Diel Models

Compatibility

diel_models is compatible with the following versions of Python:

Python 3.8
Python 3.9
Python 3.10
Python 3.11
Python 3.12

Description

diel_models is a python package generated from this project and has its own ReadtheDocs file.

Despite numerous successful studies, modeling plant metabolism remains challenging for several reasons, such as limited information, incomplete annotations, and dynamic changes in plant metabolism that occur under different conditions, including night and day. In particular, the integration of these day-night cycles (diel cycles) is complex, laborious, and time-consuming.

With this in mind, this package aims to accelerate this process by being able to transform a non-diel model into a diel model.

Table of contents:

• Installation
  • Using pip from PyPi
  • Using pip directly from GitHub
• Development (or Contributing)
• Using the tool
• Expanding the pipeline
• Where to find the publication results

Installation

Using pip from PyPi

pip install diel_models==1.2.3

Using pip directly from GitHub

pip install git+https://github.com/BioSystemsUM/diel_models.git

Development (or Contributing)

Cloning the repository and setting the conda environment:

git clone https://github.com/BioSystemsUM/diel_models.git
conda create -n dielmodels
conda activate dielmodels

pip install -r requirements.txt
pip install -e .

Using the tool

Using this package, you can handle generic or multi-tissue models by:

• Assigning day and night;
• Inserting specified metabolites into the storage pool allowing their transition between day and night, and vice versa;
• Supressing the photon reaction flux at night;
• Setting the flux of the nitrate reactions to 3:2 by default (according to the literature) or other desired ratio;
• (optional) Taking the day and night biomass reactions and creating a total biomass reaction resulting from the junctions of both. Supressing at the same time the flow of the individual reactions to zero and setting the total biomass reaction as the objective function.

If each method is to be applied individually it is essential that the first 3 steps are applied in that order specifically.

Alternatively, you can apply all methods to a given model by running the entire pipeline, with all arguments relative to the original model

This approach has been applied to multiple models, as demonstrated in the Examples folder, both generic (e.g. Athaliana13) and multi-tissue (e.g. MultiQuercus).

Briefly, the pipeline is applied as follows:

• Generic model:

import cobra
from diel_models.diel_models_creator import diel_models_creator

model = cobra.io.read_sbml_model('.../.../desired_single_tissue_model.xml')

storage_pool_metabolites = ['Metabolite_ID_1', 'Metabolite_ID_2', 'Metabolite_ID_3']

diel_models_creator(model, storage_pool_metabolites, ['Photon_Reaction_ID'], ['Nitrate_Reaction_ID'], 'Biomass_Reaction_ID')

cobra.io.write_sbml_model(model, desired_path)

where the nitrate uptake ratio is 3:2, since day_ratio_value is 3 and night_ratio_value is 2.

Alternatively, the ratio value can be set to a value other than 3:2.

import cobra
from diel_models.diel_models_creator import diel_models_creator

model = cobra.io.read_sbml_model('.../.../desired_single_tissue_model.xml')

storage_pool_metabolites = ['Metabolite_ID_1', 'Metabolite_ID_2', 'Metabolite_ID_3']

diel_models_creator(model, storage_pool_metabolites, ['Photon_Reaction_ID'], ['Nitrate_Reaction_ID'], 'Biomass_Reaction_ID', day_ratio_value=desired_value_1, night_ratio_value=desired_value_2)
cobra.io.write_sbml_model(model, desired_path)

• Multi-tissue model:

import cobra
from diel_models.diel_models_creator import diel_models_creator

model = cobra.io.read_sbml_model('.../.../desired_multi_tissue_model.xml')

storage_pool_metabolites = ['Metabolite_ID_1', 'Metabolite_ID_2', 'Metabolite_ID_3']

tissues = ['Tissue_ID_1', 'Tissue_ID_2']

diel_models_creator(model, storage_pool_metabolites, ['Photon_Reaction_ID'], ['Nitrate_Reaction_ID'], 'Biomass_Reaction_ID', tissues)
cobra.io.write_sbml_model(model, desired_path)

where the nitrate uptake ratio is 3:2, but it's also possible to adjust this ratio to different values.

Running the entire pipeline is possible due to the created Pipeline class that derives from a Step class with abstract methods - both present in this package, in the pipeline.py file.

Expanding the pipeline

It is possible to add other classes to the diel_models_creator function, if desired, for example to create a different adjustment that needs to be taken into account in the diel models. To expand the pipeline, it is necessary to create a new class that inherits from the Step class and implement the abstract methods.

Considering a new hypothetical file new_class.py, this new class, in addition to the desired methods, would have to contain the two abstract methods of the Step class, run and validate, which, respectively, runs all the methods of the class returning the model and performs asserts to validate whether the class has been implemented successfully (or simply doesn't apply any if it doesn't make sense).

from diel_models.pipeline import Step

class NewClass(Step):

  def __init__(self, model, param1):
    self.model = model
    self.param1 = param1

  def method1(self):
    pass

  def method2(self):
    pass

  def run(self):
    self.method1()
    self.method2()

    return self.model

  def validate(self):
    pass

Then you need to adjust the diel_models_creator function to integrate the new class. This function is in the diel_models_creator.py file.

from typing import List

from cobra import Model

from diel_models.new_class import NewClass

from diel_models.pipeline import Pipeline

def diel_models_creator(model: Model, storage_pool_metabolites: List[str], photon_reaction_id: List[str],
                        nitrate_exchange_reaction: List[str], param1, biomass_reaction_id: str = None, tissues: List[str] = None,
                        day_ratio_value: int = 3, night_ratio_value: int = 2) -> Model:  
  
    storage_pool_metabolites_with_day = [metabolite + "_Day" for metabolite in storage_pool_metabolites]
    photon_reaction_id_night = [photon_night_reaction + "_Night" for photon_night_reaction in photon_reaction_id]
    # ...
    
    steps = [
             # ...
            NewClass(model, param1)
        ]

    pipeline = Pipeline(model, steps)
    pipeline.run()
    return pipeline.model

Finally, you can run the diel_models_creator function with the new class.

Just as you can expand methods in the pipeline, you can modify or remove others.

Where to find the publication results

AraGEM:

• Details about the fluxes in the AraGEM diel model reactions in the day and night phases, as well as in the original model where calculated in aragem_reactions_fluxes.py file.
• Validation of the metabolites exchange reactions through simulation using pFBA where performed in simulation_sp.py file.
• DFA file and respective Test file.
• Plot from the pathway enrichment method representing the amount of differentially expressed reactions between day and night in each pathway.
• PCA plot with the sampling values filtered by the differentially expressed reactions.

Quercus suber:

• Details about the fluxes in the Quercus suber diel model reactions in the day and night phases, as well as in the original model where calculated in quercus_reactions_fluxes.py file.
• Slight adjustments to the biomass reaction in the generated diel model can be found here.
• Validation of the metabolites exchange reactions through simulation using pFBA where performed in simulation_sp_multi_quercus.py file.
• The comparison between the flux of the biomass reaction for both diel multi-tissue models are in the quercus_diel_models_comparison.py file.

QY for the several models:

• The scripts for quantum yield and assimilation quotient calculation for the Zea mays L. (2011), Arabidopsis thaliana (2010), Populus trichocarpa (2020), Solanum lycopersicum (2015), Solanum lycopersicum (2022) and Solanum tuberosum (2018) models can be found in the QY folder.