n", " del products[\"formula\"]\n", " del products[\"mol_weight*coefficient\"]\n", " products.columns = products.columns.str.replace(\n", " \"coefficient\", \"old_coefficient\"\n", " ) \n", "\n", " # Create new column with the equivalent new coefficients\n", " products[\"new_coefficient\"] = [\n", " df[\"new_coefficient\"][2],\n", " -4.1182,\n", " df[\"new_coefficient\"][1],\n", " df[\"new_coefficient\"][2],\n", " df[\"new_coefficient\"][10],\n", " df[\"new_coefficient\"][11],\n", " df[\"new_coefficient\"][2],\n", " ]\n", " # Update index and convert to absolute value\n", " products.index = range(0, len(products[\"new_coefficient\"]))\n", " products[\"new_coefficient\"] = products[\"new_coefficient\"] * -1\n", " return products\n", "\n", "W3110_df_positive = new_positive_coefficient(W3110_df_negative)\n", "SA3_df_positive = new_positive_coefficient(SA3_df_negative)\n", "\n", "# Display and format only relevant information\n", "print(\"Table with new positive coefficients for W3110 strain\")\n", "display(W3110_df_positive.loc[:,[\"identifier\",\"new_coefficient\"]])\n", "\n", "print(\"Table with new positive coefficients for SA3 strain\")\n", "display(SA3_df_positive.loc[:,[\"identifier\",\"new_coefficient\"]])" ] }, { "cell_type": "markdown", "id": "a7faacdb", "metadata": { "id": "a7faacdb" }, "source": [ "## References\n", "\n", "1. Orth, Jeffrey D., R. M. T. Fleming, and Bernhard Ø. Palsson. “Reconstruction and Use of Microbial Metabolic Networks: The Core Escherichia Coli Metabolic Model as an Educational Guide.” EcoSal Plus 4, no. 1 (February 1, 2010). https://doi.org/10.1128/ecosalplus.10.2.1.\n", "2. Chen, Xianzhong, Mingming Li, Li Zhou, Wei Shen, Govender Algasan, You Fan, and Zhengxiang Wang. “Metabolic Engineering of Escherichia Coli for Improving Shikimate Synthesis from Glucose.” Bioresource Technology 166 (August 1, 2014): 64–71. https://doi.org/10.1016/j.biortech.2014.05.035.\n" ] } ], "metadata": { "celltoolbar": "Tags", "colab": { "collapsed_sections": [ "c25b3164" ], "name": "shikimate.ipynb", "provenance": [] }, "jupytext": { "formats": "ipynb,md" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.12.2" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "05ceb9e8118a45729990c984db59e9ae": { "model_module": "escher", "model_module_version": "1.7.4", "model_name": "EscherMapModel", "state": { "_loaded_map_json": "[{\"map_name\": \"\", \"map_id\": \"\", \"map_description\": \"\", \"homepage\": \"\", \"schema\": \"https://escher.github.io/escher/jsonschema/1-0-0#\"}, {\"reactions\": {\"0\": {\"name\": \"DAHPSYN_RXN_c\", \"bigg_id\": \"DAHPSYN_RXN_c\", \"reversibility\": true, \"label_x\": 208.0, \"label_y\": 45.0, \"gene_reaction_rule\": \"\", \"genes\": [], \"segments\": {\"0\": {\"from_node_id\": \"5\", \"to_node_id\": \"6\", \"b1\": null, \"b2\": null}, \"1\": {\"from_node_id\": \"7\", \"to_node_id\": \"6\", \"b1\": null, \"b2\": null}, \"2\": {\"from_node_id\": \"0\", \"to_node_id\": \"5\", \"b1\": null, \"b2\": null}, \"3\": {\"from_node_id\": \"1\", \"to_node_id\": \"5\", \"b1\": null, \"b2\": null}, \"4\": {\"from_node_id\": \"2\", \"to_node_id\": \"5\", \"b1\": null, \"b2\": null}, \"5\": {\"from_node_id\": \"3\", \"to_node_id\": \"7\", \"b1\": null, \"b2\": null}, \"6\": {\"from_node_id\": \"4\", \"to_node_id\": \"7\", \"b1\": null, \"b2\": null}}, \"metabolites\": [{\"bigg_id\": \"e4p_c\", \"coefficient\": -1.0}, {\"bigg_id\": \"h2o_c\", \"coefficient\": -1.0}, {\"bigg_id\": \"pep_c\", \"coefficient\": -1.0}, {\"bigg_id\": \"3_DEOXY_D_ARABINO_HEPTULOSONATE_7_P_c\", \"coefficient\": 1.0}, {\"bigg_id\": \"pi_c\", \"coefficient\": 1.0}]}, \"1\": {\"name\": \"3_DEHYDROQUINATE_SYNTHASE_RXN_c\", \"bigg_id\": \"3_DEHYDROQUINATE_SYNTHASE_RXN_c\", \"reversibility\": true, \"label_x\": 696.0, \"label_y\": 45.0, \"gene_reaction_rule\": \"\", \"genes\": [], \"segments\": {\"7\": {\"from_node_id\": \"10\", \"to_node_id\": \"11\", \"b1\": null, \"b2\": null}, \"8\": {\"from_node_id\": \"12\", \"to_node_id\": \"11\", \"b1\": null, \"b2\": null}, \"9\": {\"from_node_id\": \"3\", \"to_node_id\": \"10\", \"b1\": null, \"b2\": null}, \"10\": {\"from_node_id\": \"8\", \"to_node_id\": \"12\", \"b1\": null, \"b2\": null}, \"11\": {\"from_node_id\": \"9\", \"to_node_id\": \"12\", \"b1\": null, \"b2\": null}}, \"metabolites\": [{\"bigg_id\": \"3_DEOXY_D_ARABINO_HEPTULOSONATE_7_P_c\", \"coefficient\": -1.0}, {\"bigg_id\": \"DEHYDROQUINATE_c\", \"coefficient\": 1.0}, {\"bigg_id\": \"pi_c\", \"coefficient\": 1.0}]}, \"2\": {\"name\": \"3_DEHYDROQUINATE_DEHYDRATASE_RXN_c\", \"bigg_id\": \"3_DEHYDROQUINATE_DEHYDRATASE_RXN_c\", \"reversibility\": true, \"label_x\": 1319.0, \"label_y\": 45.0, \"gene_reaction_rule\": \"\", \"genes\": [], \"segments\": {\"12\": {\"from_node_id\": \"15\", \"to_node_id\": \"16\", \"b1\": null, \"b2\": null}, \"13\": {\"from_node_id\": \"17\", \"to_node_id\": \"16\", \"b1\": null, \"b2\": null}, \"14\": {\"from_node_id\": \"8\", \"to_node_id\": \"15\", \"b1\": null, \"b2\": null}, \"15\": {\"from_node_id\": \"13\", \"to_node_id\": \"17\", \"b1\": null, \"b2\": null}, \"16\": {\"from_node_id\": \"14\", \"to_node_id\": \"17\", \"b1\": null, \"b2\": null}}, \"metabolites\": [{\"bigg_id\": \"DEHYDROQUINATE_c\", \"coefficient\": -1.0}, {\"bigg_id\": \"3_DEHYDRO_SHIKIMATE_c\", \"coefficient\": 1.0}, {\"bigg_id\": \"h2o_c\", \"coefficient\": 1.0}]}, \"3\": {\"name\": \"SHIKIMATE_5_DEHYDROGENASE_RXN_c\", \"bigg_id\": \"SHIKIMATE_5_DEHYDROGENASE_RXN_c\", \"reversibility\": true, \"label_x\": 1996.0, \"label_y\": 45.0, \"gene_reaction_rule\": \"\", \"genes\": [], \"segments\": {\"17\": {\"from_node_id\": \"23\", \"to_node_id\": \"24\", \"b1\": null, \"b2\": null}, \"18\": {\"from_node_id\": \"25\", \"to_node_id\": \"24\", \"b1\": null, \"b2\": null}, \"19\": {\"from_node_id\": \"18\", \"to_node_id\": \"23\", \"b1\": null, \"b2\": null}, \"20\": {\"from_node_id\": \"19\", \"to_node_id\": \"23\", \"b1\": null, \"b2\": null}, \"21\": {\"from_node_id\": \"20\", \"to_node_id\": \"25\", \"b1\": null, \"b2\": null}, \"22\": {\"from_node_id\": \"21\", \"to_node_id\": \"25\", \"b1\": null, \"b2\": null}, \"23\": {\"from_node_id\": \"22\", \"to_node_id\": \"25\", \"b1\": null, \"b2\": null}}, \"metabolites\": [{\"bigg_id\": \"SHIKIMATE_c\", \"coefficient\": -1.0}, 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