{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "XV2pICiM740l" }, "source": [ "# Functions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jObtNBXPw30t", "outputId": "00ae3942-af4a-4e90-f0f7-6d3626269813" }, "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", "metadata": { "id": "tjphNJOQF7B3" }, "source": [ "## Retrieving metabolic pathway information\n", "\n", "CobraMod can retrieve metabolic pathway information (metabolites, reactions or pathways) from various databases by using database-specific identifiers. It supports all databases from the [BioCyc collection](\n", "https://biocyc.org/\n", "), [Plant Metabolic Network (PMN)](\n", "https://pmn.plantcyc.org/\n", "), \n", "the [KEGG database](\n", "https://www.genome.jp/kegg/\n", ") and the [BiGG Models repository](\n", "http://bigg.ucsd.edu/\n", "). Call `cobramod.retrieval.Databases` to see all supported databases." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 339 }, "id": "FKwfLSf07408", "outputId": "855b34ca-28f5-4a34-bf20-67af248b3155" }, "outputs": [ { "data": { "text/html": [ "\n", "

CobraMod supports BioCyc, the Plant Metabolic Network (PMN), KEGG and BiGG Models repository. BioCyc includes around 18.000 sub-databases. A complete list for BioCyc can be found at: 'https://biocyc.org/biocyc-pgdb-list.shtml'.The database-specific identifiers can be found in the URL of the respective data.CobraMod uses abbreviations to represent the databases or sub-databases:\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", "
AbbreviationDatabase name
METABiocyc, subdatabase MetaCyc
ARABiocyc, subdatabase AraCyc\n", "
KEGGKyoto encyclopedia of Genes and Genomes\n", "
BIGGBiGG Models
PMN:METAPlantCyc, subdatabase META
PMN:ARAPlantCyc, subdatabase ARA
\n", "

This applies for all subdatabases from BioCyc and Plantcyc\n", "

\n", " " ], "text/plain": [ "CobraMod supports BioCyc, the Plant Metabolic Network (PMN), KEGG and BiGG Models repository. BioCyc includes around 18.000 sub-databases. A complete list for BioCyc can be found at: 'https://biocyc.org/biocyc-pgdb-list.shtml'.\n", "The database-specific identifiers can be found in the URL of the respective data.CobraMod uses abbreviations to represent the databases or sub-databases:\n", "Abbreviation Database Name\n", "META Biocyc, subdatabase MetaCyc\n", "ARA Biocyc, subdatabase AraCyc\n", "\n", "KEGG Kyoto encyclopedia of Genes and Genomes\n", "BIGG BiGG Models\n", "\n", "PMN:META Plantcyc, subdatabase META\n", "PMN:ARA Plantcyc, subdatabase ARA\n", "This applies for all subdatabases from SolCyc, BioCyc and Plantcyc" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cobramod.retrieval import Databases\n", " \n", "Databases()" ] }, { "cell_type": "markdown", "metadata": { "id": "t3ADoeWMGqCV" }, "source": [ "\n", "\n", "The user can download the metabolic pathway information using the\n", "`cobramod.get_data` function. In this example we download information from the BioCyc sub-database YEAST.\n", "\n", "\n", "**NOTE**: In order to retrieved data from Metacyc. An account is required. Create a file called `credentials.txt` and add in the first line the username and the password in the second line\n", "\n", " my_username\n", " secret_password\n", " \n", "Only after setting the credentials, CobraMod is able to download data from BioCyc" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "RFawmPqO740o" }, "outputs": [], "source": [ "from cobramod import get_data\n", "from pathlib import Path\n", "\n", "dir_data = Path.cwd().resolve().joinpath(\"data\")\n", "identifiers = [\n", " \"CPD-12575\",\n", " \"BETA-D-FRUCTOSE\",\n", " \"SUCROSE\",\n", " \"UDP\",\n", "]\n", "\n", "for metabolite in identifiers:\n", " get_data(\n", " directory=dir_data,\n", " identifier=metabolite,\n", " database=\"YEAST\"\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "GL14MNJG740q" }, "source": [ "The first argument in [cobramod.get_data()](\n", "module/cobramod/index.html#cobramod.get_data) is the system path where CobraMod stores the metabolic pathway information. CobraMod uses [pathlib](\n", "https://docs.python.org/3/library/pathlib.html#pathlib.Path) for path representation. The second argument is the data identifier used in the respective database. In this example we retrieve data from the BioCyc sub-database `YEAST`. The last argument is the abbreviation of the database. \n", "\n", "CobraMod creates a directory with the name of the database and stores the metabolic pathway information in it:\n", "\n", "```\n", "data\n", "`-- YEAST\n", " |-- CPD-12575.xml\n", " |-- BETA-D-FRUCTOSE.xml\n", " |-- SUCROSE.xml\n", " |-- UDP.xml\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "CjC8UWKk740t" }, "source": [ "## Converting stored-data into COBRApy objects\n", "\n", "CobraMod can convert metabolic pathway information (metabolites and reactions) into COBRApy objects ([cobra.Reaction](\n", " https://cobrapy.readthedocs.io/en/latest/autoapi/cobra/index.html#cobra.Reaction\n", ") and [cobra.Metabolite](\n", " https://cobrapy.readthedocs.io/en/latest/autoapi/cobra/index.html#cobra.Metabolite\n", ")). It can thus be seamlessly integrated with a COBRApy workflow.\n", "\n", "The function [cobramod.create_object()](\n", "module/cobramod/index.html#cobramod.create_object) creates COBRApy objects from metabolic pathway data retrieved by [cobramod.get_data()](\n", "module/cobramod/index.html#cobramod.get_data). If no pathway information was downloaded [cobramod.create_object()]( module/cobramod/index.html#cobramod.create_object) retrieves it automatically.\n", "\n", "In this example, we convert the metabolite *2-Oxoglutarate* with the\n", "KEGG identifier [C00026](\n", "https://www.genome.jp/dbget-bin/www_bget?C00026) into a COBRApy object.\n", "CobraMod automatically identifies the KEGG entry as a metabolite and converts it into the corresponding COBRApy metabolite.\n", "\n", "The first argument is the database-specific identifier (`C00026`) followed by\n", "the database abbreviation (`KEGG`). The third argument is the path \n", "representation for the directory of the metabolic pathway information. CobraMod\n", "downloads the data into this directory and utilize it instead of downloading it again. The last argument is the compartment of the reaction (`c` for cytosol)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 168 }, "id": "s7osopQo740t", "outputId": "ee0d87e1-6fca-4cc5-d9fb-709851702f45" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Metabolite identifierC00026_c
Namealpha-Ketoglutaric acid
Memory address0x7afc8d2703e0
FormulaC5H6O5
Compartmentc
In 0 reaction(s)\n", " \n", "
" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cobramod import create_object\n", "from pathlib import Path\n", "\n", "# Path for the metabolic pathway information directory \n", "dir_data = Path.cwd().resolve().joinpath(\"data\")\n", "\n", "new_object = create_object(\n", " identifier=\"C00026\",\n", " database=\"KEGG\",\n", " directory=dir_data,\n", " compartment=\"c\"\n", ")\n", " \n", "print(type(new_object))\n", "new_object" ] }, { "cell_type": "markdown", "metadata": { "id": "N-0zvhHTJfAz" }, "source": [ "In the second example, we convert the reaction [RXN-11502](\n", "https://pmn.plantcyc.org/CORN/NEW-IMAGE?object=RXN-11502) from the PMN sub-database CORN into a COBRApy reaction. The\n", "first argument is the database-specific identifier (`RXN-11502`) followed by\n", "the database identifier (`pmn:CORN`). CobraMod automatically takes \n", "reversibility and gene information from the database entry and adds it to the reaction object." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 285 }, "id": "60lBmpkt8qtF", "outputId": "9f44a82d-450b-4f7c-b45c-5fc64a0c975c" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mGene-reaction rule for reaction \"RXN-11501\" set to \"OR\". Please modify it if necessary.\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierRXN_11501_c
Namealkaline α- galactosidase
Memory address0x7afcc0510c80
Stoichiometry\n", "

CPD_170_c + WATER_c --> ALPHA_D_GALACTOSE_c + CPD_1099_c

\n", "

stachyose + H2O --> alpha-D-galactopyranose + raffinose

\n", "
GPRZM00001EB033890 or ZM00001EB033880 or ZM00001EB033870
Lower bound0
Upper bound1000
\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "frozenset({,\n", " ,\n", " })" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_object = create_object(\n", " identifier=\"RXN-11501\",\n", " database=\"pmn:CORN\",\n", " directory=dir_data,\n", " compartment=\"c\"\n", ")\n", " \n", "print(type(new_object))\n", "display(new_object)\n", "new_object.genes" ] }, { "cell_type": "markdown", "metadata": { "id": "jlw6Vkuy740u" }, "source": [ "\n", "## Adding metabolites\n", "\n", "The function [cobramod.add_metabolites()](\n", "module/cobramod/index.html#cobramod.add_metabolites)\n", "extends the COBRApy function [model.add_metabolites()](\n", "https://cobrapy.readthedocs.io/en/latest/autoapi/cobra/core/model/index.html#cobra.core.model.Model.add_metabolites\n", ") and can be used with a simple syntax. It can utilize a single string, a list of strings, a file path or a COBRAPy metabolite object. In the next\n", "examples we showcase these options. We use the *E. coli* core model from COBRApy as test model. This core model can be found under `cobramod.test.textbook`. If the argument `obj` is used as a string, it can be the database-specific identifier of the respective metabolite and its compartment or a user-defined metabolite. It uses the following syntax:\n", "\n", "------\n", "\n", "**SYNTAX** \n", "\n", "To add metabolite information from a database:\n", "\n", " database-specific_identifier, compartment\n", "\n", "To add user-curated metabolites:\n", "\n", " user-curated_identifier, name, compartment, chemical_formula, molecular_charge\n", "\n", "------\n", "\n", "In the first example, we add the metabolite *L-methionine* with the MetaCyc\n", "identifier [MET](\n", "https://metacyc.org/compound?orgid=META&id=MET) to the test model. The first\n", "argument is the model name. The argument `obj` contains the identifier\n", "`MET` and the compartment `c`. The second argument is the database identifier\n", "(`META`) and the third argument is the directory where CobraMod stores the metabolite information.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 168 }, "id": "TgiIWEX3740v", "outputId": "bc2a3013-ed4e-4343-c387-33f59b23fc62" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[36mMetabolite \"MET_c\" was added to model.\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Metabolite identifierMET_c
NameL-methionine
Memory address0x7afc8ca6a8a0
FormulaC5H11N1O2S1
Compartmentc
In 0 reaction(s)\n", " \n", "
" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cobramod import add_metabolites\n", "from cobramod.test import textbook\n", "from pathlib import Path\n", "\n", "# Path for the metabolic pathway information directory \n", "dir_data = Path.cwd().resolve().joinpath(\"data\")\n", "# Using copy\n", "test_model = textbook.copy()\n", "\n", "add_metabolites(\n", " model=test_model,\n", " obj=[\"MET, c\"],\n", " database=\"META\",\n", " directory=dir_data,\n", ")\n", "print(type(test_model.metabolites.get_by_id(\"MET_c\")))\n", "test_model.metabolites.get_by_id(\"MET_c\")" ] }, { "cell_type": "markdown", "metadata": { "id": "32whoodj740v" }, "source": [ "In the second example, we add two metabolites ([acetyl-coa](\n", "https://metacyc.org/compound?orgid=META&id=ACETYL-COA) and [sucrose](\n", "https://metacyc.org/compound?orgid=META&id=SUCROSE\n", ")) from MetaCyc. In the argument `obj` we define a list with the database-specific metabolite identifiers and the respecitive compartments. \n", "The rest of the arguments remains the same as in the previous example. \n", "Before adding a metabolite, CobraMod tests if the given metabolite is already present in the model (potentially with a different identifier) by checking the \n", "cross-reference database entries in the metabolite's metadata against the model's metabolite entries. If CobraMod identifies a metabolite as already present in the model it skips adding the metabolite, used the already present one, and prints a warning. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359 }, "id": "TOWdeeQD740w", "outputId": "d19e1edb-fa69-45bb-db48-a5df2add5d59" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[36mMetabolite \"ACETYL_COA_c\" was added to model.\u001b[0m\n", "\u001b[36mMetabolite \"SUCROSE_c\" was added to model.\u001b[0m\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Metabolite identifieraccoa_c
NameAcetyl-CoA
Memory address0x7afc8c95c5c0
FormulaC23H34N7O17P3S
Compartmentc
In 7 reaction(s)\n", " ACALD, MALS, PDH, CS, Biomass_Ecoli_core, PFL, PTAr\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", "
Metabolite identifierSUCROSE_c
Namesucrose
Memory address0x7afc8c9a53d0
FormulaC12H22O11
Compartmentc
In 0 reaction(s)\n", " \n", "
" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "add_metabolites(\n", " model=test_model,\n", " obj=[\"ACETYL-COA, c\", \"SUCROSE, c\"],\n", " database=\"META\",\n", " directory=dir_data,\n", ")\n", "# Show metabolites in jupyter\n", "display(test_model.metabolites.get_by_id(\"accoa_c\")) \n", "test_model.metabolites.get_by_id(\"SUCROSE_c\")" ] }, { "cell_type": "markdown", "metadata": { "id": "_r-7dIhC740x" }, "source": [ "In the third example, we use a text file to add metabolites to the test\n", "model. We use the file *metabolites.txt* in the current working directory with the following content:\n", "\n", " SUCROSE, c \n", " MET, c \n", " MALTOSE_c, MALTOSE[c], c, C12H22O11, 1\n", "\n", "In this example, CobraMod downloads the first two metabolites from MetaCyc, while `MALTOSE_c` is a user-defined metabolite. The user can specify the path to this file in the `obj` argument. The remaining arguments are the same as in the previous examples. We added two print statements to show that CobraMod adds the metabolites to the model." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 448 }, "id": "YfbHsvk0740x", "outputId": "73174ddd-ed32-48b8-85ea-a01cf07b7bb7" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[36mMetabolite \"SUCROSE_c\" was added to model.\u001b[0m\n", "\u001b[36mMetabolite \"MET_c\" was added to model.\u001b[0m\n", "\u001b[36mMetabolite \"MALTOSE_c\" was added to model.\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of metabolites prior addition: 72\n", "Number of metabolites after addition: 75\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Metabolite identifierMET_c
NameL-methionine
Memory address0x7afc8c9a7410
FormulaC5H11N1O2S1
Compartmentc
In 0 reaction(s)\n", " \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", "
Metabolite identifierSUCROSE_c
Namesucrose
Memory address0x7afc875b01a0
FormulaC12H22O11
Compartmentc
In 0 reaction(s)\n", " \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", "
Metabolite identifierMALTOSE_c
NameMALTOSE[c]
Memory address0x7afc8c7399a0
FormulaC12H22O11
Compartmentc
In 0 reaction(s)\n", " \n", "
" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cobramod.test import textbook_biocyc\n", "# Path for the metabolic pathway information directory\n", "dir_data = Path.cwd().resolve().joinpath(\"data\")\n", "# This is our file\n", "file = dir_data.joinpath(\"metabolites.txt\")\n", "# Using a copy\n", "test_model = textbook_biocyc.copy()\n", "\n", "print(f'Number of metabolites prior addition: {len(test_model.metabolites)}')\n", "# Using CobraMod\n", "add_metabolites(\n", " model=test_model,\n", " obj=file,\n", " directory=dir_data,\n", " database=\"META\",\n", ")\n", "print(f'Number of metabolites after addition: {len(test_model.metabolites)}')\n", "# Show metabolites in jupyter\n", "display(test_model.metabolites.get_by_id(\"MET_c\"))\n", "display(test_model.metabolites.get_by_id(\"SUCROSE_c\"))\n", "test_model.metabolites.get_by_id(\"MALTOSE_c\")" ] }, { "cell_type": "markdown", "metadata": { "id": "uNbBaakL740x" }, "source": [ "Since [cobramod.add_metabolites()](\n", "module/cobramod/index.html#cobramod.add_metabolites)\n", "is an extension of the COBRApy function\n", "[model.add_metabolites()](\n", "https://cobrapy.readthedocs.io/en/latest/autoapi/cobra/core/model/index.html#cobra.core.model.Model.add_metabolites)\n", "the user can also utilize COBRApy metabolites. In the following example, we use a variation of the test model (`textbook_biocyc`) which uses\n", "BioCyc metabolite identifiers. We copy a COBRApy metabolite from\n", "the test model and add it to the BioCyc test model. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 147 }, "id": "KN5D_mmH740z", "outputId": "baf6af12-a652-46f9-9cee-595910561544" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[36mMetabolite \"xu5p__D_c\" was added to model.\u001b[0m\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Metabolite identifierxu5p__D_c
NameD-Xylulose 5-phosphate
Memory address0x7afc8cb758e0
FormulaC5H9O8P
Compartmentc
In 3 reaction(s)\n", " TKT2, TKT1, RPE\n", "
" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cobramod import add_metabolites\n", "from cobramod.test import textbook, textbook_biocyc\n", " \n", "# Copying Metabolite from original model\n", "metabolite = textbook.metabolites.get_by_id(\"xu5p__D_c\")\n", "\n", "# Using a copy\n", "test_model = textbook_biocyc.copy()\n", "add_metabolites(\n", " model=test_model,\n", " obj=[metabolite]\n", ")\n", " \n", "test_model.metabolites.get_by_id(\"xu5p__D_c\")" ] }, { "cell_type": "markdown", "metadata": { "id": "P6oD20PKuJYu" }, "source": [ "If CobraMod detects large molecules (e.g. enzymes) or if the metabolite information does not include a chemical formula the user receives a warning. In this example, we use the enzyme with the MetaCyc identifier `Red-NADPH-Hemoprotein-Reductases` and add it to the test model. CobraMod raises a warning due to the missing chemical formula." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 208 }, "id": "z3WOnVwEuJi2", "outputId": "f5853e43-11de-41b4-d80b-6c7e21dd034f" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mCurated metabolite \"Red_NADPH_Hemoprotein_Reductases_c does not include a chemical formula\u001b[0m\n", "\u001b[36mMetabolite \"Red_NADPH_Hemoprotein_Reductases_c\" was added to model.\u001b[0m\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Metabolite identifierRed_NADPH_Hemoprotein_Reductases_c
NameRed-NADPH-Hemoprotein-Reductases
Memory address0x7afc86120710
FormulaX
Compartmentc
In 0 reaction(s)\n", " \n", "
" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Using a copy\n", "test_model = textbook.copy()\n", "\n", "add_metabolites(\n", " model=test_model,\n", " obj=[\"Red-NADPH-Hemoprotein-Reductases, c\"],\n", " directory=dir_data,\n", " database=\"META\",\n", ")\n", "test_model.metabolites.get_by_id(\"Red_NADPH_Hemoprotein_Reductases_c\")" ] }, { "cell_type": "markdown", "metadata": { "id": "7OCkus3m740z" }, "source": [ "----------------\n", "\n", "**NOTES**\n", "\n", "- CobraMod replaces hyphens (`-`) with underscores (`_`) in the identifiers when\n", "creating COBRApy metabolites.\n", "- When adding several metabolites the user can only specify one database identifier (It is not possible to use two databases within the same\n", "function call) or alternatively can call the function twice.\n", "- CobraMod saves the curation process in a directory 'logs'\n", "\n", "----------------\n", "\n", "## Adding reactions\n", "\n", "The function [cobramod.add_reactions()](\n", "module/cobramod/index.html#cobramod.add_reactions) extends the COBRApy\n", "function [model.add_reactions()](\n", "https://cobrapy.readthedocs.io/en/latest/autoapi/cobra/index.html?highlight=optimize#cobra.Model.add_reactions\n", ") and can be used with a simple syntax. It can utilize a single string, a \n", "list of string, a file path or a COBRApy reaction object. In the next examples \n", "we showcase these options. Again, we use the *E. coli* core model from COBRApy \n", "as test model. \n", " \n", "If the argument `obj` is used as a string, it can be the\n", "database-specific identifier of the respective reaction and its\n", "compartment or a user-defined reaction. It uses the following syntax:\n", "\n", "--------\n", "\n", "**SYNTAX** \n", "\n", "To add reaction information from a database:\n", "\n", " database-specific_identifier, compartment\n", "\n", "To add user-defined reactions (curated-reactions), the user can specify the identifier and name of the reaction following the [COBRApy reaction string syntax](\n", " https://cobrapy.readthedocs.io/en/latest/autoapi/cobra/core/reaction/index.html#cobra.core.reaction.Reaction.build_reaction_from_string\n", "):\n", "\n", " user-curated_identifier, name | coefficient_1 metabolite_1 <-> coefficient_2 metabolite_2\n", "\n", "Metabolites must contain a suffix which specifies the compartment. This is given by an underscore (`_`) followed by the compartment-abbreviation. In this example, we create a transport reaction for oxygen between the external comparment \n", "(`e`) and the cytosol (`c`): \n", "\n", " TRANS_H2O_ec, Oxygen Transport | 2 OXYGEN-MOLECULE_e <-> 2 OXYGEN_MOLECULE_c\n", "\n", "-------\n", "\n", "In the first example, we add the BIGG reaction [AKGDH](\n", " http://bigg.ucsd.edu/universal/reactions/AKGDH\n", ") to a test model. This time we use a text model which is a variaton of the original core model and uses KEGG identifiers (`textbook_kegg`). The first argument is the model name. The `obj` argument takes the identifier `AKGDH` and the compartment `c`. The next argument is the database identifier (`BIGG`) and the last argument is the directory where CobraMod stores and retrieves the metabolic pathway information from. The argument `model_id` is a BIGG-specific argument. Please read the notes below for more information. \n", " \n", "Because the test model uses KEGG identifiers, CobraMod finds the reaction `AKGDH` with the KEGG identifier `R08549_c`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 326 }, "id": "Gcf5g4dB740z", "outputId": "eb539f60-e053-491a-851e-a6e2ffb7ed88" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mReaction \"R08549_c\" is already present in the model. Skipping additions.\u001b[0m\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierR08549_c
Name2-Oxogluterate dehydrogenase
Memory address0x7afc86120fb0
Stoichiometry\n", "

C00003_c + C00010_c + C00026_c --> C00004_c + C00011_c + C00091_c

\n", "

Nicotinamide adenine dinucleotide + Coenzyme A + 2-Oxoglutarate --> Nicotinamide adenine dinucleotide - reduced + CO2 + Succinyl-CoA

\n", "
GPRb0116 and b0726 and b0727
Lower bound0.0
Upper bound1000.0
\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from cobramod.test import textbook_kegg\n", "from cobramod import add_reactions\n", "from pathlib import Path\n", " \n", "dir_data = Path.cwd().resolve().joinpath(\"data\")\n", "# Using copy\n", "test_model = textbook_kegg.copy()\n", "\n", "add_reactions(\n", " model=test_model,\n", " obj=[\"AKGDH, c\"],\n", " database=\"BIGG\",\n", " directory=dir_data,\n", " model_id=\"e_coli_core\"\n", ")\n", " \n", "display(test_model.reactions.get_by_id(\"R08549_c\"))" ] }, { "cell_type": "markdown", "metadata": { "id": "DgOH_6OQ740z" }, "source": [ "In the second example, we add two reactions ([R00315](\n", " https://www.kegg.jp/entry/R00315\n", ") and [R02736](\n", " https://www.kegg.jp/entry/R02736\n", " )) from KEGG. The argument\n", "`obj` is a list with the database-specific identifiers and the respective \n", "compartments of the reactions. The majority of the arguments remains the same \n", "as in the previous example. The argument `genome` is a KEGG-specific argument.\n", "Please read the notes below for more information.\n", "\n", "CobraMod parses the reaction information for gene identifiers and automatically adds them to the COBRApy reaction. In this example, CobraMod creates the genes\n", "`2296`, `b3115` and `b1849`, and adds it to the first reaction `R00315`." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 452 }, "id": "5hC8-O3v740z", "outputId": "658cdf0e-804f-4f9f-a452-20eed21996ba" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mGene-reaction rule for reaction \"R00315\" from KEGG set to \"OR\".\u001b[0m\n", "\u001b[33mGene-reaction rule for reaction \"R02736\" from KEGG set to \"OR\".\u001b[0m\n", "\u001b[33mReaction \"R02736_c\" unbalanced. Following atoms are affected. Please verify: {'charge': -2.0, 'H': -2.0}\u001b[0m\n", "\u001b[33mReaction \"R02736_c\" unbalanced. Following atoms are affected. Please verify: {'charge': -2.0, 'H': -2.0}\u001b[0m\n", "\u001b[33mReaction \"R00315_c\" is already present in the model. Skipping additions.\u001b[0m\n", "\u001b[33mReaction \"R02736_c\" is already present in the model. Skipping additions.\u001b[0m\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierR00315_c
Nameacetate kinase
Memory address0x7afc86121160
Stoichiometry\n", "

C00002_c + C00033_c <=> C00227_c + G11113_c

\n", "

ATP + Acetate <=> Acetyl phosphate + ADP

\n", "
GPRc2838
Lower bound-1000.0
Upper bound1000.0
\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "frozenset({})\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierR02736_c
Namebeta-D-glucose-6-phosphate:NADP+ 1-oxoreductase
Memory address0x7afc8cafdc40
Stoichiometry\n", "

C00006_c + C01172_c <=> C00005_c + C00080_c + C01236_c

\n", "

Nicotinamide adenine dinucleotide phosphate + beta-D-Glucose 6-phosphate <=> Nicotinamide adenine dinucleotide phosphate - reduced + H+ + 6-phospho-D-glucono-1,5-lactone

\n", "
GPRc2265
Lower bound-1000
Upper bound1000
\n", " " ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "add_reactions(\n", " model=test_model,\n", " obj=[\"R00315, c\", \"R02736 ,c\"],\n", " directory=dir_data,\n", " database=\"KEGG\",\n", " genome=\"ecc\"\n", ")\n", " \n", "display(test_model.reactions.get_by_id(\"R00315_c\"))\n", "print(test_model.reactions.get_by_id(\"R00315_c\").genes)\n", "test_model.reactions.get_by_id(\"R02736_c\")" ] }, { "cell_type": "markdown", "metadata": { "id": "GYPUKYn27400" }, "source": [ "In the following example, we use a text file to add reactions to the test model. To this end, we use the file *reactions.txt* in the current working directory. This file has the following content:\n", "\n", " R04382, c \n", " R02736, c \n", " C06118_ce, digalacturonate transport | 1 C06118_c <-> 1 C06118_e\n", "\n", "CobraMod downloads the first two reactions from KEGG. `C06118_ce` is a user-defined reaction.\n", "The user can specify the file path in the `obj` argument. The next arguments are the same as in the previous examples. We added two print statements to show that CobraMod adds\n", "the reaction to the model." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 619 }, "id": "KGb5w8FO7400", "outputId": "4b38496a-2c78-4e21-f0cc-f83831202076" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mGene-reaction rule for reaction \"R04382\" from KEGG set to \"OR\".\u001b[0m\n", "\u001b[33mGene-reaction rule for reaction \"R02736\" from KEGG set to \"OR\".\u001b[0m\n", "\u001b[33mReaction \"R02736_c\" unbalanced. Following atoms are affected. Please verify: {'charge': -2.0, 'H': -2.0}\u001b[0m\n", "\u001b[33mReaction \"R02736_c\" unbalanced. Following atoms are affected. Please verify: {'charge': -2.0, 'H': -2.0}\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of reactions prior addition: 95\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mReaction \"R04382_c\" is already present in the model. Skipping additions.\u001b[0m\n", "\u001b[33mReaction \"R02736_c\" is already present in the model. Skipping additions.\u001b[0m\n", "\u001b[36mReaction \"C06118_ce\" was added to model.\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of reactions after addition: 98\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierR04382_c
Name4-(4-deoxy-alpha-D-galact-4-enuronosyl)-D-galacturonate lyase
Memory address0x7afc6fcd64b0
Stoichiometry\n", "

C06118_c <=> 2.0 C04053_c

\n", "

Unsaturated digalacturonate <=> 2.0 (4S,5R)-4,5-Dihydroxy-2,6-dioxohexanoate

\n", "
GPRc0319
Lower bound-1000
Upper bound1000
\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 identifierR02736_c
Namebeta-D-glucose-6-phosphate:NADP+ 1-oxoreductase
Memory address0x7afc6fcef290
Stoichiometry\n", "

C00006_c + C01172_c <=> C00005_c + C00080_c + C01236_c

\n", "

Nicotinamide adenine dinucleotide phosphate + beta-D-Glucose 6-phosphate <=> Nicotinamide adenine dinucleotide phosphate - reduced + H+ + 6-phospho-D-glucono-1,5-lactone

\n", "
GPRc2265
Lower bound-1000
Upper bound1000
\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 identifierC06118_ce
Namedigalacturonate transport
Memory address0x7afc6fceffe0
Stoichiometry\n", "

C06118_c <=> C06118_e

\n", "

Unsaturated digalacturonate <=> Unsaturated digalacturonate

\n", "
GPR
Lower bound-1000
Upper bound1000
\n", " " ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cobramod.test import textbook_kegg\n", "from cobramod import add_reactions\n", "from pathlib import Path\n", " \n", "dir_data = Path.cwd().resolve().joinpath(\"data\")\n", "test_model = textbook_kegg.copy()\n", "# This is the file with text\n", "file = dir_data.joinpath(\"reactions.txt\")\n", "\n", "print(f'Number of reactions prior addition: {len(test_model.reactions)}')\n", " \n", "add_reactions(\n", " model=test_model,\n", " obj=file,\n", " directory=dir_data,\n", " database=\"KEGG\",\n", " genome=\"ecc\"\n", ")\n", "\n", "print(f'Number of reactions after addition: {len(test_model.reactions)}')\n", "# Show in jupyter\n", "display(test_model.reactions.get_by_id(\"R04382_c\"))\n", "display(test_model.reactions.get_by_id(\"R02736_c\"))\n", "test_model.reactions.get_by_id(\"C06118_ce\")" ] }, { "cell_type": "markdown", "metadata": { "id": "4ywVo8-X7400" }, "source": [ "Since [cobramod.add_reactions()](\n", "module/cobramod/index.html#cobramod.add_reactions\n", ") is an extension of the original COBRApy function\n", "[model.add_reactions()](https://cobrapy.readthedocs.io/en/latest/autoapi/cobra/index.html?highlight=optimize#cobra.Model.add_reactions) the user can also\n", "utilize COBRApy reactions. In this example, we use the test model\n", "(`textbook_kegg`). We copy a COBRApy reaction from the test model and add it to the KEGG test model." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 245 }, "id": "BheNVhsz7401", "outputId": "c5ac2718-3707-4456-b407-4f1459ff3f26" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mReaction \"ACALDt\" is already present in the model. Skipping additions.\u001b[0m\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierACALDt
NameR acetaldehyde reversible - transport
Memory address0x7afc6fcec560
Stoichiometry\n", "

C00084_e <=> C00084_c

\n", "

Acetaldehyde <=> Acetaldehyde

\n", "
GPRs0001
Lower bound-1000.0
Upper bound1000.0
\n", " " ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cobramod.test import textbook_kegg, textbook\n", "from cobramod import add_reactions\n", "from pathlib import Path\n", "\n", "# Using copy of test model\n", "test_model = textbook_kegg.copy()\n", "# Obtaining a reaction\n", "reaction = textbook.reactions.get_by_id(\"ACALDt\")\n", " \n", "add_reactions(model=test_model, obj=[reaction], directory=dir_data)\n", "\n", "test_model.reactions.get_by_id(\"ACALDt\")" ] }, { "cell_type": "markdown", "metadata": { "id": "1SkvGCvLnK-9" }, "source": [ " By default, COBRApy ignores metabolites that appear on\n", "both sides of a reaction equation. CobraMod identifies such reactions and assigns one of these metabolites to the extracellular compartment and raises a\n", "warning suggesting manual curation. In the following example, we\n", "add a [transport reaction for acetic acid](\n", "https://biocyc.org/META/new-image?object=TRANS-RXN-455\n", ") from BioCyc sub-database `YEAST` to the test model." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "id": "IA2OwzjmbeZJ", "outputId": "0f6c64c2-c5d2-47bc-b752-bffed576b0b0" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mGene-reaction rule for reaction \"TRANS-RXN-455\" set to \"OR\". Please modify it if necessary.\u001b[0m\n", "\u001b[33mReaction \"TRANS_RXN_455_c\" has the same metabolite on both sides of the equation (e.g transport reaction). COBRApy ignores these metabolites. To avoid this, by default, CobraMod will assign the reactant side to the extracellular compartment. Please curate the reaction if necessary.\u001b[0m\n", "\u001b[33mReaction \"TRANS_RXN_455_c\" is already present in the model. Skipping additions.\u001b[0m\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierTRANS_RXN_455_c
Nameacetic acid uptake
Memory address0x7afc86123ec0
Stoichiometry\n", "

CPD_24335_e --> CPD_24335_c

\n", "

acetic+acid --> acetic+acid

\n", "
GPRG3O-32144
Lower bound0
Upper bound1000
\n", " " ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_model = textbook_kegg.copy()\n", " \n", "add_reactions(\n", " model=test_model,\n", " obj=[\"TRANS-RXN-455, c\"],\n", " database=\"YEAST\",\n", " directory=dir_data,\n", ")\n", "# Show in jupyter\n", "test_model.reactions.get_by_id(\"TRANS_RXN_455_c\")" ] }, { "cell_type": "markdown", "metadata": { "id": "0DzULV_Q7401" }, "source": [ "---\n", "\n", "**NOTES**\n", "\n", "- CobraMod replaces hyphens (`-`) to underscores (`_`) in the identifiers\n", "when creating COBRApy reactions.\n", "- When adding several reactions the user can only specify one database\n", "identifier (It is not possible to use two databases within the same function call) or alternatively can call the function twice.\n", "- CobraMod tries to identify if a given metabolites or reactions are already present in the model and used those instead of adding redundant entries.\n", "- CobraMod saves the curation process to the file `debug.log`.\n", "- The argument `model_id` is specific for the BiGG Models repository. CobraMod\n", "can download metabolite and reaction information directly from the BIGG models. A list of all models can be found [here](\n", "http://bigg.ucsd.edu/models/\n", ").\n", "- The argument `genome` can be used with the database `KEGG` and specifies the genome for which gene information will be retrieved. The complete list of all available genomes can be found [here](\n", "https://www.genome.jp/kegg/catalog/org_list.html).\n", "If no genome is specified, no gene information will be retrieved and\n", "a warning is printed as shown below:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "id": "CvDMHPAP7401", "outputId": "5b0afb2e-e9fe-4acf-b13d-1c101ab18d8a" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mNothing was specified in argument \"genome\". Reaction \"R04382\" will not include genes. Please modify if necessary.\u001b[0m\n", "\u001b[33mReaction \"R04382_c\" is already present in the model. Skipping additions.\u001b[0m\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Reaction identifierR04382_c
Name4-(4-deoxy-alpha-D-galact-4-enuronosyl)-D-galacturonate lyase
Memory address0x7afc6fcb5c40
Stoichiometry\n", "

C06118_c <=> 2.0 C04053_c

\n", "

Unsaturated digalacturonate <=> 2.0 (4S,5R)-4,5-Dihydroxy-2,6-dioxohexanoate

\n", "
GPR
Lower bound-1000
Upper bound1000
\n", " " ], "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_model = textbook_kegg.copy()\n", " \n", "add_reactions(\n", " model=test_model,\n", " obj=[\"R04382, c\"],\n", " database=\"KEGG\",\n", " directory=dir_data,\n", ")\n", "test_model.reactions.get_by_id(\"R04382_c\")" ] }, { "cell_type": "markdown", "metadata": { "id": "pbG8COs37402" }, "source": [ "---\n", "\n", "## Adding pathways\n", " \n", "CobraMod can add metabolic pathways to a given model. The function\n", "[cobramod.add_pathway()](\n", "module/cobramod/index.html#cobramod.add_pathway) takes as an argument either a sequence \n", "of database-specific reaction identifiers or a single pathway identifier. \n", "CobraMod download the pathway information from the databases, creates the\n", "COBRApy objects and prints a short summary about the curation process when adding the respective pathway. Additionally, to ensure reproducibility and for tracking the curation process, the user can write changes to file. CobraMod creates the object [cobramod.Pathway](\n", "module/cobramod/index.html#cobramod.Pathway\n", ") which contains the pathway information. \n", "\n", "When adding serveral pathways, we recommend adding pathways one-by-one to perform neccessary curation steps inbetween. In the examples below we showcase two options. Again, we use the *E. coli* core model from COBRApy\n", "as test model.\n", "\n", "In the first example, we add the [acetoacetate degradation pathway](\n", "https://biocyc.org/ECOLI/new-image?object=ACETOACETATE-DEG-PWY\n", ") from the BioCyc sub-database `ECOLI` to the test model. This pathway contains two reactions and six metabolites.\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Ek9jKTGcx5Bq" }, "source": [ "In this example, the first argument is the model to extend. The `pathway` argument uses the database-specific identifier `ACETOACETATE-DEG-PWY` and the database identifier `ECOLI`. We define the compartment as `c` (cytosol), i.e. all metabolites and reactions will be assigned to this compartment.\n", "\n", "\n", "The argument `filename` specifies a file to which a summary of the changes is written. Additionally, [add_pathway()](\n", " module/cobramod/index.html#cobramod.add_pathway\n", ") prints a summary of the changes to the model. Calling the respective\n", "[cobramod.Pathway](\n", "module/cobramod/index.html#cobramod.Pathway\n", ") outputs a table with the main attributes of the object.\n", "\n", "All COBRApy reactions of the pathway are tested for their capacity to cary a non-zero flux. Read more about it in the\n", "[non-zero flux test](#Non-zero-flux-test) section. \n", "\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 611 }, "id": "L3kAPVwz7403", "outputId": "91059607-28d6-405c-93bf-3fcb5dc4ddf2" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mGene-reaction rule for reaction \"ACETOACETYL-COA-TRANSFER-RXN\" set to \"OR\". Please modify it if necessary.\u001b[0m\n", "\u001b[33mGene-reaction rule for reaction \"ACETYL-COA-ACETYLTRANSFER-RXN\" set to \"OR\". Please modify it if necessary.\u001b[0m\n", "\u001b[33mReaction \"ACETYL_COA_ACETYLTRANSFER_RXN_c\" unbalanced. Following atoms are affected. Please verify: {'charge': -4.0, 'C': 23.0, 'H': 34.0, 'N': 7.0, 'O': 17.0, 'P': 3.0, 'S': 1.0}\u001b[0m\n", "\u001b[33mThe model cannot turnover the following metabolites ['3_KETOBUTYRATE_c', 'accoa_c']. To overcome this, sink reactions were created to simulate their synthesis.\u001b[0m\n", "\u001b[36mNon-zero flux test for reaction 'ACETOACETYL_COA_TRANSFER_RXN_c' passed.\u001b[0m\n", "\u001b[36mReaction \"ACETOACETYL_COA_TRANSFER_RXN_c\" added to group \"ACETOACETATE-DEG-PWY\".\u001b[0m\n", "\u001b[36mNon-zero flux test for reaction 'ACETYL_COA_ACETYLTRANSFER_RXN_c' passed.\u001b[0m\n", "\u001b[36mReaction \"ACETYL_COA_ACETYLTRANSFER_RXN_c\" added to group \"ACETOACETATE-DEG-PWY\".\u001b[0m\n", "\u001b[33mAuxiliary sink reaction for \"SK_accoa_c\" created. Consider removing it and adding the synthesis reactions for the metabolite.\u001b[0m\n", "\u001b[33mAuxiliary sink reaction for \"SK_3_KETOBUTYRATE_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" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Pathway identifierACETOACETATE-DEG-PWY
Name
Memory address0x0135224490952272
Reactions involved

ACETOACETYL_COA_TRANSFER_RXN_c, ACETYL_COA_ACETYLTRANSFER_RXN_c
Genes involved

EG12432, EG11670, EG11669, EG11672
Visualization attributes
  • vertical =\n", "False
  • color_negative = None
    • \n", "
  • color_positive = None
  • color_quantile =\n", "False

 

" ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pathlib import Path\n", "from cobramod import add_pathway\n", "from cobramod.test import textbook\n", "# Defining directory\n", "dir_data = Path.cwd().resolve().joinpath(\"data\")\n", " \n", "# Using copy of test model\n", "test_model = textbook.copy()\n", "\n", "add_pathway(\n", " model=test_model,\n", " pathway=\"ACETOACETATE-DEG-PWY\",\n", " database=\"ECOLI\",\n", " compartment=\"c\",\n", " filename=\"summary.txt\",\n", " directory=dir_data,\n", ")\n", "\n", "# Display in jupyter\n", "test_model.groups.get_by_id(\"ACETOACETATE-DEG-PWY\")" ] }, { "cell_type": "markdown", "metadata": { "id": "37naM0897403" }, "source": [ "Below is an example of the summary in form of a text file. The first\n", "part lists names of all reactions, metabolites, exchange reactions,\n", "auxiliary demand and sink reactions, genes, and groups in the model. The second part of the summary lists all elements that were added or removed by the function call `add_pathway()`. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JYMb89t77404", "outputId": "37259cb5-0935-48dc-a76c-7da0430cd146" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Summary:\r\n", "Model identifier: e_coli_core\r\n", "Model name:\r\n", "None\r\n", "Reactions:\r\n", "['ACALD', 'ACALDt', 'ACKr', 'ACONTa', 'ACONTb', 'ACt2r', 'ADK1', 'AKGDH', 'AKGt2r', 'ALCD2x', 'ATPM', 'ATPS4r', 'Biomass_Ecoli_core', 'CO2t', 'CS', 'CYTBD', 'D_LACt2', 'ENO', 'ETOHt2r', 'FBA', 'FBP', 'FORt2', 'FORti', 'FRD7', 'FRUpts2', 'FUM', 'FUMt2_2', 'G6PDH2r', 'GAPD', 'GLCpts', 'GLNS', 'GLNabc', 'GLUDy', 'GLUN', 'GLUSy', 'GLUt2r', 'GND', 'H2Ot', 'ICDHyr', 'ICL', 'LDH_D', 'MALS', 'MALt2_2', 'MDH', 'ME1', 'ME2', 'NADH16', 'NADTRHD', 'NH4t', 'O2t', 'PDH', 'PFK', 'PFL', 'PGI', 'PGK', 'PGL', 'PGM', 'PIt2r', 'PPC', 'PPCK', 'PPS', 'PTAr', 'PYK', 'PYRt2', 'RPE', 'RPI', 'SUCCt2_2', 'SUCCt3', 'SUCDi', 'SUCOAS', 'TALA', 'THD2', 'TKT1', 'TKT2', 'TPI', 'ACETOACETYL_COA_TRANSFER_RXN_c', 'ACETYL_COA_ACETYLTRANSFER_RXN_c']\r\n", "Metabolites:\r\n", "['13dpg_c', '2pg_c', '3pg_c', '6pgc_c', '6pgl_c', 'ac_c', 'ac_e', 'acald_c', 'acald_e', 'accoa_c', 'acon_C_c', 'actp_c', 'adp_c', 'akg_c', 'akg_e', 'amp_c', 'atp_c', 'cit_c', 'co2_c', 'co2_e', 'coa_c', 'dhap_c', 'e4p_c', 'etoh_c', 'etoh_e', 'f6p_c', 'fdp_c', 'for_c', 'for_e', 'fru_e', 'fum_c', 'fum_e', 'g3p_c', 'g6p_c', 'glc__D_e', 'gln__L_c', 'gln__L_e', 'glu__L_c', 'glu__L_e', 'glx_c', 'h2o_c', 'h2o_e', 'h_c', 'h_e', 'icit_c', 'lac__D_c', 'lac__D_e', 'mal__L_c', 'mal__L_e', 'nad_c', 'nadh_c', 'nadp_c', 'nadph_c', 'nh4_c', 'nh4_e', 'o2_c', 'o2_e', 'oaa_c', 'pep_c', 'pi_c', 'pi_e', 'pyr_c', 'pyr_e', 'q8_c', 'q8h2_c', 'r5p_c', 'ru5p__D_c', 's7p_c', 'succ_c', 'succ_e', 'succoa_c', 'xu5p__D_c', '3_KETOBUTYRATE_c', 'ACETOACETYL_COA_c']\r\n", "Exchange:\r\n", "['EX_ac_e', 'EX_acald_e', 'EX_akg_e', 'EX_co2_e', 'EX_etoh_e', 'EX_for_e', 'EX_fru_e', 'EX_fum_e', 'EX_glc__D_e', 'EX_gln__L_e', 'EX_glu__L_e', 'EX_h_e', 'EX_h2o_e', 'EX_lac__D_e', 'EX_mal__L_e', 'EX_nh4_e', 'EX_o2_e', 'EX_pi_e', 'EX_pyr_e', 'EX_succ_e']\r\n", "Demand:\r\n", "[]\r\n", "Sinks:\r\n", "['SK_3_KETOBUTYRATE_c', 'SK_accoa_c']\r\n", "Genes:\r\n", "['b1241', 'b0351', 's0001', 'b3115', 'b1849', 'b2296', 'b1276', 'b0118', 'b0474', 'b0116', 'b0726', 'b0727', 'b2587', 'b0356', 'b1478', 'b3735', 'b3733', 'b3734', 'b3732', 'b3736', 'b3738', 'b3731', 'b3737', 'b3739', 'b0720', 'b0733', 'b0979', 'b0978', 'b0734', 'b2975', 'b3603', 'b2779', 'b1773', 'b2925', 'b2097', 'b4232', 'b3925', 'b0904', 'b2492', 'b4154', 'b4152', 'b4153', 'b4151', 'b1819', 'b1817', 'b2415', 'b1818', 'b2416', 'b1611', 'b4122', 'b1612', 'b3528', 'b1852', 'b1779', 'b1621', 'b1101', 'b2417', 'b3870', 'b1297', 'b0809', 'b0810', 'b0811', 'b1761', 'b1524', 'b1812', 'b0485', 'b3213', 'b3212', 'b4077', 'b2029', 'b0875', 'b1136', 'b4015', 'b2133', 'b1380', 'b4014', 'b2976', 'b3236', 'b1479', 'b2463', 'b2286', 'b2279', 'b2276', 'b2280', 'b2288', 'b2284', 'b2287', 'b2281', 'b2277', 'b2285', 'b2282', 'b2278', 'b2283', 'b3962', 'b1602', 'b1603', 'b0451', 'b0114', 'b0115', 'b3916', 'b1723', 'b0902', 'b0903', 'b2579', 'b3114', 'b3952', 'b3951', 'b4025', 'b2926', 'b0767', 'b0755', 'b3612', 'b4395', 'b3493', 'b2987', 'b3956', 'b3403', 'b1702', 'b2297', 'b2458', 'b1676', 'b1854', 'b4301', 'b3386', 'b2914', 'b4090', 'b0722', 'b0724', 'b0721', 'b0723', 'b0728', 'b0729', 'b2464', 'b0008', 'b2935', 'b2465', 'b3919', 'EG11670', 'EG11669', 'EG12432', 'EG11672']\r\n", "Groups:\r\n", "['ACETOACETATE-DEG-PWY']\r\n", "\r\n", "New:\r\n", "Reactions:\r\n", "['ACETOACETYL_COA_TRANSFER_RXN_c', 'ACETYL_COA_ACETYLTRANSFER_RXN_c']\r\n", "Metabolites:\r\n", "['3_KETOBUTYRATE_c', 'ACETOACETYL_COA_c']\r\n", "Exchange:\r\n", "[]\r\n", "Demand:\r\n", "[]\r\n", "Sinks:\r\n", "['SK_3_KETOBUTYRATE_c', 'SK_accoa_c']\r\n", "Genes:\r\n", "['EG11670', 'EG11669', 'EG12432', 'EG11672']\r\n", "Groups:\r\n", "['ACETOACETATE-DEG-PWY']\r\n", "\r\n", "Removed:\r\n", "Reactions:\r\n", "[]\r\n", "Metabolites:\r\n", "[]\r\n", "Exchange:\r\n", "[]\r\n", "Demand:\r\n", "[]\r\n", "Sinks:\r\n", "[]\r\n", "Genes:\r\n", "[]\r\n", "Groups:\r\n", "[]\r\n", "\r\n" ] } ], "source": [ "%cat summary.txt" ] }, { "cell_type": "markdown", "metadata": { "id": "5E3kqIV17406" }, "source": [ "In the next example, we use a list of database-specific reaction identifiers as `pathway` argument. We use the database identifier `ECOLI` and the\n", "compartment `c` (cytosol). Additionally, we define a pathway name by using the argument `group`. The user can also use this argument to merge pathways by using the same group names." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 651 }, "id": "DXh3EVxU7406", "outputId": "7d42e371-ed9e-4617-8ab4-bf38d08daad0" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mGene-reaction rule for reaction \"PEPDEPHOS-RXN\" set to \"OR\". Please modify it if necessary.\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of reaction prior addition: 95\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mGene-reaction rule for reaction \"PYRUVFORMLY-RXN\" set to \"OR\". Please modify it if necessary.\u001b[0m\n", "\u001b[33mGene-reaction rule for reaction \"FHLMULTI-RXN\" set to \"OR\". Please modify it if necessary.\u001b[0m\n", "\u001b[36mNon-zero flux test for reaction 'PEPDEPHOS_RXN_c' passed.\u001b[0m\n", "\u001b[36mReaction \"PEPDEPHOS_RXN_c\" added to group \"curated_pathway\".\u001b[0m\n", "\u001b[33mThe model cannot turnover the following metabolites ['FORMATE_c']. To overcome this, sink reactions were created to simulate their synthesis.\u001b[0m\n", "\u001b[36mNon-zero flux test for reaction 'PYRUVFORMLY_RXN_c' passed.\u001b[0m\n", "\u001b[36mReaction \"PYRUVFORMLY_RXN_c\" added to group \"curated_pathway\".\u001b[0m\n", "\u001b[36mNon-zero flux test for reaction 'FHLMULTI_RXN_c' passed.\u001b[0m\n", "\u001b[36mReaction \"FHLMULTI_RXN_c\" added to group \"curated_pathway\".\u001b[0m\n", "\u001b[33mAuxiliary sink reaction for \"SK_FORMATE_c\" created. Consider removing it and adding the synthesis reactions for the metabolite.\u001b[0m\n", "\u001b[33mAuxiliary sink reaction for \"SK_HYDROGEN_MOLECULE_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 3 | 0 \n", "Metabolites 2 | 0 \n", "Exchange 0 | 0 \n", "Demand 0 | 0 \n", "Sinks 2 | 0 \n", "Genes 12 | 0 \n", "Groups 1 | 0 \n", "\n", "Number of reactions after addition: 100\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Pathway identifiercurated_pathway
Name
Memory address0x0135224490393088
Reactions involved

PYRUVFORMLY_RXN_c, PEPDEPHOS_RXN_c, FHLMULTI_RXN_c
Genes involved

G7627, EG11784, EG10701, EG10804, EG10803, EG10477, EG10480, EG10475, EG10285, EG10476, EG10479, EG10478
Visualization attributes
  • vertical =\n", "False
  • color_negative = None
    • \n", "
  • color_positive = None
  • color_quantile =\n", "False

 

" ], "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pathlib import Path\n", "from cobramod import add_pathway\n", "from cobramod.test import textbook_biocyc\n", "# Defining directory\n", "dir_data = Path.cwd().resolve().joinpath(\"data\")\n", "\n", "test_model = textbook_biocyc.copy()\n", "# Defining database-specific identifiers\n", "sequence = [\"PEPDEPHOS-RXN\", \"PYRUVFORMLY-RXN\", \"FHLMULTI-RXN\"]\n", " \n", "print(f'Number of reaction prior addition: {len(test_model.reactions)}')\n", " \n", "add_pathway(\n", " model=test_model,\n", " pathway=sequence,\n", " directory=dir_data,\n", " database=\"ECOLI\",\n", " compartment=\"c\",\n", " group=\"curated_pathway\"\n", ")\n", "\n", "print(f'Number of reactions after addition: {len(test_model.reactions)}')\n", "# Display in jupyter\n", "test_model.groups.get_by_id(\"curated_pathway\")" ] }, { "cell_type": "markdown", "metadata": { "id": "wYJWosLv7407" }, "source": [ "--------------------\n", "\n", "**NOTES**\n", "\n", "- A pathway is a set of COBRApy reactions. All the notes listed for `add_metabolites()` and `add_reactions()` also apply to pathways, i. e., handling of duplicate elements, transport reactions and the argument `genome` for KEGG.\n", "- CobraMod saves the curation process in the directory `logs`. The logs are saved by date.\n", "\n", "--------------------\n" ] }, { "cell_type": "markdown", "metadata": { "id": "WZ78z82Mgbdl" }, "source": [ "## Non-zero flux test\n", "\n", "When calling the function `add_pathway()`, CobraMod tests each reaction of the `Pathway` object for its capability to carry a non-zero flux, i.e., if the involved metabolites can be turned over. Additionally, the user can test individual COBRApy reactions for their capability to cary a non-zero flux by using the function [cobramod.test_non_zero_flux()](\n", "module/cobramod/index.html#cobramod.test_non_zero_flux\n", ").\n", "When running `add_pathway()` CobraMod tries to maximize (minimize if operating backwards) the flux through the tested reaction. If the flux is below [solver tolerance](\n", "https://cobrapy.readthedocs.io/en/latest/autoapi/cobra/index.html?highlight=tolerance#cobra.Configuration.tolerance ) CobraMod adds auxiliary\n", "[sink reactions](\n", " https://cobrapy.readthedocs.io/en/latest/building_model.html#Exchanges,-Sinks-and-Demands\n", ") to all metabolites participating in the reaction and subsequently remove them one by one if the respective metabolites can be turned over by reactions in the model. CobraMod then raises a warning about the remaining auxiliary sink reactions.\n", "\n", " Based on this information the user can then manually curate the model and if necessary add the missing synthesis reactions for the respective metabolites.\n", "If a reaction passes the test CobraMod prints a short message that the test was sucessfully passed.\n", "\n", "\n", "The user can also use the argument `ignore_list` when adding a pathway to skip\n", "the non-zero-flux test for the specified reactions.\n", "\n", "In the following artifical example (*E. coli* is a prokaryote without compartmentation), we introduce the glutathione synthase reaction (`GLUTATHIONE-SYN-RXN`) into a new comparment (`x`) and test the reaction for its capability to carry a non-zero flux. This reaction has the following equation:\n", "\n", " ATP_x + GLY_x + L_GAMMA_GLUTAMYLCYSTEINE_x --> ADP_x + GLUTATHIONE_x + PROTON_x + Pi_x\n", "\n", "Because we are testing a reaction in a new, empty compartment, CobraMod creates auxiliary sink reactions for the involved metabolites and prints a warning." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "1eLXkls4qmph", "outputId": "95c55f6a-fcfe-402a-aa7e-ad0e07532f36", "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mGene-reaction rule for reaction \"GLUTATHIONE-SYN-RXN\" set to \"OR\". Please modify it if necessary.\u001b[0m\n", "\u001b[33mReaction \"GLUTATHIONE_SYN_RXN_x\" is already present in the model. Skipping additions.\u001b[0m\n", "\u001b[36mNon-zero flux test for reaction 'GLUTATHIONE_SYN_RXN_x' passed.\u001b[0m\n", "\u001b[33mAuxiliary sink reaction for \"SK_GLUTATHIONE_x\" created. Consider removing it and adding the synthesis reactions for the metabolite.\u001b[0m\n", "\u001b[33mAuxiliary sink reaction for \"SK_ADP_x\" created. Consider removing it and adding the synthesis reactions for the metabolite.\u001b[0m\n", "\u001b[33mAuxiliary sink reaction for \"SK_PROTON_x\" created. Consider removing it and adding the synthesis reactions for the metabolite.\u001b[0m\n", "\u001b[33mAuxiliary sink reaction for \"SK_L_GAMMA_GLUTAMYLCYSTEINE_x\" created. Consider removing it and adding the synthesis reactions for the metabolite.\u001b[0m\n", "\u001b[33mAuxiliary sink reaction for \"SK_GLY_x\" created. Consider removing it and adding the synthesis reactions for the metabolite.\u001b[0m\n", "\u001b[33mAuxiliary sink reaction for \"SK_ATP_x\" created. Consider removing it and adding the synthesis reactions for the metabolite.\u001b[0m\n", "\u001b[33mAuxiliary sink reaction for \"SK_Pi_x\" created. Consider removing it and adding the synthesis reactions for the metabolite.\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Reaction GLUTATHIONE_SYN_RXN_x passed the non-zero-flux test.\n" ] } ], "source": [ "from cobramod import test_non_zero_flux, add_reactions\n", "from cobramod.test import textbook_biocyc\n", "\n", "test_model = textbook_biocyc.copy()\n", "\n", "# Adding the reaction into the test model\n", "add_reactions(\n", " model=test_model,\n", " # The list has 4 metabolites\n", " obj=[\"GLUTATHIONE-SYN-RXN, x\"],\n", " directory=dir_data,\n", " database=\"ECOLI\",\n", ")\n", "# Running test for GLUTATHIONE_SYN_RXN_x\n", "test_non_zero_flux(\n", " model=test_model,\n", " reaction=\"GLUTATHIONE_SYN_RXN_x\",\n", ")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "XMVGghQRW0dt" }, "source": [ "## Curation process\n", "\n", "CobraMod automatically performs the following curation steps during the creation of COBRApy reaction and metabolite objects and CobraMod \n", "pathway objects:\n", "\n", "\n", "1. If CobraMod encounters biochemical information (e.g. reactions or metabolites) with missing formulas or entries, a warning is printed and CobraMod informs the user about the missing\n", "data. For instance, CobraMod assigns an \"X\" to metabolites with missing\n", "formulas.\n", "2. When adding a metabolite, reaction or pathway to the model, CobraMod tries\n", "to identify if these elements are already present in the model (potentially with a different name) and use the exiting elements instead of creating redundant ones (by checking the cross-reference database entries in the elements's metadata against the model entries).\n", " - In the case of user-defined, curated reactions CobraMod tries to find the respective metabolites in the model. If a metabolites is not found, a warning is printed and the metabolite is created with \"X\" as chemical formula.\n", "3. When adding metabolites, reactions or pathways, CobraMod can query [MetaNetX](\n", " https://www.metanetx.org/) and [PubChem](\n", " https://pubchem.ncbi.nlm.nih.gov/) to find cross-references and adds the information\n", " to the corresponding Reactions and Metabolites (See Using the automatic cross-reference expansion in the documentation).\n", "4. CobraMod utilizes the COBRApy method [cobra.Reaction.check_mass_balance()](\n", "https://cobrapy.readthedocs.io/en/latest/autoapi/cobra/core/reaction/index.html#cobra.core.reaction.Reaction.check_mass_balance \n", ") and returns a warning if mass imbalances are found. \n", "5. CobraMod uses the reaction reversibility information provided with the obtained reaction data. If the reversibility information is missing, CobraMod raises a warning.\n", "6. When CobraMod adds a pathway, each pathway reaction undergos a *non-zero flux test*. If a reaction cannot carry a non-zero flux, CobraMod adds\n", "auxiliary sink reactions to unblock the reaction and suggests manual curation steps based on these auxiliary modifications. (See Non-zero flux test in the documentation).\n", "7. CobraMod offers the function `add_crossreferences` which queries the genome annotation database [MetaNetX](https://www.metanetx.org/) to adds the missing meta-data about the cross-references." ] }, { "cell_type": "markdown", "metadata": { "id": "lndble907408" }, "source": [ "## Converting COBRApy groups back to CobraMod pathways \n", "\n", "The COBRApy function [cobra.io.write_sbml_model()](\n", " https://cobrapy.readthedocs.io/en/latest/autoapi/cobra/io/index.html#cobra.io.write_sbml_model\n", ") writes cobra models to sbml files. If the user calls this function with a model that contains [cobramod.Pathway](\n", "module/cobramod/index.html#cobramod.Pathway\n", ") elements these will be saved as [COBRApy Group](\n", " https://cobrapy.readthedocs.io/en/latest/autoapi/cobra/core/group/index.html#cobra.core.group.Group\n", ") elements and the CobraMod pathway objects are lost. To convert COBRApy groups back to CobraMod pathways (and use the associated functionalities), the user can call the function [cobramod.model_convert()](\n", "module/cobramod/core/pathway/index.html#cobramod.core.pathway.model_convert\n", "). In the following example, we create a `Group` with four reactions and add it to the model.\n", "We then use the [cobramod.model_convert()](\n", "module/cobramod/core/pathway/index.html#cobramod.core.pathway.model_convert\n", ") function with the argument `model` and can then call the newly converted respective CobraMod pathway objects." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "id": "wu5oKFEe7408", "outputId": "82260219-afe0-4dc9-a764-aabd7aa22e41" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[36mGroup-object \"curated_pathway\" was transformed to a Pathway-object.\u001b[0m\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Pathway identifiercurated_pathway
Name
Memory address0x0135225782733520
Reactions involved

GLCpts, G6PDH2r, PGL, GND
Genes involved

b1819, b1621, b1817, b1101, b2415, b2417, b1818, b2416, b1852, b0767, b2029
Visualization attributes
  • vertical =\n", "False
  • color_negative = None
    • \n", "
  • color_positive = None
  • color_quantile =\n", "False

 

" ], "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from cobramod import model_convert\n", "from cobramod.test import textbook_biocyc\n", "from cobra.core.group import Group\n", "\n", "test_model = textbook_biocyc.copy()\n", "# Creation of group\n", "test_group = Group(id=\"curated_pathway\")\n", "for reaction in (\"GLCpts\", \"G6PDH2r\", \"PGL\", \"GND\"):\n", " test_group.add_members([test_model.reactions.get_by_id(reaction)])\n", "test_model.add_groups([test_group])\n", "\n", "# Conversion to a Pathway\n", "model_convert(model=test_model)\n", "# Display to Jupyter\n", "test_model.groups.get_by_id(\"curated_pathway\")" ] } ], "metadata": { "celltoolbar": "Tags", "colab": { "collapsed_sections": [], "name": "how_to.ipynb", "provenance": [] }, "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": { "0197759ddeee4dc7a876354ff818870a": { "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\": \"G6PDH2r\", \"bigg_id\": \"G6PDH2r\", \"reversibility\": true, \"label_x\": 265.0, \"label_y\": 325.0, \"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}, 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