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JR-1991 committed Mar 20, 2022
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2 changes: 1 addition & 1 deletion docs/_examples/01_KineticModeling_PySCeS.ipynb
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"id": "67d1ee98-ca30-46d8-88ed-5d316b59d3f2",
"metadata": {},
"source": [
"## Scenario 5: Modeling a reaction by mass action cascades using PySCeS\n",
"# Modeling a reaction by mass action cascades using PySCeS\n",
"\n",
"This notebook is part of the publication \"EnzymeML at Work\" from Lauterbach et al. 2022 and adds a given micro-kinetic model to an EnzymeML document. Prior to this, experimental data was collected using the EnzymeML spreadsheet and converted to EnzymeML. The following notebook adresses the following key procedures using PyEnzyme:\n",
"\n",
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100 changes: 42 additions & 58 deletions docs/_getstarted/01_BasicUsage.ipynb
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"id": "515b1663-1f09-4085-ade9-bacd0fcbcb74",
"metadata": {},
"source": [
"## Initializing an EnzymeML document\n",
"# Setting up an EnzymeML document\n",
"\n",
"In order to write an EnzymeML document it needs to be initialized by calling the ```EnzymeMLDocument``` object. At this point it is possible to add metadata such as a name, URL, DOI or PubmedID to the document. In addition, it is necessary but not mandatory to add author information. Please note, that for a Dataverse upload adding an author is mandatory though.\n",
"\n",
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"id": "b1293c5f-00f6-482a-85b8-fc3f1977060c",
"metadata": {},
"source": [
"### Vessels\n",
"__Vessels__\n",
"\n",
"- ```Vessel``` carries the metadata for vessels that are used.\n",
"- ```addVessel``` adds a ```Vessel``` object to the document and returns the ID."
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"id": "3288f19a-56ca-4d33-bf71-ebf91fa9a49f",
"metadata": {},
"source": [
"### Proteins\n",
"__Proteins__\n",
"\n",
"- ```Protein``` carries the metadata for proteins that are part of the experiment.\n",
"- ```addProtein``` adds a ```Protein``` object to the document and returns the ID."
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"metadata": {},
"outputs": [],
"source": [
"enzyme = pe.Protein(name=\"Enzyme\", vessel_id=vessel_id, sequence=\"MAVKLT\", constant=False)\n",
"enzyme = pe.Protein(name=\"Enzyme\", vessel_id=vessel_id,\n",
" sequence=\"MAVKLT\", constant=False)\n",
"\n",
"enzyme_id = enzmldoc.addProtein(enzyme)"
]
},
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"id": "e6dc7af3-f9b5-4a26-8cee-d6da1da662c9",
"metadata": {},
"source": [
"### Reactants\n",
"__Reactants__\n",
"\n",
"- ```Reactant``` carries the metadata for reactants that are part of the experiment.\n",
"- ```addReactant``` adds a ```Reactant``` object to the document and returns the ID."
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"id": "2548347a-2ea9-483e-b4b0-93872a368f08",
"metadata": {},
"source": [
"### Complexes\n",
"__Complexes__\n",
"\n",
"- ```Complex``` carries the metadata for complexes that are part of the experiment.\n",
"- ```addComplex``` adds a ```Complex``` object to the document and returns the ID."
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"reaction_1 = pe.EnzymeReaction(name=\"reaction-1\", reversible=True)\n",
"\n",
"# Add each element\n",
"reaction_1.addEduct(species_id=substrate_id, stoichiometry=1.0, enzmldoc=enzmldoc)\n",
"reaction_1.addEduct(species_id=enzyme_id, stoichiometry=1.0, enzmldoc=enzmldoc)\n",
"reaction_1.addProduct(species_id=product_id, stoichiometry=1.0, enzmldoc=enzmldoc)"
"reaction_1.addEduct(species_id=substrate_id, stoichiometry=1.0, \n",
" enzmldoc=enzmldoc)\n",
"\n",
"reaction_1.addEduct(species_id=enzyme_id, stoichiometry=1.0,\n",
" enzmldoc=enzmldoc)\n",
"\n",
"reaction_1.addProduct(species_id=product_id, stoichiometry=1.0,\n",
" enzmldoc=enzmldoc)"
]
},
{
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"id": "21624256-889c-4a3a-ba57-d2d5df19fb12",
"metadata": {},
"source": [
"### Measurement 1: 'Run 1'\n",
"__Measurement 1: 'Run 1'__\n",
"\n",
"- ```Measurement``` carries the metadata for measurements that are conducted in the experiment.\n",
"- ```addData``` appends measurement data to the ```Measurement``` object and checks consistency."
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")\n",
"\n",
"# Add each entity that will be measured\n",
"measurement_1.addData(reactant_id=substrate_id, init_conc=10.0, unit=\"mmole / l\")\n",
"measurement_1.addData(reactant_id=product_id, unit=\"mmole / l\")\n",
"measurement_1.addData(protein_id=enzyme_id, init_conc=20.0, unit=\"fmole / l\")\n",
"measurement_1.addData(reactant_id=substrate_id,\n",
" init_conc=10.0, unit=\"mmole / l\")\n",
"\n",
"measurement_1.addData(reactant_id=product_id,\n",
" unit=\"mmole / l\")\n",
"\n",
"measurement_1.addData(protein_id=enzyme_id, init_conc=20.0,\n",
" unit=\"fmole / l\")\n",
"\n",
"# Add it to the EnzymeML document\n",
"meas_1_id = enzmldoc.addMeasurement(measurement_1)"
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"id": "e7707c8a-5d04-4e53-92cf-d8c45e29c935",
"metadata": {},
"source": [
"### Measurement 2: 'Run 2'\n",
"__Measurement 2: 'Run 2'__\n",
"\n",
"- ```Measurement``` carries the metadata for measurements that are conducted in the experiment.\n",
"- ```addData``` appends measurement data to the ```Measurement``` object and checks consistency."
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")\n",
"\n",
"# Add each entity that will be measured\n",
"measurement_2.addData(reactant_id=substrate_id, init_conc=100.0, unit=\"mmole / l\")\n",
"measurement_2.addData(reactant_id=product_id, unit=\"mmole / l\")\n",
"measurement_2.addData(protein_id=enzyme_id, init_conc=40.0, unit=\"fmole / l\")\n",
"measurement_2.addData(reactant_id=substrate_id,\n",
" init_conc=100.0, unit=\"mmole / l\")\n",
"\n",
"measurement_2.addData(reactant_id=product_id,\n",
" unit=\"mmole / l\")\n",
"\n",
"measurement_2.addData(protein_id=enzyme_id,\n",
" init_conc=40.0, unit=\"fmole / l\")\n",
"\n",
"# Add it to the EnzymeML document\n",
"meas_2_id = enzmldoc.addMeasurement(measurement_2)"
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"id": "bfe87c90-2c79-4daf-a99c-f302d37f2adc",
"metadata": {},
"source": [
"### Data for 'Run 1'\n",
"__Data for 'Run 1'__\n",
"\n",
"- ```Replicate``` carries the tim-courses and metadata for each measured entity.\n",
"- ```addReplicates``` adds ```Replicate``` objects to a measurement to the corresponding ```MeasurementData``` container where the concentrations are also stored."
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"id": "e7602d58-29a3-4a8a-93bf-40bedc1f82bb",
"metadata": {},
"source": [
"### Data for 'Run 2'\n",
"__Data for 'Run 2'__\n",
"\n",
"- ```Replicate``` carries the tim-courses and metadata for each measured entity.\n",
"- ```addReplicates``` adds ```Replicate``` objects to a measurement to the corresponding ```MeasurementData``` container where the concentrations are also stored."
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"id": "1b5a7d92-f997-4c17-817e-64f1427f6466",
"metadata": {},
"source": [
"### Export\n",
"__Export__\n",
"\n",
"- ```toFile``` writes the EnzymeML document to an OMEX archive at the specified path.\n",
"- ```json``` converts the EnzymeML document to a JSON string, which in turn can be used for REST interfaces or data storage.\n",
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"id": "5decfe8a-bb2d-460d-86c9-9e700162afb9",
"metadata": {},
"source": [
"### Upload\n",
"__Upload__\n",
"\n",
"- ```uploadToDataverse``` uploads the document to a Dataverse installation.\n",
"- Please note, that in order to work, your environment should contain these variables\n",
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"id": "e99a7220-d586-44bc-8507-cef3ee1de231",
"metadata": {},
"source": [
"## Loading an EnzymeML document\n",
"# Loading and editing\n",
"\n",
"It is not expected to create an EnzymeML document in a single session, but to let it evolve over the course of an experiment. Thus it is necessary to load and edit an EnzymeML document, without re-creating everything from start. PyEnzyme's ```EnzymeMLDocument``` object offers an initialization method ```fromFile``` to edit an already existing document. In addition, it is also possible to use the aforementioned JSON \n",
"\n",
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},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": null,
"id": "98715329-b341-4bfd-b749-1e2647f7fdd0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pyruvate Decarboxylation\n",
">>> Reactants\n",
"\tID: s0 \t Name: Pyruvate\n",
"\tID: s1 \t Name: Acetaldehyde\n",
"\tID: s2 \t Name: CO2\n",
">>> Proteins\n",
"\tID: p0 \t Name: Pyruvate decarboxylase isozyme 1\n",
">>> Complexes\n",
">>> Reactions\n",
"\tID: r0 \t Name: Pyruvate decarboxylation\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/homebrew/Caskroom/miniforge/base/envs/enzymeml/lib/python3.9/site-packages/openpyxl/worksheet/_reader.py:315: UserWarning: Data Validation extension is not supported and will be removed\n",
" warn(msg)\n"
]
}
],
"outputs": [],
"source": [
"# Similar to the OMEX and JSON loaders, its a simple call\n",
"enzmldoc = pe.EnzymeMLDocument.fromTemplate(\"EnzymeML_Template_Example.xlsm\")\n",
"enzmldoc.printDocument()"
]
},
{
"cell_type": "markdown",
"id": "81201665-aebe-4db2-bbe1-de9f78edc8a2",
"metadata": {},
"source": [
"--------"
]
},
{
"cell_type": "markdown",
"id": "65b3a935-9b0d-4266-9aab-001f96241fe6",
"metadata": {},
"source": [
"## Editing EnzymeML: Kinetic Modeling\n",
"#### Adding a kinetic model\n",
"\n",
"Building on top of the previous section about loading an EnzymeML document, this example will demonstrate how to interact with an already created EnzymeML document using the OMEX loader. Since the purpose of an experiment is to generate data from a theory, modeling takes care of the interpretation of an experiment outcome. However, PyEnzyme and EnzymeML are no modeling platforms, but provides a convinient way to interface to such. Hence, this example will demonstrate how such an interfacing could look like.\n",
"\n",
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7 changes: 4 additions & 3 deletions docs/_getstarted/My_Experiment.json
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"stoichiometry": 1.0,
"constant": false,
"ontology": "SBO:0000015"
},
}
],
"products": [
{
"species_id": "s1",
"stoichiometry": 1.0,
"constant": false,
"ontology": "SBO:0000015"
"ontology": "SBO:0000011"
}
],
"products": [],
"modifiers": []
},
"r1": {
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19 changes: 10 additions & 9 deletions docs/conf.py
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#
import os
import sys
sys.path.insert(0, os.path.abspath('.'))

sys.path.insert(0, os.path.abspath("."))


# -- Project information -----------------------------------------------------

project = 'PyEnzyme'
copyright = '2021, Jan Range'
author = 'Jan Range'
project = "PyEnzyme"
copyright = "2022, Jan Range"
author = "Jan Range, Dr. Frank Bergmann, Prof. Dr. Johann Rohwer"

# The full version, including alpha/beta/rc tags
release = '1.2.0'
release = "1.1.3"


# -- General configuration ---------------------------------------------------
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autodoc_pydantic_settings_show_json = False

# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
templates_path = ["_templates"]

# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]


# -- Options for HTML output -------------------------------------------------

# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = "sphinx_rtd_theme"
html_theme = "furo"

# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
html_static_path = ["_static"]

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