diff --git a/README.md b/README.md index 4a6280f..ba66cdc 100644 --- a/README.md +++ b/README.md @@ -24,7 +24,7 @@ We have extended this package with the following features: Note that the package has been tested with the [Gurobi](https://www.mathworks.com/products/connections/product_detail/gurobi-optimizer.html) solver. ## What can you find where in this repository? -This repository contains not only the source code, but also examples and scripts which were used in [publication](linktopublication). +This repository contains not only the source code, but also examples and scripts which were used in **INSERT PUBLICATION HERE**. - **Data** - *eGFP_expression_Bienick2014*: measured growth rate and eGFP expression by [Bienick et al. (2014)](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0109105) - *proteinAllocationModel_iML1515_EnzymaticData_py*: information about the proteinsectors of the PAM for *Escherichia coli* (*E.coli*) diff --git a/docs/.gitignore b/docs/.gitignore new file mode 100644 index 0000000..7195f59 --- /dev/null +++ b/docs/.gitignore @@ -0,0 +1,23 @@ +# Dependencies +/node_modules + +# Production +/build + +# Generated files +.docusaurus +.cache-loader + +# Misc +.DS_Store +.env.local +.env.development.local +.env.test.local +.env.production.local + +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# Python venv for temporary build of pydoc-markdown +.venv diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000..8485236 --- /dev/null +++ b/docs/README.md @@ -0,0 +1,49 @@ +# Website + +This website is built using [Docusaurus](https://docusaurus.io/), a modern static website generator. +You will need NodeJS to be able to run Docusaurus for development. + +### Installation +To use Docusaurus, install NodeJS. You will find a comprehensive guide [here](https://nodejs.org/en/download/package-manager#debian-and-ubuntu-based-linux-distributions) for linux systems. + +``` +$ npm install +``` + +### Local Development + +``` +$ npm run start +``` + +This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server. + +### Build + +``` +$ npm run build +``` + +This command generates static content into the `build` directory and can be served using any static contents hosting service. + +### Deployment + +Using SSH: + +``` +$ USE_SSH=true npm run deploy +``` + +Not using SSH: + +``` +$ GIT_USER= npm run deploy +``` + +If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch. + +# Documenting your code + +## Setup + +This installation makes use of the Python module "pydoc-markdown". If you want to use it, install this module and execute it in the base directory of this repo. The provided pydoc-markdown.yml file contains all necessary configurations needed. It will generate the API reference from the docstrings in the PAModelpy package. Please note, that you have to use Google docstring format if you want to make changes to the PAModelpy project. diff --git a/docs/babel.config.js b/docs/babel.config.js new file mode 100644 index 0000000..e00595d --- /dev/null +++ b/docs/babel.config.js @@ -0,0 +1,3 @@ +module.exports = { + presets: [require.resolve('@docusaurus/core/lib/babel/preset')], +}; diff --git a/docs/default.nix b/docs/default.nix new file mode 100644 index 0000000..953b467 --- /dev/null +++ b/docs/default.nix @@ -0,0 +1,45 @@ +with import { }; + +let + venvDir = "./.venv"; + pythonPackages = pkgs.python310Packages; +in pkgs.mkShell rec { + name = "impurePythonEnv"; + buildInputs = [ + pythonPackages.python + pythonPackages.virtualenv + pythonPackages.numpy + pkgs.zlib + pkgs.expat + nodejs_20 + ]; + + # This is very close to how venvShellHook is implemented, but + # adapted to use 'virtualenv' + shellHook = '' + SOURCE_DATE_EPOCH=$(date +%s) + + if [ -d "${venvDir}" ]; then + echo "Skipping venv creation, '${venvDir}' already exists" + else + echo "Creating new venv environment in path: '${venvDir}'" + ${pythonPackages.python.interpreter} -m venv "${venvDir}" + fi + + source "${venvDir}/bin/activate" + + pip install -r requirements.txt + + LD_LIBRARY_PATH="${pkgs.lib.makeLibraryPath buildInputs}:$LD_LIBRARY_PATH" + LD_LIBRARY_PATH="${pkgs.stdenv.cc.cc.lib.outPath}/lib:$LD_LIBRARY_PATH" + + # Add the expat library path to LD_LIBRARY_PATH + LD_LIBRARY_PATH=${pkgs.expat}/lib:$LD_LIBRARY_PATH + + # fixes libstdc++ issues and libgl.so issues + LD_LIBRARY_PATH=${pkgs.stdenv.cc.cc.lib}/lib/:/run/opengl-driver/lib/:$LD_LIBRARY_PATH + # fixes xcb issues : + QT_PLUGIN_PATH=${pkgs.qt5.qtbase}/${pkgs.qt5.qtbase.qtPluginPrefix} + PATH="$PWD/node_modules/.bin/:$PATH" + ''; +} diff --git a/docs/docs/api_reference/CatalyticEvent.md b/docs/docs/api_reference/CatalyticEvent.md new file mode 100644 index 0000000..3a5a54f --- /dev/null +++ b/docs/docs/api_reference/CatalyticEvent.md @@ -0,0 +1,183 @@ +--- +sidebar_label: CatalyticEvent +title: CatalyticEvent +--- + +CatalyticEvent object which relates Reaction variables to the EnzymeVariable and Enzyme objects. +It contains multiple functions which enable easy mapping and handling of one Event of catalysis +(e.g. one conversion of substrate to product, can be catalyzed by multiple enzymes) + +## CatalyticEvent Objects + +```python +class CatalyticEvent(Object) +``` + +CatalyticEvent is a class for holding information regarding the catalysis of a Reaction in a cobra.Model object. +It serves as an interface between the metabolic reaction and the associated enzyme constraints and variables. + +**Notes**: + + There are three different scenarios: + - Enzyme complex: multiple enzymes together are associated with an EnzymeComplex object + - Isozymes: multiple enzymes independently associated with a single catalytic event + - Other: a single enzyme is associated with a single catalytic event + + +**Arguments**: + +- `kcats2enzymes` _dict_ - A dictionary with enzyme, kcat key, value pairs to connect the enzyme with the associated reaction. + The kcat is another dictionary with 'f' and 'b' for the forward and backward reactions, respectively. +- `id` _str, optional_ - The identifier to associate with this catalytic event (default None). +- `rxn_id` _str, optional_ - The reaction with which this catalytic event is associated. +- `name` _str, optional_ - A human-readable name for the reaction (default ""). + +#### kcat\_values + +```python +@property +def kcat_values() +``` + +returns a dictionary with kcat values and enzymes + +#### flux + +```python +@property +def flux() -> float +``` + +Get the flux value in the most recent solution. + +Flux is the primal value of the corresponding variable in the model. + +**Returns**: + +- `flux` _float_ - Flux is the primal value of the corresponding variable in the model. + + +**Warnings**: + + * Accessing reaction fluxes through a `Solution` object is the safer, + preferred, and only guaranteed to be correct way. You can see how to + do so easily in the examples. + * Reaction flux is retrieved from the currently defined + `self._model.solver`. The solver status is checked but there are no + guarantees that the current solver state is the one you are looking + for. + * If you modify the underlying model after an optimization, you will + retrieve the old optimization values. + + +**Raises**: + +- `RuntimeError` - If the underlying model was never optimized beforehand or the + reaction is not part of a model. +- `OptimizationError` - If the solver status is anything other than `optimal`. +- `AssertionError` - If the flux value is not within the bounds. + + +**Examples**: + + ``` + >>> from cobra.io import load_model + >>> model = load_model("textbook") + >>> solution = model.optimize() + >>> model.variables.PFK.flux + 7.477381962160283 + >>> solution.fluxes.PFK + 7.4773819621602833 + ``` + +#### concentration + +```python +@property +def concentration() -> float +``` + +Get the enzyme concentration value of the most recent solution. +The enzyme concentration equals the flux value. + +**Returns**: + +- `float` - Enzyme concentration in [mmol/gDW]. + +#### add\_enzymes + +```python +def add_enzymes(enzyme_kcat_dict: dict) +``` + +Add enzymes to the catalytic event and create bindings to the related model. +The enzymes in the enzyme_kcat_dict are individual isozymes. Enzyme complexes +should be added as an EnzymeComplex object with a single kcat value. + +**Arguments**: + +- `enzyme_kcat_dict` - Dict + A nested dictionary with enzyme, kcat key, value pairs to connect the + enzyme with the associated reaction. The kcat is another dictionary with `f` and `b` + for the forward and backward reactions respectively. + +#### remove\_enzymes + +```python +def remove_enzymes(enzyme_list: list) +``` + +Remove enzymes from the catalytic event and remove the catalytic event from the +constraint expressions related to the enzyme. + +**Arguments**: + +- `enzyme_list` - List[Union[str, PAModelpy.Package.Enzyme]] + A list with PAModelpy.Package.Enzyme objects to be removed. If a list of identifiers (str) + is provided, the corresponding enzyme will be obtained from the CatalyticEvent.enzymes attribute. + +#### change\_kcat\_values + +```python +def change_kcat_values(enzyme_kcat_dict: dict) +``` + +Change kcat values for the enzyme variable. + +**Arguments**: + +- `enzyme_kcat_dict` - Dict[str, Dict[str, float]] + A nested dictionary with enzyme identifiers as keys and kcat dictionaries as values. + The kcat dictionary should have `f` and `b` keys for the forward and backward reactions, respectively. + +#### \_\_copy\_\_ + +```python +def __copy__() -> "CatalyticEvent" +``` + +Copy the CatalyticEvent. + +**Returns**: + + CatalyticEvent: + A new CatalyticEvent that is a copy of the original CatalyticEvent. + +#### \_\_deepcopy\_\_ + +```python +def __deepcopy__(memo: dict) -> "CatalyticEvent" +``` + +Copy the CatalyticEvent with memo. + +**Arguments**: + +- `memo` _dict_ - Automatically passed parameter. + + +**Returns**: + + CatalyticEvent: + A new CatalyticEvent that is a copy of the original CatalyticEvent with memo. + diff --git a/docs/docs/api_reference/Constraints.md b/docs/docs/api_reference/Constraints.md new file mode 100644 index 0000000..126e3fb --- /dev/null +++ b/docs/docs/api_reference/Constraints.md @@ -0,0 +1,21 @@ +--- +sidebar_label: Constraints +title: Constraints +--- + +## Constraint Objects + +```python +class Constraint(Metabolite) +``` + +Class for information about a Constraint in a protein Sector. + +Constraint is a class for holding information similar to +a metabolite in a cobra.Reaction object. + +**Arguments**: + +- `id` _str_ - The identifier to associate with the constraint. +- `name` _str_ - A human-readable name. + diff --git a/docs/docs/api_reference/Enzyme.md b/docs/docs/api_reference/Enzyme.md new file mode 100644 index 0000000..5104f38 --- /dev/null +++ b/docs/docs/api_reference/Enzyme.md @@ -0,0 +1,410 @@ +--- +sidebar_label: Enzyme +title: Enzyme +--- + +Classes related to enzymes: +- Enzyme: Constraints relating enzymes to reactions. Including upper and lower bound enzyme constraints +- EnzymeVariable: Variable related to an enzyme. The value of this variable represent the concentration. + +## Enzyme Objects + +```python +class Enzyme(Object) +``` + +Upper level Enzyme object containing information about the enzyme and links to the EnzymeVariables for each reaction the enzyme catalyzes. + +**Arguments**: + + - id (str): Identifier for the enzyme (e.g., Uniprot ID). + - rxn2kcat (Dict): Dictionary with reaction ID, kcat value pairs for the forward (f) and backward (b) reaction, + e.g. `{'PGI': {'f': 30, 'b': 0.1}}` + - upper_bound (float): Upper bound for the enzyme variable (default 1000.0). + - lower_bound (float): Lower bound for the enzyme variable (default 0). + - name (str): Name of the enzyme (default None). + - molmass (float): Molar mass of the enzyme (default 3.947778784340140e04). + + +**Notes**: + + - This class is used to generate enzyme instances from kcat values and contains information about the forward as well as the backward catalysis. + - The enzyme is linked to individual cobra.Reaction variables with CatalyticEvent objects. + - There are two scenarios: + - Promiscuous enzymes: a single enzyme can catalyze multiple reactions. + - Other: a single enzyme catalyzes a single reaction. + +#### DEFAULT\_ENZYME\_MOL\_MASS + +mean enzymes mass E.coli [g/mol] + +#### kcat\_values + +```python +@property +def kcat_values() +``` + +Returns a dictionary with kcat values for each associated reaction. + +**Returns**: + +- `dict` - A dictionary containing kcat values for associated reactions. + +#### concentration + +```python +@property +def concentration(units: str = "mmol/gDW", + return_units: bool = False) -> float +``` + +Returns the enzyme's total concentration considering any associated reactions. + +**Arguments**: + +- `units` _str, optional_ - Units in which the concentration is calculated (default is 'mmol/gDW'), other option is 'g/gDW'. +- `return_units` _bool, optional_ - Determines whether the units should be returned as well. + + +**Returns**: + +- `float` - Enzyme concentration as a float. + +#### add\_catalytic\_event + +```python +def add_catalytic_event(ce: CatalyticEvent, kcats: Dict) +``` + +Adds a catalytic event associated with a reaction to an enzyme. + +**Arguments**: + +- `ce` _PAModelpy.Variables.CatalyticEvent_ - A CatalyticEvent object to which the enzyme should be added. +- `kcats` _dict_ - A dictionary containing direction and kcat key-value pairs. + + +**Returns**: + +- `NoneType` - None + +#### create\_catalytic\_event + +```python +def create_catalytic_event(rxn_id: str, kcats: Dict) +``` + +Creates enzyme variables that link to reactions. + +**Arguments**: + +- `rxn_id` _str_ - ID of the associated reaction in the model. +- `kcats` _Dict_ - A dictionary containing kcat values for the forward and backward reaction. + + +**Returns**: + +- `Variables.CatalyticEvent` - Enzyme variable object of type Variables.CatalyticEvent. + +#### create\_enzyme\_variable + +```python +def create_enzyme_variable() +``` + +Creates enzyme variables that link enzyme to reactions. + +#### change\_kcat\_values + +```python +def change_kcat_values(rxn2kcat: Dict) +``` + +Changes the kcat values for the enzyme and updates the enzyme variable (enzymatic reaction) accordingly. + +**Arguments**: + +- `rxn2kcat` _Dict_ - A dictionary with reaction ID, kcat value pairs for the forward (f) and backward (b) reaction, + e.g. `{'PGI': {'f': 30, 'b': 0.1}}` + +#### get\_kcat\_values + +```python +def get_kcat_values(rxn_ids: Union[str, list] = None) -> Dict +``` + +Returns the kcat values for a specific enzyme and all enzyme-associated reactions. + +**Arguments**: + +- `rxn_ids` _Union[str, list], optional_ - ID of the reactions for which the kcat values should be returned. It can be a single reaction ID (str) or a list of reaction IDs. + + +**Returns**: + +- `Dict` - A dictionary containing kcat values for the forward (f) and backward (b) reactions. + +#### remove\_catalytic\_event + +```python +def remove_catalytic_event(catalytic_event: Union[CatalyticEvent, str]) +``` + +Function to remove a catalytic event from an enzyme. + +**Arguments**: + +- `catalytic_event` _Union[CatalyticEvent, str]_ - CatalyticEvent or str, catalytic event or identifier to remove. + +#### \_\_copy\_\_ + +```python +def __copy__() -> "Enzyme" +``` + +Copy the enzyme variable. + +**Returns**: + +- `PAModelpy.Enzyme.Enzyme` - A new enzyme that is a copy of the original enzyme. + +#### \_\_deepcopy\_\_ + +```python +def __deepcopy__(memo: dict) -> "Enzyme" +``` + +Copy the enzyme variable with memo. + +**Arguments**: + +- `memo` _dict_ - Automatically passed parameter. + + +**Returns**: + +- `PAModelpy.Enzyme.Enzyme` - A new enzyme that is a copy of the original enzyme with memo. + +## EnzymeComplex Objects + +```python +class EnzymeComplex(Enzyme) +``` + +Upper-level EnzymeComplex object containing information about the enzymes in a complex +and a link to the enzyme variables (CatalyticEvents) for each reaction the enzyme complex catalyzes. + +This class is used to generate enzyme instances from kcat values and contains +information about the forward as well as the backward catalysis. + +**Arguments**: + +- `id` _str_ - Identifier for the enzyme complex (e.g., Uniprot ID). +- `enzymes` _DictList of cobra.core.Enzyme_ - Enzyme objects associated with the enzyme complex. +- `rxn2kcat` _Dict_ - Dictionary with reaction ID, kcat value pairs for the forward (f) and backward (b) reaction, + e.g. `{'PGI': {'f': 30, 'b': 0.1}}` +- `upper_bound` _float, optional_ - Upper bound for the enzyme variable (default 1000.0). +- `name` _str, optional_ - Name of the enzyme (default None). +- `molmass` _float, optional_ - Molar mass of the enzyme (default 3.947778784340140e04). + +#### DEFAULT\_ENZYME\_MOL\_MASS + +mean enzymes mass E.coli [g/mol] + +## EnzymeVariable Objects + +```python +class EnzymeVariable(Reaction) +``` + +EnzymeVariable is a class for holding information regarding the variable representing an enzyme in the model. +For each reaction, the enzyme variables are summarized in a CatalyticEvent. + +There are three different scenarios: +- Enzyme complex: multiple enzymes together are associated with an EnzymeComplex object. +- Isozymes: multiple enzymes independently associated with a single catalytic event. +- Other: a single enzyme is associated with a single catalytic event. + +**Arguments**: + +- `kcats2rxns` _Dict_ - A dictionary with reaction_id, kcat key, value pairs to connect the enzyme with the associated reaction. + The kcat is another dictionary with `f` and `b` for the forward and backward reactions, respectively. +- `id` _str, optional_ - The identifier to associate with this enzyme (default None). +- `name` _str, optional_ - A human-readable name for the enzyme (default ""). +- `subsystem` _str, optional_ - Subsystem where the enzyme is meant to function (default ""). +- `lower_bound` _float_ - The lower flux bound (default 0.0). +- `upper_bound` _float, optional_ - The upper flux bound (default None). +- `**kwargs` - Additional keyword arguments are passed on to the parent class. + +#### DEFAULT\_ENZYME\_MOL\_MASS + +mean enzymes mass E.coli [g/mol] + +#### kcat\_values + +```python +@property +def kcat_values() +``` + +Returns a dictionary with kcat values and reactions. + +**Returns**: + +- `dict` - A dictionary containing kcat values and their associated reactions. + +#### flux + +```python +@property +def flux() -> float +``` + +Get the flux value in the most recent solution. + +Flux is the primal value of the corresponding variable in the model. + +**Returns**: + +- `float` - Flux, which is the primal value of the corresponding variable in the model. + + +**Raises**: + +- `RuntimeError` - If the underlying model was never optimized beforehand or the reaction is not part of a model. +- `OptimizationError` - If the solver status is anything other than 'optimal'. +- `AssertionError` - If the flux value is not within the bounds. + + +**Warnings**: + + - Accessing reaction fluxes through a `Solution` object is the safer, preferred, and only guaranteed to be correct way. + - Reaction flux is retrieved from the currently defined `self._model.solver`. The solver status is checked but there are no guarantees that the current solver state is the one you are looking for. + - If you modify the underlying model after an optimization, you will retrieve the old optimization values. + + +**Examples**: + + ``` + >>> from cobra.io import load_model + >>> model = load_model("textbook") + >>> solution = model.optimize() + >>> model.variables.PFK.flux + 7.477381962160283 + >>> solution.fluxes.PFK + 7.4773819621602833 + ``` + +#### concentration + +```python +@property +def concentration() -> float +``` + +Get the enzyme concentration value of the most recent solution. + +The enzyme concentration equals the flux value. + +**Returns**: + +- `float` - Enzyme concentration in mmol/gDW. + +#### add\_catalytic\_events + +```python +def add_catalytic_events(catalytic_events: list, kcats: list) +``` + +Adding catalytic events to an enzyme variable. + +**Arguments**: + +- `catalytic_events` _list_ - A list of catalytic events to add. +- `kcats` _list_ - A list with dictionaries containing direction and kcat key-value pairs. + +#### add\_reactions + +```python +def add_reactions(reaction_kcat_dict: dict) +``` + +Add reactions to the enzyme variable and create bindings to the related model. +If there are multiple reactions related to a single enzyme, this is an isozyme. + +**Arguments**: + +- `reaction_kcat_dict` _dict_ - A nested dictionary with the reaction_id, kcat key, value pairs to connect the + enzyme with the associated reaction. The kcat is another dictionary with `f` and `b` for the forward and + backward reactions, respectively. + +#### remove\_catalytic\_event + +```python +def remove_catalytic_event(catalytic_event: Union[CatalyticEvent, str]) +``` + +Remove a catalytic event from an enzyme. + +**Arguments**: + +- `catalytic_event` _Union[CatalyticEvent, str]_ - CatalyticEvent or str, catalytic event or identifier to remove. + +#### remove\_reactions + +```python +def remove_reactions(reaction_list: list) +``` + +Remove reactions from the enzyme variable and remove the reactions from the +constraint expressions related to the enzyme. + +**Arguments**: + +- `reaction_list` _list_ - A list with Cobra.Reaction objects which should be removed. If a list of identifiers (str) + is provided, the corresponding enzyme will be obtained from the EnzymeVariables.reaction attribute. + +#### change\_kcat\_values + +```python +def change_kcat_values(reaction_kcat_dict: dict) +``` + +Changes kcat values for the enzyme variable. + +**Arguments**: + +- `reaction_kcat_dict` _dict_ - A nested dictionary with Cobra.Reaction, kcat key, value pairs to connect the + enzyme with the associated reaction. The kcat is another dictionary with `f` and `b` for the forward and + backward reactions, respectively. + +#### \_\_copy\_\_ + +```python +def __copy__() -> "PAModelpy.Enzyme.EnzymeVariable" +``` + +Copy the enzyme variable. + +**Returns**: + +- `PAModelpy.Enzyme.EnzymeVariable` - A new enzyme variable that is a copy of the original enzyme variable. + +#### \_\_deepcopy\_\_ + +```python +def __deepcopy__(memo: dict) -> "PAModelpy.Enzyme.EnzymeVariable" +``` + +Copy the enzyme variable with memo. + +**Arguments**: + +- `memo` _dict_ - Automatically passed parameter. + + +**Returns**: + +- `PAModelpy.Enzyme.EnzymeVariable` - A new enzyme variable that is a copy of the original enzyme variable with memo. + diff --git a/docs/docs/api_reference/EnzymeSectors.md b/docs/docs/api_reference/EnzymeSectors.md new file mode 100644 index 0000000..9939cae --- /dev/null +++ b/docs/docs/api_reference/EnzymeSectors.md @@ -0,0 +1,146 @@ +--- +sidebar_label: EnzymeSectors +title: EnzymeSectors +--- + +## Sector Objects + +```python +class Sector(Object) +``` + +#### \_\_copy\_\_ + +```python +def __copy__() -> "Sector" +``` + +Copy the Sector. + +**Returns**: + +- `Sector` - A new Sector that is a copy of the original Sector. + +#### \_\_deepcopy\_\_ + +```python +def __deepcopy__(memo: dict) -> "Sector" +``` + +Copy the Sector with memo. + +**Arguments**: + +- `memo` _dict_ - Automatically passed parameter. + + +**Returns**: + +- `Sector` - A new Sector that is a copy of the original Sector with memo. + +## EnzymeSector Objects + +```python +class EnzymeSector(Sector) +``` + +#### DEFAULT\_MOL\_MASS + +mean enzymes mass E.coli [g/mol] + +## ActiveEnzymeSector Objects + +```python +class ActiveEnzymeSector(Sector) +``` + +#### DEFAULT\_MOL\_MASS + +mean enzymes mass E.coli [g/mol] + +#### \_\_init\_\_ + +```python +def __init__(rxn2protein: dict, configuration: Config = Config) +``` + +_summary_ + +**Arguments**: + +- `rxn2protein` _dict_ - A dictionary with reaction ID, enzymes_dict key, value pairs for each reaction in the active_enzyme sector. + The enzymes_dict contains the enzyme identifiers of the enzymes related to the specific reaction as keys, and a dict + with information about the enzyme as values. The information included in this dict includes the turnover number for + the forward and backward reaction (1/s), molar mass of the enzyme (mol/g), gene identifiers related to the enzyme, + and with which other enzymes it forms a complex. +- `configuration` _Config object, optional_ - Information about the general configuration of the model, including identifier conventions. + Default is as defined in the `PAModelpy.configuration` script for the E.coli iML1515 model. + + +**Example**: + + ``` + For the Parameter rxn2protein a dictionary may look like this: + { + 'R1': + { + 'E1': {'f': forward kcat, 'b': backward kcat, 'molmass': molar mass, 'genes': ['G1', 'G2'], + 'complex_with': 'E2'}, + 'E2': {'f': forward kcat, 'b': backward kcat, 'molmass': molar mass, 'genes': ['G3', 'G4'], + 'complex_with': 'E1'} + } + } + ``` + +#### check\_kcat\_values + +```python +def check_kcat_values(model, reaction, kcat) +``` + +Function to check if the kcat values provided for an enzyme are consistent with the direction of the reaction. + +**Arguments**: + +- `model` _cobra.Model or PAModel_ - Model to which the kcat values should be added. +- `reaction` _cobra.Reaction_ - Reaction that is catalyzed with the enzyme related to the kcats. +- `kcat` _dict_ - A dictionary with the turnover values for the forward and/or backward reaction for different enzymes [/s]. + + +**Example**: + + Example dictionary for the `kcat` parameter + ``` + {'E1': {'f': forward kcat, 'b': backward kcat}} + ``` + +## TransEnzymeSector Objects + +```python +class TransEnzymeSector(EnzymeSector) +``` + +#### DEFAULT\_MOL\_MASS + +default E. coli ribosome molar mass [g/mol] + +## UnusedEnzymeSector Objects + +```python +class UnusedEnzymeSector(EnzymeSector) +``` + +#### DEFAULT\_MOL\_MASS + +mean enzymes mass E.coli [g/mol] + +## CustomSector Objects + +```python +class CustomSector(EnzymeSector) +``` + +#### DEFAULT\_ENZYME\_MOL\_MASS + +mean enzymes mass E.coli [g/mol] + diff --git a/docs/docs/api_reference/PAMValidator.md b/docs/docs/api_reference/PAMValidator.md new file mode 100644 index 0000000..a719ef7 --- /dev/null +++ b/docs/docs/api_reference/PAMValidator.md @@ -0,0 +1,96 @@ +--- +sidebar_label: PAMValidator +title: PAMValidator +--- + +## PAMValidator Objects + +```python +class PAMValidator(object) +``` + +#### MW\_GLC + +g/mol + +#### GRADIENT\_MAX + +mmol/gdw/h + +#### GRADIENT\_STEP + +mmol/gdw/h + +#### GRADIENT\_MIN + +mmol/gdw/h + +#### run\_simulations\_glc\_o2\_gradient + +```python +def run_simulations_glc_o2_gradient( + oxygen_gradient: list, + params_to_save: Union[str, list] = "R_TranslationalProteinSector") +``` + +Function to run simulations of different oxygen gradients for a range of growth rates. + +This will simulate growth for the entire range of glucose concentrations for each oxygen uptake rate as given by the input. + +**Arguments**: + +- `oxygen_gradient` _list_ - List of upper bounds for the oxygen uptake reaction to loop over. +- `params_to_save` _optional_ - string or list, which parameter(s) to save for further analysis (default: translational protein sector constraint). + + +**Returns**: + +- `results` _list of dataframes_ - Saves the growth rate, glucose uptake rate, and the user-defined parameters for each oxygen uptake rate in separate dataframes. + +#### run\_simulations\_ups + +```python +def run_simulations_ups( + ups_gradient: list, + params_to_save: Union[str, list] = "R_TranslationalProteinSector") +``` + +Function to run simulations with increasing unused enzyme sectors proportions for a range of growth rates. + +This will simulate growth for the entire range of glucose concentrations for a range of fractions of ups_0 as given by the input. + +**Arguments**: + +- `ups_gradient` _list_ - List of upper bounds for the oxygen uptake reaction to loop over. +- `params_to_save` _optional_ - string or list, which parameter(s) to save for further analysis (default: translational protein sector constraint). + + +**Returns**: + +- `results` _list of dataframes_ - Saves the growth rate, glucose uptake rate, and the user-defined parameters for each oxygen uptake rate in separate dataframes. + +#### custom\_plot + +```python +def custom_plot(rxn_ids: list, + valid_dataframe: pd.DataFrame = None, + xaxis: str = None, + c_uptake_rxn: str = GLUCOSE_EXCHANGE_RXNID) +``` + +Function to plot the results of custom reactions. + +**Arguments**: + +- `rxn_ids` _list of str_ - Reaction identifiers of the reactions to be plotted. +- `valid_dataframe` _pandas.DataFrame, optional_ - A DataFrame with experimental data to validate the results with. + The columns should be the same as the rxn_id of the reaction to be plotted and the reaction which should be plotted + on the x-axis (by default the glucose exchange reaction `EX_glc__D_e_b`). If the DataFrame is not provided, + only the simulation results will be plotted. +- `xaxis` _str, optional_ - The reaction identifier of the reaction which should be plotted on the x-axis (default: `EX_glc__D_e_b`). + + +**Returns**: + + Prints scatter plots of the model simulations vs. experimental data points (if provided). + diff --git a/docs/docs/api_reference/PAModel.md b/docs/docs/api_reference/PAModel.md new file mode 100644 index 0000000..9305f5c --- /dev/null +++ b/docs/docs/api_reference/PAModel.md @@ -0,0 +1,616 @@ +--- +sidebar_label: PAModel +title: PAModel +--- + +## PAModel Objects + +```python +class PAModel(Model) +``` + +Class representation for a cobra model extended with enzyme kinetics as published in Alter et al. (2021). + +**Arguments**: + +- `id_or_model` _str or Model_ - String to use as model id, or actual model to base new model on. + If a string, it is used as input to load a model from. If a model, a new model object is instantiated with + the same properties as the original model (default None). +- `name` _str, optional_ - Human-readable string to be model description (default None). +- `p_tot` _float, optional_ - Total protein concentration (condition-dependent) (unit g_prot/g_cdw) (default 0.285). +- `senstitivity` _bool_ - Boolean value whether or not a sensitivity analysis should be performed during each simulation. + This sensitivity analysis will indicate to which extent individual constraints contribute to the objective value. + Enzyme sectors (EnzymeSector objects, optional): Information about the different enzyme sectors, including: + - Active_enzyme: Metabolic active proteins. + - Transl_enzyme: Enzymes related to translation. + - Unused_enzymes: Excess enzymes. + - Custom_enzymes (list): Custom enzyme sectors. +- `configuration` _Config object, optional_ - Information about the general configuration of the model, including + identifier conventions. Default as defined in the `PAModelpy.configuration` script for the E.coli iML1515 model. + + +**Attributes**: + +- `p_tot` _float_ - The fraction of biomass allocated to proteins (units: g_prot/g_cdw). +- `reactions` _DictList_ - A DictList where the key is the reaction identifier and the value is a Reaction. +- `metabolites` _DictList_ - A DictList where the key is the metabolite identifier and the value is a Metabolite. +- `genes` _DictList_ - A DictList where the key is the gene identifier and the value is a Gene. +- `name`0 _DictList_ - A DictList where the key is the group identifier and the value is a Group. +- `name`1 _DictList_ - A DictList where the key is the enzyme identifier and the value is an Enzyme. +- `name`2 _DictList_ - A DictList where the key is the enzyme variable identifier and the value is an EnzymeVariable. +- `name`3 _DictList_ - A DictList where the key is the catalytic event identifier and the value is a CatalyticEvent. +- `name`4 _dict_ - A dictionary containing sector-specific constraints. +- `name`5 _DictList_ - A DictList where the key is the sector identifier and the value is an EnzymeSector. + +#### P\_TOT\_DEFAULT + +g_protein/g_cdw + +#### \_\_init\_\_ + +```python +def __init__(id_or_model: Union[str, "Model", None] = None, + name: Optional[str] = None, + p_tot: Optional[float] = Config.P_TOT_DEFAULT, + sensitivity: bool = True, + active_sector: Optional[ActiveEnzymeSector] = None, + translational_sector: Optional[TransEnzymeSector] = None, + unused_sector: Optional[UnusedEnzymeSector] = None, + custom_sectors: Union[List, CustomSector] = None, + configuration=Config) +``` + +Constants + +#### add\_enzymes + +```python +def add_enzymes(enzyme_list: list) -> None +``` + +Add new enzymes to a model. +Adapted from Cobra.core.model.add_reactions and Cobra.core.model.add_metabolites. + +This function will add a DictList of enzymes to the model object and add new variables accordingly. +For each enzyme-associated reaction, a constraint in each direction is added to the model. +The change is reverted upon exit when using the model as a context. + +**Arguments**: + +- `enzyme_list` _list or Enzyme_ - A list of `Enzyme` objects. If it isn't an iterable container, the enzyme will + be placed into a list. + +#### add\_sectors + +```python +def add_sectors(sectors: List = None) +``` + +Adds sector variables to the model and adds these to the total protein constraint. + +**Arguments**: + +- `sectors` _list_ - A list of PAModelpy.EnzymeSectors to add to the model. + +#### add\_sector + +```python +def add_sector(sector) +``` + +Adds the sector variable for a specific sector to the model and adds this to the total protein constraint. +Also stores the sector variables in the model attributes. + +**Arguments**: + +- `sector` _PAModelpy.EnzymeSector_ - The specific EnzymeSector to add to the model. + +#### add\_catalytic\_events + +```python +def add_catalytic_events(catalytic_events: Optional[Iterable]) +``` + +Add a new CatalyticEvent to the model. +Will add a list of CatalyticEvent variables to the model object using the function defined in the CatalyticEvent object. + +**Arguments**: + +- `catalytic_events` _list or variables.CatalyticEvent_ - A list of `variables.CatalyticEvent` objects. If it isn't + an iterable container, the catalytic event will be placed into a list. + +#### add\_enzyme\_constraints + +```python +def add_enzyme_constraints(constraint_list: Optional[list]) +``` + +Add new enzyme constraints to a model. +Will add a list of constraints to the model object and add new constraints accordingly. +The change is reverted upon exit when using the model as a context. + +**Arguments**: + +- `constraint_list` _list, str, or constraints.Constraint_ - A list of `constraints.Constraint` objects. If it isn't + an iterable container, the constraint will be placed into a list. Also, a string with the constraint id + can be provided. A constraint will be created before adding it to the model. + +#### add\_sector\_constraints + +```python +def add_sector_constraints(constraint_list: Optional[list]) +``` + +Add a new constraint related to a sector to a model. +Will add a list of constraints to the model object and add new constraints accordingly. +The change is reverted upon exit when using the model as a context. + +**Arguments**: + +- `constraint_list` _list or constraints.Constraint_ - A list of `constraints.Constraint` objects. If it isn't an iterable + container, the constraint will be placed into a list. + +#### add\_total\_protein\_constraint + +```python +def add_total_protein_constraint(p_tot: Optional[float] = P_TOT_DEFAULT) +``` + +Function which adds the total protein constraint to the model. +This limits the amount of available enzymes and thus the resulting fluxes. + +**Notes**: + + The constraint expression looks like this: +- ``Etot` - sum(E) + E_translprot + E_unusedprot == p_tot - E_trsn_0 - E_ue_0` + + +**Arguments**: + +- `p_tot` _float, optional_ - Fraction of biomass which consists of protein (g_protein/g_cdw). + Default is 0.258 (E.coli). + +#### add\_reactions + +```python +def add_reactions(reaction_list: Iterable[Reaction]) -> None +``` + +Add reactions to the model. +This method is superimposed upon the cobra.Model.add_reactions() function. +As a new feature, it will add constraints to determine the lower and upper bound if a sensitivity analysis should +be performed (which is determined by the model attribute: PAModel.sensitivity). +Reactions with identifiers identical to a reaction already in the model are ignored. +The change is reverted upon exit when using the model as a context. + +**Arguments**: + +- `reaction_list` _list_ - A list of `cobra.Reaction` objects. + +#### add\_lb\_ub\_constraints + +```python +def add_lb_ub_constraints() +``` + +Makes additional constraints for the reaction lower bounds and upperbounds. +By adding these constraints the shadow prices of the reaction bounds can be +calculated and used in sensitivity analysis + +#### make\_lb\_ub\_constraint + +```python +@staticmethod +def make_lb_ub_constraint(m: Optional[Model], rxn: Reaction, + lower_bound: float, upper_bound: float) +``` + +Adding variables and constraints for the lower and upper bounds of a reaction to a model. +When solving the model, shadow prices for the lower and upper bounds will be calculated. +This allows for the calculation of sensitivity coefficients. The constraints are formulated as follows: + +**Notes**: + + Constraints are formulated as follows: + - `R_ub: R_fwd - R_rev <= UB` + - `R_lb: -(R_fwd - R_rev) <= -LB` + + +**Arguments**: + +- `m` _cobra.Model or PAModelpy.PAModel_ - The model to which the upper and lower bound constraints and variables + should be added. +- `rxn` _cobra.Reaction_ - The reaction for which upper and lower bound constraints should be generated. +- `lower_bound` _float_ - The value of the lower bound. +- `upper_bound` _float_ - The value of the upper bound. + + +**Returns**: + +- `m` _cobra.Model or PAModelpy.PAModel_ - The model with additional constraints and variables for the reactions. + +#### make\_enzyme\_min\_max\_constraint + +```python +@staticmethod +def make_enzyme_min_max_constraint(m: Optional[Model], enz: Enzyme, + lower_bound: float, upper_bound: float) +``` + +Adding variables and constraints for the lower and upper bounds of an enzyme's concentration to a model. +When solving the model, shadow prices for the lower and upper bounds will be calculated. +This allows for the calculation of sensitivity coefficients. + +**Notes**: + + The constraints are formulated as follows: + - `E_ub: E <= Emax` + - `E_lb: -E <= -Emin` + + +**Arguments**: + +- `m` _cobra.Model or PAModelpy.PAModel_ - The model to which the upper and lower bound constraints and variables + should be added. +- `rxn` _PAModelpy.Enzyme_ - The enzyme for which minimal and maximal concentration constraints should be generated. +- `lower_bound` _float_ - The value of the lower bound. +- `upper_bound` _float_ - The value of the upper bound. + + +**Returns**: + +- `m` _cobra.Model or PAModelpy.PAModel_ - The model with additional constraints and variables for the enzyme's + concentration. + +#### parse\_shadow\_prices + +```python +@staticmethod +def parse_shadow_prices(shadow_prices) +``` + +Parse the shadow prices to a DataFrame where each constraint corresponds to a row, and shadow prices and directions are columns. + +#### calculate\_csc + +```python +def calculate_csc(obj_value, mu, mu_ub, mu_lb, mu_ec_f, mu_ec_b) +``` + +Calculate the capacity sensitivity coefficient for all inequality constraints in the model. +The sum of all capacity sensitivity coefficients should equal 1 for growth maximization. + +Capacity Sensitivity Coefficient Calculation: +Capacity Sensitivity Coefficient = constraint_UB * shadowprice / obj_value + +**Arguments**: + +- `obj_value` _float_ - The objective value of the model. +- `mu` _DataFrame_ - Shadow prices for all constraints. +- `mu_ub` _DataFrame_ - Shadow prices for the reaction upper bound (UB) constraints. +- `mu_lb` _DataFrame_ - Shadow prices for the reaction lower bound (LB) constraints. +- `mu_ec_f` _DataFrame_ - Shadow prices for the constraints related to enzymatic catalysis of the forward reaction. +- `mu_ec_b` _DataFrame_ - Shadow prices for the constraints related to enzymatic catalysis of the backward reaction. + + +**Returns**: + + None + + Results are saved in the `self.capacity_sensitivity_coefficients` attribute as a DataFrame. + +#### calculate\_esc + +```python +def calculate_esc(obj_value, mu_ec_f, mu_ec_b) +``` + +Calculate enzyme sensitivity coefficients for the enzyme variables using their primal values, +the objective value, and shadow prices according to the following relations: + +Enzyme Sensitivity Coefficient Calculation: +esc = enzyme_variable.primal * constraint.shadowprice / obj_value + +**Arguments**: + +- `obj_value` _float_ - The objective value from the most recent optimal solution. +- `mu_ec_f` _pd.DataFrame_ - Shadow prices for maximizing enzyme concentrations (forward variables). +- `mu_ec_b` _pd.DataFrame_ - Shadow prices for minimizing enzyme concentrations (reverse variables). + + +**Returns**: + + None + + Fills the `PAModel.enzyme_sensitivity_coefficients` dataframe with the calculated enzyme sensitivity coefficients. + +#### calculate\_sum\_of\_enzymes + +```python +def calculate_sum_of_enzymes() +``` + +Calculate the sum of all enzyme variables for a feasible solution. + +**Returns**: + +- `float` - The sum of all enzyme variables in milligrams per gram of cell dry weight per hour (mg/gCDW/h). + +#### change\_total\_protein\_constraint + +```python +def change_total_protein_constraint(p_tot) +``` + +Change the fraction of biomass that is allocated to active proteins. + +**Arguments**: + +- `p_tot` _float_ - The new proteome fraction in grams of protein per gram of cell dry weight (g_protein/g_cdw). + +#### change\_reaction\_bounds + +```python +def change_reaction_bounds(rxn_id: str, + lower_bound: float = None, + upper_bound: float = None) +``` + +Change the reaction bounds. If a sensitivity analysis is required, the bounds of the upper and lower bound +constraints are adjusted. + +**Arguments**: + +- `rxn_id` _str_ - The string representing the reaction identifier to change. +- `lower_bound` _float, optional_ - The new value for the lower bound of the reaction (default is None). +- `upper_bound` _float, optional_ - The new value for the upper bound of the reaction (default is None). + +#### change\_enzyme\_bounds + +```python +def change_enzyme_bounds(enzyme_id: str, + lower_bound: float = None, + upper_bound: float = None) +``` + +Change the enzyme bounds. If the model should be primed for performing a sensitivity analysis, +the upper bound of the minimum and maximum enzyme concentration constraints are adjusted. + +**Arguments**: + +- `enzyme_id` _str_ - The string representing the enzyme identifier to change. +- `lower_bound` _float, optional_ - The new value for the minimal enzyme concentration (default is None). +- `upper_bound` _float, optional_ - The new value for the maximal enzyme concentration (default is None). + +#### get\_enzymes\_with\_reaction\_id + +```python +def get_enzymes_with_reaction_id(rxn_id: str) +``` + +Return Enzyme objects associated with the reaction identifier through CatalyticEvent objects. + +**Arguments**: + +- `rxn_id` _str_ - The reaction identifier. + + +**Returns**: + +- `DictList` - A DictList of Enzyme objects associated with the reaction. + +#### get\_reactions\_with\_enzyme\_id + +```python +def get_reactions_with_enzyme_id(enz_id: str) +``` + +Return a list of reaction identifiers associated with the enzyme identifier (EC number) through CatalyticEvent objects. + +**Arguments**: + +- `enz_id` _str_ - The enzyme identifier (EC number). + + +**Returns**: + +- `List[str]` - A list of reaction identifiers associated with the enzyme. + +#### change\_kcat\_value + +```python +def change_kcat_value(enzyme_id: str, kcats: dict) +``` + +Change the turnover number (kcat) of the enzyme for a specific reaction. + +**Arguments**: + +- `enzyme_id` _str_ - The enzyme identifier. +- `kcats` _dict_ - A dictionary with reaction identifiers as keys and kcat values as values. + Each kcat value should be a nested dictionary with `f` (forward) and `b` (backward) as keys, + and the corresponding kcat values as values. + + +**Example**: + + Example dictionary for the `kcat` parameter + ``` + {'R1': {'f': 10.0, 'b': 5.0}, 'R2': {'f': 7.0, 'b': 3.0}} + ``` + +#### remove\_enzymes + +```python +def remove_enzymes( + enzymes: Union[str, Enzyme, List[Union[str, Enzyme]]]) -> None +``` + +Remove enzymes from the model. + +**Arguments**: + +- `enzymes` _list, reaction, or str_ - A list with enzymes (`Enzyme`), or their IDs, to remove. + Enzymes will be placed in a list. Strings will be placed in a list + and used to find the enzymes in the model. + +**Notes**: + + The change is reverted upon exit when using the model as a context. + +#### remove\_reactions + +```python +def remove_reactions(reactions: Union[str, Reaction, List[Union[str, + Reaction]]], + remove_orphans: bool = False) -> None +``` + +Remove reactions from the model. Inherited from the cobrapy.core.remove_reactions() function. + +**Arguments**: + +- `reactions` _list, reaction, or str_ - A list with reactions (`cobra.Reaction`), or their IDs, to remove. + Reactions will be placed in a list. Strings will be placed in a list + and used to find the reactions in the model. +- `remove_orphans` _bool, optional_ - Remove orphaned genes and metabolites from the model as well (default False). + +**Notes**: + + The change is reverted upon exit when using the model as a context. Also removes associated CatalyticEvents if they exist. + +#### remove\_catalytic\_events + +```python +def remove_catalytic_events(catalytic_events: Union[ + str, CatalyticEvent, List[Union[str, CatalyticEvent]]], + remove_orphans: bool = False) -> None +``` + +Remove catalytic events from the model. + +**Arguments**: + +- `reactions` _list, reaction, or str_ - A list with reactions (`cobra.Reaction`), or their IDs, to remove. + Reactions will be placed in a list. Strings will be placed in a list + and used to find the reactions in the model. +- `remove_orphans` _bool, optional_ - Remove orphaned genes and metabolites from the model as well (default False). + + +**Notes**: + + The change is reverted upon exit when using the model as a context. + +#### remove\_sectors + +```python +def remove_sectors( + sectors: Union[ + str, + Sector, + ActiveEnzymeSector, + List[Union[str, Sector, ActiveEnzymeSector]], + ] +) -> None +``` + +Remove sections from the model. + +Also removes associated CatalyticEvents if they exist. + +**Arguments**: + +- `sectors` _list, sector, or str_ - A list with sector (`PAModelpy.Sector` or `PAModelpy.ActiveEnzymeSector`), + or their IDs, to remove. A single sector will be placed in a list. + Strings will be placed in a list and used to find the sector in the model. + +#### test + +```python +def test(glc_flux: Union[int, float] = 10) +``` + +Test the proteome allocation model. + +**Arguments**: + +- `glc_flux` _float, optional_ - The glucose flux which limits the growth rate (units: mmol_glc/g_cdw/h, default=10). + +#### pfba + +```python +def pfba(fraction_of_optimum: float = 1.0, + proteins: bool = False, + reactions: bool = True, + exclude: List["str"] = [], + objective: Union[Dict, "Objective", None] = None) +``` + +Perform pFBA (parsimonious Enzyme Usage Flux Balance Analysis) with a custom objective including: +- All reactions +- All proteins +- All proteins and all reactions. + +pFBA [1] adds the minimization of all fluxes to the objective of the model. This approach is motivated by the idea that high fluxes have a higher enzyme turnover, and since producing enzymes is costly, the cell will try to minimize overall flux while still maximizing the original objective function, e.g., the growth rate. + +**Arguments**: + +- `fraction_of_optimum` _float, optional_ - The fraction of optimum which must be maintained. The original objective reaction is constrained to be greater than the maximal value times the `fraction_of_optimum` (default 1.0). +- `objective` _dict or cobra.Model.objective, optional_ - A desired objective to use during optimization in addition to the pFBA objective. Dictionaries (reaction as the key, coefficient as the value) can be used for linear objectives (default None). +- `proteins` _bool, optional_ - Determines whether to include enzyme variables in the pFBA objective. +- `reactions` _bool, optional_ - Determines whether to include reaction variables in the pFBA objective. +- `exclude` _list of reaction ids, optional_ - Reactions to exclude from the minimization objective. + + +**References**: + + - [1] Lewis, N. E., Hixson, K. K., Conrad, T. M., Lerman, J. A., Charusanti, P., Polpitiya, A. D., Palsson, B. O. (2010). Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Molecular Systems Biology, 6, 390. doi:10.1038/msb.2010.47 + +#### reset\_objective + +```python +def reset_objective() +``` + +Reseting the objective to the standard biomass maximization objective after pFBA + +#### optimize + +```python +def optimize(objective_sense: Optional[str] = None, + raise_error: bool = False) -> "Solution" +``` + +Optimize the model using flux balance analysis. Inherits from the cobra.Model.optimize() function and performs a sensitivity analysis after optimization if this is desired (by setting the PAModel.sensitivity attribute to True). + +**Arguments**: + +- `objective_sense` _`{None, 'maximize', 'minimize'}`, optional_ - Whether fluxes should be maximized or minimized. In case of None, the previous direction is used (default None). +- `raise_error` _bool_ - If true, raise an OptimizationError if solver status is not optimal (default False). + + +**Returns**: + + Solution + + +**Notes**: + + Only the most commonly used parameters are presented here. Additional parameters for cobra.solver may be available and specified with the appropriate keyword argument. + +#### copy + +```python +def copy() -> "PAModel" +``` + +Provide a partial 'deepcopy' of the Model. + +Adjusted from cobra.Model.copy(). + +All the Metabolite, Gene, Reaction, Enzyme, EnzymeVariable, Sector, and CatalyticEvent objects are created anew but in a faster fashion than deepcopy. + +**Returns**: + +- `PAModelpy.PAModel` - A new model copy. + diff --git a/docs/docs/api_reference/_category_.json b/docs/docs/api_reference/_category_.json new file mode 100644 index 0000000..3c28986 --- /dev/null +++ b/docs/docs/api_reference/_category_.json @@ -0,0 +1,11 @@ +{ + "position": 3, + "label": "API Reference", + "collapsible": true, + "collapsed": true, + "className": "red", + "link": { + "type": "generated-index", + "title": "API Reference" + } + } \ No newline at end of file diff --git a/docs/docs/api_reference/configuration.md b/docs/docs/api_reference/configuration.md new file mode 100644 index 0000000..e8c487e --- /dev/null +++ b/docs/docs/api_reference/configuration.md @@ -0,0 +1,36 @@ +--- +sidebar_label: configuration +title: configuration +--- + +## Config Objects + +```python +class Config() +``` + +Object with information about model defaults which are used throughout the package: +- TOTAL_PROTEIN_CONSTRAINT_ID: str, `TotalProteinConstraint` +- P_TOT_DEFAULT: float, 0.258 g_p/g_cdw +- CO2_EXHANGE_RXNID: str, `EX_co2_e` +- GLUCOSE_EXCHANGE_RXNID: str, `EX_glc__D_e` +- BIOMASS_REACTION: str, `BIOMASS_Ec_iML1515_core_75p37M` +- OXYGEN_UPTAKE_RXNID: str, `EX_o2_e` +- ACETATE_EXCRETION_RXNID: str, `EX_ac_e` +- PHYS_RXN_IDS: List of str, `[BIOMASS_REACTION, GLUCOSE_EXCHANGE_RXNID, ACETATE_EXCRETION_RXNID, CO2_EXHANGE_RXNID, OXYGEN_UPTAKE_RXNID, + 'PGI', 'G6PDH2r', 'EDA', 'CS', 'ICL', 'PPC', 'ME1', 'ME2']` + +Defaults are configured for the iML1515 E.coli model + +#### P\_TOT\_DEFAULT + +g_protein/g_cdw + +#### reset + +```python +def reset() +``` + +Reset the config object to the standard settings for E.coli iML1515. + diff --git a/docs/docs/api_reference/sidebar.json b/docs/docs/api_reference/sidebar.json new file mode 100644 index 0000000..42da33b --- /dev/null +++ b/docs/docs/api_reference/sidebar.json @@ -0,0 +1,13 @@ +{ + "items": [ + "docs/api_reference/CatalyticEvent", + "docs/api_reference/Constraints", + "docs/api_reference/Enzyme", + "docs/api_reference/EnzymeSectors", + "docs/api_reference/PAMValidator", + "docs/api_reference/PAModel", + "docs/api_reference/configuration" + ], + "label": "Reference", + "type": "category" +} \ No newline at end of file diff --git a/docs/source/toy-model.png b/docs/docs/assets/toy-model.png similarity index 100% rename from docs/source/toy-model.png rename to docs/docs/assets/toy-model.png diff --git a/docs/source/example.md b/docs/docs/example.md similarity index 96% rename from docs/source/example.md rename to docs/docs/example.md index 28299ab..d094710 100644 --- a/docs/source/example.md +++ b/docs/docs/example.md @@ -1,3 +1,9 @@ +--- +title: 'Examples' +sidebar_position: 2 +sidebar_title: 'Examples' +--- + # Example usage of PAModelpy ************* @@ -70,13 +76,15 @@ active_enzyme_info[nan_values] We need to collect the data from this table and put it in the correct structure to be parsed into the ActiveEnzymeSector object. The main input in this object is the rxn2protein dictionary, where all the information about protein-reaction associations required to build the protein-reaction relations in the model. It has the following format: -``` - {'R1': - {'E1': - {'f': forward kcat, 'b': backward kcat, 'molmass': molar mass}, - 'E2': - {'f': forward kcat, 'b': backward kcat, 'molmass': molar mass} - } + +```json +{'R1': + {'E1': + {'f': forward kcat, 'b': backward kcat, 'molmass': molar mass}, + 'E2': + {'f': forward kcat, 'b': backward kcat, 'molmass': molar mass} + } +} ``` We need to take the following steps to get the right format: @@ -233,16 +241,18 @@ we get the sensitivity of the objective function to slight changes in the enzyme coefficients, ESC) as a result from the model simulations. In this example we'll use a toy model to illustrate how these sensitivities can help us explain concepts of protein allocation. -[//]: # (image reference) -[toy_model_image]: toy-model.png "Figure 1. Toy model network and parameters" +
+ +![toy_model_image](./assets/toy-model.png) -![toy_model][toy_model_image] -**Figure 1. Toy model network and parameters.** *This toy model represents a schematic overview of a microbial metabolism, +
**Figure 1. Toy model network and parameters** +*This toy model represents a schematic overview of a microbial metabolism, with an energy efficient (R1-R2-R4+R5-R6-R7) and an enzyme efficient (R1-R2-R3+R5-R6-R7) pathway. Besides the enzymes catalyzing the reactions (denoted with an 'E') and corresponding catalytic efficiency (kcat), also the relation with the reactions and the enzyme sectors are given. UES: Unused Enzyme Sector, TES: Translational Enzyme Sector, AES: -Active Enzyme Sector.* +Active Enzyme Sector.*
+
First, all import statements you'll need in this example: @@ -373,7 +383,7 @@ print_heatmap(x_axis_esc, Cesc, yaxis=substrate_axis) ``` ### Step 4: Interpret the results -Compare the [toy model network structure][toy_model_image] with the results from the heatmap. Did you expect these results? Do they make +Compare the [toy model network structure](#toy_model_image) with the results from the heatmap. Did you expect these results? Do they make sense? Which mechanisms to explain these observations. If the observations are not inline with you're expectations, you can use the enzyme sensitivities to point to the enzymatic parameters which might need to be adjusted (in this dummy example this makes no sense off course, but in reality this is a very plausible outcome). diff --git a/docs/source/introduction.md b/docs/docs/introduction.md similarity index 96% rename from docs/source/introduction.md rename to docs/docs/introduction.md index 29e1dc8..ec38bcd 100644 --- a/docs/source/introduction.md +++ b/docs/docs/introduction.md @@ -1,3 +1,10 @@ +--- +title: 'Introduction' +sidebar_position: 1 +sidebar_title: 'Introduction' +slug: '/' +--- + # PAModelpy - Protein Allocation Model reconstruction in Python ******* @@ -25,7 +32,7 @@ We have extended this package with the following features: Note that the package has been tested with the [Gurobi](https://www.mathworks.com/products/connections/product_detail/gurobi-optimizer.html) solver. ## What can you find where in this repository? -This repository contains not only the source code, but also examples and scripts which were used in [publication](linktopublication). +This repository contains not only the source code, but also examples and scripts which were used in **INSERT PUBLICATION HERE**. - **Data** - *eGFP_expression_Bienick2014*: measured growth rate and eGFP expression by [Bienick et al. (2014)](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0109105) - *proteinAllocationModel_iML1515_EnzymaticData_py*: information about the proteinsectors of the PAM for *Escherichia coli* (*E.coli*) diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js new file mode 100644 index 0000000..68159ee --- /dev/null +++ b/docs/docusaurus.config.js @@ -0,0 +1,126 @@ +// @ts-check +// `@type` JSDoc annotations allow editor autocompletion and type checking +// (when paired with `@ts-check`). +// There are various equivalent ways to declare your Docusaurus config. +// See: https://docusaurus.io/docs/api/docusaurus-config + +import {themes as prismThemes} from 'prism-react-renderer'; +import remarkMath from 'remark-math'; +import rehypeKatex from 'rehype-katex'; + +/** @type {import('@docusaurus/types').Config} */ +const config = { + title: 'PAModelpy', + tagline: 'PAModelpy Documentation', + favicon: 'img/favicon.ico', + + // Set the production url of your site here + url: 'https://SamiralVdB.github.io', + // Set the // pathname under which your site is served + // For GitHub pages deployment, it is often '//' + baseUrl: '/', + + // GitHub pages deployment config. + // If you aren't using GitHub pages, you don't need these. + organizationName: 'Samira van den Bogaard', // Usually your GitHub org/user name. + projectName: 'PAModelpy', // Usually your repo name. + + onBrokenLinks: 'throw', + onBrokenMarkdownLinks: 'warn', + + // Even if you don't use internationalization, you can use this field to set + // useful metadata like html lang. For example, if your site is Chinese, you + // may want to replace "en" with "zh-Hans". + i18n: { + defaultLocale: 'en', + locales: ['en'], + }, + + markdown: { + mermaid: true, + }, + themes: ['@docusaurus/theme-mermaid'], + + presets: [ + [ + 'classic', + /** @type {import('@docusaurus/preset-classic').Options} */ + ({ + docs: { + sidebarPath: './sidebars.js', + // Please change this to your repo. + // Remove this to remove the "edit this page" links. + editUrl: + 'https://github.com/SamiralVdB/PAModelpy/tree/main/docs', + routeBasePath: '/', + remarkPlugins: [remarkMath], + rehypePlugins: [rehypeKatex], + }, + blog:false, + theme: { + customCss: './src/css/custom.css', + }, + }), + ], + ], + stylesheets: [ + { + href: 'https://cdn.jsdelivr.net/npm/katex@0.13.24/dist/katex.min.css', + type: 'text/css', + integrity: + 'sha384-odtC+0UGzzFL/6PNoE8rX/SPcQDXBJ+uRepguP4QkPCm2LBxH3FA3y+fKSiJ+AmM', + crossorigin: 'anonymous', + }, + ], + themeConfig: + /** @type {import('@docusaurus/preset-classic').ThemeConfig} */ + ({ + colorMode: { + defaultMode: 'dark', + }, + docs: { + sidebar: { + hideable: true, + }, + }, + navbar: { + title: 'PAModelpy', + logo: { + alt: 'PAModelpy', + src: 'img/iamb_logo.png', + }, + items: [ + { + type: 'docSidebar', + sidebarId: 'tutorialSidebar', + position: 'left', + label: 'Documentation', + }, + { + href: 'https://github.com/palaract/bachelorthesis', + label: 'GitHub', + position: 'right', + }, + ], + }, + footer: { + logo: { + alt: 'PAModelpy Project', + src: 'img/rwth_abbt_iamb_en_rgb.png', + }, + style: 'dark', + copyright: `Copyright © ${new Date().getFullYear()} Institute of Applied Microbiology, RWTH Aachen University, Aachen, Germany`, + }, + tableOfContents: { + minHeadingLevel: 2, + maxHeadingLevel: 4, + }, + prism: { + theme: prismThemes.github, + darkTheme: prismThemes.dracula, + additionalLanguages: ['json', 'docker'], + }, + }), +}; + +export default config; diff --git a/docs/package-lock.json 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"@docusaurus/core": "3.1.0", + "@docusaurus/preset-classic": "3.1.0", + "@docusaurus/theme-mermaid": "^3.1.0", + "@mdx-js/react": "^3.0.0", + "clsx": "^2.0.0", + "prism-react-renderer": "^2.3.0", + "react": "^18.0.0", + "react-dom": "^18.0.0", + "rehype-katex": "^7.0.0", + "remark-math": "^6.0.0" + }, + "devDependencies": { + "@docusaurus/module-type-aliases": "3.1.0", + "@docusaurus/types": "3.1.0" + }, + "browserslist": { + "production": [ + ">0.5%", + "not dead", + "not op_mini all" + ], + "development": [ + "last 3 chrome version", + "last 3 firefox version", + "last 5 safari version" + ] + }, + "engines": { + "node": ">=18.0" + } +} diff --git a/docs/sidebars.js b/docs/sidebars.js new file mode 100644 index 0000000..3327580 --- /dev/null +++ b/docs/sidebars.js @@ -0,0 +1,33 @@ +/** + * Creating a sidebar enables you to: + - create an ordered group of docs + - render a sidebar for each doc of that group + - provide next/previous navigation + + The sidebars can be generated from the filesystem, or explicitly defined here. + + Create as many sidebars as you want. + */ + +// @ts-check + +/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */ +const sidebars = { + // By default, Docusaurus generates a sidebar from the docs folder structure + tutorialSidebar: [{type: 'autogenerated', dirName: '.'}], + + // But you can create a sidebar manually + /* + tutorialSidebar: [ + 'intro', + 'hello', + { + type: 'category', + label: 'Tutorial', + items: ['tutorial-basics/create-a-document'], + }, + ], + */ +}; + +export default sidebars; diff --git a/docs/src/css/custom.css b/docs/src/css/custom.css new file mode 100644 index 0000000..f5fefb2 --- /dev/null +++ b/docs/src/css/custom.css @@ -0,0 +1,64 @@ +/** + * Any CSS included here will be global. The classic template + * bundles Infima by default. Infima is a CSS framework designed to + * work well for content-centric websites. + */ + + @import url('https://fonts.googleapis.com/css2?family=Open+Sans&display=swap'); + +/* You can override the default Infima variables here. */ +:root { + --ifm-color-primary: #3578e5; /*#25c2a0;*/ + --ifm-color-primary-dark: #1d68e1; /*rgb(33, 175, 144);*/ + --ifm-color-primary-darker: #1b62d4; /*rgb(31, 165, 136);*/ + --ifm-color-primary-darkest: #1751af; /*rgb(26, 136, 112);*/ + --ifm-color-primary-light: #4e89e8; /*rgb(70, 203, 174);*/ + --ifm-color-primary-lighter: #5a91ea; /*rgb(102, 212, 189);*/ + --ifm-color-primary-lightest: #80aaef; /*rgb(146, 224, 208);*/ + --ifm-code-background: #ededed; + --ifm-code-font-size: 90%; /*95%;*/ + --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.1); + --ifm-font-family-base: 'Arial'; + } + +/* For readability concerns, you should choose a lighter palette in dark mode. */ +[data-theme='dark'] { + --ifm-color-primary: #25c2a0; + --ifm-color-primary-dark: #21af90; + --ifm-color-primary-darker: #1fa588; + --ifm-color-primary-darkest: #1a8870; + --ifm-color-primary-light: #29d5b0; + --ifm-color-primary-lighter: #32d8b4; + --ifm-color-primary-lightest: #4fddbf; + --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.3); +} + +[data-theme='light'] pre code { + background-color: var(--ifm-code-background); +} + +html[data-theme='dark'] { + --ifm-background-color: #1e2125; + --ifm-background-surface-color: #121212; +} + +@media screen and (max-width: 996px) { + :root { + --ifm-font-size-base: 18px; + } + article header h1 { + font-size: 1.5rem !important; + } + .hero .hero__title { + font-size: 2.5rem; + } +} + +@media screen and (min-width: 997px) { + :root { + --ifm-font-size-base: 17px; + } + article header h1 { + font-size: 2rem !important; + } +} \ No newline at end of file diff --git a/docs/static/.nojekyll b/docs/static/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/docs/static/img/iamb_logo.png b/docs/static/img/iamb_logo.png new file mode 100644 index 0000000..005289c Binary files /dev/null and b/docs/static/img/iamb_logo.png differ diff --git a/docs/static/img/rwth_abbt_iamb_en_rgb.png b/docs/static/img/rwth_abbt_iamb_en_rgb.png new file mode 100644 index 0000000..f432bbf Binary files /dev/null and b/docs/static/img/rwth_abbt_iamb_en_rgb.png differ diff --git a/pydoc-markdown.yml b/pydoc-markdown.yml new file mode 100644 index 0000000..3682326 --- /dev/null +++ b/pydoc-markdown.yml @@ -0,0 +1,13 @@ +loaders: + - type: python + search_path: ['./src/PAModelpy'] +processors: + - type: filter + skip_empty_modules: true + - type: smart + - type: crossref +renderer: + type: docusaurus + docs_base_path: docs + relative_output_path: docs/api_reference + relative_sidebar_path: sidebar.json diff --git a/requirements.txt b/requirements.txt index 7733bbe..9025402 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,4 @@ PAModelpy pytest -plotly \ No newline at end of file +plotly +pydoc-markdown==4.8.2 \ No newline at end of file diff --git a/src/PAModelpy/CatalyticEvent.py b/src/PAModelpy/CatalyticEvent.py index 29bf765..4baedf7 100644 --- a/src/PAModelpy/CatalyticEvent.py +++ b/src/PAModelpy/CatalyticEvent.py @@ -12,109 +12,106 @@ from warnings import warn from copy import copy, deepcopy + class CatalyticEvent(Object): """ - CatalyticEvent is a class for holding information regarding the - catalysis of a Reaction in a cobra.Model object. It serves as an interface - between the metabolic reaction and the associated enzyme constraints and variables. + CatalyticEvent is a class for holding information regarding the catalysis of a Reaction in a cobra.Model object. + It serves as an interface between the metabolic reaction and the associated enzyme constraints and variables. + + Notes: There are three different scenarios: - Enzyme complex: multiple enzymes together are associated with an EnzymeComplex object - - isozymes: multiple enzymes independently associated with a single catalytic event + - Isozymes: multiple enzymes independently associated with a single catalytic event - Other: a single enzyme is associated with a single catalytic event - Parameters - ---------- - kcats2enzymes: Dict - A Dict with enzyme, kcat key, value pairs to connect the - enzyme with the associated reaction the kcat is another dict with 'f' and 'b' - for the forward and backward reactions respectively. - id : str, optional - The identifier to associate with this catalytic event (default None). - rxn_id: str, Optional - The reaction with which this catalytic event is associated - name : str, optional - A human-readable name for the reaction (default ""). - """ + Parameters: + kcats2enzymes (dict): A dictionary with enzyme, kcat key, value pairs to connect the enzyme with the associated reaction. + The kcat is another dictionary with 'f' and 'b' for the forward and backward reactions, respectively. + id (str, optional): The identifier to associate with this catalytic event (default None). + rxn_id (str, optional): The reaction with which this catalytic event is associated. + name (str, optional): A human-readable name for the reaction (default ""). + """ + def __init__( - self, - kcats2enzymes: Dict, - id: Optional[str] = None, # ID of enzymatic reaction - rxn_id: str = '', # ID of reaction - name: str = "" + self, + kcats2enzymes: Dict, + id: Optional[str] = None, # ID of enzymatic reaction + rxn_id: str = "", # ID of reaction + name: str = "", ): - #identification + # identification self.id = id self.name = name - #relation to reaction + # relation to reaction self.rxn_id = rxn_id self.rxn = None - #relation to enzymes + # relation to enzymes self.kcats = kcats2enzymes self.enzymes = DictList() for enzyme in kcats2enzymes.keys(): self.enzymes.append(enzyme) self.enzyme_variables = DictList() - #other attributes - self.constraints = {} # store IDs of constraint the catalytic event is associated with + # other attributes + self.constraints = ( + {} + ) # store IDs of constraint the catalytic event is associated with self.variables = dict() self._model = None - self.annotation = {'type':'Constraint'} - + self.annotation = {"type": "Constraint"} @property def kcat_values(self): - """returns a dictionary with kcat values and enzymes - """ + """returns a dictionary with kcat values and enzymes""" return self.kcats @property def flux(self) -> float: """ Get the flux value in the most recent solution. + Flux is the primal value of the corresponding variable in the model. - Returns - ------- - flux: float - Flux is the primal value of the corresponding variable in the model. - Warnings - -------- - * Accessing reaction fluxes through a `Solution` object is the safer, - preferred, and only guaranteed to be correct way. You can see how to - do so easily in the examples. - * Reaction flux is retrieved from the currently defined - `self._model.solver`. The solver status is checked but there are no - guarantees that the current solver state is the one you are looking - for. - * If you modify the underlying model after an optimization, you will - retrieve the old optimization values. - Raises - ------ - RuntimeError - If the underlying model was never optimized beforehand or the - reaction is not part of a model. - OptimizationError - If the solver status is anything other than 'optimal'. - AssertionError - If the flux value is not within the bounds. - Examples - -------- - >>> from cobra.io import load_model - >>> model = load_model("textbook") - >>> solution = model.optimize() - >>> model.variables.PFK.flux - 7.477381962160283 - >>> solution.fluxes.PFK - 7.4773819621602833 + + Returns: + flux (float): Flux is the primal value of the corresponding variable in the model. + + Warnings: + * Accessing reaction fluxes through a `Solution` object is the safer, + preferred, and only guaranteed to be correct way. You can see how to + do so easily in the examples. + * Reaction flux is retrieved from the currently defined + `self._model.solver`. The solver status is checked but there are no + guarantees that the current solver state is the one you are looking + for. + * If you modify the underlying model after an optimization, you will + retrieve the old optimization values. + + Raises: + RuntimeError: If the underlying model was never optimized beforehand or the + reaction is not part of a model. + OptimizationError: If the solver status is anything other than `optimal`. + AssertionError: If the flux value is not within the bounds. + + Examples: + ``` + >>> from cobra.io import load_model + >>> model = load_model("textbook") + >>> solution = model.optimize() + >>> model.variables.PFK.flux + 7.477381962160283 + >>> solution.fluxes.PFK + 7.4773819621602833 + ``` """ + try: check_solver_status(self._model.solver.status) total_flux = 0 for enzyme_variable in self.enzyme_variables: - fwd_flux = enzyme_variable.variables['forward'].primal - rev_flux = enzyme_variable.variables['reverse'].primal + fwd_flux = enzyme_variable.variables["forward"].primal + rev_flux = enzyme_variable.variables["reverse"].primal total_flux += fwd_flux + rev_flux return total_flux except AttributeError: @@ -132,61 +129,66 @@ def flux(self) -> float: def concentration(self) -> float: """ Get the enzyme concentration value of the most recent solution. - The enzyme concentration equals the flux value + The enzyme concentration equals the flux value. - Returns - ------- - float - enzyme concentration [mmol/gDW] + Returns: + float: Enzyme concentration in [mmol/gDW]. """ + return self.flux @property def model(self): - return self._model + return self._model @model.setter def model(self, model): self._model = model - #add reaction instance + # add reaction instance if self.rxn_id in self._model.reactions: self.rxn = self._model.reactions.get_by_id(self.rxn_id) - else: #create new reaction and add to model - rxn = cobra.Reaction(id = self.rxn_id) + else: # create new reaction and add to model + rxn = cobra.Reaction(id=self.rxn_id) self._model.add_reactions([rxn]) self.rxn = rxn - #add enzymes to the model if they are not there already + # add enzymes to the model if they are not there already for enzyme in self.enzymes: if enzyme in self._model.enzymes: - self.constraints ={**self.constraints, **enzyme._constraints} + self.constraints = {**self.constraints, **enzyme._constraints} enzyme_model = self._model.enzymes.get_by_id(enzyme.id) if self.rxn_id in enzyme_model.rxn2kcat.keys(): if self not in enzyme_model.catalytic_events: - enzyme_model.add_catalytic_event(self, kcats= {enzyme: enzyme.rxn2kcat[self.rxn_id]}) + enzyme_model.add_catalytic_event( + self, kcats={enzyme: enzyme.rxn2kcat[self.rxn_id]} + ) else: - print(f'Reaction {self.rxn_id} is not related to enzyme {enzyme.id}') + print( + f"Reaction {self.rxn_id} is not related to enzyme {enzyme.id}" + ) else: self._model.add_enzymes([enzyme]) - def add_enzymes(self,enzyme_kcat_dict: dict): + def add_enzymes(self, enzyme_kcat_dict: dict): """ Add enzymes to the catalytic event and create bindings to the related model. The enzymes in the enzyme_kcat_dict are individual isozymes. Enzyme complexes should be added as an EnzymeComplex object with a single kcat value. - Parameters - ---------- - enzyme_kcat_dict: nested dict - A Dict with enzyme, kcat key, value pairs to connect the - enzyme with the associated reaction the kcat is another dict with 'f' and 'b' - for the forward and backward reactions respectively. + Parameters: + enzyme_kcat_dict: Dict + A nested dictionary with enzyme, kcat key, value pairs to connect the + enzyme with the associated reaction. The kcat is another dictionary with `f` and `b` + for the forward and backward reactions respectively. """ + for enzyme, kcat in enzyme_kcat_dict.items(): # check if the enzyme is already associated to the catalytic event if enzyme in self.enzymes: # print(enzyme) # warn(f'Enzyme {enzyme.id} is already associated with catalytic event {self.id}. This enzyme will be updated') - self.change_kcat_values({enzyme.id: enzyme.get_kcat_values(rxn_ids = [self.rxn_id])}) + self.change_kcat_values( + {enzyme.id: enzyme.get_kcat_values(rxn_ids=[self.rxn_id])} + ) # print(self.enzymes) continue @@ -195,54 +197,65 @@ def add_enzymes(self,enzyme_kcat_dict: dict): if self._model is None: continue - #check if enzyme is in the model - try: self._model.enzymes.get_by_id(enzyme.id) - #if not: add the enzyme to the model - except: self._model.add_enzymes([enzyme]) + # check if enzyme is in the model + try: + self._model.enzymes.get_by_id(enzyme.id) + # if not: add the enzyme to the model + except: + self._model.add_enzymes([enzyme]) for direction in kcat.keys(): - if direction != 'f' and direction != 'b': - warn(f'Invalid direction {direction} for kcat value for enzyme variable {self.id}! Skip!') + if direction != "f" and direction != "b": + warn( + f"Invalid direction {direction} for kcat value for enzyme variable {self.id}! Skip!" + ) continue - #add enzyme to catalytic event and the related variable - #get enzyme variable + # add enzyme to catalytic event and the related variable + # get enzyme variable enzyme_var = self._model.enzyme_variables.get_by_id(enzyme.id) self.enzyme_variables.append(enzyme_var) - #add constraints to the catalytic event + # add constraints to the catalytic event self.constraints = {**self.constraints, **enzyme._constraints} - #connect the enzyme variable to the enzyme in the model and the reaction + # connect the enzyme variable to the enzyme in the model and the reaction for direction, kcatvalue in kcat.items(): coeff = kcatvalue * 3600 * 1e-6 - #add enzyme to the associated reaction with kinetic constants - #and relate enzyme to the catalytic event - if direction == 'f': - self.constraints[f'EC_{enzyme.id}_{direction}'].set_linear_coefficients({ - self.rxn.forward_variable: 1/coeff, - enzyme_var.forward_variable: -1 - }) - - elif direction == 'b': - self.constraints[f'EC_{enzyme.id}_{direction}'].set_linear_coefficients({ - self.rxn.reverse_variable: 1/coeff, - enzyme_var.reverse_variable: -1 - }) - + # add enzyme to the associated reaction with kinetic constants + # and relate enzyme to the catalytic event + if direction == "f": + self.constraints[ + f"EC_{enzyme.id}_{direction}" + ].set_linear_coefficients( + { + self.rxn.forward_variable: 1 / coeff, + enzyme_var.forward_variable: -1, + } + ) + + elif direction == "b": + self.constraints[ + f"EC_{enzyme.id}_{direction}" + ].set_linear_coefficients( + { + self.rxn.reverse_variable: 1 / coeff, + enzyme_var.reverse_variable: -1, + } + ) def remove_enzymes(self, enzyme_list: list): """ - Remove enzymes from the catalytic event and remove catalytic event from the - constraint expressions related to the enzyme - - Parameters - ---------- - enzyme_list: list - A list with PAModelpy.Package.Enzyme objects which should be removed. If a list of identifiers (str) - is provided, the corresponding enzyme will be obtained from the CatalyticEvent.enzymes attribute + Remove enzymes from the catalytic event and remove the catalytic event from the + constraint expressions related to the enzyme. + + Parameters: + enzyme_list: List[Union[str, PAModelpy.Package.Enzyme]] + A list with PAModelpy.Package.Enzyme objects to be removed. If a list of identifiers (str) + is provided, the corresponding enzyme will be obtained from the CatalyticEvent.enzymes attribute. """ - #check the input + + # check the input if not hasattr(enzyme_list, "__iter__"): enzyme_list = [enzyme_list] if len(enzyme_list) == 0: @@ -254,20 +267,24 @@ def remove_enzymes(self, enzyme_list: list): try: enzyme_list[i] = self.enzymes.get_by_id(enz) except: - print(f'Enzyme {enz} is not associated with the catalytic event {self.id}. This enzyme will not be removed. \n') + print( + f"Enzyme {enz} is not associated with the catalytic event {self.id}. This enzyme will not be removed. \n" + ) pass for enz in enzyme_list: - #remove from kcat dict + # remove from kcat dict del self.kcats[enz] - #remove from enzymes dictlist + # remove from enzymes dictlist self.enzymes.remove(enz) - #remove enzyme variable from dictlist if it still exists + # remove enzyme variable from dictlist if it still exists if self.enzyme_variables.has_id(enz.id): enzyme_var = self.enzyme_variables.get_by_id(enz.id) self.enzyme_variables.remove(enzyme_var) - #set coefficient in constraint to 0 - for constraint in set([cons for name, cons in self.constraints.items() if enz.id in name]): + # set coefficient in constraint to 0 + for constraint in set( + [cons for name, cons in self.constraints.items() if enz.id in name] + ): # self.constraints[constraint.name] = constraint # coeff = 0 # #set coefficients to 0 @@ -282,92 +299,104 @@ def remove_enzymes(self, enzyme_list: list): # }) if constraint in self._model.constraints.values(): self._model.remove_cons_vars([constraint]) - #remove constraint from list of r=constraints + # remove constraint from list of r=constraints del self.constraints[constraint.name] - #if there are no enzymes associated to the reaction anymore, the reaction flux will be 0 - if len(self.enzymes)==0: - no_enz_constraint_f = self._model.problem.Constraint(Zero, name = f'{self.rxn_id}_no_enzyme_f', lb=0, ub=0) - no_enz_constraint_b = self._model.problem.Constraint(Zero, name=f'{self.rxn_id}_no_enzyme_b', lb=0, ub=0) - self._model.add_cons_vars([no_enz_constraint_f,no_enz_constraint_b]) - - self._model.constraints[no_enz_constraint_f.name].set_linear_coefficients({ - self.rxn.forward_variable:1 - }) + # if there are no enzymes associated to the reaction anymore, the reaction flux will be 0 + if len(self.enzymes) == 0: + no_enz_constraint_f = self._model.problem.Constraint( + Zero, name=f"{self.rxn_id}_no_enzyme_f", lb=0, ub=0 + ) + no_enz_constraint_b = self._model.problem.Constraint( + Zero, name=f"{self.rxn_id}_no_enzyme_b", lb=0, ub=0 + ) + self._model.add_cons_vars([no_enz_constraint_f, no_enz_constraint_b]) + + self._model.constraints[ + no_enz_constraint_f.name + ].set_linear_coefficients({self.rxn.forward_variable: 1}) self.constraints[no_enz_constraint_f.name] = no_enz_constraint_f - self._model.constraints[no_enz_constraint_b.name].set_linear_coefficients({ - self.rxn.forward_variable: 1 - }) + self._model.constraints[ + no_enz_constraint_b.name + ].set_linear_coefficients({self.rxn.forward_variable: 1}) self.constraints[no_enz_constraint_b.name] = no_enz_constraint_b - def change_kcat_values(self, enzyme_kcat_dict : dict): - """changes kcat values for the enzyme variable - Parameters - ---------- - enzyme_kcat_dict: nested Dict - A Dict with enzyme, kcat key, value pairs to connect the - enzyme with the associated reaction the kcat is another dict with 'f' and 'b' - for the forward and backward reactions respectively. - + def change_kcat_values(self, enzyme_kcat_dict: dict): """ + Change kcat values for the enzyme variable. + + Parameters: + enzyme_kcat_dict: Dict[str, Dict[str, float]] + A nested dictionary with enzyme identifiers as keys and kcat dictionaries as values. + The kcat dictionary should have `f` and `b` keys for the forward and backward reactions, respectively. + """ + # apply changes to internal dicts (one by one to avoid deleting kcat values) kcats_change = {} for enzyme, kcat_dict in enzyme_kcat_dict.items(): # save change in dict self.kcats[enzyme] = kcat_dict for direction, kcat in kcat_dict.items(): - if direction != 'f' and direction != 'b': - warn(f'Invalid direction {direction} for kcat value for enzyme variable {self.id}! Skip!') + if direction != "f" and direction != "b": + warn( + f"Invalid direction {direction} for kcat value for enzyme variable {self.id}! Skip!" + ) continue - + kcats_change[direction] = kcat - + # is enzyme variable already integrated into a model if self._model is None: # warn(f'Catalytic event {self.id} is not integrated into a model!') return enzyme_var = self._model.enzyme_variables.get_by_id(enzyme) - if enzyme_var not in self.enzyme_variables: self.enzyme_variables.add(enzyme_var) + if enzyme_var not in self.enzyme_variables: + self.enzyme_variables.add(enzyme_var) enzyme_obj = self._model.enzymes.get_by_id(enzyme) enzyme_var.change_kcat_values({self.rxn: kcat_dict}) for direction, kcat in kcats_change.items(): - #change enzyme variable + # change enzyme variable enzyme_var.kcats[self.rxn_id][direction] = kcat # get constraint - constraint_id = f'EC_{enzyme}_{direction}' + constraint_id = f"EC_{enzyme}_{direction}" constraint = enzyme_obj._constraints[constraint_id] # change kcat value in the constraint - coeff = kcat * 3600 * 1e-6 - if direction == 'f': - self._model.constraints[constraint_id].set_linear_coefficients({ - self.rxn.forward_variable: 1/coeff - }) - elif direction == 'b': - self._model.constraints[constraint_id].set_linear_coefficients({ - self.rxn.reverse_variable: 1/coeff - }) + coeff = kcat * 3600 * 1e-6 + if direction == "f": + self._model.constraints[constraint_id].set_linear_coefficients( + {self.rxn.forward_variable: 1 / coeff} + ) + elif direction == "b": + self._model.constraints[constraint_id].set_linear_coefficients( + {self.rxn.reverse_variable: 1 / coeff} + ) self._model.solver.update() - def __copy__(self) -> 'CatalyticEvent': - """ Copy the CatalyticEvent - :return: CatalyticEvent: - A new CatalyticEvent that is a copy of the original CatalyticEvent + def __copy__(self) -> "CatalyticEvent": + """ + Copy the CatalyticEvent. + + Returns: + CatalyticEvent: + A new CatalyticEvent that is a copy of the original CatalyticEvent. """ cop = copy(super(CatalyticEvent, self)) return cop - def __deepcopy__(self, memo: dict) -> 'CatalyticEvent': - """ Copy the CatalyticEvent with memo + def __deepcopy__(self, memo: dict) -> "CatalyticEvent": + """ + Copy the CatalyticEvent with memo. - :param: memo:dict: - Automatically passed parameter + Parameters: + memo (dict): Automatically passed parameter. - :return: CatalyticEvent: - A new CatalyticEvent that is a copy of the original CatalyticEvent with memo + Returns: + CatalyticEvent: + A new CatalyticEvent that is a copy of the original CatalyticEvent with memo. """ cop = deepcopy(super(CatalyticEvent, self), memo) - return cop \ No newline at end of file + return cop diff --git a/src/PAModelpy/Constraints.py b/src/PAModelpy/Constraints.py index cdf75c5..f566dbe 100644 --- a/src/PAModelpy/Constraints.py +++ b/src/PAModelpy/Constraints.py @@ -1,33 +1,33 @@ from cobra import Reaction, Metabolite from typing import Optional + class Constraint(Metabolite): - """Class for information about a Constraint in a protein Sector. - Constraint is a class for holding information similar to - a metabolite in a cobra.Reaction object. - Parameters which are important in this class: - ---------- - id : str - the identifier to associate with the constraint - name : str - A human readable name. - """ + """ + Class for information about a Constraint in a protein Sector. + + Constraint is a class for holding information similar to + a metabolite in a cobra.Reaction object. + + Parameters: + id (str): The identifier to associate with the constraint. + name (str): A human-readable name. + """ # noinspection PyShadowingBuiltins def __init__( - self, - id: Optional[str] = None, - formula: Optional[str] = None, - name: Optional[str] = "", - charge: Optional[float] = None, - compartment: Optional[str] = 'cytosol', + self, + id: Optional[str] = None, + formula: Optional[str] = None, + name: Optional[str] = "", + charge: Optional[float] = None, + compartment: Optional[str] = "cytosol", ) -> None: super().__init__( id=id, - formula= formula, + formula=formula, name=name, charge=charge, compartment=compartment, - ) - self.annotation = {'type':'Constraint'} - + ) + self.annotation = {"type": "Constraint"} diff --git a/src/PAModelpy/Enzyme.py b/src/PAModelpy/Enzyme.py index 9b604e8..c1b5356 100644 --- a/src/PAModelpy/Enzyme.py +++ b/src/PAModelpy/Enzyme.py @@ -19,42 +19,29 @@ from warnings import warn - class Enzyme(Object): - """Upper level Enzyme object containing information about the enzyme - and link to the EnzymeVariables for each reaction the - enzyme catalyzes. - This class is used to generate enzyme instances from kcat values and contains - information about the forward as well as the backward catalysis. - - The enzyme is linked to individual cobra.Reaction variables with CatalyticEvent objects. - - There are two scenarios: - - Promiscuous enzymes: a single enzyme can catalyze multiple reactions - - Other: a single enzyme catalyzes a single reaction - - Parameters - ------- - id : str - Identifier for the enzyme (e.g. Uniprot ID) - rxn2kcat: Dict - Dictionary with reaction ID, kcat value pairs for the forward (f) - and backward (b) reaction - (Example: {'PGI': {'f': 30, 'b': 0.1}}) - upper_bound: float - Upper bound for the enzyme variable (default 1000.0) - lower_bound: float - Lower bound for the enzyme variable (default 0) - name: str - Name of the enzyme (default None) - molmass: float - Molar mass of the enzyme (default 3.947778784340140e04) + """Upper level Enzyme object containing information about the enzyme and links to the EnzymeVariables for each reaction the enzyme catalyzes. + + Args: + - id (str): Identifier for the enzyme (e.g., Uniprot ID). + - rxn2kcat (Dict): Dictionary with reaction ID, kcat value pairs for the forward (f) and backward (b) reaction, + e.g. `{'PGI': {'f': 30, 'b': 0.1}}` + - upper_bound (float): Upper bound for the enzyme variable (default 1000.0). + - lower_bound (float): Lower bound for the enzyme variable (default 0). + - name (str): Name of the enzyme (default None). + - molmass (float): Molar mass of the enzyme (default 3.947778784340140e04). + + Notes: + - This class is used to generate enzyme instances from kcat values and contains information about the forward as well as the backward catalysis. + - The enzyme is linked to individual cobra.Reaction variables with CatalyticEvent objects. + - There are two scenarios: + - Promiscuous enzymes: a single enzyme can catalyze multiple reactions. + - Other: a single enzyme catalyzes a single reaction. """ - # constant parameters DEFAULT_ENZYME_MOL_MASS = 3.947778784340140e04 # mean enzymes mass E.coli [g/mol] - + def __init__( self, id: str, @@ -64,59 +51,63 @@ def __init__( name: Optional[str] = None, molmass: Union[int, float] = DEFAULT_ENZYME_MOL_MASS, ): - self.rxn2kcat = rxn2kcat self.molmass = molmass - self.id = id # use e.g. Uniprot ID + self.id = id # use e.g. Uniprot ID self.name = name self.upper_bound = upper_bound self.lower_bound = lower_bound - #create enzyme variable associated with theis enzyme + # create enzyme variable associated with theis enzyme self.enzyme_variable = None self.create_enzyme_variable() - self.catalytic_event_id = 'CE_' + '{0}' # generic template for the catalytic events IDs + self.catalytic_event_id = ( + "CE_" + "{0}" + ) # generic template for the catalytic events IDs # initialize CatalyticEvent interfaces for each associated reaction (promiscuity) self.catalytic_events = DictList() for rxn_id, kcats in rxn2kcat.items(): self.create_catalytic_event(rxn_id, kcats) - self._constraints = {} # dict with constraint_id:optlang.Constraint, key:value pairs. + self._constraints = ( + {} + ) # dict with constraint_id:optlang.Constraint, key:value pairs. self._model = None - self.enzyme_complex = [] #is the enzyme in a complex? - self.annotation = {'type':'Constraint'}#you can add an annotation for an enzyme - + self.enzyme_complex = [] # is the enzyme in a complex? + self.annotation = { + "type": "Constraint" + } # you can add an annotation for an enzyme + @property def kcat_values(self): - """returns a dictionary with kcat values for each associated reaction + """Returns a dictionary with kcat values for each associated reaction. + + Returns: + dict: A dictionary containing kcat values for associated reactions. """ return self.get_kcat_values() @property - def concentration(self, units:str = 'mmol/gDW', return_units:bool = False) -> float: - """returns the enzyme's total concentration - Any associated reaction is considered - - Parameters - ------- - units: optional, string - units in which the concentration is calculated (default is mmol/gDW), other option is 'g/gDW' - return_units: optional, bool - determines wheter the units should be returned as well - - Returns - ------- - float - Enzyme concentration + def concentration( + self, units: str = "mmol/gDW", return_units: bool = False + ) -> float: + """Returns the enzyme's total concentration considering any associated reactions. + + Args: + units (str, optional): Units in which the concentration is calculated (default is 'mmol/gDW'), other option is 'g/gDW'. + return_units (bool, optional): Determines whether the units should be returned as well. + + Returns: + float: Enzyme concentration as a float. """ # sum up concentrations (aka fluxes) of all enzyme objects concentration = 0.0 for catalytic_event in self.catalytic_events: concentration += catalytic_event.flux() - if units == 'g/gDW': - #converting mmol to grams of protein: + if units == "g/gDW": + # converting mmol to grams of protein: # [g] = [mmol]* 1e-3 [mol/mmol] * MW[g/mol] concentration = concentration * 1e-3 * self.molmass if return_units: @@ -124,62 +115,56 @@ def concentration(self, units:str = 'mmol/gDW', return_units:bool = False) -> fl return concentration def create_constraint(self, extension: str = None): - if extension is None: extension = '' - else: extension = '_' + extension + if extension is None: + extension = "" + else: + extension = "_" + extension # return cobra.core.Metabolite(id = f'EC_{self.id}{extension}', compartment='Enzymes') - return optlang.Constraint(Zero, name= f'EC_{self.id}{extension}', lb=0, ub=0) + return optlang.Constraint(Zero, name=f"EC_{self.id}{extension}", lb=0, ub=0) def add_catalytic_event(self, ce: CatalyticEvent, kcats: Dict): - """ - Adding catalytic event associated to a reaction to an enzyme - Parameters - ---------- - ce: PAModelpy.Variables.CatalyticEvent - The catalytic event object to which the enzyme should be added - kcats: dict - A list with dicts containing direction, kcat key value pairs - Returns - ------- + """Adds a catalytic event associated with a reaction to an enzyme. + + Args: + ce (PAModelpy.Variables.CatalyticEvent): A CatalyticEvent object to which the enzyme should be added. + kcats (dict): A dictionary containing direction and kcat key-value pairs. + Returns: + NoneType: None """ self.catalytic_events += [ce] - self.enzyme_variable.add_catalytic_events([ce],[kcats]) + self.enzyme_variable.add_catalytic_events([ce], [kcats]) def create_catalytic_event(self, rxn_id: str, kcats: Dict): - """creates enzyme variables that link to reactions - - Parameters - ---------- - rxn_id : str - ID of the associated reaction in the model - kcats : Dict - kcat values for the forward and backward reaction - Returns - ------- - Variables.CatalyticEvent - Enzyme variable object + """Creates enzyme variables that link to reactions. + + Args: + rxn_id (str): ID of the associated reaction in the model. + kcats (Dict): A dictionary containing kcat values for the forward and backward reaction. + + Returns: + Variables.CatalyticEvent: Enzyme variable object of type Variables.CatalyticEvent. """ - + # create unique enzyme object name and id catalytic_event_id = self.catalytic_event_id.format(rxn_id) if self.name is not None: - enzyme_object_name = rxn_id + '_' + self.name + enzyme_object_name = rxn_id + "_" + self.name else: - enzyme_object_name = rxn_id + '_' + self.id - + enzyme_object_name = rxn_id + "_" + self.id + # create enzymatic reaction object inherited from cobra.Reaction - catalytic_event= CatalyticEvent( + catalytic_event = CatalyticEvent( id=catalytic_event_id, rxn_id=rxn_id, kcats2enzymes={self: kcats}, - name=enzyme_object_name + name=enzyme_object_name, ) self.add_catalytic_event(catalytic_event, kcats) def create_enzyme_variable(self): - """creates enzyme variables that link enzyme to reactions - """ + """Creates enzyme variables that link enzyme to reactions.""" # create enzymatic reaction object inherited from cobra.Reaction enzyme_variable = EnzymeVariable( id=self.id, @@ -190,49 +175,41 @@ def create_enzyme_variable(self): self.enzyme_variable = enzyme_variable def change_kcat_values(self, rxn2kcat: Dict): - """changes the kcat values for the enzyme - and updates the enzyme variable (enzymatic reaction) accordingly - - Parameters - ---------- - rxn2kcat : Dict - Dictionary with reaction ID, kcat value pairs for the forward (f) - and backward (b) reaction - (Example: {'PGI': {'f': 30, 'b': 0.1}}) + """Changes the kcat values for the enzyme and updates the enzyme variable (enzymatic reaction) accordingly. + + Args: + rxn2kcat (Dict): A dictionary with reaction ID, kcat value pairs for the forward (f) and backward (b) reaction, + e.g. `{'PGI': {'f': 30, 'b': 0.1}}` """ # update the enzyme variables for rxn_id, kcats in rxn2kcat.items(): catalytic_event_id = self.catalytic_event_id.format(rxn_id) - #change rxn2kcat dictionary + # change rxn2kcat dictionary self.rxn2kcat[rxn_id] = kcats # is there already a link between enzyme and reaction? if catalytic_event_id not in self.catalytic_events: - warn(f'Reaction {rxn_id} is not associated with enzyme {self.id}. Skip') + warn(f"Reaction {rxn_id} is not associated with enzyme {self.id}. Skip") else: # change kcat values of existing enzyme variable - self.catalytic_events.get_by_id(catalytic_event_id).change_kcat_values({self.id: kcats}) - + self.catalytic_events.get_by_id(catalytic_event_id).change_kcat_values( + {self.id: kcats} + ) def get_kcat_values(self, rxn_ids: Union[str, list] = None) -> Dict: - """returns the kcat values for a specific enzyme and all - enzyme-associated reactions - - Parameters - ---------- - rxn_ids : str or list - ID of the reactions for which the kcat values should be returned - - Returns - ------- - Dict - kcat values for the forward and backward reaction + """Returns the kcat values for a specific enzyme and all enzyme-associated reactions. + + Args: + rxn_ids (Union[str, list], optional): ID of the reactions for which the kcat values should be returned. It can be a single reaction ID (str) or a list of reaction IDs. + + Returns: + Dict: A dictionary containing kcat values for the forward (f) and backward (b) reactions. """ - + if isinstance(rxn_ids, str): rxn_ids = [rxn_ids] - - rxn2kcat = {} + + rxn2kcat = {} if rxn_ids is None: # return all kcat values rxn2kcat = self.rxn2kcat @@ -241,50 +218,51 @@ def get_kcat_values(self, rxn_ids: Union[str, list] = None) -> Dict: for rxn_id in rxn_ids: catalytic_event_id = self.catalytic_event_id.format(rxn_id) if catalytic_event_id not in self.catalytic_events: - warn('No reaction {0} found'.format(rxn_id)) + warn("No reaction {0} found".format(rxn_id)) else: # get kcat values rxn2kcat[rxn_id] = self.rxn2kcat[rxn_id] - if len(rxn_ids)==1: + if len(rxn_ids) == 1: return rxn2kcat[rxn_id] - + return rxn2kcat def remove_catalytic_event(self, catalytic_event: Union[CatalyticEvent, str]): - """ - Function to remove a catalytic event from an enzyme - Parameters - ---------- - catalytic_event: CatalyticEvent or str - catalytic event or identifier to remove + """Function to remove a catalytic event from an enzyme. + + Args: + catalytic_event (Union[CatalyticEvent, str]): CatalyticEvent or str, catalytic event or identifier to remove. """ if isinstance(catalytic_event, str): try: catalytic_event = self.catalytic_events.get_by_id(catalytic_event) except: - print(f'Catalytic event {catalytic_event} is not related to this enzyme and can thus not be removed!') + print( + f"Catalytic event {catalytic_event} is not related to this enzyme and can thus not be removed!" + ) - #remove the event from the DictList + # remove the event from the DictList self.catalytic_events.remove(catalytic_event) - def __copy__(self) -> 'Enzyme': - """ Copy the enzyme variable - :return: PAModelpy.Enzyme.Enzyme: - A new enzyme that is a copy of the original enzyme + def __copy__(self) -> "Enzyme": + """Copy the enzyme variable. + + Returns: + PAModelpy.Enzyme.Enzyme: A new enzyme that is a copy of the original enzyme. """ cop = copy(super(Enzyme, self)) return cop - def __deepcopy__(self, memo: dict) -> 'Enzyme': - """ Copy the enzyme variable with memo + def __deepcopy__(self, memo: dict) -> "Enzyme": + """Copy the enzyme variable with memo. - :param: memo:dict: - Automatically passed parameter + Args: + memo (dict): Automatically passed parameter. - :return: PAModelpy.Enzyme.Enzyme: - A new enzyme that is a copy of the original enzyme with memo + Returns: + PAModelpy.Enzyme.Enzyme: A new enzyme that is a copy of the original enzyme with memo. """ cop = deepcopy(super(Enzyme, self), memo) @@ -292,53 +270,40 @@ def __deepcopy__(self, memo: dict) -> 'Enzyme': class EnzymeComplex(Enzyme): - """Upper level EnzymeComplex object containing information about the enzymes in a complex - and link to the enzyme variables (CatalyticEvents) for each reaction the - enzyme complex catalyzes. - This class is used to generate enzyme instances from kcat values and contains - information about the forward as well as the backward catalysis. - - There are two scenarios: - - Promiscuous enzymes: a single enzyme complex can catalyze multiple reactions - - Other: a single enzyme complex catalyzes a single reaction - - Parameters - ------- - id : str - Identifier for the enzyme complex (e.g. Uniprot ID) - enzymes: DictList of cobra.core.Enzyme - Enzyme objects associated with the enzyme complex - rxn2kcat: Dict - Dictionary with reaction ID, kcat value pairs for the forward (f) - and backward (b) reaction - (Example: {'PGI': {'f': 30, 'b': 0.1}}) - upper_bound: float - Upper bound for the enzyme variable (default 1000.0) - name: str - Name of the enzyme (default None) - molmass: float - Molar mass of the enzyme (default 3.947778784340140e04) - - """ + """Upper-level EnzymeComplex object containing information about the enzymes in a complex + and a link to the enzyme variables (CatalyticEvents) for each reaction the enzyme complex catalyzes. + + This class is used to generate enzyme instances from kcat values and contains + information about the forward as well as the backward catalysis. + + Args: + id (str): Identifier for the enzyme complex (e.g., Uniprot ID). + enzymes (DictList of cobra.core.Enzyme): Enzyme objects associated with the enzyme complex. + rxn2kcat (Dict): Dictionary with reaction ID, kcat value pairs for the forward (f) and backward (b) reaction, + e.g. `{'PGI': {'f': 30, 'b': 0.1}}` + upper_bound (float, optional): Upper bound for the enzyme variable (default 1000.0). + name (str, optional): Name of the enzyme (default None). + molmass (float, optional): Molar mass of the enzyme (default 3.947778784340140e04). + """ # constant parameters DEFAULT_ENZYME_MOL_MASS = 3.947778784340140e04 # mean enzymes mass E.coli [g/mol] def __init__( - self, - id: str, - enzymes: DictList, - rxn2kcat: Dict, - upper_bound: Union[int, float] = 1000.0, - name: Optional[str] = None, - molmass: Union[int, float] = DEFAULT_ENZYME_MOL_MASS, + self, + id: str, + enzymes: DictList, + rxn2kcat: Dict, + upper_bound: Union[int, float] = 1000.0, + name: Optional[str] = None, + molmass: Union[int, float] = DEFAULT_ENZYME_MOL_MASS, ): super().__init__( - id = id, + id=id, rxn2kcat=rxn2kcat, - upper_bound = upper_bound, + upper_bound=upper_bound, name=name, - molmass=molmass + molmass=molmass, ) self.enzymes = None @@ -352,109 +317,97 @@ def add_enzymes(self, enzymes: DictList): class EnzymeVariable(Reaction): + """EnzymeVariable is a class for holding information regarding the variable representing an enzyme in the model. + For each reaction, the enzyme variables are summarized in a CatalyticEvent. + + There are three different scenarios: + - Enzyme complex: multiple enzymes together are associated with an EnzymeComplex object. + - Isozymes: multiple enzymes independently associated with a single catalytic event. + - Other: a single enzyme is associated with a single catalytic event. + + Args: + kcats2rxns (Dict): A dictionary with reaction_id, kcat key, value pairs to connect the enzyme with the associated reaction. + The kcat is another dictionary with `f` and `b` for the forward and backward reactions, respectively. + id (str, optional): The identifier to associate with this enzyme (default None). + name (str, optional): A human-readable name for the enzyme (default ""). + subsystem (str, optional): Subsystem where the enzyme is meant to function (default ""). + lower_bound (float): The lower flux bound (default 0.0). + upper_bound (float, optional): The upper flux bound (default None). + **kwargs: Additional keyword arguments are passed on to the parent class. """ - EnzymeVariable is a class for holding information regarding the - variable representing an enzyme in the model. For each reaction, the enzyme variables are - summarized in a CatalyticEvent - There are three different scenarios: - - Enzyme complex: multiple enzymes together are associated with an EnzymeComplex object - - isozymes: multiple enzymes independently associated with a single catalytic event - - Other: a single enzyme is associated with a single catalytic event - - Parameters - ---------- - kcats2rxns: Dict - A Dict with reaction_id, kcat key, value pairs to connect the - enzyme with the associated reaction the kcat is another dict with 'f' and 'b' - for the forward and backward reactions respectively. - id : str, optional - The identifier to associate with this enzyme (default None) - name : str, optional - A human-readable name for the reaction (default ""). - subsystem : str, optional - Subsystem where the reaction is meant to occur (default ""). - lower_bound : float - The lower flux bound (default 0.0). - upper_bound : float, optional - The upper flux bound (default None). - **kwargs: - Further keyword arguments are passed on to the parent class. - """ + DEFAULT_ENZYME_MOL_MASS = 3.947778784340140e04 # mean enzymes mass E.coli [g/mol] def __init__( - self, - kcats2rxns: Dict, - molmass: Union[int, float] = DEFAULT_ENZYME_MOL_MASS, - id: Optional[str] = None, # ID of enzymatic reaction, - name: str = "", - upper_bound: Optional[float] = None, - **kwargs + self, + kcats2rxns: Dict, + molmass: Union[int, float] = DEFAULT_ENZYME_MOL_MASS, + id: Optional[str] = None, # ID of enzymatic reaction, + name: str = "", + upper_bound: Optional[float] = None, + **kwargs, ): super().__init__( id=id, name=name, - subsystem='Enzymes', + subsystem="Enzymes", lower_bound=-upper_bound, upper_bound=upper_bound, - **kwargs + **kwargs, ) self.kcats = kcats2rxns self.molmass = molmass self.rxn_ids = [rxn for rxn in kcats2rxns.keys()] - self.enzyme = None #store the enzyme associated to the enzyme variable - self.catalytic_events = DictList() #store the interfaces to reactions related to this enzyme + self.enzyme = None # store the enzyme associated to the enzyme variable + self.catalytic_events = ( + DictList() + ) # store the interfaces to reactions related to this enzyme self.reactions = DictList() self.constraints = {} # store IDs of constraint the enzyme is associated with self._model = None - self.annotation = {'type': 'Variable'} + self.annotation = {"type": "Variable"} self.variables = dict() @property def kcat_values(self): - """returns a dictionary with kcat values and reactions + """Returns a dictionary with kcat values and reactions. + + Returns: + dict: A dictionary containing kcat values and their associated reactions. """ return self.kcats @property def flux(self) -> float: - """ - Get the flux value in the most recent solution. + """Get the flux value in the most recent solution. + Flux is the primal value of the corresponding variable in the model. - Returns - ------- - flux: float - Flux is the primal value of the corresponding variable in the model. - Warnings - -------- - * Accessing reaction fluxes through a `Solution` object is the safer, - preferred, and only guaranteed to be correct way. You can see how to - do so easily in the examples. - * Reaction flux is retrieved from the currently defined - `self._model.solver`. The solver status is checked but there are no - guarantees that the current solver state is the one you are looking - for. - * If you modify the underlying model after an optimization, you will - retrieve the old optimization values. - Raises - ------ - RuntimeError - If the underlying model was never optimized beforehand or the - reaction is not part of a model. - OptimizationError - If the solver status is anything other than 'optimal'. - AssertionError - If the flux value is not within the bounds. - Examples - -------- - >>> from cobra.io import load_model - >>> model = load_model("textbook") - >>> solution = model.optimize() - >>> model.variables.PFK.flux - 7.477381962160283 - >>> solution.fluxes.PFK - 7.4773819621602833 + + Returns: + float: Flux, which is the primal value of the corresponding variable in the model. + + Raises: + RuntimeError: If the underlying model was never optimized beforehand or the reaction is not part of a model. + OptimizationError: If the solver status is anything other than 'optimal'. + AssertionError: If the flux value is not within the bounds. + + Warnings: + - Accessing reaction fluxes through a `Solution` object is the safer, preferred, and only guaranteed to be correct way. + - Reaction flux is retrieved from the currently defined `self._model.solver`. The solver status is checked but there are no guarantees that the current solver state is the one you are looking for. + - If you modify the underlying model after an optimization, you will retrieve the old optimization values. + + Examples: + ``` + >>> from cobra.io import load_model + >>> model = load_model("textbook") + >>> solution = model.optimize() + >>> model.variables.PFK.flux + 7.477381962160283 + >>> solution.fluxes.PFK + 7.4773819621602833 + ``` """ + try: check_solver_status(self._model.solver.status) return self.forward_variable.primal + self.reverse_variable.primal @@ -471,15 +424,12 @@ def flux(self) -> float: @property def concentration(self) -> float: - """ - Get the enzyme concentration value of the most recent solution. - The enzyme concentration equals the flux value + """Get the enzyme concentration value of the most recent solution. - Returns - ------- - float - enzyme concentration [mmol/gDW] - """ + The enzyme concentration equals the flux value. + + Returns: + float: Enzyme concentration in mmol/gDW.""" return self.flux @property @@ -493,22 +443,25 @@ def model(self, model): # add enzyme instance enzyme_ids = [enz.id for enz in self._model.enzymes] if self.id not in enzyme_ids: - enzyme = Enzyme(id =self.id, - rxn2kcat = self.kcat, - molmass=self.molmass - ) + enzyme = Enzyme(id=self.id, rxn2kcat=self.kcat, molmass=self.molmass) self._model.add_enzymes([enzyme]) self.enzyme = self._model.enzymes.get_by_id(self.id) self.constraints = self.enzyme._constraints self._model.enzyme_variables.append(self) # create new forward and reverse variables - forward_variable = self._model.problem.Variable(name=self.id, ub=self.upper_bound, lb=0) - reverse_variable = self._model.problem.Variable(name=self.reverse_id, ub=-self.lower_bound, lb=0) - self.variables = {'forward_variable': forward_variable, 'reverse_variable': reverse_variable} + forward_variable = self._model.problem.Variable( + name=self.id, ub=self.upper_bound, lb=0 + ) + reverse_variable = self._model.problem.Variable( + name=self.reverse_id, ub=-self.lower_bound, lb=0 + ) + self.variables = { + "forward_variable": forward_variable, + "reverse_variable": reverse_variable, + } self._model.add_cons_vars([forward_variable, reverse_variable]) - # add catalytic event and reaction instances for rxn_id in self.rxn_ids: if not rxn_id in self._model.reactions: @@ -517,51 +470,53 @@ def model(self, model): self._model.add_reactions([rxn]) if not rxn_id in self.reactions: self.reactions.append(self._model.reactions.get_by_id(rxn_id)) - #create a catalytic event if it doesn't exist already - if not f'CE_{rxn_id}' in self._model.catalytic_events: + # create a catalytic event if it doesn't exist already + if not f"CE_{rxn_id}" in self._model.catalytic_events: kcats2enzymes = {self.enzyme: self.kcats[rxn_id]} - ce = CatalyticEvent(id = f'CE_{rxn_id}', - kcats2enzymes=kcats2enzymes, - rxn_id=rxn_id) + ce = CatalyticEvent( + id=f"CE_{rxn_id}", kcats2enzymes=kcats2enzymes, rxn_id=rxn_id + ) ce.model = model self._model.catalytic_events.append(ce) # if catalytic event exist, add the enzyme to it else: - self._model.catalytic_events.get_by_id(f'CE_{rxn_id}').add_enzymes({self.enzyme: self.kcats[rxn_id]}) + self._model.catalytic_events.get_by_id(f"CE_{rxn_id}").add_enzymes( + {self.enzyme: self.kcats[rxn_id]} + ) # if catalytic event is not related to the enzyme variable yet, add it. - if f'CE_{rxn_id}' not in self.catalytic_events: - self.catalytic_events.append(self._model.catalytic_events.get_by_id(f'CE_{rxn_id}')) + if f"CE_{rxn_id}" not in self.catalytic_events: + self.catalytic_events.append( + self._model.catalytic_events.get_by_id(f"CE_{rxn_id}") + ) @property def forward_variable(self): if self._model is not None: return self._model.variables[self.id] else: - return self.variables['forward_variable'] + return self.variables["forward_variable"] @property def reverse_variable(self): if self._model is not None: return self._model.variables[self.reverse_id] else: - return self.variables['reverse_variable'] + return self.variables["reverse_variable"] - def add_catalytic_events(self, catalytic_events:list, kcats:list): - """ - Adding a catalytic event to an enzyme variable - - Parameters - ---------- - catalytic_events: list - Catalytic events to add - kcats:list - A list with dicts containing direction, kcat key value pairs + def add_catalytic_events(self, catalytic_events: list, kcats: list): + """Adding catalytic events to an enzyme variable. + + Args: + catalytic_events (list): A list of catalytic events to add. + kcats (list): A list with dictionaries containing direction and kcat key-value pairs. """ for i, ce in enumerate(catalytic_events): if ce in self.catalytic_events: - warn(f'Catalytic event {ce.id} is already associated with enzyme variable {self.id}. ' - f'Continue with other catalytic events') + warn( + f"Catalytic event {ce.id} is already associated with enzyme variable {self.id}. " + f"Continue with other catalytic events" + ) else: if not ce.enzyme_variables.has_id(self.id): ce.enzyme_variables.append(self) @@ -570,25 +525,21 @@ def add_catalytic_events(self, catalytic_events:list, kcats:list): self.add_reactions({ce.rxn_id: kcats[i]}) def add_reactions(self, reaction_kcat_dict: dict): - """ - Add reactions to the enzyme variable and create bindings to the related model. + """Add reactions to the enzyme variable and create bindings to the related model. If there are multiple reactions related to a single enzyme, this is an isozyme. - Parameters - ---------- - reaction_kcat_dict: nested dict - A Dict with the reaction_id, kcat key, value pairs to connect the - enzyme with the associated reaction the kcat is another dict with 'f' and 'b' - for the forward and backward reactions respectively. - - + Args: + reaction_kcat_dict (dict): A nested dictionary with the reaction_id, kcat key, value pairs to connect the + enzyme with the associated reaction. The kcat is another dictionary with `f` and `b` for the forward and + backward reactions, respectively. """ for reaction, kcat in reaction_kcat_dict.items(): # check if the enzyme is already associated to the catalytic event try: self.reactions.get_by_id(reaction.id) warn( - f'Reaction {reaction.id} is already associated with the enzyme {self.id}. The enzyme variable will be updated') + f"Reaction {reaction.id} is already associated with the enzyme {self.id}. The enzyme variable will be updated" + ) self.change_kcat_values(kcat) return except: @@ -604,15 +555,17 @@ def add_reactions(self, reaction_kcat_dict: dict): self._model.reactions.get_by_id(reaction) # if not: add the enzyme to the model except: - rxn = Reaction(id = reaction) + rxn = Reaction(id=reaction) self._model.add_reactions([rxn]) rxn = self._model.reactions.get_by_id(reaction) self.reactions.append(rxn) for direction in kcat.keys(): - if direction != 'f' and direction != 'b': - warn(f'Invalid direction {direction} for kcat value for enzyme variable {self.id}! Skip!') + if direction != "f" and direction != "b": + warn( + f"Invalid direction {direction} for kcat value for enzyme variable {self.id}! Skip!" + ) continue # add enzyme to catalytic event and the related variable @@ -620,45 +573,44 @@ def add_reactions(self, reaction_kcat_dict: dict): coeff = kcatvalue * 3600 * 1e-6 # add enzyme to the associated reaction with kinetic constants # and relate enzyme to the catalytic event - if direction == 'f': - self.constraints[f'EC_{self.id}_{direction}'].set_linear_coefficients({ - rxn.forward_variable: 1 / coeff, - self.forward_variable: -1 - }) - - elif direction == 'b': - self.constraints[f'EC_{self.id}_{direction}'].set_linear_coefficients({ - rxn.reverse_variable: 1 / coeff, - self.reverse_variable: -1 - }) + if direction == "f": + self.constraints[ + f"EC_{self.id}_{direction}" + ].set_linear_coefficients( + {rxn.forward_variable: 1 / coeff, self.forward_variable: -1} + ) + + elif direction == "b": + self.constraints[ + f"EC_{self.id}_{direction}" + ].set_linear_coefficients( + {rxn.reverse_variable: 1 / coeff, self.reverse_variable: -1} + ) def remove_catalytic_event(self, catalytic_event: Union[CatalyticEvent, str]): - """ - Function to remove a catalytic event from an enzyme - Parameters - ---------- - catalytic_event: CatalyticEvent or str - catalytic event or identifier to remove + """Remove a catalytic event from an enzyme. + + Args: + catalytic_event (Union[CatalyticEvent, str]): CatalyticEvent or str, catalytic event or identifier to remove. """ if isinstance(catalytic_event, str): try: catalytic_event = self.catalytic_events.get_by_id(catalytic_event) except: - print(f'Catalytic event {catalytic_event} is not related to this enzyme and can thus not be removed!') + print( + f"Catalytic event {catalytic_event} is not related to this enzyme and can thus not be removed!" + ) - #remove the event from the DictList + # remove the event from the DictList self.catalytic_events.remove(catalytic_event.id) def remove_reactions(self, reaction_list: list): - """ - Remove reaction from the enzyme variable and remove the reaction from the - constraint expressions related to the enzyme - - Parameters - ---------- - reaction_list: list - A list with Cbra.Reaction objects which should be removed. If a list of identifiers (str) - is provided, the corresponding enzyme will be obtained from the EnzymeVariables.reaction attribute + """Remove reactions from the enzyme variable and remove the reactions from the + constraint expressions related to the enzyme. + + Args: + reaction_list (list): A list with Cobra.Reaction objects which should be removed. If a list of identifiers (str) + is provided, the corresponding enzyme will be obtained from the EnzymeVariables.reaction attribute. """ # check the input if not hasattr(reaction_list, "__iter__"): @@ -673,7 +625,8 @@ def remove_reactions(self, reaction_list: list): reaction_list[i] = self.reactions.get_by_id(rxn) except: print( - f'Reaction {rxn} is not associated with the enzyme variable {self.id}. This reaction cannot be removed. \n') + f"Reaction {rxn} is not associated with the enzyme variable {self.id}. This reaction cannot be removed. \n" + ) pass for rxn in reaction_list: @@ -686,29 +639,25 @@ def remove_reactions(self, reaction_list: list): self.constraints[constraint.name] = constraint coeff = 0 # set coefficients to 0 - if constraint.name[-1] == 'f': - constraint.set_linear_coefficients({ - rxn.forward_variable: coeff, - self.forward_variable: 0 - }) - - elif constraint.name[-1] == 'b': - constraint.set_linear_coefficients({ - rxn.reverse_variable: coeff, - self.reverse_variable: 0 - }) + if constraint.name[-1] == "f": + constraint.set_linear_coefficients( + {rxn.forward_variable: coeff, self.forward_variable: 0} + ) + + elif constraint.name[-1] == "b": + constraint.set_linear_coefficients( + {rxn.reverse_variable: coeff, self.reverse_variable: 0} + ) # remove constraint from list of r=constraints del self.constraints[constraint.name] def change_kcat_values(self, reaction_kcat_dict: dict): - """changes kcat values for the enzyme variable - Parameters - ---------- - reaction_kcat_dict: nested Dict - A Dict with Cobra.Reaction, kcat key, value pairs to connect the - enzyme with the associated reaction the kcat is another dict with 'f' and 'b' - for the forward and backward reactions respectively. + """Changes kcat values for the enzyme variable. + Args: + reaction_kcat_dict (dict): A nested dictionary with Cobra.Reaction, kcat key, value pairs to connect the + enzyme with the associated reaction. The kcat is another dictionary with `f` and `b` for the forward and + backward reactions, respectively. """ # apply changes to internal dicts (one by one to avoid deleting kcat values) kcats_change = {} @@ -716,49 +665,52 @@ def change_kcat_values(self, reaction_kcat_dict: dict): # save change in dict self.kcats[rxn.id] = kcat_dict for direction, kcat in kcat_dict.items(): - if direction != 'f' and direction != 'b': - warn(f'Invalid direction {direction} for kcat value for enzyme variable {self.id}! Skip!') + if direction != "f" and direction != "b": + warn( + f"Invalid direction {direction} for kcat value for enzyme variable {self.id}! Skip!" + ) continue kcats_change[direction] = kcat # is enzyme variable already integrated into a model if self._model is None: - warn(f'Catalytic event {self.id} is not integrated into a model!') + warn(f"Catalytic event {self.id} is not integrated into a model!") for direction, kcat in kcats_change.items(): # get constraint - constraint_id = f'EC_{self.id}_{direction}' + constraint_id = f"EC_{self.id}_{direction}" constraint = self.enzyme._constraints[constraint_id] # change kcat value in the constraint coeff = kcat * 3600 * 1e-6 - if direction == 'f': - self._model.constraints[constraint_id].set_linear_coefficients({ - rxn.forward_variable: 1 / coeff - }) - elif direction == 'b': - self._model.constraints[constraint_id].set_linear_coefficients({ - rxn.reverse_variable: 1 / coeff - }) + if direction == "f": + self._model.constraints[constraint_id].set_linear_coefficients( + {rxn.forward_variable: 1 / coeff} + ) + elif direction == "b": + self._model.constraints[constraint_id].set_linear_coefficients( + {rxn.reverse_variable: 1 / coeff} + ) self._model.solver.update() - def __copy__(self) -> 'PAModelpy.Enzyme.EnzymeVariable': - """ Copy the enzyme variable - :return: PAModelpy.Enzyme.EnzymeVariable: - A new enzyme variable that is a copy of the original enzyme variable + def __copy__(self) -> "PAModelpy.Enzyme.EnzymeVariable": + """Copy the enzyme variable. + + Returns: + PAModelpy.Enzyme.EnzymeVariable: A new enzyme variable that is a copy of the original enzyme variable. """ cop = copy(super(EnzymeVariable, self)) return cop - def __deepcopy__(self, memo: dict) -> 'PAModelpy.Enzyme.EnzymeVariable': - """ Copy the enzyme variable with memo + def __deepcopy__(self, memo: dict) -> "PAModelpy.Enzyme.EnzymeVariable": + """Copy the enzyme variable with memo. - :param: memo:dict: - Automatically passed parameter + Args: + memo (dict): Automatically passed parameter. - :return: PAModelpy.Enzyme.EnzymeVariable: - A new enzyme variable that is a copy of the original enzyme variable with memo + Returns: + PAModelpy.Enzyme.EnzymeVariable: A new enzyme variable that is a copy of the original enzyme variable with memo. """ cop = deepcopy(super(EnzymeVariable, self), memo) diff --git a/src/PAModelpy/EnzymeSectors.py b/src/PAModelpy/EnzymeSectors.py index d9139b3..29130fc 100644 --- a/src/PAModelpy/EnzymeSectors.py +++ b/src/PAModelpy/EnzymeSectors.py @@ -8,6 +8,7 @@ class Sector(Object): TOTAL_PROTEIN_CONSTRAINT_ID = Config.TOTAL_PROTEIN_CONSTRAINT_ID + def get_tpc_metabolite(self, model): tpc = self.TOTAL_PROTEIN_CONSTRAINT_ID try: @@ -18,37 +19,42 @@ def get_tpc_metabolite(self, model): totprot = False return totprot, tpc_metabolite - def __copy__(self) -> 'Sector': - """ Copy the Sector - :return: Sector: - A new Sector that is a copy of the original Sector + def __copy__(self) -> "Sector": + """Copy the Sector. + + Returns: + Sector: A new Sector that is a copy of the original Sector. """ cop = copy(super(Sector, self)) return cop - def __deepcopy__(self, memo: dict) -> 'Sector': - """ Copy the Sector with memo + def __deepcopy__(self, memo: dict) -> "Sector": + """Copy the Sector with memo. - :param: memo:dict: - Automatically passed parameter + Args: + memo (dict): Automatically passed parameter. - :return: Sector: - A new Sector that is a copy of the original Sector with memo + Returns: + Sector: A new Sector that is a copy of the original Sector with memo. """ cop = deepcopy(super(Sector, self), memo) return cop + class EnzymeSector(Sector): DEFAULT_MOL_MASS = 3.947778784340140e04 # mean enzymes mass E.coli [g/mol] - def __init__(self, id_list, mol_mass, configuration = Config): - self.id_list = id_list #list with reaction ids which are associated with the specific sector - self.mol_mass = mol_mass #dict with molar masses for each enzyme in the id_list (g/mol) + + def __init__(self, id_list, mol_mass, configuration=Config): + self.id_list = id_list # list with reaction ids which are associated with the specific sector + self.mol_mass = ( + mol_mass # dict with molar masses for each enzyme in the id_list (g/mol) + ) self.variables = [] self.constraints = [] self.model = None - self.id = '' + self.id = "" self.slope = 0 self.intercept = 0 @@ -57,35 +63,41 @@ def __init__(self, id_list, mol_mass, configuration = Config): class ActiveEnzymeSector(Sector): DEFAULT_MOL_MASS = 3.947778784340140e04 # mean enzymes mass E.coli [g/mol] - #class with all the information on enzymes related to metabolic reactions - def __init__(self, rxn2protein: dict, configuration:Config =Config): - """_summary_ - - Parameters - ---------- - rxn2protein : nested dict - reaction id, enzymes_dict key, value pairs for each reaction in the active_enzyme sector. - The enzymes_dict contains the enzyme identifiers of the enzymes which are related to the specific reaction - as keys and a dict with information about the enzyme as values. The information included in this dict is: - turnover number for forward and backward reaction [1/s], molar mass of the enzyme [mol/g], gene identifiers - related to the enzyme, with which other enzymes it forms a complex. - example: {R1: - {E1: - {'f': forward kcat, 'b': backward kcat, 'molmass': molar mass, 'genes': [G1, G2], - 'complex_with': 'E2'}, - E2: - {'f': forward kcat, 'b': backward kcat, 'molmass': molar mass, 'genes': [G3, G4], - 'complex_with': 'E1'}} - - configuration: Config object, optional - Information about general configuration of the model including identifier conventions. - Default as defined in the `PAModelpy.configuration` script for the E.coli iML1515 model. + + # class with all the information on enzymes related to metabolic reactions + def __init__(self, rxn2protein: dict, configuration: Config = Config): """ + _summary_ + + Parameters: + rxn2protein (dict): A dictionary with reaction ID, enzymes_dict key, value pairs for each reaction in the active_enzyme sector. + The enzymes_dict contains the enzyme identifiers of the enzymes related to the specific reaction as keys, and a dict + with information about the enzyme as values. The information included in this dict includes the turnover number for + the forward and backward reaction (1/s), molar mass of the enzyme (mol/g), gene identifiers related to the enzyme, + and with which other enzymes it forms a complex. + configuration (Config object, optional): Information about the general configuration of the model, including identifier conventions. + Default is as defined in the `PAModelpy.configuration` script for the E.coli iML1515 model. + + Example: + ``` + For the Parameter rxn2protein a dictionary may look like this: + { + 'R1': + { + 'E1': {'f': forward kcat, 'b': backward kcat, 'molmass': molar mass, 'genes': ['G1', 'G2'], + 'complex_with': 'E2'}, + 'E2': {'f': forward kcat, 'b': backward kcat, 'molmass': molar mass, 'genes': ['G3', 'G4'], + 'complex_with': 'E1'} + } + } + ``` + """ + self.TOTAL_PROTEIN_CONSTRAINT_ID = configuration.TOTAL_PROTEIN_CONSTRAINT_ID self.rxn2protein = rxn2protein # ID of the sector - self.id = 'ActiveEnzymeSector' + self.id = "ActiveEnzymeSector" self.model = None # for easy removal @@ -94,13 +106,15 @@ def __init__(self, rxn2protein: dict, configuration:Config =Config): def add(self, model): self.model = model - #make and enzymatic constraint for an existing stoichiometric reaction in the form of: e_i=v_i*kcat_i + # make and enzymatic constraint for an existing stoichiometric reaction in the form of: e_i=v_i*kcat_i # c.f. the GECKO formulation of Sanchez et al. 2017 - print('Add active protein sector\n') - #check if the constraint exists + print("Add active protein sector\n") + # check if the constraint exists totprot, tpc_metabolite = self.get_tpc_metabolite(model) if not totprot: - warn('Total protein constraint is not in the model. Skip the Active Enzyme Sector') + warn( + "Total protein constraint is not in the model. Skip the Active Enzyme Sector" + ) return model rxn2protein = self.rxn2protein.copy() @@ -108,8 +122,8 @@ def add(self, model): for rxn_id, enzymes in self.rxn2protein.items(): # extract reaction from model if rxn_id not in model.reactions: - # reaction = model.reactions.get_by_id(rxn_id) - # else: + # reaction = model.reactions.get_by_id(rxn_id) + # else: try: reaction = model.reactions.query(rxn_id) for rxn in reaction: @@ -117,7 +131,9 @@ def add(self, model): del rxn2protein[rxn_id] except: - warn(f'Reaction {rxn_id} is not in the model, this reaction will be skipped') + warn( + f"Reaction {rxn_id} is not in the model, this reaction will be skipped" + ) if rxn_id in rxn2protein.keys(): del rxn2protein[rxn_id] @@ -125,14 +141,17 @@ def add(self, model): reaction = model.reactions.get_by_id(rxn_id) # skip the reaction if a problem is encountered in check_kcat_values() consistent = self.check_kcat_values(model, reaction, enzymes) - if not consistent: continue + if not consistent: + continue for enzyme_id, enzyme_dict in enzymes.items(): - kcat = dict((k, v) for k, v in enzyme_dict.items() if k == 'f' or k == 'b') + kcat = dict( + (k, v) for k, v in enzyme_dict.items() if k == "f" or k == "b" + ) # get molar mass of enzyme or replace with default value - if 'molmass' in enzyme_dict.keys(): - molmass = enzyme_dict['molmass'] + if "molmass" in enzyme_dict.keys(): + molmass = enzyme_dict["molmass"] else: molmass = self.DEFAULT_MOL_MASS @@ -141,27 +160,27 @@ def add(self, model): if enzyme_id in model.enzymes: enzyme = model.enzymes.get_by_id(enzyme_id) - #check if catalytic event already exists: - catal_event_id = f'CE_{rxn_id}' + # check if catalytic event already exists: + catal_event_id = f"CE_{rxn_id}" if catal_event_id in model.catalytic_events: ce = model.catalytic_events.get_by_id(catal_event_id) enzyme.add_catalytic_event(ce, kcat) else: enzyme.create_catalytic_event(rxn_id=rxn_id, kcats=kcat) - model.add_catalytic_events([enzyme.catalytic_events.get_by_id(f'CE_{rxn_id}')]) + model.add_catalytic_events( + [enzyme.catalytic_events.get_by_id(f"CE_{rxn_id}")] + ) else: enzyme = Enzyme( - id = enzyme_id, - rxn2kcat= {rxn_id: kcat}, - molmass = molmass + id=enzyme_id, rxn2kcat={rxn_id: kcat}, molmass=molmass ) - #in the add enzymes function the enzyme with corresponding catalytic events will be added to the model - #and connected to the reaction and total protein constraint + # in the add enzymes function the enzyme with corresponding catalytic events will be added to the model + # and connected to the reaction and total protein constraint model.add_enzymes([enzyme]) - #adding to the enzyme sector object for easy removal + # adding to the enzyme sector object for easy removal self.constraints += [enzyme] model.tpc += 1 self.variables.append(enzyme.enzyme_variable) @@ -169,82 +188,101 @@ def add(self, model): def check_kcat_values(self, model, reaction, kcat): """ - Function to check if the kcat values provided for an enzyme are consistent with the direction of the reaction - Parameters - ---------- - model: cobra.Model or PAModel - model to which the kcat values should be added - reaction: cobra.Reaction - reaction which is catalyzed with the enzyme related to the kcats - kcat: dict - a dict with the turnover values for the forward and/or backward reaction for different enzymes [/s] - {'E1':{'f': forward kcat, 'b': backward kcat}} - - Returns - ------- - + Function to check if the kcat values provided for an enzyme are consistent with the direction of the reaction. + + Parameters: + model (cobra.Model or PAModel): Model to which the kcat values should be added. + reaction (cobra.Reaction): Reaction that is catalyzed with the enzyme related to the kcats. + kcat (dict): A dictionary with the turnover values for the forward and/or backward reaction for different enzymes [/s]. + + Example: + Example dictionary for the `kcat` parameter + ``` + {'E1': {'f': forward kcat, 'b': backward kcat}} + ``` """ + if reaction.id not in model.reactions: - warn('Reaction ' + reaction.id + ' not in the model. Skip enzyme constraint') + warn( + "Reaction " + reaction.id + " not in the model. Skip enzyme constraint" + ) return False # check if there is an EC number associated to each reaction, otherwise, use the reaction id if reaction.id not in self.rxn2protein.keys(): self.rxn2protein[reaction.id] = reaction.id - # check consistency between provided kcat values and reaction direction + # check consistency between provided kcat values and reaction direction directions = [] - for val in kcat.values():# get all directions from the kcat dict + for val in kcat.values(): # get all directions from the kcat dict directions += list(val.keys()) kcats = [] - for val in kcat.values():# get all directions from the kcat dict + for val in kcat.values(): # get all directions from the kcat dict kcats += list(val.values()) - rxn_dir = 'consistent' + rxn_dir = "consistent" if reaction.lower_bound >= 0 and reaction.upper_bound > 0: # reaction is irreversible in the forward direction - if not 'f' in directions: - rxn_dir = 'inconsistent' + if not "f" in directions: + rxn_dir = "inconsistent" elif reaction.lower_bound < 0 and reaction.upper_bound <= 0: # reaction is irreversible in the backward direction - if not 'b' in directions: - rxn_dir = 'inconsistent' + if not "b" in directions: + rxn_dir = "inconsistent" else: # reaction is reversible - if not 'f' in directions and 'b' in directions: - rxn_dir = 'inconsistent' - - if rxn_dir == 'inconsistent': - warn(reaction.id + ': reaction directionality does not match provided kcat values. Skip reaction') + if not "f" in directions and "b" in directions: + rxn_dir = "inconsistent" + + if rxn_dir == "inconsistent": + warn( + reaction.id + + ": reaction directionality does not match provided kcat values. Skip reaction" + ) return False # check kcat values input for k in kcats: if k < 0: - warn(['Turnover number for reaction "', reaction.id, '" is invalid. Skip for active enzyme sector']) + warn( + [ + 'Turnover number for reaction "', + reaction.id, + '" is invalid. Skip for active enzyme sector', + ] + ) return False - return rxn_dir == 'consistent' + return rxn_dir == "consistent" + class TransEnzymeSector(EnzymeSector): DEFAULT_MOL_MASS = 4.0590394e05 # default E. coli ribosome molar mass [g/mol] BIOMASS_RXNID = Config.BIOMASS_REACTION - #class with all the information on enzymes related to translation/ribosomes (which is linearly related) - def __init__(self, tps_0, tps_mu,id_list= [BIOMASS_RXNID], mol_mass=None, configuration = Config): + + # class with all the information on enzymes related to translation/ribosomes (which is linearly related) + def __init__( + self, + tps_0, + tps_mu, + id_list=[BIOMASS_RXNID], + mol_mass=None, + configuration=Config, + ): super().__init__(id_list, mol_mass, configuration) BIOMASS_RXNID = configuration.BIOMASS_REACTION if self.mol_mass is None: self.mol_mass = [self.DEFAULT_MOL_MASS] - #id_list only contains the model identifier of the biomass formation reaction - #mol_mass is the molar mass of a fictional ribosome, if assigned, the computed enzyme concentration resamble + # id_list only contains the model identifier of the biomass formation reaction + # mol_mass is the molar mass of a fictional ribosome, if assigned, the computed enzyme concentration resamble # ribosome concentrations - self.tps_0 =tps_0 #amount of protein allocated to the translational sector at zero growth (g_p/g_cdw) + self.tps_0 = tps_0 # amount of protein allocated to the translational sector at zero growth (g_p/g_cdw) self.tps_mu = tps_mu # amount of protein allocated to the translational sector at zero growth (g_p/g_cdw)' - self.id = 'TranslationalProteinSector' + self.id = "TranslationalProteinSector" # *1000 to convert units from g/g_cdw to mg/g_cdw - self.tps_0_coeff = self.tps_0[0] *1e3 + self.tps_0_coeff = self.tps_0[0] * 1e3 self.slope = None self.set_slope() self.intercept = None @@ -255,42 +293,46 @@ def __init__(self, tps_0, tps_mu,id_list= [BIOMASS_RXNID], mol_mass=None, config self.constraints = [] def add(self, model): - print('Add translational protein sector\n') + print("Add translational protein sector\n") if self.mol_mass is None: self.mol_mass = [self.DEFAULT_MOL_MASS] self.set_tps_0_coeff() - return self.add_sector(model= model, slope = self.tps_mu[0]* 1e3, intersect=self.tps_0_coeff) + return self.add_sector( + model=model, slope=self.tps_mu[0] * 1e3, intersect=self.tps_0_coeff + ) def set_tps_0_coeff(self): # *1000 to convert units from g/g_cdw to mg/g_cdw - self.tps_0_coeff = self.tps_0[0] *1e3 + self.tps_0_coeff = self.tps_0[0] * 1e3 def set_slope(self): # *1000 to convert units from g/g_cdw to mg/g_cdw - self.slope = self.tps_mu[0] *1e3#/ (self.mol_mass[0] * 1e-3) + self.slope = self.tps_mu[0] * 1e3 # / (self.mol_mass[0] * 1e-3) def set_intercept(self): self.set_tps_0_coeff() self.intercept = self.tps_0_coeff + class UnusedEnzymeSector(EnzymeSector): DEFAULT_MOL_MASS = 3.947778784340140e04 # mean enzymes mass E.coli [g/mol] - #class with all the information on 'excess' proteins (linear dependent on substrate uptake rate) - def __init__(self, ups_0, ups_mu ,id_list, mol_mass=None, configuration = Config): + + # class with all the information on 'excess' proteins (linear dependent on substrate uptake rate) + def __init__(self, ups_0, ups_mu, id_list, mol_mass=None, configuration=Config): super().__init__(id_list, mol_mass, configuration) if self.mol_mass is None: self.mol_mass = [self.DEFAULT_MOL_MASS] # id_list only contains the model identifier of the substrate uptake # mol_mass is the molar mass of a concentration unit of the unused protein sector - self.ups_0 = ups_0 #amount of protein allocated to the excess enzyme sector at zero substrate uptake + self.ups_0 = ups_0 # amount of protein allocated to the excess enzyme sector at zero substrate uptake if isinstance(ups_mu, list): self.ups_mu = ups_mu[0] # slope of linear relation with growth else: self.ups_mu = ups_mu # *1000 to convert units from g/g_cdw to mg/g_cdw - self.ups_0_coeff = self.ups_0[0]*1e3 + self.ups_0_coeff = self.ups_0[0] * 1e3 - self.slope = self.ups_mu*1e3 + self.slope = self.ups_mu * 1e3 self.intercept = self.ups_0_coeff # for easy removal @@ -298,27 +340,34 @@ def __init__(self, ups_0, ups_mu ,id_list, mol_mass=None, configuration = Config self.constraints = [] def add_to_model(self, model): - print('Add unused protein sector\n') + print("Add unused protein sector\n") if self.mol_mass is None: self.mol_mass = [self.DEFAULT_MOL_MASS] - return self.add_sector(model= model, slope = self.ups_mu*1e3, intersect=self.ups_0_coeff) + return self.add_sector( + model=model, slope=self.ups_mu * 1e3, intersect=self.ups_0_coeff + ) + class CustomSector(EnzymeSector): DEFAULT_ENZYME_MOL_MASS = 3.947778784340140e04 # mean enzymes mass E.coli [g/mol] - #class with all the information on a custom protein allocation secotr (linear dependent on a user defined model variable) - def __init__(self, id_list, name, cps_0,cps_s, mol_mass=None, configuration=Config): + # class with all the information on a custom protein allocation secotr (linear dependent on a user defined model variable) + def __init__( + self, id_list, name, cps_0, cps_s, mol_mass=None, configuration=Config + ): super().__init__(id_list, mol_mass, configuration) - #id_list containts the name of the reaction on which the custom protein sector is linearly dependent - self.name = name #name of the protein sector - self.lin_rxn_id = id_list[0] #model identifier of the reaction from which the custom protein sector is linearly dependent - self.cps_0 = cps_0 #intercept of the linear function describing protein allocation of the custom protein sector - self.cps_s = cps_s #slope of the linear function describing protein allocation of the custom protein sector - self.cps_0_coeff = self.cps_0[0] *1e3 - self.id = f'CustomProteinSector_{self.name}' - - #*1000 to convert units from g/g_cdw to mg/g_cdw - self.slope = self.cps_s[0]*1e3 + # id_list containts the name of the reaction on which the custom protein sector is linearly dependent + self.name = name # name of the protein sector + self.lin_rxn_id = id_list[ + 0 + ] # model identifier of the reaction from which the custom protein sector is linearly dependent + self.cps_0 = cps_0 # intercept of the linear function describing protein allocation of the custom protein sector + self.cps_s = cps_s # slope of the linear function describing protein allocation of the custom protein sector + self.cps_0_coeff = self.cps_0[0] * 1e3 + self.id = f"CustomProteinSector_{self.name}" + + # *1000 to convert units from g/g_cdw to mg/g_cdw + self.slope = self.cps_s[0] * 1e3 self.intercept = self.cps_0_coeff # for easy removal @@ -326,5 +375,7 @@ def __init__(self, id_list, name, cps_0,cps_s, mol_mass=None, configuration=Conf self.constraints = [] def add_to_model(self, model): - print(f'Add custom protein sector {self.name}\n') - return self.add_sector(model= model, slope = self.cps_mu[0]*1e3, intersect=self.cps_0_coeff) + print(f"Add custom protein sector {self.name}\n") + return self.add_sector( + model=model, slope=self.cps_mu[0] * 1e3, intersect=self.cps_0_coeff + ) diff --git a/src/PAModelpy/PAMValidator.py b/src/PAModelpy/PAMValidator.py index fa0217c..967ab0a 100644 --- a/src/PAModelpy/PAMValidator.py +++ b/src/PAModelpy/PAMValidator.py @@ -14,21 +14,22 @@ from typing import Union + class PAMValidator(object): - RESULT_DIR = os.path.join(os.path.split(os.getcwd())[0], 'Results') - OUT_FILE = os.path.join(RESULT_DIR, 'flux_rates_vs_glc.xls') + RESULT_DIR = os.path.join(os.path.split(os.getcwd())[0], "Results") + OUT_FILE = os.path.join(RESULT_DIR, "flux_rates_vs_glc.xls") GLUCOSE_EXCHANGE_RXNID = Config.GLUCOSE_EXCHANGE_RXNID OXYGEN_UPTAKE_RXNID = Config.OXYGEN_UPTAKE_RXNID BIOMASS_REACTION = Config.BIOMASS_REACTION ACETATE_EXCRETION_RXNID = Config.ACETATE_EXCRETION_RXNID CO2_EXHANGE_RXNID = Config.CO2_EXHANGE_RXNID PHYS_RXN_IDS = Config.PHYS_RXN_IDS - MW_GLC = 180.15588 #g/mol + MW_GLC = 180.15588 # g/mol MW_ACETATE = 59.04 MW_CO2 = 44.009 - GRADIENT_MAX = 11 #mmol/gdw/h - GRADIENT_STEP = 0.5 #mmol/gdw/h - GRADIENT_MIN = 0 #mmol/gdw/h + GRADIENT_MAX = 11 # mmol/gdw/h + GRADIENT_STEP = 0.5 # mmol/gdw/h + GRADIENT_MIN = 0 # mmol/gdw/h def __init__(self, model, phys_file, configuration=Config): self.GLUCOSE_EXCHANGE_RXNID = configuration.GLUCOSE_EXCHANGE_RXNID @@ -39,20 +40,22 @@ def __init__(self, model, phys_file, configuration=Config): self.PHYS_RXN_IDS = configuration.PHYS_RXN_IDS self.model = model - self.physiology_data = pd.read_excel(phys_file, sheet_name='Fluxes') - self.yield_data = pd.read_excel(phys_file, sheet_name='Yields') + self.physiology_data = pd.read_excel(phys_file, sheet_name="Fluxes") + self.yield_data = pd.read_excel(phys_file, sheet_name="Yields") - self.results = pd.DataFrame(columns=['Y_biomass', 'Y_acetate', 'Y_co2']+self.PHYS_RXN_IDS) + self.results = pd.DataFrame( + columns=["Y_biomass", "Y_acetate", "Y_co2"] + self.PHYS_RXN_IDS + ) self._additional_rxns = [] @staticmethod def check_kernel_type(): try: ipy_str = str(type(get_ipython())) - if 'zmqshell' in ipy_str: + if "zmqshell" in ipy_str: # jupyter return True - if 'terminal' in ipy_str: + if "terminal" in ipy_str: # ipython return False except: @@ -75,354 +78,619 @@ def validate_literature(self): jupyter = self.check_kernel_type() if jupyter: - pd.set_option('display.max_rows', None) + pd.set_option("display.max_rows", None) display(self.results) else: - print(tabulate(self.results, headers='keys', tablefmt='psql')) + print(tabulate(self.results, headers="keys", tablefmt="psql")) self.plot_mu() - def validate_range(self, c_uptake_rxn: str = GLUCOSE_EXCHANGE_RXNID, pfba: bool = False, show: bool = True, save: bool = False): - # reset result dataframe - self.results = pd.DataFrame(columns=['Y_biomass', 'Y_acetate', 'Y_co2'] + self.PHYS_RXN_IDS) - try: - self.parse_data() - except: - pass - self.run_simulations_gradient(c_uptake_rxn=c_uptake_rxn, pfba=pfba) - jupyter = self.check_kernel_type() - - if jupyter: - pd.set_option('display.max_rows', None) - display(self.results) - else: - print(tabulate(self.results, headers='keys', tablefmt='psql')) - if show: - self.plot_vs_mu(c_uptake_rxn) - if self._additional_rxns != []: - self.custom_plot(self._additional_rxns, c_uptake_rxn=c_uptake_rxn) - if save: - # make result directory if it not already exists - if not os.path.exists(self.RESULT_DIR): - os.mkdir(self.RESULT_DIR) - self.results.to_excel(self.OUT_FILE, engine='openpyxl') + def validate_range( + self, + c_uptake_rxn: str = GLUCOSE_EXCHANGE_RXNID, + pfba: bool = False, + show: bool = True, + save: bool = False, + ): + # reset result dataframe + self.results = pd.DataFrame( + columns=["Y_biomass", "Y_acetate", "Y_co2"] + self.PHYS_RXN_IDS + ) + try: + self.parse_data() + except: + pass + self.run_simulations_gradient(c_uptake_rxn=c_uptake_rxn, pfba=pfba) + jupyter = self.check_kernel_type() + if jupyter: + pd.set_option("display.max_rows", None) + display(self.results) + else: + print(tabulate(self.results, headers="keys", tablefmt="psql")) + if show: + self.plot_vs_mu(c_uptake_rxn) + if self._additional_rxns != []: + self.custom_plot(self._additional_rxns, c_uptake_rxn=c_uptake_rxn) + if save: + # make result directory if it not already exists + if not os.path.exists(self.RESULT_DIR): + os.mkdir(self.RESULT_DIR) + self.results.to_excel(self.OUT_FILE, engine="openpyxl") def parse_data(self): - self.physiology_data = pd.melt(self.physiology_data, id_vars='Reaction_ID', var_name='Reference', - value_vars=['Rijsewijk_2011', 'Nanchen_2006_1', 'Nanchen_2006_2', 'Nanchen_2006_3', 'Nanchen_2006_4']) + self.physiology_data = pd.melt( + self.physiology_data, + id_vars="Reaction_ID", + var_name="Reference", + value_vars=[ + "Rijsewijk_2011", + "Nanchen_2006_1", + "Nanchen_2006_2", + "Nanchen_2006_3", + "Nanchen_2006_4", + ], + ) def run_simulations(self): - i=0 - print('Running simulations to validate the model...\n') - #physiology dataset + i = 0 + print("Running simulations to validate the model...\n") + # physiology dataset for ref in pd.unique(self.physiology_data.Reference): - #set glucose uptake rate + # set glucose uptake rate df = self.physiology_data[self.physiology_data.Reference == ref] bound = df[df.Reaction_ID == self.GLUCOSE_EXCHANGE_RXNID].value.iloc[0] self.set_glc_uptake_bounds(bound) self.model.optimize() - if self.model.solver.status == 'optimal': + if self.model.solver.status == "optimal": self.store_results() - print('Still validating...\n') + print("Still validating...\n") for ref in pd.unique(self.yield_data.Reference): df = self.yield_data[self.yield_data.Reference == ref] for glc_flux in pd.unique(df.Glucose_IN): self.set_glc_uptake_bounds(glc_flux) self.model.optimize() - if self.model.solver.status == 'optimal': + if self.model.solver.status == "optimal": self.store_results() if i == 2: cobra.core.solution.get_solution(self.model).fluxes.to_csv( - os.path.join(os.path.split(os.getcwd())[0], 'Results', 'test_out_mu.tsv'), sep='\t') - i+=1 - - def run_simulations_gradient(self, pfba: bool = False, c_uptake_rxn: str = GLUCOSE_EXCHANGE_RXNID): - #run simulations for increasing glucose consumption rates - print('Running simulations to validate the model...\n') + os.path.join( + os.path.split(os.getcwd())[0], "Results", "test_out_mu.tsv" + ), + sep="\t", + ) + i += 1 + + def run_simulations_gradient( + self, pfba: bool = False, c_uptake_rxn: str = GLUCOSE_EXCHANGE_RXNID + ): + # run simulations for increasing glucose consumption rates + print("Running simulations to validate the model...\n") self.model.reset_objective() for flux in np.arange(self.GRADIENT_MIN, self.GRADIENT_MAX, self.GRADIENT_STEP): self.model.change_reaction_bounds(c_uptake_rxn, -flux, -flux) - #if glucose is not the substrate turn off glucose uptake reaction + # if glucose is not the substrate turn off glucose uptake reaction if c_uptake_rxn != self.GLUCOSE_EXCHANGE_RXNID: self.set_glc_uptake_bounds(0) - if pfba: self.model.reset_objective() + if pfba: + self.model.reset_objective() self.model.optimize() - if self.model.solver.status == 'optimal': - if pfba: self.model.pfba(fraction_of_optimum=0.1, proteins=True, reactions=False) + if self.model.solver.status == "optimal": + if pfba: + self.model.pfba( + fraction_of_optimum=0.1, proteins=True, reactions=False + ) self.store_results(c_uptake_rxn=c_uptake_rxn) - print('Done running simulations\n') + print("Done running simulations\n") - def run_simulations_glc_o2_gradient(self, oxygen_gradient: list, params_to_save:Union[str, list] = 'R_TranslationalProteinSector'): + def run_simulations_glc_o2_gradient( + self, + oxygen_gradient: list, + params_to_save: Union[str, list] = "R_TranslationalProteinSector", + ): """ - Function to run simulations of different oxygen gradients for a range of growth rates + Function to run simulations of different oxygen gradients for a range of growth rates. + This will simulate growth for the entire range of glucose concentrations for each oxygen uptake rate as given by the input. - :param - oxygen_gradient: list - List of upperbounds for the oxygen uptake reaction to loop over - params_to_save: optional, string or list - which parameter(s) to save for further analysis (default: translational protein sector constraint) - :return: - results: list of dataframes - saves the growth rate, glucose uptake rate and the user defined parameters for each oxygen uptake rate - separate dataframes + + Parameters: + oxygen_gradient (list): List of upper bounds for the oxygen uptake reaction to loop over. + params_to_save (optional): string or list, which parameter(s) to save for further analysis (default: translational protein sector constraint). + + Returns: + results (list of dataframes): Saves the growth rate, glucose uptake rate, and the user-defined parameters for each oxygen uptake rate in separate dataframes. """ - print('Running simulations of glucose and oxygen gradients...\n') + + print("Running simulations of glucose and oxygen gradients...\n") result = list() - if isinstance(params_to_save,str): params_to_save = [params_to_save] - rxns_to_save = [self.BIOMASS_REACTION, self.GLUCOSE_EXCHANGE_RXNID, self.OXYGEN_UPTAKE_RXNID] + params_to_save + if isinstance(params_to_save, str): + params_to_save = [params_to_save] + rxns_to_save = [ + self.BIOMASS_REACTION, + self.GLUCOSE_EXCHANGE_RXNID, + self.OXYGEN_UPTAKE_RXNID, + ] + params_to_save for o2 in oxygen_gradient: df = pd.DataFrame(columns=rxns_to_save) self.set_rxn_uptake_bounds(self.OXYGEN_UPTAKE_RXNID, o2) print(self.model.reactions.get_by_id(self.OXYGEN_UPTAKE_RXNID).bounds) for glc_flux in range(0, self.GRADIENT_MAX, self.GRADIENT_STEP): - self.set_glc_uptake_bounds(glc_flux/10) + self.set_glc_uptake_bounds(glc_flux / 10) self.model.reset_objective() self.model.optimize() - if self.model.solver.status == 'optimal': + if self.model.solver.status == "optimal": df = self.store_manual_results(rxns_to_save, df) result += [df] return result - def run_simulations_ups(self, ups_gradient: list, params_to_save:Union[str, list] = 'R_TranslationalProteinSector'): + def run_simulations_ups( + self, + ups_gradient: list, + params_to_save: Union[str, list] = "R_TranslationalProteinSector", + ): """ - Function to run simulations with increasing unused enzyme sectors proportions for a range of growth rates - This will simulate growth for the entire range of glucose concentrations for a range of fractions of ups_0 as - given by the input. - :param: - ups_gradient: list - List of upperbounds for the oxygen uptake reaction to loop over - params_to_save: optional, string or list - which parameter(s) to save for further analysis (default: translational protein sector constraint) - :return: - results: list of dataframes - saves the growth rate, glucose uptake rate and the user defined parameters for each oxygen uptake rate - separate dataframes + Function to run simulations with increasing unused enzyme sectors proportions for a range of growth rates. + + This will simulate growth for the entire range of glucose concentrations for a range of fractions of ups_0 as given by the input. + + Parameters: + ups_gradient (list): List of upper bounds for the oxygen uptake reaction to loop over. + params_to_save (optional): string or list, which parameter(s) to save for further analysis (default: translational protein sector constraint). + + Returns: + results (list of dataframes): Saves the growth rate, glucose uptake rate, and the user-defined parameters for each oxygen uptake rate in separate dataframes. """ - print('Running simulations of different unused protein intercepts and glucose gradients...\n') + + print( + "Running simulations of different unused protein intercepts and glucose gradients...\n" + ) result = list() - if isinstance(params_to_save,str): params_to_save = [params_to_save] - rxns_to_save = [self.BIOMASS_REACTION, self.GLUCOSE_EXCHANGE_RXNID, self.OXYGEN_UPTAKE_RXNID] + params_to_save - unused_protein_sector = self.model.sectors.get_by_id('UnusedProteinSector') + if isinstance(params_to_save, str): + params_to_save = [params_to_save] + rxns_to_save = [ + self.BIOMASS_REACTION, + self.GLUCOSE_EXCHANGE_RXNID, + self.OXYGEN_UPTAKE_RXNID, + ] + params_to_save + unused_protein_sector = self.model.sectors.get_by_id("UnusedProteinSector") ups_0 = unused_protein_sector.ups_0[0] for perc in ups_gradient: df = pd.DataFrame(columns=rxns_to_save) - unused_protein_sector.ups_0[0] = ups_0*perc + unused_protein_sector.ups_0[0] = ups_0 * perc # self.set_rxn_uptake_bounds(self.OXYGEN_UPTAKE_RXNID, perc) for glc_flux in range(0, self.GRADIENT_MAX, self.GRADIENT_STEP): - self.set_glc_uptake_bounds(glc_flux/10) + self.set_glc_uptake_bounds(glc_flux / 10) self.model.reset_objective() self.model.optimize() - if self.model.solver.status == 'optimal': + if self.model.solver.status == "optimal": df = self.store_manual_results(rxns_to_save, df) result += [df] return result - - def set_glc_uptake_bounds(self, bound:int): + def set_glc_uptake_bounds(self, bound: int): rxn_glc_in = self.model.reactions.get_by_id(self.GLUCOSE_EXCHANGE_RXNID) - #check if model is reversible - if self.GLUCOSE_EXCHANGE_RXNID[-1] == 'b': + # check if model is reversible + if self.GLUCOSE_EXCHANGE_RXNID[-1] == "b": rxn_glc_in._lower_bound, rxn_glc_in._upper_bound = bound, bound else: rxn_glc_in._lower_bound, rxn_glc_in._upper_bound = -bound, -bound rxn_glc_in.update_variable_bounds() - def set_rxn_uptake_bounds(self, rxn_id:str, bound:int, - upper:bool = True, lower:bool = False): + def set_rxn_uptake_bounds( + self, rxn_id: str, bound: int, upper: bool = True, lower: bool = False + ): rxn = self.model.reactions.get_by_id(rxn_id) - if rxn_id[-1] == 'b': - if upper: rxn._upper_bound = bound - if lower: rxn._lower_bound = bound + if rxn_id[-1] == "b": + if upper: + rxn._upper_bound = bound + if lower: + rxn._lower_bound = bound else: - if upper: rxn._lower_bound = -bound - if lower: rxn._upper_bound = -bound + if upper: + rxn._lower_bound = -bound + if lower: + rxn._upper_bound = -bound rxn.update_variable_bounds() def store_results(self, c_uptake_rxn: str = GLUCOSE_EXCHANGE_RXNID): result = {} c_upt_flux = self.model.reactions.get_by_id(c_uptake_rxn).flux - #store the relevant flux values + # store the relevant flux values if c_uptake_rxn != self.GLUCOSE_EXCHANGE_RXNID: for i, rxn in enumerate(self.PHYS_RXN_IDS): if rxn == self.GLUCOSE_EXCHANGE_RXNID: self.PHYS_RXN_IDS[i] = c_uptake_rxn - self.results = pd.DataFrame(columns= self.PHYS_RXN_IDS) + self.results = pd.DataFrame(columns=self.PHYS_RXN_IDS) # self.results = pd.DataFrame(columns=[ 'Y_biomass', 'Y_acetate', 'Y_co2']+self.PHYS_RXN_IDS) for id in self.PHYS_RXN_IDS: - #getting reaction or variable - try:result[id]=self.model.reactions.get_by_id(id).flux - except:result[id]=self.model.variables[id].primal - #calculate yields based on glucose for exchange reactions - if ('EX' in id or 'BIOMASS' in id) and 'glc' not in id and '_o2' not in id and abs(c_upt_flux)> 0: - if 'BIOMASS' in id: - id_ext = 'biomass' + # getting reaction or variable + try: + result[id] = self.model.reactions.get_by_id(id).flux + except: + result[id] = self.model.variables[id].primal + # calculate yields based on glucose for exchange reactions + if ( + ("EX" in id or "BIOMASS" in id) + and "glc" not in id + and "_o2" not in id + and abs(c_upt_flux) > 0 + ): + if "BIOMASS" in id: + id_ext = "biomass" MW = 1 - if 'ac' in id: - id_ext = 'acetate' - MW = 1#self.MW_ACETATE * 1e-3 #[mmol/mol] - if '_co2' in id: - id_ext = 'co2' - MW = 1#self.MW_CO2 * 1e-3 #[mmol/mol] - yield_id = 'Y_'+ id_ext - result[yield_id]= (self.model.reactions.get_by_id(id).flux * MW) / (c_upt_flux * self.MW_GLC * 1e-3) - #appending the results to the dataframe + if "ac" in id: + id_ext = "acetate" + MW = 1 # self.MW_ACETATE * 1e-3 #[mmol/mol] + if "_co2" in id: + id_ext = "co2" + MW = 1 # self.MW_CO2 * 1e-3 #[mmol/mol] + yield_id = "Y_" + id_ext + result[yield_id] = (self.model.reactions.get_by_id(id).flux * MW) / ( + c_upt_flux * self.MW_GLC * 1e-3 + ) + # appending the results to the dataframe result_series = pd.Series(result) - #sort the series to match the format of the output dataframe + # sort the series to match the format of the output dataframe result_series_sorted = result_series[self.results.columns.to_list()] - #add the resulting row to the result dataframe + # add the resulting row to the result dataframe self.results.loc[len(self.results.index)] = result_series_sorted.to_list() - def store_manual_results(self, vars_to_save:list, df:pd.DataFrame): + def store_manual_results(self, vars_to_save: list, df: pd.DataFrame): row = dict() for id in vars_to_save: try: - #for variables + # for variables row[id] = self.model.variables[id].primal except: - #for reactions: + # for reactions: row[id] = self.model.reactions.get_by_id(id).flux # appending the results to the dataframe - df = pd.concat([df, pd.DataFrame({key: pd.Series(value) for key, value in row.items()})], ignore_index=True) + df = pd.concat( + [df, pd.DataFrame({key: pd.Series(value) for key, value in row.items()})], + ignore_index=True, + ) return df def plot_mu(self): - print('Plotting the results against reference values. \n') + print("Plotting the results against reference values. \n") # Initialize figure with subplots fig = make_subplots( - rows=2, cols=2, - specs=[[{"type": "scatter"}, {"type": "scatter"}], [{"type": "scatter"}, {"type": "scatter"}]], - subplot_titles= ('Growth rate', 'Biomass yield', 'CO2 excretion', 'CO2 yield', 'Acetate excretion', 'Acetate yield')) + rows=2, + cols=2, + specs=[ + [{"type": "scatter"}, {"type": "scatter"}], + [{"type": "scatter"}, {"type": "scatter"}], + ], + subplot_titles=( + "Growth rate", + "Biomass yield", + "CO2 excretion", + "CO2 yield", + "Acetate excretion", + "Acetate yield", + ), + ) result = self.results.__deepcopy__() - result = result.merge(self.yield_data, left_on=self.GLUCOSE_EXCHANGE_RXNID, right_on='Glucose_IN') - #print(tabulate(result, headers='keys', tablefmt='psql')) + result = result.merge( + self.yield_data, left_on=self.GLUCOSE_EXCHANGE_RXNID, right_on="Glucose_IN" + ) + # print(tabulate(result, headers='keys', tablefmt='psql')) # growth rate - fig.add_trace(go.Scatter(x=result[self.BIOMASS_REACTION], y=result['mu'], mode='markers', showlegend=False, marker=dict(color='blue')), row=1, col=1) - fig.add_trace(go.Scatter(x=[0,0.5,1.4],y=[0,0.5,1.4], mode='lines', showlegend=False, - line=dict(color='black', dash='dash', width=1)),row=1, col=1) - fig.update_xaxes(title_text='Simulated growth rate [1/h]',row=1, col=1) - fig.update_yaxes(title_text='Experimentally determined growth rate [1/h]',row=1, col=1) + fig.add_trace( + go.Scatter( + x=result[self.BIOMASS_REACTION], + y=result["mu"], + mode="markers", + showlegend=False, + marker=dict(color="blue"), + ), + row=1, + col=1, + ) + fig.add_trace( + go.Scatter( + x=[0, 0.5, 1.4], + y=[0, 0.5, 1.4], + mode="lines", + showlegend=False, + line=dict(color="black", dash="dash", width=1), + ), + row=1, + col=1, + ) + fig.update_xaxes(title_text="Simulated growth rate [1/h]", row=1, col=1) + fig.update_yaxes( + title_text="Experimentally determined growth rate [1/h]", row=1, col=1 + ) # biomass yield - fig.add_trace(go.Scatter(x=result['Y_biomass'], y=result['Yield_biomass'], mode='markers',showlegend=False, marker=dict(color='blue')), row=1, col=2) - fig.add_trace(go.Scatter(x=[0, 0.5, 0.6], y=[0, 0.5, 0.6], mode='lines', - showlegend=False, - line=dict(color='black', dash='dash', width =1)), row=1, col=2) - fig.update_xaxes(title_text='Simulated biomass yield [g_x/g_glc]', row=1, col=2) - fig.update_yaxes(title_text='Experimentally determined biomass yield [g_x/g_glc]', row=1, col=2) + fig.add_trace( + go.Scatter( + x=result["Y_biomass"], + y=result["Yield_biomass"], + mode="markers", + showlegend=False, + marker=dict(color="blue"), + ), + row=1, + col=2, + ) + fig.add_trace( + go.Scatter( + x=[0, 0.5, 0.6], + y=[0, 0.5, 0.6], + mode="lines", + showlegend=False, + line=dict(color="black", dash="dash", width=1), + ), + row=1, + col=2, + ) + fig.update_xaxes(title_text="Simulated biomass yield [g_x/g_glc]", row=1, col=2) + fig.update_yaxes( + title_text="Experimentally determined biomass yield [g_x/g_glc]", + row=1, + col=2, + ) # CO2 yield - fig.add_trace(go.Scatter(x=result['Y_co2'], y=result['Yield_co2'], mode='markers', showlegend=False, marker=dict(color='blue')), row=2, col=2) - fig.add_trace(go.Scatter(x=[0, 15, 30], y=[0, 15, 30], mode='lines', showlegend=False, - line=dict(color='black', dash='dash', width =1)), row=2, col=2) - fig.update_xaxes(title_text='Simulated CO2 yield [mmol_x/g_glc]', row=2, col=2) - fig.update_yaxes(title_text='Experimentally determined CO2 yield [mmol_co2/g_glc]', row=2, col=2) + fig.add_trace( + go.Scatter( + x=result["Y_co2"], + y=result["Yield_co2"], + mode="markers", + showlegend=False, + marker=dict(color="blue"), + ), + row=2, + col=2, + ) + fig.add_trace( + go.Scatter( + x=[0, 15, 30], + y=[0, 15, 30], + mode="lines", + showlegend=False, + line=dict(color="black", dash="dash", width=1), + ), + row=2, + col=2, + ) + fig.update_xaxes(title_text="Simulated CO2 yield [mmol_x/g_glc]", row=2, col=2) + fig.update_yaxes( + title_text="Experimentally determined CO2 yield [mmol_co2/g_glc]", + row=2, + col=2, + ) # CO2 out - fig.add_trace(go.Scatter(x=result[self.CO2_EXHANGE_RXNID], y=result['CO2_OUT'], mode='markers', showlegend=False, - marker=dict(color='blue')), row=2, col=1) - fig.add_trace(go.Scatter(x=[0, 15, 30], y=[0, 15, 30], mode='lines', showlegend=False, - line=dict(color='black', dash='dash', width=1)), row=2, col=1) - fig.update_xaxes(title_text='Simulated CO2 exchange rate [mmol/g_cdw/h]', row=2, col=1) - fig.update_yaxes(title_text='Experimentally determined CO2 exchange rate [mmol/g_cdw/h]', row=2, col=1) - #fig.write_image('Results/Images/mu_co2_plots.png') #depends on the kaleido or orca package + fig.add_trace( + go.Scatter( + x=result[self.CO2_EXHANGE_RXNID], + y=result["CO2_OUT"], + mode="markers", + showlegend=False, + marker=dict(color="blue"), + ), + row=2, + col=1, + ) + fig.add_trace( + go.Scatter( + x=[0, 15, 30], + y=[0, 15, 30], + mode="lines", + showlegend=False, + line=dict(color="black", dash="dash", width=1), + ), + row=2, + col=1, + ) + fig.update_xaxes( + title_text="Simulated CO2 exchange rate [mmol/g_cdw/h]", row=2, col=1 + ) + fig.update_yaxes( + title_text="Experimentally determined CO2 exchange rate [mmol/g_cdw/h]", + row=2, + col=1, + ) + # fig.write_image('Results/Images/mu_co2_plots.png') #depends on the kaleido or orca package fig.show() - #fig.write_image('here.png') + # fig.write_image('here.png') - def plot_vs_mu(self, c_uptake_rxn:str = GLUCOSE_EXCHANGE_RXNID): - print('Plotting the results against reference values. \n') + def plot_vs_mu(self, c_uptake_rxn: str = GLUCOSE_EXCHANGE_RXNID): + print("Plotting the results against reference values. \n") # Initialize figure with subplots fig = make_subplots( - rows=2, cols=2, - specs=[[{"type": "scatter"}, {"type": "scatter"}], [{"type": "scatter"}, {"type": "scatter"}]], - subplot_titles= ('Glucose consumption', 'Acetate production', 'O2 consumption','CO2 production')) + rows=2, + cols=2, + specs=[ + [{"type": "scatter"}, {"type": "scatter"}], + [{"type": "scatter"}, {"type": "scatter"}], + ], + subplot_titles=( + "Glucose consumption", + "Acetate production", + "O2 consumption", + "CO2 production", + ), + ) self.results[c_uptake_rxn] = abs(self.results[c_uptake_rxn]) - self.results[self.OXYGEN_UPTAKE_RXNID] = abs(self.results[self.OXYGEN_UPTAKE_RXNID]) + self.results[self.OXYGEN_UPTAKE_RXNID] = abs( + self.results[self.OXYGEN_UPTAKE_RXNID] + ) self.yield_data[c_uptake_rxn] = abs(self.yield_data[c_uptake_rxn]) - self.yield_data[self.OXYGEN_UPTAKE_RXNID] = abs(self.yield_data[self.OXYGEN_UPTAKE_RXNID]) - self.yield_data[self.BIOMASS_REACTION] = abs(self.yield_data[self.BIOMASS_REACTION]) - - #glucose consumption - fig.add_trace(go.Scatter(x=self.results[c_uptake_rxn], y=self.results[self.BIOMASS_REACTION], - showlegend=False, mode='lines', marker=dict(color='blue')), row=1, col=1) - fig.add_trace(go.Scatter(x=self.yield_data[c_uptake_rxn], - y=self.yield_data[self.BIOMASS_REACTION], mode='markers', showlegend=False, - line=dict(color='black', dash='dash', width=1)),row=1, col=1) - fig.update_yaxes(title_text='Growth rate [1/h]',row=1, col=1) - fig.update_xaxes(title_text='Glucose consumption [mmol/gdw/h]',row=1, col=1) - - #acetate excretion - fig.add_trace(go.Scatter(x=self.results[c_uptake_rxn], y=self.results[ - self.ACETATE_EXCRETION_RXNID], - showlegend= False, mode='lines', marker=dict(color='blue')), row=1, col=2) - fig.add_trace(go.Scatter(x=self.yield_data[c_uptake_rxn], - y=self.yield_data[self.ACETATE_EXCRETION_RXNID], - mode='markers', showlegend=False, - line=dict(color='black', dash='dash', width=1)), row=1, col=2) - fig.update_xaxes(title_text='Glucose consumption [mmol/gdw/h]', row=1, col=2) - fig.update_yaxes(title_text='Acetate production rate [mmol/gdw/h]', row=1, col=2) + self.yield_data[self.OXYGEN_UPTAKE_RXNID] = abs( + self.yield_data[self.OXYGEN_UPTAKE_RXNID] + ) + self.yield_data[self.BIOMASS_REACTION] = abs( + self.yield_data[self.BIOMASS_REACTION] + ) + + # glucose consumption + fig.add_trace( + go.Scatter( + x=self.results[c_uptake_rxn], + y=self.results[self.BIOMASS_REACTION], + showlegend=False, + mode="lines", + marker=dict(color="blue"), + ), + row=1, + col=1, + ) + fig.add_trace( + go.Scatter( + x=self.yield_data[c_uptake_rxn], + y=self.yield_data[self.BIOMASS_REACTION], + mode="markers", + showlegend=False, + line=dict(color="black", dash="dash", width=1), + ), + row=1, + col=1, + ) + fig.update_yaxes(title_text="Growth rate [1/h]", row=1, col=1) + fig.update_xaxes(title_text="Glucose consumption [mmol/gdw/h]", row=1, col=1) + + # acetate excretion + fig.add_trace( + go.Scatter( + x=self.results[c_uptake_rxn], + y=self.results[self.ACETATE_EXCRETION_RXNID], + showlegend=False, + mode="lines", + marker=dict(color="blue"), + ), + row=1, + col=2, + ) + fig.add_trace( + go.Scatter( + x=self.yield_data[c_uptake_rxn], + y=self.yield_data[self.ACETATE_EXCRETION_RXNID], + mode="markers", + showlegend=False, + line=dict(color="black", dash="dash", width=1), + ), + row=1, + col=2, + ) + fig.update_xaxes(title_text="Glucose consumption [mmol/gdw/h]", row=1, col=2) + fig.update_yaxes( + title_text="Acetate production rate [mmol/gdw/h]", row=1, col=2 + ) # O2 uptake fig.add_trace( - go.Scatter(x=self.results[c_uptake_rxn], y=self.results[self.OXYGEN_UPTAKE_RXNID], mode='lines', - showlegend=False, marker=dict(color='blue')), row=2, col=1) - fig.add_trace(go.Scatter(x=self.yield_data[c_uptake_rxn], - y=self.yield_data[self.OXYGEN_UPTAKE_RXNID], - mode='markers', showlegend=False, - line=dict(color='black', dash='dash', width=1)), row=2, col=1) - fig.update_xaxes(title_text='Glucose consumption [mmol/gdw/h]', row=2, col=1) - fig.update_yaxes(title_text='O2 consumption rate [mmol/gdw/h]', row=2, col=1) - - #CO2 excretion - fig.add_trace(go.Scatter(x=self.results[c_uptake_rxn], y=self.results[ - self.CO2_EXHANGE_RXNID], mode='lines', - showlegend=False, marker=dict(color='blue')), row=2, col=2) - fig.add_trace(go.Scatter(x=self.yield_data[c_uptake_rxn], - y=self.yield_data[self.CO2_EXHANGE_RXNID], - mode='markers', showlegend=False, - line=dict(color='black', dash='dash', width=1)), row=2, col=2) - fig.update_xaxes(title_text='Glucose consumption [mmol/gdw/h]', row=2, col=2) - fig.update_yaxes(title_text='CO2 production rate [mmol/gdw/h]', row=2, col=2) + go.Scatter( + x=self.results[c_uptake_rxn], + y=self.results[self.OXYGEN_UPTAKE_RXNID], + mode="lines", + showlegend=False, + marker=dict(color="blue"), + ), + row=2, + col=1, + ) + fig.add_trace( + go.Scatter( + x=self.yield_data[c_uptake_rxn], + y=self.yield_data[self.OXYGEN_UPTAKE_RXNID], + mode="markers", + showlegend=False, + line=dict(color="black", dash="dash", width=1), + ), + row=2, + col=1, + ) + fig.update_xaxes(title_text="Glucose consumption [mmol/gdw/h]", row=2, col=1) + fig.update_yaxes(title_text="O2 consumption rate [mmol/gdw/h]", row=2, col=1) + + # CO2 excretion + fig.add_trace( + go.Scatter( + x=self.results[c_uptake_rxn], + y=self.results[self.CO2_EXHANGE_RXNID], + mode="lines", + showlegend=False, + marker=dict(color="blue"), + ), + row=2, + col=2, + ) + fig.add_trace( + go.Scatter( + x=self.yield_data[c_uptake_rxn], + y=self.yield_data[self.CO2_EXHANGE_RXNID], + mode="markers", + showlegend=False, + line=dict(color="black", dash="dash", width=1), + ), + row=2, + col=2, + ) + fig.update_xaxes(title_text="Glucose consumption [mmol/gdw/h]", row=2, col=2) + fig.update_yaxes(title_text="CO2 production rate [mmol/gdw/h]", row=2, col=2) fig.show() - def custom_plot(self, rxn_ids:list, valid_dataframe: pd.DataFrame = None, xaxis:str = None, c_uptake_rxn:str = GLUCOSE_EXCHANGE_RXNID): + def custom_plot( + self, + rxn_ids: list, + valid_dataframe: pd.DataFrame = None, + xaxis: str = None, + c_uptake_rxn: str = GLUCOSE_EXCHANGE_RXNID, + ): """ - Function to plot the results of custom reactions - Parameters - ---------- - rxn_ids: list of rxn_ids (str) - reaction identifiers of the reactions to be plotted - valid_dataframe: pandas.DataFrame, optional - A dataframe with experimental data to validate the results with. The columns should be - the same as the rxn_id of the reaction to be plotted and the reaction which should be - plotted on the x-axis (by default the glucose exchange reaction 'EX_glc__D_e_b'). - If the dataframe is not provided, only the simulation results will be plotted. - xaxis: str, optional - the reaction identifier of the reaction which should be plotted on the x-axis - (default: 'EX_glc__D_e_b') - - Returns - ------- - prints scatter plots of the model simulations vs experimental datapoints (if provided) + Function to plot the results of custom reactions. + + Parameters: + rxn_ids (list of str): Reaction identifiers of the reactions to be plotted. + valid_dataframe (pandas.DataFrame, optional): A DataFrame with experimental data to validate the results with. + The columns should be the same as the rxn_id of the reaction to be plotted and the reaction which should be plotted + on the x-axis (by default the glucose exchange reaction `EX_glc__D_e_b`). If the DataFrame is not provided, + only the simulation results will be plotted. + xaxis (str, optional): The reaction identifier of the reaction which should be plotted on the x-axis (default: `EX_glc__D_e_b`). + + Returns: + Prints scatter plots of the model simulations vs. experimental data points (if provided). """ - if xaxis is None: xaxis = c_uptake_rxn + + if xaxis is None: + xaxis = c_uptake_rxn fig = make_subplots( - rows=1, cols=len(rxn_ids), - specs=[[{"type": "scatter"}]*len(rxn_ids)], - subplot_titles=tuple(rxn_id[0] for rxn_id in rxn_ids)) + rows=1, + cols=len(rxn_ids), + specs=[[{"type": "scatter"}] * len(rxn_ids)], + subplot_titles=tuple(rxn_id[0] for rxn_id in rxn_ids), + ) for i, rxn in enumerate(rxn_ids): fig.add_trace( - go.Scatter(x=self.results[xaxis], y=self.results[rxn[0]], mode='lines', - showlegend=False, marker=dict(color='blue')), row=1, col=i+1) + go.Scatter( + x=self.results[xaxis], + y=self.results[rxn[0]], + mode="lines", + showlegend=False, + marker=dict(color="blue"), + ), + row=1, + col=i + 1, + ) if valid_dataframe is not None: - fig.add_trace(go.Scatter(x=valid_dataframe[xaxis], - y=valid_dataframe[rxn], - mode='markers', showlegend=False, - line=dict(color='black', dash='dash', width=1)), row=1, col=i+1) - fig.update_xaxes(title_text=xaxis+' [mmol/gdw/h]', row=1, col=i+1) - fig.update_yaxes(title_text=rxn[0] +' [mmol/gdw/h]', row=1, col=i+1) + fig.add_trace( + go.Scatter( + x=valid_dataframe[xaxis], + y=valid_dataframe[rxn], + mode="markers", + showlegend=False, + line=dict(color="black", dash="dash", width=1), + ), + row=1, + col=i + 1, + ) + fig.update_xaxes(title_text=xaxis + " [mmol/gdw/h]", row=1, col=i + 1) + fig.update_yaxes(title_text=rxn[0] + " [mmol/gdw/h]", row=1, col=i + 1) fig.show() - diff --git a/src/PAModelpy/PAModel.py b/src/PAModelpy/PAModel.py index 0a65989..e3dda3c 100644 --- a/src/PAModelpy/PAModel.py +++ b/src/PAModelpy/PAModel.py @@ -1,13 +1,15 @@ -#cobra tools +# cobra tools import cobra from cobra import Model, DictList, Reaction, Metabolite, Solution from cobra.io import load_model from cobra.util.context import get_context -#type checking + +# type checking from optlang.symbolics import Zero from optlang.interface import Objective from typing import List, Optional, Union, Dict, Iterable -#other + +# other from functools import partial import warnings import pandas as pd @@ -15,7 +17,13 @@ import inspect # sys.path.append('../') -from .EnzymeSectors import ActiveEnzymeSector, TransEnzymeSector, UnusedEnzymeSector, CustomSector, Sector +from .EnzymeSectors import ( + ActiveEnzymeSector, + TransEnzymeSector, + UnusedEnzymeSector, + CustomSector, + Sector, +) from .CatalyticEvent import CatalyticEvent from .Constraints import Constraint from .Enzyme import Enzyme @@ -23,80 +31,56 @@ class PAModel(Model): - """Class representation for a cobra model extended with enyzme kinetics as published in Alter et al. (2021) - Parameters - ---------- - id_or_model: str or Model - String to use as model id, or actual model to base new model one. - If string, it is used as input to load a model from. If model, a new model object is - instantiated with the same properties as the original model (default None). - name: str, optional - Human readable string to be model description (default None). - p_tot : float, optional - Total protein concentration (condition dependent) (unit g_p/g_cdw) (default 0.285) - senstitivity: bool - Boolean value wheter or not a sensitivity analysis should be performed during each simulation. - This sensitivity analysis will indicate to which extent individual constraints contribute to the - objective value. - Enzyme sectors: EnzymeSector objects, optional - Information about the different enzyme sectors, being: - - Active_enzyme - metabolic active proteins - - Transl_enzyme - Enzymes related to translation - - Unused_enzymes - Excess enzymes - - Custom_enzymes: list - custom enzyme sectors - configuration: Config object, optional - Information about general configuration of the model including identifier conventions. - Default as defined in the `PAModelpy.configuration` script for the E.coli iML1515 model. - - Attributes - ---------- - p_tot : float - The fraction of biomass allocated to proteins (units: g_prot/g_cdw) - reactions : DictList - A DictList where the key is the reaction identifier and the value a - Reaction - metabolites : DictList - A DictList where the key is the metabolite identifier and the value a - Metabolite - genes : DictList - A DictList where the key is the gene identifier and the value a - Gene - groups : DictList - A DictList where the key is the group identifier and the value a - Group - enzymes : DictList - A DictList where the key is the enzyme identifier and the value an - Enzyme - enzyme_variables: DictList - - catalytic_events: DictList - - sector_constraints: dict - - - sectors : DictList - A DictList where the key is the sector identifier and the value an - EnyzmeSector - """ + """ + Class representation for a cobra model extended with enzyme kinetics as published in Alter et al. (2021). + + Parameters: + id_or_model (str or Model): String to use as model id, or actual model to base new model on. + If a string, it is used as input to load a model from. If a model, a new model object is instantiated with + the same properties as the original model (default None). + name (str, optional): Human-readable string to be model description (default None). + p_tot (float, optional): Total protein concentration (condition-dependent) (unit g_prot/g_cdw) (default 0.285). + senstitivity (bool): Boolean value whether or not a sensitivity analysis should be performed during each simulation. + This sensitivity analysis will indicate to which extent individual constraints contribute to the objective value. + Enzyme sectors (EnzymeSector objects, optional): Information about the different enzyme sectors, including: + - Active_enzyme: Metabolic active proteins. + - Transl_enzyme: Enzymes related to translation. + - Unused_enzymes: Excess enzymes. + - Custom_enzymes (list): Custom enzyme sectors. + configuration (Config object, optional): Information about the general configuration of the model, including + identifier conventions. Default as defined in the `PAModelpy.configuration` script for the E.coli iML1515 model. + + Attributes: + p_tot (float): The fraction of biomass allocated to proteins (units: g_prot/g_cdw). + reactions (DictList): A DictList where the key is the reaction identifier and the value is a Reaction. + metabolites (DictList): A DictList where the key is the metabolite identifier and the value is a Metabolite. + genes (DictList): A DictList where the key is the gene identifier and the value is a Gene. + groups (DictList): A DictList where the key is the group identifier and the value is a Group. + enzymes (DictList): A DictList where the key is the enzyme identifier and the value is an Enzyme. + enzyme_variables (DictList): A DictList where the key is the enzyme variable identifier and the value is an EnzymeVariable. + catalytic_events (DictList): A DictList where the key is the catalytic event identifier and the value is a CatalyticEvent. + sector_constraints (dict): A dictionary containing sector-specific constraints. + sectors (DictList): A DictList where the key is the sector identifier and the value is an EnzymeSector. + """ + TOTAL_PROTEIN_CONSTRAINT_ID = Config.TOTAL_PROTEIN_CONSTRAINT_ID P_TOT_DEFAULT = Config.P_TOT_DEFAULT # g_protein/g_cdw CO2_EXHANGE_RXNID = Config.CO2_EXHANGE_RXNID GLUCOSE_EXCHANGE_RXNID = Config.GLUCOSE_EXCHANGE_RXNID BIOMASS_REACTION = Config.BIOMASS_REACTION - def __init__(self, id_or_model: Union[str, "Model", None] = None, - name: Optional[str] = None, - p_tot: Optional[float] = Config.P_TOT_DEFAULT, - sensitivity: bool = True, - active_sector: Optional[ActiveEnzymeSector]=None, - translational_sector: Optional[TransEnzymeSector]=None, - unused_sector: Optional[UnusedEnzymeSector]=None, - custom_sectors: Union[List, CustomSector] =None, - configuration = Config): + def __init__( + self, + id_or_model: Union[str, "Model", None] = None, + name: Optional[str] = None, + p_tot: Optional[float] = Config.P_TOT_DEFAULT, + sensitivity: bool = True, + active_sector: Optional[ActiveEnzymeSector] = None, + translational_sector: Optional[TransEnzymeSector] = None, + unused_sector: Optional[UnusedEnzymeSector] = None, + custom_sectors: Union[List, CustomSector] = None, + configuration=Config, + ): """Constants""" self.TOTAL_PROTEIN_CONSTRAINT_ID = configuration.TOTAL_PROTEIN_CONSTRAINT_ID self.P_TOT_DEFAULT = configuration.P_TOT_DEFAULT # g_protein/g_cdw @@ -110,33 +94,50 @@ def __init__(self, id_or_model: Union[str, "Model", None] = None, id_or_model = load_model(id_or_model) super().__init__(id_or_model=id_or_model, name=name) - self.m_model = id_or_model.copy() #save a copy of the original m_model - - self.p_tot = p_tot # fraction of biomass allocated to proteins (units: g_prot/g_cdw) - self.enzymes = DictList() # a list of Enzyme.Enzyme - self.enzyme_variables = DictList() # a list of variables related to the enzymes (Variables.EnzymeVariable) - self.catalytic_events = DictList() # a list of objects storing the relation of a single reaction to the enzymes necessary for the catalysis of that reaction (Variables.CatalyticEvent) - self.enzyme_constraints = {} # a dict with enzyme constraint id (format: 'EC_{ecnmbr}_{direction}'), optlang.Constraint (enzymes) key, value pairs - self.sectors = DictList() # a list of EnzymeSectors (protein sectors) - self.sector_constraints = {} # a dict with sector constraint id, optlang.Constraint (enzymes) key, value pairs for constraints related to the protein sectors - self.tpc = 0 # counter of number of CatalyticEvents which contribute to the total protein constraint - self.sensitivity = sensitivity - self.capacity_sensitivity_coefficients = pd.DataFrame() #dataframe to store the result of the sensitivity analysis (capacity sensitivity coefficients for each constraint). The sensitivity coefficients are splitted on LB, UB and the different sectors - self.enzyme_sensitivity_coefficients = pd.DataFrame() #dataframe to store the result of the sensitivity analysis (sensitivity coefficients for each constraint). The sensitivity coefficients are splitted on LB, UB and the different sectors - + self.m_model = id_or_model.copy() # save a copy of the original m_model - #initialize the model - print(f'Setting up the proteome allocation model {self.id}\n') + self.p_tot = ( + p_tot # fraction of biomass allocated to proteins (units: g_prot/g_cdw) + ) + self.enzymes = DictList() # a list of Enzyme.Enzyme + self.enzyme_variables = ( + DictList() + ) # a list of variables related to the enzymes (Variables.EnzymeVariable) + self.catalytic_events = ( + DictList() + ) # a list of objects storing the relation of a single reaction to the enzymes necessary for the catalysis of that reaction (Variables.CatalyticEvent) + self.enzyme_constraints = ( + {} + ) # a dict with enzyme constraint id (format: 'EC_{ecnmbr}_{direction}'), optlang.Constraint (enzymes) key, value pairs + self.sectors = DictList() # a list of EnzymeSectors (protein sectors) + self.sector_constraints = ( + {} + ) # a dict with sector constraint id, optlang.Constraint (enzymes) key, value pairs for constraints related to the protein sectors + self.tpc = 0 # counter of number of CatalyticEvents which contribute to the total protein constraint + self.sensitivity = sensitivity + self.capacity_sensitivity_coefficients = ( + pd.DataFrame() + ) # dataframe to store the result of the sensitivity analysis (capacity sensitivity coefficients for each constraint). The sensitivity coefficients are splitted on LB, UB and the different sectors + self.enzyme_sensitivity_coefficients = ( + pd.DataFrame() + ) # dataframe to store the result of the sensitivity analysis (sensitivity coefficients for each constraint). The sensitivity coefficients are splitted on LB, UB and the different sectors + + # initialize the model + print(f"Setting up the proteome allocation model {self.id}\n") self.add_total_protein_constraint(p_tot) - if sensitivity: #perform sensitivity analysis when the model is run + if sensitivity: # perform sensitivity analysis when the model is run self.add_lb_ub_constraints() - for sector in [active_sector, translational_sector, unused_sector, custom_sectors]: + for sector in [ + active_sector, + translational_sector, + unused_sector, + custom_sectors, + ]: if sector is not None: self.add_sectors([sector]) - print(f'Done with setting up the proteome allocation model {self.id}\n') - + print(f"Done with setting up the proteome allocation model {self.id}\n") @property def total_protein_fraction(self): @@ -148,78 +149,91 @@ def total_protein_fraction(self, p_tot): @property def translational_enzymes(self): - return self.sectors.get_by_id('TranslationalEnzymeSector') + return self.sectors.get_by_id("TranslationalEnzymeSector") @translational_enzymes.setter - def tranlational_enzymes(self, slope:float, intercept:float, lin_rxn_id: str = 'BIOMASS_Ec_iML1515_WT_75p37M'): - #input is in g/gDW - self.change_sector_parameters(self.translational_enzymes, slope, intercept, lin_rxn_id) + def tranlational_enzymes( + self, + slope: float, + intercept: float, + lin_rxn_id: str = "BIOMASS_Ec_iML1515_WT_75p37M", + ): + # input is in g/gDW + self.change_sector_parameters( + self.translational_enzymes, slope, intercept, lin_rxn_id + ) @property def unused_enzymes(self): - return self.sectors.get_by_id('UnusedEnzymeSector') + return self.sectors.get_by_id("UnusedEnzymeSector") @unused_enzymes.setter - def unused_enzymes(self, slope: float, intercept: float, lin_rxn_id: str = 'EX_glc__D_e_b'): + def unused_enzymes( + self, slope: float, intercept: float, lin_rxn_id: str = "EX_glc__D_e_b" + ): self.change_sector_parameters(self.unused_enzymes, slope, intercept, lin_rxn_id) @property def stoichiometric_matrix(self): - #TODO check solver, only works for gurobi solver + # TODO check solver, only works for gurobi solver matrix = self.problem.solver.getA() return matrix.toarray() def add_enzymes(self, enzyme_list: list) -> None: - """Add new enzymes to a model. - Adapted from Cobra.core.model.add_reactions and Cobra.core.model.add_metabolites() + """ + Add new enzymes to a model. + Adapted from Cobra.core.model.add_reactions and Cobra.core.model.add_metabolites. + + This function will add a DictList of enzymes to the model object and add new variables accordingly. + For each enzyme-associated reaction, a constraint in each direction is added to the model. + The change is reverted upon exit when using the model as a context. + + Parameters: + enzyme_list (list or Enzyme): A list of `Enzyme` objects. If it isn't an iterable container, the enzyme will + be placed into a list. + """ - Will add a DictList of enzymes to the model object and add new - variables accordingly. - For each enzyme-associated reaction a constraint in each direction - is added to the model. - The change is reverted upon exit when using the model as a context. - :param - enzyme_list : list or Enzyme. - A list of `Enzyme` objects. If it isn't an iterable - container, the enzyme will be placed into a list. - """ - def existing_filter(enz: Enzyme) -> bool: - """Check if the enzyme does not exist in the model. - Parameters - ---------- - enz: Variables.Enzyme - Returns - ------- - bool - False if enzyme exists, True if it doesn't. - If the enzyme exists, will log a warning. """ + Check if the enzyme does not exist in the model. + + Parameters: + enz (Variables.Enzyme): Enzyme object to check for existence. + + Returns: + bool: + - False if the enzyme exists. + - True if it doesn't. + + If the enzyme exists, a warning will be logged. + """ + if enz.id in self.enzymes: warnings.warn(f"Ignoring enzyme '{enz.id}' since it already exists.") return False return True - + def parameter_filter(enz: Enzyme) -> bool: - """Check enzyme parameters are consistent with the model + """ + Check if enzyme parameters are consistent with the model. + + Parameters: + enz (Enzyme): Enzyme object inferred from cobra.core.reaction. - Parameters - ---------- - enz : Enzyme - Enzyme object inferred from cobra.core.reaction + Returns: + bool: + - True if enzyme parameters are valid and consistent with the model. + - False if they are not valid. - Returns - ------- - bool - enzyme parameter validity + The function checks whether the enzyme parameters provided in the `enz` object are consistent with the model. """ # extract parameters from Enzyme molmass = enz.molmass - + # check molar mass if molmass <= 0: - warnings.warn(f'Molar mass for {enz.id} is invalid: {molmass}') + warnings.warn(f"Molar mass for {enz.id} is invalid: {molmass}") return False # check if enzyme objects are valid @@ -227,10 +241,14 @@ def parameter_filter(enz: Enzyme) -> bool: # extract parameters from enzyme object kcats = catalytic_event.kcats rxn_id = catalytic_event.rxn_id - + # check if reaction associated with the enzyme object exists if rxn_id not in self.reactions: - warnings.warn('Reaction '+ rxn_id +' not in the model. Skip enzyme constraint') + warnings.warn( + "Reaction " + + rxn_id + + " not in the model. Skip enzyme constraint" + ) return False # check kcat values @@ -238,43 +256,58 @@ def parameter_filter(enz: Enzyme) -> bool: for kcat in kcatdict.values(): if kcat < 0: # invalid kcat value - warnings.warn('Turnover number for reaction "'+rxn_id+'" is invalid. Skip for active enzyme sector') + warnings.warn( + 'Turnover number for reaction "' + + rxn_id + + '" is invalid. Skip for active enzyme sector' + ) return False - + # extract reaction from model - reaction = self.reactions.get_by_id(rxn_id) + reaction = self.reactions.get_by_id(rxn_id) for kcats in kcats.values(): # check consistency between provided kcat values and reaction direction if self.sensitivity: - lower_bound = -self.constraints[f'{rxn_id}_lb'].ub - upper_bound = self.constraints[f'{rxn_id}_ub'].ub + lower_bound = -self.constraints[f"{rxn_id}_lb"].ub + upper_bound = self.constraints[f"{rxn_id}_ub"].ub else: lower_bound = reaction.lower_bound upper_bound = reaction.upper_bound if lower_bound >= 0 and upper_bound > 0: # reaction is irreversible in the forward direction - if 'f' not in kcats: # or 'b' in kcats: - warnings.warn(rxn_id+': Inconsistencies between the reaction reversibility and the provided kcat values') + if "f" not in kcats: # or 'b' in kcats: + warnings.warn( + rxn_id + + ": Inconsistencies between the reaction reversibility and the provided kcat values" + ) return False elif lower_bound < 0 and upper_bound <= 0: # reaction is irreversible in the backward direction - if 'b' not in kcats or 'f' in kcats: - warnings.warn(rxn_id+': Inconsistencies between the reaction reversibility and the provided kcat values') + if "b" not in kcats or "f" in kcats: + warnings.warn( + rxn_id + + ": Inconsistencies between the reaction reversibility and the provided kcat values" + ) return False else: # reaction is reversible - if 'f' not in kcats:# or 'b' not in kcats: - warnings.warn(rxn_id+': Inconsistencies between the reaction reversibility and the provided kcat values') + if "f" not in kcats: # or 'b' not in kcats: + warnings.warn( + rxn_id + + ": Inconsistencies between the reaction reversibility and the provided kcat values" + ) return False return True - #check if the input is a list or an enzyme + # check if the input is a list or an enzyme if not hasattr(enzyme_list, "__iter__"): enzyme_list = [enzyme_list] for enz in enzyme_list: if not isinstance(enz, Enzyme): - raise AttributeError('The input was neither an Iterable nor an Enzyme. Please provide only (lists of) Enzyme objects.') + raise AttributeError( + "The input was neither an Iterable nor an Enzyme. Please provide only (lists of) Enzyme objects." + ) # First check whether the enzymes exist in the model. pruned = filter(existing_filter, enzyme_list) @@ -299,43 +332,49 @@ def parameter_filter(enz: Enzyme) -> bool: # populate solver for enzyme in pruned: - #add constraint related to the enzyme - self.add_enzyme_constraints([f'EC_{enzyme.id}_f', f'EC_{enzyme.id}_b']) - #store the constraints connected to the model in the enzyme object + # add constraint related to the enzyme + self.add_enzyme_constraints([f"EC_{enzyme.id}_f", f"EC_{enzyme.id}_b"]) + # store the constraints connected to the model in the enzyme object new_constraints = {} - new_constraints[f'EC_{enzyme.id}_f'] = self.enzyme_constraints[f'EC_{enzyme.id}_f'] - new_constraints[f'EC_{enzyme.id}_b'] = self.enzyme_constraints[f'EC_{enzyme.id}_b'] + new_constraints[f"EC_{enzyme.id}_f"] = self.enzyme_constraints[ + f"EC_{enzyme.id}_f" + ] + new_constraints[f"EC_{enzyme.id}_b"] = self.enzyme_constraints[ + f"EC_{enzyme.id}_b" + ] enzyme._constraints = new_constraints - #add enzyme variable + # add enzyme variable enzyme_variable = enzyme.enzyme_variable if enzyme_variable.id not in self.variables: - # make enzyme variable aware of the model. - # The enzyme variable will be added as variable to the model in the model.setter magic function - enzyme_variable.model = self - # self.enzyme_variables.append(enzyme_variable) - if context: - context(partial(setattr, enzyme_variable, "_model", None)) + # make enzyme variable aware of the model. + # The enzyme variable will be added as variable to the model in the model.setter magic function + enzyme_variable.model = self + # self.enzyme_variables.append(enzyme_variable) + if context: + context(partial(setattr, enzyme_variable, "_model", None)) # connect the enzyme to total protein constraint enzyme_variable = self.enzyme_variables.get_by_id(enzyme.id) self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].set_linear_coefficients( - { #*1e-6 to make calculations more accurate (solver accuracy) - enzyme_variable.forward_variable: enzyme.molmass * 1e-6, - enzyme_variable.reverse_variable: enzyme.molmass * 1e-6, + { # *1e-6 to make calculations more accurate (solver accuracy) + enzyme_variable.forward_variable: enzyme.molmass * 1e-6, + enzyme_variable.reverse_variable: enzyme.molmass * 1e-6, } ) - self.tpc+=1 + self.tpc += 1 # add enzyme constraints to the model and link to enzyme and reaction variables - #get the enzyme variable + # get the enzyme variable enzyme_var_model = self.enzyme_variables.get_by_id(enzyme.id) - #connect enzyme variable to its upper and lower bound - if enzyme.upper_bound > self.p_tot*1e3: + # connect enzyme variable to its upper and lower bound + if enzyme.upper_bound > self.p_tot * 1e3: ub = enzyme.upper_bound else: - ub = self.p_tot*1e3 - self, enzyme = self.make_enzyme_min_max_constraint(self, enzyme, lower_bound=enzyme.lower_bound, upper_bound=ub) + ub = self.p_tot * 1e3 + self, enzyme = self.make_enzyme_min_max_constraint( + self, enzyme, lower_bound=enzyme.lower_bound, upper_bound=ub + ) # add the enzyme to the interface between the enzyme constraints, enzyme variables and the reaction for catalytic_event in enzyme.catalytic_events: @@ -348,65 +387,84 @@ def parameter_filter(enz: Enzyme) -> bool: context(partial(setattr, catalytic_event, "_model", None)) else: # add enzyme to the catalytic event - catalytic_event_model = self.catalytic_events.get_by_id(catalytic_event.id) - #remove block on the reaction of no enzyme constraint if this is present + catalytic_event_model = self.catalytic_events.get_by_id( + catalytic_event.id + ) + # remove block on the reaction of no enzyme constraint if this is present ce_constraints = catalytic_event_model.constraints.copy() for name, constraint in ce_constraints.items(): - if 'no_enzyme' in name: + if "no_enzyme" in name: del catalytic_event_model.constraints[name] self.remove_cons_vars([constraint]) - catalytic_event_model.add_enzymes({enzyme: enzyme.rxn2kcat[catalytic_event.rxn_id]}) + catalytic_event_model.add_enzymes( + {enzyme: enzyme.rxn2kcat[catalytic_event.rxn_id]} + ) # replace the catalytic event with the already existing event from the model enzyme.catalytic_events._replace_on_id(catalytic_event_model) - #connect the enzyme to each of the reactions it is associated with + # connect the enzyme to each of the reactions it is associated with for rxn, kcatdict in enzyme.rxn2kcat.items(): # link enzymatic variable to reaction via the turnover number kcat for direction, kcat in kcatdict.items(): # check direction - if direction != 'f' and direction != 'b': - warnings.warn(['Invalid kcat direction encountered for ', catalytic_event.id, direction]) + if direction != "f" and direction != "b": + warnings.warn( + [ + "Invalid kcat direction encountered for ", + catalytic_event.id, + direction, + ] + ) continue # create enzyme constraint for the reaction if not existent already - constraint_id = 'EC_' + enzyme.id + '_' + direction + constraint_id = "EC_" + enzyme.id + "_" + direction if constraint_id not in self.enzyme_constraints.keys(): self.add_enzyme_constraints([constraint_id]) # kcat is used as a coefficient for the enzyme concentration # Get existent forward/reverse variables - coeff = kcat*3600*1e-6 #3600 to convert to /h to /s *1e-6 to make calculations more accurate - if direction == 'f': + coeff = ( + kcat * 3600 * 1e-6 + ) # 3600 to convert to /h to /s *1e-6 to make calculations more accurate + if direction == "f": self.constraints[constraint_id].set_linear_coefficients( - {enzyme_var_model.forward_variable: -1, - self.reactions.get_by_id(rxn).forward_variable: 1/coeff - }) - elif direction == 'b': + { + enzyme_var_model.forward_variable: -1, + self.reactions.get_by_id(rxn).forward_variable: 1 + / coeff, + } + ) + elif direction == "b": self.constraints[constraint_id].set_linear_coefficients( - {enzyme_var_model.reverse_variable: -1, - self.reactions.get_by_id(rxn).reverse_variable: 1/coeff - }) + { + enzyme_var_model.reverse_variable: -1, + self.reactions.get_by_id(rxn).reverse_variable: 1 + / coeff, + } + ) # make reaction-enzyme interface and the enzyme variable aware of its participation in the constraint - catalytic_event_model.constraints[enzyme.id] = self.constraints[constraint_id] - enzyme_var_model.constraints[constraint_id] = self.constraints[constraint_id] + catalytic_event_model.constraints[enzyme.id] = self.constraints[ + constraint_id + ] + enzyme_var_model.constraints[constraint_id] = self.constraints[ + constraint_id + ] self.solver.update() def add_sectors(self, sectors: List = None): """ - Adds sector variables to the model and add these tot the total protein constraint. - - Parameters - ---------- - sectors: list - list of PAModelpy.EnzymeSectors to add to the model + Adds sector variables to the model and adds these to the total protein constraint. + Parameters: + sectors (list): A list of PAModelpy.EnzymeSectors to add to the model. """ self.sectors += sectors for sector in sectors: - #different method to add the active enzyme_sector + # different method to add the active enzyme_sector if isinstance(sector, ActiveEnzymeSector): self = sector.add(self) else: @@ -414,92 +472,91 @@ def add_sectors(self, sectors: List = None): def add_sector(self, sector): """ - Adds the sector variable for a specific sector to the model and add this tot the total protein constraint. - Also stores the sector variables in the model attributes - - Parameters - ---------- - sectors: PAModelpy.EnzymeSectors to add to the model + Adds the sector variable for a specific sector to the model and adds this to the total protein constraint. + Also stores the sector variables in the model attributes. - """ - print(f'Add the following protein sector: {sector.id}\n') - #make sector aware of the model + Parameters: + sector (PAModelpy.EnzymeSector): The specific EnzymeSector to add to the model. + """ + print(f"Add the following protein sector: {sector.id}\n") + # make sector aware of the model sector.model = self lin_rxn = self.reactions.get_by_id(sector.id_list[0]) - - #relate the sector to the total protein if it is there + # relate the sector to the total protein if it is there if self.TOTAL_PROTEIN_CONSTRAINT_ID in self.constraints.keys(): # add parts of constraint corresponding to the enzyme sector to the total_protein_constraint # 1. subtract the intercept value from the sum of protein (Total_protein == Etot - phi_sector tpc_ub = self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].ub - self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].ub = tpc_ub - sector.intercept + self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].ub = ( + tpc_ub - sector.intercept + ) - #2. link flux to enzyme concentration + # 2. link flux to enzyme concentration # link enzyme concentration in the sector to the total enzyme concentration - self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].set_linear_coefficients({ - lin_rxn.forward_variable: sector.slope, - lin_rxn.reverse_variable: -sector.slope - }) + self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].set_linear_coefficients( + { + lin_rxn.forward_variable: sector.slope, + lin_rxn.reverse_variable: -sector.slope, + } + ) else: # constraint corresponding to the enzyme sector constraint = self.problem.Constraint(Zero, name=sector.id, lb=0, ub=0) self.add_sector_constraints([constraint]) # add variable which represents the concentration of enzymes in this enzyme sector - var = self.problem.Variable('R_' + sector.id) + var = self.problem.Variable("R_" + sector.id) self.add_cons_vars(var) # link flux to enzyme concentration - self.constraints[sector.id].set_linear_coefficients({ - var: 1, - lin_rxn.forward_variable: -sector.slope, - lin_rxn.reverse_variable: sector.slope - }) + self.constraints[sector.id].set_linear_coefficients( + { + var: 1, + lin_rxn.forward_variable: -sector.slope, + lin_rxn.reverse_variable: sector.slope, + } + ) # Make a variable which represents the intercept - var_0 = self.problem.Variable('R0_' + sector.id) + var_0 = self.problem.Variable("R0_" + sector.id) var_0.lb, var_0.ub = sector.intercept, sector.intercept self.add_cons_vars(var_0) - #add intercept to the constraint definition - self.constraints[sector.id].set_linear_coefficients({ - var_0: -1}) + # add intercept to the constraint definition + self.constraints[sector.id].set_linear_coefficients({var_0: -1}) - #save the intercept variable for easy removal of the sector + # save the intercept variable for easy removal of the sector sector.variables += [var_0] # save variable and constraint for easy removal of the sector sector.constraints += [self.sector_constraints[sector.id]] sector.variables += [var] - #add sector to sectorlist is it isn't included already + # add sector to sectorlist is it isn't included already if not self.sectors.has_id(sector.id): self.sectors += [sector] - def add_catalytic_events(self, catalytic_events: Optional[Iterable]): """ Add a new CatalyticEvent to the model. - Will add a list of CatalyticEvent variables to the model object using the function defined in the - CatalyticEvent object. - - Parameters - ---------- - catalytic_events : list or variables.CatalyticEvent. - A list of `variables.CatalyticEvent` objects. If it isn't an iterable - container, the catalytic event will be placed into a list. + Will add a list of CatalyticEvent variables to the model object using the function defined in the CatalyticEvent object. + Parameters: + catalytic_events (list or variables.CatalyticEvent): A list of `variables.CatalyticEvent` objects. If it isn't + an iterable container, the catalytic event will be placed into a list. """ - #check if the input is an iterable + # check if the input is an iterable if not hasattr(catalytic_events, "__iter__"): catalytic_events = [catalytic_events] if len(catalytic_events) == 0: return None # First check whether the catalytic exist in the model - catalytic_events = [x for x in catalytic_events if x.id not in self.catalytic_events] + catalytic_events = [ + x for x in catalytic_events if x.id not in self.catalytic_events + ] # if there already exists a catalytic event, the enzymes of the new event should be added to the old one present_events = [x for x in catalytic_events if x.id in self.catalytic_events] @@ -511,44 +568,45 @@ def add_catalytic_events(self, catalytic_events: Optional[Iterable]): self.catalytic_events += catalytic_events bad_ids = [ - m for m in catalytic_events if not isinstance(m.id, str) or len(m.id) < 1 - ] + m for m in catalytic_events if not isinstance(m.id, str) or len(m.id) < 1 + ] if len(bad_ids) != 0: raise ValueError(f"invalid identifiers in {repr(bad_ids)}") for event in catalytic_events: - #make catalytic event aware of the model. This will automatically add the catalytic event to the model + # make catalytic event aware of the model. This will automatically add the catalytic event to the model event.model = self - def add_enzyme_constraints(self, constraint_list:Optional[list]): - - """Add new enzyme constraint to a model. - Will add a list of constraints to the model object and add new - constraints accordingly. + def add_enzyme_constraints(self, constraint_list: Optional[list]): + """ + Add new enzyme constraints to a model. + Will add a list of constraints to the model object and add new constraints accordingly. The change is reverted upon exit when using the model as a context. - Parameters - ---------- - constraint_list : list, str or constraints.Constraint. - A list of `constraints.Constraint` objects. If it isn't an iterable - container, the constraint will be placed into a list. Also, a string with - the constraint id can be provided. A constraint will be created before adding - it to the model + + Parameters: + constraint_list (list, str, or constraints.Constraint): A list of `constraints.Constraint` objects. If it isn't + an iterable container, the constraint will be placed into a list. Also, a string with the constraint id + can be provided. A constraint will be created before adding it to the model. """ if not hasattr(constraint_list, "__iter__"): constraint_list = [constraint_list] if len(constraint_list) == 0: return None - #check wether the input is a cobra.Metabolite or string and convert it to constraint + # check wether the input is a cobra.Metabolite or string and convert it to constraint for i, cons in enumerate(constraint_list): if isinstance(cons, Metabolite) or isinstance(cons, Constraint): - constraint_list[i] = self.problem.Constraint(Zero, name=cons.id, lb=0, ub=0) + constraint_list[i] = self.problem.Constraint( + Zero, name=cons.id, lb=0, ub=0 + ) if isinstance(cons, str): constraint = self.problem.Constraint(Zero, name=cons, lb=0, ub=0) constraint_list[i] = constraint # First check whether the metabolites exist in the model - constraint_list = [x for x in constraint_list if x.name not in self.enzyme_constraints.keys()] + constraint_list = [ + x for x in constraint_list if x.name not in self.enzyme_constraints.keys() + ] bad_ids = [ m for m in constraint_list if not isinstance(m.name, str) or len(m.name) < 1 ] @@ -574,18 +632,16 @@ def add_enzyme_constraints(self, constraint_list:Optional[list]): context(partial(setattr, x, "_model", None)) self.solver.update() + def add_sector_constraints(self, constraint_list: Optional[list]): + """ + Add a new constraint related to a sector to a model. + Will add a list of constraints to the model object and add new constraints accordingly. + The change is reverted upon exit when using the model as a context. - def add_sector_constraints(self, constraint_list:Optional[list]): - """Add new constraint related to an sector to a model. - Will add a list of constraint to the model object and add new - constraints accordingly. - The change is reverted upon exit when using the model as a context. - Parameters - ---------- - constraint_list : list or constraints.Constraint. - A list of `constraints.Constraint` objects. If it isn't an iterable - container, the constraint will be placed into a list. - """ + Parameters: + constraint_list (list or constraints.Constraint): A list of `constraints.Constraint` objects. If it isn't an iterable + container, the constraint will be placed into a list. + """ if not hasattr(constraint_list, "__iter__"): constraint_list = [constraint_list] @@ -593,7 +649,9 @@ def add_sector_constraints(self, constraint_list:Optional[list]): return None # First check whether the metabolites exist in the model - constraint_list = [x for x in constraint_list if x.name not in self.sector_constraints.keys()] + constraint_list = [ + x for x in constraint_list if x.name not in self.sector_constraints.keys() + ] bad_ids = [ m for m in constraint_list if not isinstance(m.name, str) or len(m.name) < 1 @@ -621,28 +679,29 @@ def add_sector_constraints(self, constraint_list:Optional[list]): context(partial(setattr, x, "_model", None)) self.solver.update() - def add_total_protein_constraint(self, p_tot: Optional[float]=P_TOT_DEFAULT): + def add_total_protein_constraint(self, p_tot: Optional[float] = P_TOT_DEFAULT): """ Function which adds the total protein constraint to the model. - This limits the amount of available enzymes and thus the resulting fluxes - constraint expression: - Etot: sum(E) + E_translprot + E_unusedprot == ptot - E_trsn_0 - E_ue_0 - - :param - p_tot: float, optional - Fraction of biomass which consists of protein (g_protein/g_cdw) - default: 0.258 (E.coli) + This limits the amount of available enzymes and thus the resulting fluxes. + + Notes: + The constraint expression looks like this: + `Etot: sum(E) + E_translprot + E_unusedprot == p_tot - E_trsn_0 - E_ue_0` + + Parameters: + p_tot (float, optional): Fraction of biomass which consists of protein (g_protein/g_cdw). + Default is 0.258 (E.coli). """ - #check if we should add total protein constraint + # check if we should add total protein constraint if isinstance(self.p_tot, bool) and isinstance(p_tot, bool): - print('Total condition-dependent protein constraint is not added \n') + print("Total condition-dependent protein constraint is not added \n") return - print('Add total condition-dependent protein constraint') + print("Add total condition-dependent protein constraint") if isinstance(p_tot, float): self.p_tot = p_tot - print(f'\tTotal protein concentration: {p_tot} g/gDW\n') + print(f"\tTotal protein concentration: {p_tot} g/gDW\n") # check if there already is a total protein constraint in the solver if self.TOTAL_PROTEIN_CONSTRAINT_ID in self.constraints.keys(): @@ -650,30 +709,32 @@ def add_total_protein_constraint(self, p_tot: Optional[float]=P_TOT_DEFAULT): else: # Create the pseudometabolite associated with the constraint, this metabolite will be 'produced' in the enzyme 'reactions' # *1e3 to convert gp/gcdw to mg/gcdw for easier computing - tpc_constraint = self.problem.Constraint(Zero, name=self.TOTAL_PROTEIN_CONSTRAINT_ID, lb=0, - ub=self.p_tot * 1e3) + tpc_constraint = self.problem.Constraint( + Zero, name=self.TOTAL_PROTEIN_CONSTRAINT_ID, lb=0, ub=self.p_tot * 1e3 + ) self.sector_constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID] = tpc_constraint self.add_cons_vars([tpc_constraint]) def add_reactions(self, reaction_list: Iterable[Reaction]) -> None: - """Add reactions to the model. + """ + Add reactions to the model. This method is superimposed upon the cobra.Model.add_reactions() function. As a new feature, it will add constraints to determine the lower and upper bound if a sensitivity analysis should be performed (which is determined by the model attribute: PAModel.sensitivity). - Reactions with identifiers identical to a reaction already in the - model are ignored. + Reactions with identifiers identical to a reaction already in the model are ignored. The change is reverted upon exit when using the model as a context. - Parameters - ---------- - reaction_list : list - A list of `cobra.Reaction` objects + + Parameters: + reaction_list (list): A list of `cobra.Reaction` objects. """ super().add_reactions(reaction_list=reaction_list) - #add upper and lower bound constraints if you want to perform a sensitivity analysis + # add upper and lower bound constraints if you want to perform a sensitivity analysis if self.sensitivity: for rxn in reaction_list: - self = self.make_lb_ub_constraint(self, rxn, rxn.lower_bound, rxn.upper_bound) - #reset the reaction bounds + self = self.make_lb_ub_constraint( + self, rxn, rxn.lower_bound, rxn.upper_bound + ) + # reset the reaction bounds rxn.lower_bound, rxn.upper_bound = -1e6, 1e6 def add_lb_ub_constraints(self): @@ -683,146 +744,155 @@ def add_lb_ub_constraints(self): calculated and used in sensitivity analysis """ for rxn in self.reactions: - self = self.make_lb_ub_constraint(self, rxn, rxn.lower_bound, rxn.upper_bound) + self = self.make_lb_ub_constraint( + self, rxn, rxn.lower_bound, rxn.upper_bound + ) rxn.lower_bound, rxn.upper_bound = -1e6, 1e6 @staticmethod - def make_lb_ub_constraint(m: Optional[Model], rxn: Reaction, lower_bound: float, upper_bound:float): + def make_lb_ub_constraint( + m: Optional[Model], rxn: Reaction, lower_bound: float, upper_bound: float + ): """ - Adding variables and constraints for the lower and upperbounds of a reaction to a model. - When solving the model, shadow prices for the lower and upperbounds will be calculated. + Adding variables and constraints for the lower and upper bounds of a reaction to a model. + When solving the model, shadow prices for the lower and upper bounds will be calculated. This allows for the calculation of sensitivity coefficients. The constraints are formulated as follows: - R_ub : R_fwd-R_rev <= UB - R_lb : -(R_fwd-R_rev) <= -LB - - Parameters - ---------- - m: cobra.Model or PAModelpy.PAModel - model to which the upper and lowerbound constraints and variables should be added - rxn: cobra.Reaction - reaction for which an upper and lowerbound constraints should be generated - lower_bound: float - Value of the lowerbound - upper_bound: float - Value of the upperbound - - Returns - ------- - m : cobra.Model or PAModelpy.PAModel - model with additional constraints and variables for the reactions + + Notes: + Constraints are formulated as follows: + - `R_ub: R_fwd - R_rev <= UB` + - `R_lb: -(R_fwd - R_rev) <= -LB` + + Parameters: + m (cobra.Model or PAModelpy.PAModel): The model to which the upper and lower bound constraints and variables + should be added. + rxn (cobra.Reaction): The reaction for which upper and lower bound constraints should be generated. + lower_bound (float): The value of the lower bound. + upper_bound (float): The value of the upper bound. + + Returns: + m (cobra.Model or PAModelpy.PAModel): The model with additional constraints and variables for the reactions. """ - #check if the constraints already exists in the model. If it does, change the bounds - if f'{rxn.id}_ub' in m.constraints.keys(): - m.constraints[f'{rxn.id}_ub'].ub = upper_bound - if f'{rxn.id}_lb' in m.constraints.keys(): - m.constraints[f'{rxn.id}_lb'].ub = -lower_bound + # check if the constraints already exists in the model. If it does, change the bounds + if f"{rxn.id}_ub" in m.constraints.keys(): + m.constraints[f"{rxn.id}_ub"].ub = upper_bound + if f"{rxn.id}_lb" in m.constraints.keys(): + m.constraints[f"{rxn.id}_lb"].ub = -lower_bound else: - ub_constraint = m.problem.Constraint(Zero, name=f'{rxn.id}_ub', lb=-1e6, ub=upper_bound) - lb_constraint = m.problem.Constraint(Zero, name=f'{rxn.id}_lb', lb=-1e6, ub=-lower_bound) + ub_constraint = m.problem.Constraint( + Zero, name=f"{rxn.id}_ub", lb=-1e6, ub=upper_bound + ) + lb_constraint = m.problem.Constraint( + Zero, name=f"{rxn.id}_lb", lb=-1e6, ub=-lower_bound + ) m.add_cons_vars([ub_constraint, lb_constraint]) # setting up the constraints - m.constraints[f'{rxn.id}_ub'].set_linear_coefficients({ - rxn.forward_variable: 1, - rxn.reverse_variable: -1 - }) - m.constraints[f'{rxn.id}_lb'].set_linear_coefficients({ - rxn.forward_variable: -1, - rxn.reverse_variable: 1 - }) + m.constraints[f"{rxn.id}_ub"].set_linear_coefficients( + {rxn.forward_variable: 1, rxn.reverse_variable: -1} + ) + m.constraints[f"{rxn.id}_lb"].set_linear_coefficients( + {rxn.forward_variable: -1, rxn.reverse_variable: 1} + ) return m @staticmethod - def make_enzyme_min_max_constraint(m: Optional[Model], enz: Enzyme, lower_bound: float, upper_bound:float): + def make_enzyme_min_max_constraint( + m: Optional[Model], enz: Enzyme, lower_bound: float, upper_bound: float + ): """ - Adding variables and constraints for the lower and upperbounds of a reaction to a model. - When solving the model, shadow prices for the lower and upperbounds will be calculated. - This allows for the calculation of sensitivity coefficients. The constraints are formulated as follows: - R_ub : E <= Emax - R_lb : -E <= -Emin - - Parameters - ---------- - m: cobra.Model or PAModelpy.PAModel - model to which the upper and lowerbound constraints and variables should be added - rxn: PAModelpy.Enzyme - Enzyme for which minimal and maximal concentration constraints should be generated - lower_bound: float - Value of the lowerbound - upper_bound: float - Value of the upperbound - - Returns - ------- - m : cobra.Model or PAModelpy.PAModel - model with additional constraints and variables for the reactions + Adding variables and constraints for the lower and upper bounds of an enzyme's concentration to a model. + When solving the model, shadow prices for the lower and upper bounds will be calculated. + This allows for the calculation of sensitivity coefficients. + + Notes: + The constraints are formulated as follows: + - `E_ub: E <= Emax` + - `E_lb: -E <= -Emin` + + Parameters: + m (cobra.Model or PAModelpy.PAModel): The model to which the upper and lower bound constraints and variables + should be added. + rxn (PAModelpy.Enzyme): The enzyme for which minimal and maximal concentration constraints should be generated. + lower_bound (float): The value of the lower bound. + upper_bound (float): The value of the upper bound. + + Returns: + m (cobra.Model or PAModelpy.PAModel): The model with additional constraints and variables for the enzyme's + concentration. """ + # make separate constraints for forward and reverse enzyme variables fwd_var = m.enzyme_variables.get_by_id(enz.id).forward_variable rev_var = m.enzyme_variables.get_by_id(enz.id).reverse_variable - - if f'{enz.id}_max' in m.constraints.keys(): - m.constraints[f'{enz.id}_max'].ub = upper_bound - if f'{enz.id}_min' in m.constraints.keys(): - m.constraints[f'{enz.id}_min'].ub = -lower_bound + if f"{enz.id}_max" in m.constraints.keys(): + m.constraints[f"{enz.id}_max"].ub = upper_bound + if f"{enz.id}_min" in m.constraints.keys(): + m.constraints[f"{enz.id}_min"].ub = -lower_bound else: - max_constraint = m.problem.Constraint(Zero, name=f'{enz.id}_max', lb=-1e6, ub=upper_bound) - min_constraint = m.problem.Constraint(Zero, name=f'{enz.id}_min', lb=-1e6, ub=-lower_bound) + max_constraint = m.problem.Constraint( + Zero, name=f"{enz.id}_max", lb=-1e6, ub=upper_bound + ) + min_constraint = m.problem.Constraint( + Zero, name=f"{enz.id}_min", lb=-1e6, ub=-lower_bound + ) m.add_cons_vars([max_constraint, min_constraint]) # setting up the constraints - m.constraints[f'{enz.id}_max'].set_linear_coefficients({ - fwd_var: 1, - rev_var:1 - }) - m.constraints[f'{enz.id}_min'].set_linear_coefficients({ - fwd_var: -1, - rev_var: -1 - }) + m.constraints[f"{enz.id}_max"].set_linear_coefficients( + {fwd_var: 1, rev_var: 1} + ) + m.constraints[f"{enz.id}_min"].set_linear_coefficients( + {fwd_var: -1, rev_var: -1} + ) - #save the constraints in the enzyme object - enz._constraints = {**enz._constraints, **{f'{enz.id}_max':m.constraints[f'{enz.id}_max'], f'{enz.id}_min':m.constraints[f'{enz.id}_min']}} + # save the constraints in the enzyme object + enz._constraints = { + **enz._constraints, + **{ + f"{enz.id}_max": m.constraints[f"{enz.id}_max"], + f"{enz.id}_min": m.constraints[f"{enz.id}_min"], + }, + } - return m,enz + return m, enz def determine_sensitivity_coefficients(self): obj_value = self.objective.value # v_z if obj_value == 0: - print('Objective value is 0, thus sensitivity coefficients cannot be calculated') + print( + "Objective value is 0, thus sensitivity coefficients cannot be calculated" + ) return mu = self.parse_shadow_prices(self.solver.shadow_prices) - mu_ub = mu[(mu['direction'] == 'ub')].reset_index() - mu_lb = mu[( - mu['direction'] == 'lb')].reset_index() - mu_ec_max = mu[(mu['direction'] == 'max')].reset_index() - mu_ec_min = mu[(mu['direction'] == 'min')].reset_index() - mu_ec_f = mu[(mu['direction'] == 'f')].reset_index() - mu_ec_b = mu[(mu['direction'] == 'b')].reset_index() + mu_ub = mu[(mu["direction"] == "ub")].reset_index() + mu_lb = mu[(mu["direction"] == "lb")].reset_index() + mu_ec_max = mu[(mu["direction"] == "max")].reset_index() + mu_ec_min = mu[(mu["direction"] == "min")].reset_index() + mu_ec_f = mu[(mu["direction"] == "f")].reset_index() + mu_ec_b = mu[(mu["direction"] == "b")].reset_index() self.calculate_csc(obj_value, mu, mu_ub, mu_lb, mu_ec_max, mu_ec_min) self.calculate_esc(obj_value, mu_ec_f, mu_ec_b) # self.validate_sensitivity_coefficients() - @staticmethod def parse_shadow_prices(shadow_prices): """ - Parsing the shadow prices to a dataframe which has for each constraint a row and the shadowprices and directions - in the columns - :param shadow_prices: - :return: + Parse the shadow prices to a DataFrame where each constraint corresponds to a row, and shadow prices and directions are columns. """ - df = pd.DataFrame(pd.Series(shadow_prices), columns=['shadow_prices']) + + df = pd.DataFrame(pd.Series(shadow_prices), columns=["shadow_prices"]) # extract only reaction bounds - df['rxn_id'] = df.index + df["rxn_id"] = df.index splitted_rxn_df = pd.DataFrame( - [x.rsplit('_', 1) for x in df.rxn_id.tolist()], - columns=['rxn_id', 'direction'] + [x.rsplit("_", 1) for x in df.rxn_id.tolist()], + columns=["rxn_id", "direction"], ) - df = df.drop('rxn_id', axis=1) + df = df.drop("rxn_id", axis=1) df = df.reset_index() df_long = pd.concat([df, splitted_rxn_df], axis=1) # df_long[['rxn_id', 'direction']] = df_long['rxn_id'].str.rsplit('_', 1, expand = True).rename(columns=lambda x: 'col{}'.format(x + 1)) @@ -830,326 +900,422 @@ def parse_shadow_prices(shadow_prices): def calculate_csc(self, obj_value, mu, mu_ub, mu_lb, mu_ec_f, mu_ec_b): """ - Calculates the capacity sensitivity coefficient for all inequality constraints in the model. + Calculate the capacity sensitivity coefficient for all inequality constraints in the model. The sum of all capacity sensitivity coefficients should equal 1 for growth maximization. - Definition: constraint_UB*shadowprice/obj_value. - - :param obj_value: Float - :param mu: DataFrame - Shadowprices for all constraints - :param mu_ub: DataFrame - Shadowprices for the reaction UB constraints - :param mu_lb: DataFrame - Shadowprices for the reaction LB constraints - :param mu_ec_f: DataFrame - Shadowprices for the constraint related to an enzymatic catalysis of the forward reaction - :param mu_ec_b: DataFrame - Shadowprices for the constraint related to an enzymatic catalysis of the backward reaction - - Results will be saved in the self.capacity_sensitivity_coefficients attribute as a dataframe + Capacity Sensitivity Coefficient Calculation: + Capacity Sensitivity Coefficient = constraint_UB * shadowprice / obj_value + + Parameters: + obj_value (float): The objective value of the model. + mu (DataFrame): Shadow prices for all constraints. + mu_ub (DataFrame): Shadow prices for the reaction upper bound (UB) constraints. + mu_lb (DataFrame): Shadow prices for the reaction lower bound (LB) constraints. + mu_ec_f (DataFrame): Shadow prices for the constraints related to enzymatic catalysis of the forward reaction. + mu_ec_b (DataFrame): Shadow prices for the constraints related to enzymatic catalysis of the backward reaction. + + Returns: + None + + Results are saved in the `self.capacity_sensitivity_coefficients` attribute as a DataFrame. """ - self.capacity_sensitivity_coefficients = pd.DataFrame(columns=['rxn_id', 'enzyme_id', 'constraint', 'coefficient']) + + self.capacity_sensitivity_coefficients = pd.DataFrame( + columns=["rxn_id", "enzyme_id", "constraint", "coefficient"] + ) # add capacity sensitivity coefficients for sectors if they are there if self.TOTAL_PROTEIN_CONSTRAINT_ID in self.constraints.keys(): for sector in self.sectors: - constraint = 'proteome' + constraint = "proteome" if isinstance(sector, ActiveEnzymeSector): rxn_id = self.TOTAL_PROTEIN_CONSTRAINT_ID enzyme_id = self.TOTAL_PROTEIN_CONSTRAINT_ID - ca_coefficient = self.constraints[enzyme_id].ub * mu[mu['rxn_id'] == self.TOTAL_PROTEIN_CONSTRAINT_ID]['shadow_prices'].iloc[0] / obj_value + ca_coefficient = ( + self.constraints[enzyme_id].ub + * mu[mu["rxn_id"] == self.TOTAL_PROTEIN_CONSTRAINT_ID][ + "shadow_prices" + ].iloc[0] + / obj_value + ) new_row = [rxn_id, enzyme_id, constraint, ca_coefficient] # add new_row to dataframe - self.capacity_sensitivity_coefficients.loc[len(self.capacity_sensitivity_coefficients)] = new_row + self.capacity_sensitivity_coefficients.loc[ + len(self.capacity_sensitivity_coefficients) + ] = new_row - #treat sectors separately if there is not a total protein constraint + # treat sectors separately if there is not a total protein constraint else: for sector in self.sectors: - constraint = 'sector' - rxn_id = 'R_' + sector.id + constraint = "sector" + rxn_id = "R_" + sector.id enzyme_id = sector.id - ca_coefficient = self.constraints[enzyme_id].ub * mu[mu['rxn_id'] == sector.id]['shadow_prices'].iloc[0] / obj_value + ca_coefficient = ( + self.constraints[enzyme_id].ub + * mu[mu["rxn_id"] == sector.id]["shadow_prices"].iloc[0] + / obj_value + ) new_row = [rxn_id, enzyme_id, constraint, ca_coefficient] # add new_row to dataframe - self.capacity_sensitivity_coefficients.loc[len(self.capacity_sensitivity_coefficients)] = new_row + self.capacity_sensitivity_coefficients.loc[ + len(self.capacity_sensitivity_coefficients) + ] = new_row for rxn in self.reactions: # LB - sign=1 + sign = 1 - if 'EX_' in rxn.id or self.constraints[f'{rxn.id}_lb'].ub<0: + if "EX_" in rxn.id or self.constraints[f"{rxn.id}_lb"].ub < 0: sign = -1 - ca_coefficient_LB = -sign*self.constraints[f'{rxn.id}_lb'].ub * mu_lb[mu_lb['rxn_id'] == rxn.id]['shadow_prices'].iloc[0] / obj_value + ca_coefficient_LB = ( + -sign + * self.constraints[f"{rxn.id}_lb"].ub + * mu_lb[mu_lb["rxn_id"] == rxn.id]["shadow_prices"].iloc[0] + / obj_value + ) # UB - ca_coefficient_UB = self.constraints[f'{rxn.id}_ub'].ub * mu_ub[mu_ub['rxn_id'] == rxn.id]['shadow_prices'].iloc[0] / obj_value + ca_coefficient_UB = ( + self.constraints[f"{rxn.id}_ub"].ub + * mu_ub[mu_ub["rxn_id"] == rxn.id]["shadow_prices"].iloc[0] + / obj_value + ) - new_row_UB = [rxn.id,'', 'flux_ub', ca_coefficient_UB] - new_row_LB = [rxn.id,'', 'flux_lb', ca_coefficient_LB] + new_row_UB = [rxn.id, "", "flux_ub", ca_coefficient_UB] + new_row_LB = [rxn.id, "", "flux_lb", ca_coefficient_LB] # add new_row to dataframe - self.capacity_sensitivity_coefficients.loc[len(self.capacity_sensitivity_coefficients)] = new_row_UB - self.capacity_sensitivity_coefficients.loc[len(self.capacity_sensitivity_coefficients)] = new_row_LB + self.capacity_sensitivity_coefficients.loc[ + len(self.capacity_sensitivity_coefficients) + ] = new_row_UB + self.capacity_sensitivity_coefficients.loc[ + len(self.capacity_sensitivity_coefficients) + ] = new_row_LB for enzyme in self.enzymes: # get reactions associated with this enzyme - reactions = ','.join(self.get_reactions_with_enzyme_id(enzyme.id)) + reactions = ",".join(self.get_reactions_with_enzyme_id(enzyme.id)) # get the right row from the shadow price dataframes # min constraints can be skipped as they always equal to 0 (the enzymes cannot have a concentration lower than 0) - mu_ec_max_row = mu_ec_f[mu_ec_f['index'] == f'{enzyme.id}_max'] - mu_ec_min_row = mu_ec_b[mu_ec_b['index'] == f'{enzyme.id}_min'] + mu_ec_max_row = mu_ec_f[mu_ec_f["index"] == f"{enzyme.id}_max"] + mu_ec_min_row = mu_ec_b[mu_ec_b["index"] == f"{enzyme.id}_min"] # max Enzyme constraint - ca_coefficient_EC_max= self.constraints[f'{enzyme.id}_max'].ub * mu_ec_max_row['shadow_prices'].iloc[0] / obj_value - new_enzyme_row_EC_max =[reactions, enzyme.id, 'enzyme_max', ca_coefficient_EC_max] + ca_coefficient_EC_max = ( + self.constraints[f"{enzyme.id}_max"].ub + * mu_ec_max_row["shadow_prices"].iloc[0] + / obj_value + ) + new_enzyme_row_EC_max = [ + reactions, + enzyme.id, + "enzyme_max", + ca_coefficient_EC_max, + ] # add new_row to dataframe - self.capacity_sensitivity_coefficients.loc[len(self.capacity_sensitivity_coefficients)] = new_enzyme_row_EC_max + self.capacity_sensitivity_coefficients.loc[ + len(self.capacity_sensitivity_coefficients) + ] = new_enzyme_row_EC_max # min Enzyme constraint - ca_coefficient_EC_min = self.constraints[f'{enzyme.id}_min'].ub * mu_ec_min_row['shadow_prices'].iloc[0] / obj_value - new_enzyme_row_EC_min =[reactions, enzyme.id, 'enzyme_min', ca_coefficient_EC_min] + ca_coefficient_EC_min = ( + self.constraints[f"{enzyme.id}_min"].ub + * mu_ec_min_row["shadow_prices"].iloc[0] + / obj_value + ) + new_enzyme_row_EC_min = [ + reactions, + enzyme.id, + "enzyme_min", + ca_coefficient_EC_min, + ] # add new_row to dataframe - self.capacity_sensitivity_coefficients.loc[len(self.capacity_sensitivity_coefficients)] = new_enzyme_row_EC_min + self.capacity_sensitivity_coefficients.loc[ + len(self.capacity_sensitivity_coefficients) + ] = new_enzyme_row_EC_min def calculate_esc(self, obj_value, mu_ec_f, mu_ec_b): """ - Calculates enzyme sensitivity coefficients for the enzyme variables using their primal values - the objective value and shadow prices according to the following relations: + Calculate enzyme sensitivity coefficients for the enzyme variables using their primal values, + the objective value, and shadow prices according to the following relations: + + Enzyme Sensitivity Coefficient Calculation: + esc = enzyme_variable.primal * constraint.shadowprice / obj_value - esc = enzyme_variable.primal * constraint.shadowprice/obj.value + Parameters: + obj_value (float): The objective value from the most recent optimal solution. + mu_ec_f (pd.DataFrame): Shadow prices for maximizing enzyme concentrations (forward variables). + mu_ec_b (pd.DataFrame): Shadow prices for minimizing enzyme concentrations (reverse variables). - :param obj_value: float: objective value from the most recent optimal solution - :param mu_ec_f: pd.DataFrame: shadow prices for max enzyme concentrations (forward variables) - :param mu_ec_b: pd.DataFrame: shadow prices for the min enzyme concentrations (reverse variables) + Returns: + None - :return: fills the PAModel.enzyme_sensitivity_coefficients dataframe + Fills the `PAModel.enzyme_sensitivity_coefficients` dataframe with the calculated enzyme sensitivity coefficients. """ - self.enzyme_sensitivity_coefficients = pd.DataFrame(columns=['rxn_id', 'enzyme_id', 'coefficient']) - #calculate enzyme sensitivity coefficient + self.enzyme_sensitivity_coefficients = pd.DataFrame( + columns=["rxn_id", "enzyme_id", "coefficient"] + ) + + # calculate enzyme sensitivity coefficient for enzyme in self.enzymes: - #get the reactions associated with the enzyme - reactions = ','.join(self.get_reactions_with_enzyme_id(enzyme.id)) + # get the reactions associated with the enzyme + reactions = ",".join(self.get_reactions_with_enzyme_id(enzyme.id)) - #get the right row from the shadow price dataframes - sp_ec_f = mu_ec_f[mu_ec_f['rxn_id']== f'EC_{enzyme.id}']['shadow_prices'].iloc[0] - sp_ec_b = mu_ec_b[mu_ec_b['rxn_id'] == f'EC_{enzyme.id}']['shadow_prices'].iloc[0] + # get the right row from the shadow price dataframes + sp_ec_f = mu_ec_f[mu_ec_f["rxn_id"] == f"EC_{enzyme.id}"][ + "shadow_prices" + ].iloc[0] + sp_ec_b = mu_ec_b[mu_ec_b["rxn_id"] == f"EC_{enzyme.id}"][ + "shadow_prices" + ].iloc[0] e_fwd = self.enzyme_variables.get_by_id(enzyme.id).forward_variable.primal e_rev = self.enzyme_variables.get_by_id(enzyme.id).reverse_variable.primal - #EC: enzyme constraint - enzyme_sensitivity_coefficient = (e_fwd * sp_ec_f + e_rev * sp_ec_b)/obj_value - #add new_row to dataframe - self.enzyme_sensitivity_coefficients.loc[len(self.enzyme_sensitivity_coefficients)] = [reactions, enzyme.id, - enzyme_sensitivity_coefficient] + # EC: enzyme constraint + enzyme_sensitivity_coefficient = ( + e_fwd * sp_ec_f + e_rev * sp_ec_b + ) / obj_value + # add new_row to dataframe + self.enzyme_sensitivity_coefficients.loc[ + len(self.enzyme_sensitivity_coefficients) + ] = [reactions, enzyme.id, enzyme_sensitivity_coefficient] def calculate_sum_of_enzymes(self): """ - Calculates the sum of all enzyme variables for a feasible solution + Calculate the sum of all enzyme variables for a feasible solution. - :return: sum: float: sum of all enzyme variables in mg/gcdw/h + Returns: + float: The sum of all enzyme variables in milligrams per gram of cell dry weight per hour (mg/gCDW/h). """ - if self.solver.status != 'optimal': - warnings.warn('Cannot calculate sum of enzymes: solution is not optimal') + + if self.solver.status != "optimal": + warnings.warn("Cannot calculate sum of enzymes: solution is not optimal") return - sum = 0 #mg/gcdw/h + sum = 0 # mg/gcdw/h for enzyme in self.enzyme_variables: - #convert from mmol to mg using the same formulation as the coefficients in the protein pool constraint - sum+= enzyme.concentration *enzyme.molmass *1e-6 + # convert from mmol to mg using the same formulation as the coefficients in the protein pool constraint + sum += enzyme.concentration * enzyme.molmass * 1e-6 return sum - def change_total_protein_constraint(self, p_tot): """ - Changing the fraction of biomass which is allocated to active proteins - Parameters - ---------- - p_tot: float - new proteome fraction in g_protein/g_cdw + Change the fraction of biomass that is allocated to active proteins. + + Parameters: + p_tot (float): The new proteome fraction in grams of protein per gram of cell dry weight (g_protein/g_cdw). """ - print(f'Change total condition-dependent protein constraint from {self.p_tot} to {p_tot}') + + print( + f"Change total condition-dependent protein constraint from {self.p_tot} to {p_tot}" + ) tot_prot_constraint = self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID] protein_availability = tot_prot_constraint.ub # correct for the difference between old and new total protein to keep the correction for the protein sections (ptot = Etot - phi_t,0 - phi_ue,0) new_protein_fraction = p_tot * 1e3 for sector in self.sectors: - if hasattr(sector, 'intercept'): + if hasattr(sector, "intercept"): new_protein_fraction -= sector.intercept self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].ub = new_protein_fraction self.p_tot = p_tot self.solver.update() - def change_sector_parameters(self, sector, slope:float, intercept:float, lin_rxn_id:str): + def change_sector_parameters( + self, sector, slope: float, intercept: float, lin_rxn_id: str + ): # input in g/gDW - print(f'Changing the slope and intercept of the {sector.id}') - print(f'Changing slope from {sector.slope} to {slope*1e3} mg/gcdw/h') - print(f'Changing intercept from {sector.intercept} to {intercept*1e3} mg/gcdw') + print(f"Changing the slope and intercept of the {sector.id}") + print(f"Changing slope from {sector.slope} to {slope*1e3} mg/gcdw/h") + print(f"Changing intercept from {sector.intercept} to {intercept*1e3} mg/gcdw") prev_intercept = sector.intercept - #*1e3 to convert g to mg + # *1e3 to convert g to mg sector.slope = slope * 1e3 sector.intercept = intercept * 1e3 lin_rxn = self.reactions.get_by_id(lin_rxn_id) if self.TOTAL_PROTEIN_CONSTRAINT_ID in self.constraints.keys(): intercept_diff = sector.intercept - prev_intercept - #set the intercept - self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].ub = self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].ub - intercept_diff - #reset the slope - self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].set_linear_coefficients({ - lin_rxn.forward_variable: -slope, - lin_rxn.reverse_variable: slope - }) + # set the intercept + self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].ub = ( + self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].ub - intercept_diff + ) + # reset the slope + self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].set_linear_coefficients( + {lin_rxn.forward_variable: -slope, lin_rxn.reverse_variable: slope} + ) else: - var = self.variables['R_' + sector.id] + var = self.variables["R_" + sector.id] # update the constraint - self.constraints[sector.id].set_linear_coefficients({ - var: 1, - lin_rxn.forward_variable: -slope, #/ (sector.mol_mass[0] * 1e-6), - lin_rxn.reverse_variable: slope #/ (sector.mol_mass[0] * 1e-6) - }) + self.constraints[sector.id].set_linear_coefficients( + { + var: 1, + lin_rxn.forward_variable: -slope, # / (sector.mol_mass[0] * 1e-6), + lin_rxn.reverse_variable: slope, # / (sector.mol_mass[0] * 1e-6) + } + ) # update the sector object sector.variables = [var] sector.constraints = [self.constraints[sector.id]] - def change_reaction_bounds(self, rxn_id:str, lower_bound: float = None, upper_bound: float = None): + def change_reaction_bounds( + self, rxn_id: str, lower_bound: float = None, upper_bound: float = None + ): """ - Change the reaction bounds. If there should be a sensitivity analysis, the bounds of the upper and lower bound - constraints are adjusted - Parameters - ---------- - rxn_id: str - string of reaction id to change - lower_bound: float, optional - value of the lower bound - upper_bound: float, optional - value of the upper bound + Change the reaction bounds. If a sensitivity analysis is required, the bounds of the upper and lower bound + constraints are adjusted. + + Parameters: + rxn_id (str): The string representing the reaction identifier to change. + lower_bound (float, optional): The new value for the lower bound of the reaction (default is None). + upper_bound (float, optional): The new value for the upper bound of the reaction (default is None). """ + if rxn_id not in self.reactions: - warnings.warn(f'Reaction {rxn_id} does not exist in the model. Cannot change the upper and lowerbound.') + warnings.warn( + f"Reaction {rxn_id} does not exist in the model. Cannot change the upper and lowerbound." + ) return rxn = self.reactions.get_by_id(rxn_id) - #make sure the order of setting is right to prevent errors + # make sure the order of setting is right to prevent errors if lower_bound is not None and lower_bound >= rxn.upper_bound: - if upper_bound is not None: self.change_reaction_ub(rxn_id, upper_bound) - self.change_reaction_lb(rxn_id,lower_bound) + if upper_bound is not None: + self.change_reaction_ub(rxn_id, upper_bound) + self.change_reaction_lb(rxn_id, lower_bound) elif upper_bound is not None: - if lower_bound is not None: self.change_reaction_lb(rxn_id, lower_bound) - self.change_reaction_ub(rxn_id,upper_bound) + if lower_bound is not None: + self.change_reaction_lb(rxn_id, lower_bound) + self.change_reaction_ub(rxn_id, upper_bound) - def change_reaction_ub(self, rxn_id:str, upper_bound: float = None): + def change_reaction_ub(self, rxn_id: str, upper_bound: float = None): if self.sensitivity: - self.constraints[rxn_id + '_ub'].ub = upper_bound + self.constraints[rxn_id + "_ub"].ub = upper_bound else: self.reactions.get_by_id(rxn_id).upper_bound = upper_bound - def change_reaction_lb(self,rxn_id:str, lower_bound: float = None): + def change_reaction_lb(self, rxn_id: str, lower_bound: float = None): if self.sensitivity: - self.constraints[rxn_id + '_lb'].ub = -lower_bound + self.constraints[rxn_id + "_lb"].ub = -lower_bound else: self.reactions.get_by_id(rxn_id).lower_bound = lower_bound - def change_enzyme_bounds(self, enzyme_id: str, lower_bound: float = None, upper_bound: float = None): + def change_enzyme_bounds( + self, enzyme_id: str, lower_bound: float = None, upper_bound: float = None + ): + """ + Change the enzyme bounds. If the model should be primed for performing a sensitivity analysis, + the upper bound of the minimum and maximum enzyme concentration constraints are adjusted. + + Parameters: + enzyme_id (str): The string representing the enzyme identifier to change. + lower_bound (float, optional): The new value for the minimal enzyme concentration (default is None). + upper_bound (float, optional): The new value for the maximal enzyme concentration (default is None). """ - Change the enzyme bounds. If the model should be primed for performing a sensitivity analysis, - the upper bound of the min and max enzyme concentration constraints are adjusted - Parameters - ---------- - enzyme_id: str - string of enzyme id to change - lower_bound: float, optional - value of the minimal enzyme concentration - upper_bound: float, optional - value of the maximal enzyme concentration - """ + if enzyme_id not in self.enzyme_variables: - warnings.warn(f'Enzyme {enzyme_id} does not exist in the model. Cannot change the minimal and maximal concentrations.') + warnings.warn( + f"Enzyme {enzyme_id} does not exist in the model. Cannot change the minimal and maximal concentrations." + ) return enzyme = self.enzyme_variables.get_by_id(enzyme_id) # make sure the order of setting is right to prevent errors if lower_bound is not None and lower_bound > enzyme.upper_bound: - if upper_bound is not None: self.change_enzyme_max(enzyme_id, upper_bound) + if upper_bound is not None: + self.change_enzyme_max(enzyme_id, upper_bound) self.change_enzyme_min(enzyme_id, lower_bound) elif upper_bound is not None: - if lower_bound is not None: self.change_enzyme_min(enzyme_id, lower_bound) + if lower_bound is not None: + self.change_enzyme_min(enzyme_id, lower_bound) self.change_enzyme_max(enzyme_id, upper_bound) def change_enzyme_max(self, enzyme_id: str, upper_bound: float = None): if self.sensitivity: - self.constraints[enzyme_id + '_max'].ub = upper_bound + self.constraints[enzyme_id + "_max"].ub = upper_bound else: self.enzyme_variables.get_by_id(enzyme_id).upper_bound = upper_bound def change_enzyme_min(self, enzyme_id: str, lower_bound: float = None): if self.sensitivity: - self.constraints[enzyme_id + '_min'].ub = -lower_bound + self.constraints[enzyme_id + "_min"].ub = -lower_bound else: self.enzyme_variables.get_by_id(enzyme_id).lower_bound = lower_bound - def get_enzymes_with_reaction_id(self, rxn_id:str): + def get_enzymes_with_reaction_id(self, rxn_id: str): """ - Returns Enzyme objects associated with the reaction id through CatalyticEvent objects - :param rxn_id: str - reaction identifier + Return Enzyme objects associated with the reaction identifier through CatalyticEvent objects. + + Parameters: + rxn_id (str): The reaction identifier. - Returns - ------- - DictList of Enzyme objects associated with the reaction + Returns: + DictList: A DictList of Enzyme objects associated with the reaction. """ - catalytic_event_id = 'CE_' + rxn_id + + catalytic_event_id = "CE_" + rxn_id catalytic_event = self.catalytic_events.get_by_id(catalytic_event_id) enzymes = catalytic_event.enzymes return enzymes - def get_reactions_with_enzyme_id(self, enz_id:str): + def get_reactions_with_enzyme_id(self, enz_id: str): """ - Returns Enzyme objects associated with the reaction id through CatalyticEvent objects - :param enz_id: str - enzyme identifier (EC number) + Return a list of reaction identifiers associated with the enzyme identifier (EC number) through CatalyticEvent objects. + + Parameters: + enz_id (str): The enzyme identifier (EC number). - Returns - ------- - List of reaction ids associated with the enzyme + Returns: + List[str]: A list of reaction identifiers associated with the enzyme. """ + enzyme = self.enzymes.get_by_id(enz_id) rxn_ids = list(enzyme.rxn2kcat.keys()) return rxn_ids - def change_kcat_value(self, enzyme_id:str, kcats:dict): + def change_kcat_value(self, enzyme_id: str, kcats: dict): """ - Change the turnover number of the enzyme for a specific reaction - :param enzyme_id: Enzyme identifier - :param kcats: dict with reaction id, kcat key, value pairs. kcat is a dict with direction, kcat value - key, value pairs {'f': fwd_value, 'b': bckwrd_value} + Change the turnover number (kcat) of the enzyme for a specific reaction. + + Parameters: + enzyme_id (str): The enzyme identifier. + kcats (dict): A dictionary with reaction identifiers as keys and kcat values as values. + Each kcat value should be a nested dictionary with `f` (forward) and `b` (backward) as keys, + and the corresponding kcat values as values. + + Example: + Example dictionary for the `kcat` parameter + ``` + {'R1': {'f': 10.0, 'b': 5.0}, 'R2': {'f': 7.0, 'b': 3.0}} + ``` """ + if self.enzymes.has_id(enzyme_id): enzyme = self.enzymes.get_by_id(enzyme_id) enzyme.change_kcat_values(kcats) - #also change the active enzyme sector - active_enzyme = self.sectors.get_by_id('ActiveEnzymeSector') + # also change the active enzyme sector + active_enzyme = self.sectors.get_by_id("ActiveEnzymeSector") for rxn, kcat_f_b in kcats.items(): active_enzyme.rxn2protein[rxn][enzyme_id] = kcat_f_b else: - warnings.warn(f'The enzyme {enzyme_id} does not exist in the model. The kcat can thus not be changed.') - - def remove_enzymes(self, - enzymes: Union[str, Enzyme, List[Union[str, Enzyme]]] - )-> None: - """Remove enzymes from the model. Adapted from the cobra.core.remove_reactions() function. - If the reaction is not catalyzed by any enzyme anymore, the reaction ub will become 0 - - The change is reverted upon exit when using the model as a context. - - Parameters - ---------- - enzymes : list or reaction or str - A list with reactions (`cobra.Reaction`), or their id's, to remove. - Enzyme will be placed in a list. Str will be placed in a list and used to - find the reaction in the model. - """ + warnings.warn( + f"The enzyme {enzyme_id} does not exist in the model. The kcat can thus not be changed." + ) + + def remove_enzymes( + self, enzymes: Union[str, Enzyme, List[Union[str, Enzyme]]] + ) -> None: + """ + Remove enzymes from the model. + + Parameters: + enzymes (list, reaction, or str): A list with enzymes (`Enzyme`), or their IDs, to remove. + Enzymes will be placed in a list. Strings will be placed in a list + and used to find the enzymes in the model. + Note: + The change is reverted upon exit when using the model as a context. + """ + if isinstance(enzymes, str) or hasattr(enzymes, "id"): warnings.warn("need to pass in a list") enzymes = [enzymes] @@ -1169,14 +1335,14 @@ def remove_enzymes(self, self.enzymes.remove(enzyme) enzyme._model = None - #remove variable related to the enzyme + # remove variable related to the enzyme self.enzyme_variables.remove(enzyme.enzyme_variable) forward = enzyme.enzyme_variable.forward_variable reverse = enzyme.enzyme_variable.reverse_variable self.remove_cons_vars([forward, reverse]) - #remove enzyme from catalytic event + # remove enzyme from catalytic event for ce in enzyme.catalytic_events: ce.remove_enzymes([enzyme]) @@ -1190,27 +1356,23 @@ def remove_reactions( reactions: Union[str, Reaction, List[Union[str, Reaction]]], remove_orphans: bool = False, ) -> None: + """ + Remove reactions from the model. Inherited from the cobrapy.core.remove_reactions() function. + + Parameters: + reactions (list, reaction, or str): A list with reactions (`cobra.Reaction`), or their IDs, to remove. + Reactions will be placed in a list. Strings will be placed in a list + and used to find the reactions in the model. + remove_orphans (bool, optional): Remove orphaned genes and metabolites from the model as well (default False). + Note: + The change is reverted upon exit when using the model as a context. Also removes associated CatalyticEvents if they exist. + """ - """Remove reactions from the model. Inherited from the cobrapy.core.remove_reactions() function. - - The change is reverted upon exit when using the model as a context. Also removes associated - CatalyticEvents if they exist. - - Parameters - ---------- - reactions : list or reaction or str - A list with reactions (`cobra.Reaction`), or their id's, to remove. - Reaction will be placed in a list. Str will be placed in a list and used to - find the reaction in the model. - remove_orphans : bool, optional - Remove orphaned genes and metabolites from the model as - well (default False). - """ if not hasattr(reactions, "__iter__"): reactions = [reactions] super().remove_reactions(reactions, remove_orphans) - #remove the catalytic events if they exist for the reactions + # remove the catalytic events if they exist for the reactions for rxn in reactions: # catalytic_event = self.catalytic_events.get_by_id('CE_' + rxn.id) # self.catalytic_events.remove(catalytic_event) @@ -1231,7 +1393,7 @@ def remove_reactions( # self.remove_cons_vars(enzyme._constraints) try: - catalytic_event = self.catalytic_events.get_by_id('CE_' + rxn.id) + catalytic_event = self.catalytic_events.get_by_id("CE_" + rxn.id) self.catalytic_events.remove(catalytic_event) # removing catalytic event from the enzymes @@ -1241,39 +1403,42 @@ def remove_reactions( for enzyme_var in catalytic_event.enzyme_variables: enzyme_var.remove_catalytic_event(catalytic_event) - #removing orphaned enzymes + # removing orphaned enzymes if remove_orphans and len(enzyme.catalytic_events) == 0: - print(f'Enzyme {enzyme.id} is orphaned. This enzyme will be removed from the model') + print( + f"Enzyme {enzyme.id} is orphaned. This enzyme will be removed from the model" + ) self.remove_cons_vars(enzyme._constraints) enzyme._model = None self.enzymes.remove(enzyme) self.remove_cons_vars(enzyme._constraints) if self.sensitivity: - lb_constraint = self.constraints[rxn.id + '_lb'] - ub_constraint = self.constraints[rxn.id + '_ub'] + lb_constraint = self.constraints[rxn.id + "_lb"] + ub_constraint = self.constraints[rxn.id + "_ub"] self.remove_cons_vars([lb_constraint, ub_constraint]) except: continue - def remove_catalytic_events(self, - catalytic_events:Union[str, CatalyticEvent, List[Union[str,CatalyticEvent]]], - remove_orphans: bool = False - )-> None: - """Remove catalytic events from the model. - - - Parameters - ---------- - reactions : list or reaction or str - A list with reactions (`cobra.Reaction`), or their id's, to remove. - Reaction will be placed in a list. Str will be placed in a list and used to - find the reaction in the model. - remove_orphans : bool, optional - Remove orphaned genes and metabolites from the model as - well (default False). - """ + def remove_catalytic_events( + self, + catalytic_events: Union[str, CatalyticEvent, List[Union[str, CatalyticEvent]]], + remove_orphans: bool = False, + ) -> None: + """ + Remove catalytic events from the model. + + Parameters: + reactions (list, reaction, or str): A list with reactions (`cobra.Reaction`), or their IDs, to remove. + Reactions will be placed in a list. Strings will be placed in a list + and used to find the reactions in the model. + remove_orphans (bool, optional): Remove orphaned genes and metabolites from the model as well (default False). + + Note: + The change is reverted upon exit when using the model as a context. + """ + for catalytic_event in catalytic_events: self.catalytic_events.remove(catalytic_event) @@ -1283,31 +1448,39 @@ def remove_catalytic_events(self, # removing orphaned enzymes if remove_orphans and len(enzyme.catalytic_events) == 0: - print(f'Enzyme {enzyme.id} is orphaned. This enzyme will be removed from the model') + print( + f"Enzyme {enzyme.id} is orphaned. This enzyme will be removed from the model" + ) self.remove_cons_vars(enzyme._constraints) enzyme._model = None self.enzymes.remove(enzyme) self.remove_cons_vars(enzyme._constraints) - def remove_sectors(self, - sectors: Union[str, Sector, ActiveEnzymeSector, List[Union[str, Sector, ActiveEnzymeSector]]] - )-> None: - """Remove sections from the model. + def remove_sectors( + self, + sectors: Union[ + str, + Sector, + ActiveEnzymeSector, + List[Union[str, Sector, ActiveEnzymeSector]], + ], + ) -> None: + """ + Remove sections from the model. - Also removes associated CatalyticEvents if they exist. + Also removes associated CatalyticEvents if they exist. + + Parameters: + sectors (list, sector, or str): A list with sector (`PAModelpy.Sector` or `PAModelpy.ActiveEnzymeSector`), + or their IDs, to remove. A single sector will be placed in a list. + Strings will be placed in a list and used to find the sector in the model. + """ - Parameters - ---------- - secotrs : list or sector or str - A list with sector (`PAModelpy.Sector` or `PAModelpy.ActiveEnzymeSector`), or their id's, to remove. - A single sector will be placed in a list. Str will be placed in a list and used to - find the secotr in the model. - """ if isinstance(sectors, str) or hasattr(sectors, "id"): sectors = [sectors] for sector in sectors: - print(f'Removing the following protein sector: {sector.id}\n') + print(f"Removing the following protein sector: {sector.id}\n") # remove the connection to the model sector._model = None @@ -1322,28 +1495,30 @@ def remove_sectors(self, # remove parts of constraint corresponding to the enzyme sector from the total_protein_constraint # 1. add the intercept value from the sum of protein (Total_protein == Etot) tpc_ub = self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].ub - self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].ub = tpc_ub + sector.intercept + self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].ub = ( + tpc_ub + sector.intercept + ) # 2. remove link between flux and enzyme concentration # link enzyme concentration in the sector to the total enzyme concentration lin_rxn = self.reactions.get_by_id(sector.id_list[0]) - self.constraints[self.TOTAL_PROTEIN_CONSTRAINT_ID].set_linear_coefficients({ - lin_rxn.forward_variable: 0, - lin_rxn.reverse_variable: 0 - }) + self.constraints[ + self.TOTAL_PROTEIN_CONSTRAINT_ID + ].set_linear_coefficients( + {lin_rxn.forward_variable: 0, lin_rxn.reverse_variable: 0} + ) else: - - #remove the associated constraints + # remove the associated constraints for constraint in sector.constraints: if isinstance(constraint, Enzyme): self.remove_enzymes([constraint]) - #check if constraint is in the solver + # check if constraint is in the solver if constraint in self.constraints.values(): self.remove_cons_vars([constraint]) self.solver.update() - #remove the associated variables + # remove the associated variables for variable in sector.variables: if isinstance(variable, CatalyticEvent): self.remove_catalytic_events([variable]) @@ -1358,94 +1533,94 @@ def remove_sectors(self, # remove sector constraint from model easy lookup: del self.sector_constraints[sector.id] - #remove the sector and its connection to the model + # remove the sector and its connection to the model self.sectors.remove(sector) sector._model = None - def test(self, glc_flux: Union[int, float]=10): + def test(self, glc_flux: Union[int, float] = 10): """ - Test the proteome allocation model - :param glc_flux: glc flux which limits the growth rate (mmol_glc/g_cdw/h, default = 10) + Test the proteome allocation model. + + Parameters: + glc_flux (float, optional): The glucose flux which limits the growth rate (units: mmol_glc/g_cdw/h, default=10). """ + self.set_glc_uptake_bounds(glc_flux) self.optimize() - if self.solver.status == 'optimal': - print('Protein allocation model test case was successful.\n') - print('Optimal objective function value: ',self.objective.value,'\n') - print('Predicted glucose uptake rate: ',self.reactions.get_by_id(self.GLUCOSE_EXCHANGE_RXNID).flux) + if self.solver.status == "optimal": + print("Protein allocation model test case was successful.\n") + print("Optimal objective function value: ", self.objective.value, "\n") + print( + "Predicted glucose uptake rate: ", + self.reactions.get_by_id(self.GLUCOSE_EXCHANGE_RXNID).flux, + ) def set_glc_uptake_bounds(self, bound: Union[int, float]): - #check if the model is reversible - if self.GLUCOSE_EXCHANGE_RXNID[-1] == 'b': - self.change_reaction_bounds(self.GLUCOSE_EXCHANGE_RXNID,bound, bound) + # check if the model is reversible + if self.GLUCOSE_EXCHANGE_RXNID[-1] == "b": + self.change_reaction_bounds(self.GLUCOSE_EXCHANGE_RXNID, bound, bound) else: - self.change_reaction_bounds(self.GLUCOSE_EXCHANGE_RXNID,-bound, -bound) - - - def pfba(self, - fraction_of_optimum: float = 1.0, - proteins:bool = False, - reactions:bool = True, - exclude:List['str'] =[], - objective: Union[Dict, "Objective", None] = None): - """Perform pFBA (parsimonious Enzyme Usage Flux Balance Analysis) with a custom objective including: - all reactions, all proteins, all proteins and all reactions. - pFBA [1] adds the minimization of all fluxes the objective of the - model. This approach is motivated by the idea that high fluxes have a - higher enzyme turn-over and that since producing enzymes is costly, - the cell will try to minimize overall flux while still maximizing the - original objective function, e.g. the growth rate. - Parameters - ---------- - fraction_of_optimum : float, optional - The fraction of optimum which must be maintained. The original - objective reaction is constrained to be greater than maximal value - times the `fraction_of_optimum` (default 1.0). - objective : dict or cobra.Model.objective, optional - A desired objective to use during optimization in addition to the - pFBA objective. Dictionaries (reaction as key, coefficient as value) - can be used for linear objectives (default None). - proteins: bool, optional - Determines whether to include enzyme variables in the pFBA objective - reactions: bool, optional - Determines whether to include reaction variables in the pFBA objective - exclude: list of reaction ids, optional - Reactions to exclude fom the minimalization objective - - - References - ---------- - .. [1] Lewis, N. E., Hixson, K. K., Conrad, T. M., Lerman, J. A., - Charusanti, P., Polpitiya, A. D., Palsson, B. O. (2010). Omic data - from evolved E. coli are consistent with computed optimal growth from - genome-scale models. Molecular Systems Biology, 6, - 390. doi:10.1038/msb.2010.47 - """ - #get the variables which should be included in the objective function + self.change_reaction_bounds(self.GLUCOSE_EXCHANGE_RXNID, -bound, -bound) + + def pfba( + self, + fraction_of_optimum: float = 1.0, + proteins: bool = False, + reactions: bool = True, + exclude: List["str"] = [], + objective: Union[Dict, "Objective", None] = None, + ): + """ + Perform pFBA (parsimonious Enzyme Usage Flux Balance Analysis) with a custom objective including: + - All reactions + - All proteins + - All proteins and all reactions. + + pFBA [1] adds the minimization of all fluxes to the objective of the model. This approach is motivated by the idea that high fluxes have a higher enzyme turnover, and since producing enzymes is costly, the cell will try to minimize overall flux while still maximizing the original objective function, e.g., the growth rate. + + Parameters: + fraction_of_optimum (float, optional): The fraction of optimum which must be maintained. The original objective reaction is constrained to be greater than the maximal value times the `fraction_of_optimum` (default 1.0). + objective (dict or cobra.Model.objective, optional): A desired objective to use during optimization in addition to the pFBA objective. Dictionaries (reaction as the key, coefficient as the value) can be used for linear objectives (default None). + proteins (bool, optional): Determines whether to include enzyme variables in the pFBA objective. + reactions (bool, optional): Determines whether to include reaction variables in the pFBA objective. + exclude (list of reaction ids, optional): Reactions to exclude from the minimization objective. + + References: + - [1] Lewis, N. E., Hixson, K. K., Conrad, T. M., Lerman, J. A., Charusanti, P., Polpitiya, A. D., Palsson, B. O. (2010). Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Molecular Systems Biology, 6, 390. doi:10.1038/msb.2010.47 + """ + + # get the variables which should be included in the objective function variables = list() if reactions: for rxn in self.reactions: - if rxn not in exclude and rxn not in self.enzymes: variables += [rxn] + if rxn not in exclude and rxn not in self.enzymes: + variables += [rxn] if proteins: for enzyme_var in self.enzyme_variables: - if enzyme_var.id not in exclude: variables += [enzyme_var.forward_variable, enzyme_var.reverse_variable] + if enzyme_var.id not in exclude: + variables += [ + enzyme_var.forward_variable, + enzyme_var.reverse_variable, + ] - #set custom objective + # set custom objective if objective is not None: self.objective = objective - #set previous objective as constraint + # set previous objective as constraint if self.solver.objective.name == "_pfba_objective": raise ValueError("The model already has a pFBA objective.") - cobra.util.solver.fix_objective_as_constraint(self, fraction=fraction_of_optimum) + cobra.util.solver.fix_objective_as_constraint( + self, fraction=fraction_of_optimum + ) - #add new pFBA objective + # add new pFBA objective self.objective = self.problem.Objective( Zero, direction="min", sloppy=True, name="_pfba_objective" ) self.objective.set_linear_coefficients({v: 1.0 for v in variables}) - #run pFBA + # run pFBA self.optimize() def reset_objective(self): @@ -1453,51 +1628,47 @@ def reset_objective(self): Reseting the objective to the standard biomass maximization objective after pFBA """ biomass_rxn = self.reactions.get_by_id(self.BIOMASS_REACTION) - #reset the biomass reaction to standard UB and LB + # reset the biomass reaction to standard UB and LB biomass_rxn.lower_bound = 0 - biomass_rxn.upper_bound= 1e3 + biomass_rxn.upper_bound = 1e3 - #reseting the objective + # reseting the objective self.objective = {biomass_rxn: 1.0} - self.objective.direction = 'max' + self.objective.direction = "max" def optimize( self, objective_sense: Optional[str] = None, raise_error: bool = False ) -> "Solution": - """ Optimize the model using flux balance analysis. - Inherits from the cobra.Model.optimize() function and performs a sensitivity analysis after optimization if - this is desired (by setting the PAModel.sensitivity attribute to True) - Parameters - ---------- - objective_sense : {None, 'maximize' 'minimize'}, optional - Whether fluxes should be maximized or minimized. In case of None, - the previous direction is used (default None). - raise_error : bool - If true, raise an OptimizationError if solver status is not - optimal (default False). - Returns - ------- - Solution - Notes - ----- - Only the most commonly used parameters are presented here. Additional - parameters for cobra.solver may be available and specified with the - appropriate keyword argument. - """ - solution = super().optimize(objective_sense,raise_error) - if self.sensitivity and self.solver.status == 'optimal': + """ + Optimize the model using flux balance analysis. Inherits from the cobra.Model.optimize() function and performs a sensitivity analysis after optimization if this is desired (by setting the PAModel.sensitivity attribute to True). + + Parameters: + objective_sense (`{None, 'maximize', 'minimize'}`, optional): Whether fluxes should be maximized or minimized. In case of None, the previous direction is used (default None). + raise_error (bool): If true, raise an OptimizationError if solver status is not optimal (default False). + + Returns: + Solution + + Notes: + Only the most commonly used parameters are presented here. Additional parameters for cobra.solver may be available and specified with the appropriate keyword argument. + """ + + solution = super().optimize(objective_sense, raise_error) + if self.sensitivity and self.solver.status == "optimal": self.determine_sensitivity_coefficients() return solution - def copy(self) -> 'PAModel': - """Provide a partial 'deepcopy' of the Model. + def copy(self) -> "PAModel": + """ + Provide a partial 'deepcopy' of the Model. + + Adjusted from cobra.Model.copy(). - Adjusted from cobra.Model.copy() - All the Metabolite, Gene, Reaction, Enzyme, EnzymeVariable, Sector and CatalyticEvent - objects are created anew but in a faster fashion than deepcopy. + All the Metabolite, Gene, Reaction, Enzyme, EnzymeVariable, Sector, and CatalyticEvent objects are created anew but in a faster fashion than deepcopy. - :return: PAModelpy.PAModel: new model copy - """ + Returns: + PAModelpy.PAModel: A new model copy. + """ do_not_copy_by_ref = { "metabolites", @@ -1507,20 +1678,19 @@ def copy(self) -> 'PAModel': "enzyme_variables", "enzyme_constraints", "sectors", - "catalytic_events" - "notes", + "catalytic_events" "notes", "annotation", "groups", } # setting up the dict for the initialization of the model copy model_init_attr = { - 'm_model', - 'name', - 'p_tot', - 'sensitivity', - 'sectors', - 'configuration' + "m_model", + "name", + "p_tot", + "sensitivity", + "sectors", + "configuration", } new_dict = {} @@ -1530,7 +1700,7 @@ def copy(self) -> 'PAModel': # else: new_attr = attr # new_dict[new_attr] = self.__dict__[attr] new_dict = self.find_init_args(self) - new_dict['id_or_model'] = self.m_model.copy() + new_dict["id_or_model"] = self.m_model.copy() # initialize new model new = self.__class__(**new_dict) # also adjust the constants @@ -1601,7 +1771,12 @@ def copy(self) -> 'PAModel': new.sectors += [sector] # if some manual enzymes are added, copy them separately - do_not_copy_by_ref = {"_model", "enzyme_variables", "catalytic_events", "_constraints"} + do_not_copy_by_ref = { + "_model", + "enzyme_variables", + "catalytic_events", + "_constraints", + } for enzyme in self.enzymes: if enzyme not in new.enzymes: # TODO init_args = self.find_init_args(enzyme) @@ -1656,7 +1831,7 @@ def copy(self) -> 'PAModel': # assign a new empty context new._contexts = [] - #copy bounds + # copy bounds for key, var in self.variables.items(): new.variables[key].lb = var.lb new.variables[key].ub = var.ub @@ -1670,6 +1845,6 @@ def copy(self) -> 'PAModel': def find_init_args(self, object): init_args = {} for param, default in inspect.signature(object.__init__).parameters.items(): - if param != 'self' and default.default == inspect.Parameter.empty: + if param != "self" and default.default == inspect.Parameter.empty: init_args[param] = getattr(object, param) - return init_args \ No newline at end of file + return init_args diff --git a/src/PAModelpy/configuration.py b/src/PAModelpy/configuration.py index 68d3975..b1a3480 100644 --- a/src/PAModelpy/configuration.py +++ b/src/PAModelpy/configuration.py @@ -1,41 +1,66 @@ class Config: """ Object with information about model defaults which are used throughout the package: - - TOTAL_PROTEIN_CONSTRAINT_ID: str, 'TotalProteinConstraint' + - TOTAL_PROTEIN_CONSTRAINT_ID: str, `TotalProteinConstraint` - P_TOT_DEFAULT: float, 0.258 g_p/g_cdw - - CO2_EXHANGE_RXNID: str, 'EX_co2_e' - - GLUCOSE_EXCHANGE_RXNID: str, 'EX_glc__D_e' - - BIOMASS_REACTION: str, 'BIOMASS_Ec_iML1515_core_75p37M' - - OXYGEN_UPTAKE_RXNID: str, 'EX_o2_e' - - ACETATE_EXCRETION_RXNID: str, 'EX_ac_e' - - PHYS_RXN_IDS: List of str, [BIOMASS_REACTION, GLUCOSE_EXCHANGE_RXNID, ACETATE_EXCRETION_RXNID, CO2_EXHANGE_RXNID, OXYGEN_UPTAKE_RXNID, - 'PGI', 'G6PDH2r', 'EDA', 'CS', 'ICL', 'PPC', 'ME1', 'ME2'] + - CO2_EXHANGE_RXNID: str, `EX_co2_e` + - GLUCOSE_EXCHANGE_RXNID: str, `EX_glc__D_e` + - BIOMASS_REACTION: str, `BIOMASS_Ec_iML1515_core_75p37M` + - OXYGEN_UPTAKE_RXNID: str, `EX_o2_e` + - ACETATE_EXCRETION_RXNID: str, `EX_ac_e` + - PHYS_RXN_IDS: List of str, `[BIOMASS_REACTION, GLUCOSE_EXCHANGE_RXNID, ACETATE_EXCRETION_RXNID, CO2_EXHANGE_RXNID, OXYGEN_UPTAKE_RXNID, + 'PGI', 'G6PDH2r', 'EDA', 'CS', 'ICL', 'PPC', 'ME1', 'ME2']` Defaults are configured for the iML1515 E.coli model - """ - TOTAL_PROTEIN_CONSTRAINT_ID = 'TotalProteinConstraint' - P_TOT_DEFAULT = 0.258 #g_protein/g_cdw - CO2_EXHANGE_RXNID = 'EX_co2_e' - GLUCOSE_EXCHANGE_RXNID = 'EX_glc__D_e' - BIOMASS_REACTION = 'BIOMASS_Ec_iML1515_core_75p37M' - OXYGEN_UPTAKE_RXNID = 'EX_o2_e' - ACETATE_EXCRETION_RXNID = 'EX_ac_e' - PHYS_RXN_IDS = [BIOMASS_REACTION, GLUCOSE_EXCHANGE_RXNID, ACETATE_EXCRETION_RXNID, CO2_EXHANGE_RXNID, OXYGEN_UPTAKE_RXNID, - 'PGI', 'G6PDH2r', 'EDA', 'CS', 'ICL', 'PPC', 'ME1', 'ME2'] + + TOTAL_PROTEIN_CONSTRAINT_ID = "TotalProteinConstraint" + P_TOT_DEFAULT = 0.258 # g_protein/g_cdw + CO2_EXHANGE_RXNID = "EX_co2_e" + GLUCOSE_EXCHANGE_RXNID = "EX_glc__D_e" + BIOMASS_REACTION = "BIOMASS_Ec_iML1515_core_75p37M" + OXYGEN_UPTAKE_RXNID = "EX_o2_e" + ACETATE_EXCRETION_RXNID = "EX_ac_e" + PHYS_RXN_IDS = [ + BIOMASS_REACTION, + GLUCOSE_EXCHANGE_RXNID, + ACETATE_EXCRETION_RXNID, + CO2_EXHANGE_RXNID, + OXYGEN_UPTAKE_RXNID, + "PGI", + "G6PDH2r", + "EDA", + "CS", + "ICL", + "PPC", + "ME1", + "ME2", + ] def reset(self): """ - Reset the config object to the standard settings for E.coli iML1515 - :return: + Reset the config object to the standard settings for E.coli iML1515. """ - self.TOTAL_PROTEIN_CONSTRAINT_ID = 'TotalProteinConstraint' + + self.TOTAL_PROTEIN_CONSTRAINT_ID = "TotalProteinConstraint" self.P_TOT_DEFAULT = 0.258 # g_protein/g_cdw - self.CO2_EXHANGE_RXNID = 'EX_co2_e' - self.GLUCOSE_EXCHANGE_RXNID = 'EX_glc__D_e' - self.BIOMASS_REACTION = 'BIOMASS_Ec_iML1515_core_75p37M' - self.OXYGEN_UPTAKE_RXNID = 'EX_o2_e' - self.ACETATE_EXCRETION_RXNID = 'EX_ac_e' - self.PHYS_RXN_IDS = [self.BIOMASS_REACTION, self.GLUCOSE_EXCHANGE_RXNID, self.ACETATE_EXCRETION_RXNID, self.CO2_EXHANGE_RXNID, - self.OXYGEN_UPTAKE_RXNID, - 'PGI', 'G6PDH2r', 'EDA', 'CS', 'ICL', 'PPC', 'ME1', 'ME2'] + self.CO2_EXHANGE_RXNID = "EX_co2_e" + self.GLUCOSE_EXCHANGE_RXNID = "EX_glc__D_e" + self.BIOMASS_REACTION = "BIOMASS_Ec_iML1515_core_75p37M" + self.OXYGEN_UPTAKE_RXNID = "EX_o2_e" + self.ACETATE_EXCRETION_RXNID = "EX_ac_e" + self.PHYS_RXN_IDS = [ + self.BIOMASS_REACTION, + self.GLUCOSE_EXCHANGE_RXNID, + self.ACETATE_EXCRETION_RXNID, + self.CO2_EXHANGE_RXNID, + self.OXYGEN_UPTAKE_RXNID, + "PGI", + "G6PDH2r", + "EDA", + "CS", + "ICL", + "PPC", + "ME1", + "ME2", + ]