Context-Specific Metabolic Modeling

Transcriptome Data Driven Analysis for Epithelial and Mesenchymal States in Tumour Microenvironment.

Karthik M Dani
Namyatha N Mulbagal

Introduction

Metastasis

Epithelial to Mesenchymal Transition

Building Context Specific Metabolic Models

Link to the python notebook this link

Importing packages

  • run_model_reconstruction from gsmm.csm.build_csm: Used for running the model reconstruction pipeline.
  • cobra: A package for constraint-based reconstruction and analysis.
from gsmm.csm.build_csm import run_model_reconstruction

import cobra
import pandas as pd

GSMM package can be downloaded with pip install gsmm from PyPI site

Load expression data

  • emt_expression_data_path: Path to the CSV file containing the EMT expression data.
  • emt_expression_data: DataFrame holding the loaded EMT expression data.
emt_expression_data_path = "../../Data/InputData/EMT_FINAL_DATA.csv"
emt_expression_data = pd.read_csv(emt_expression_data_path)
emt_expression_data.head()

Load Recon3D model from the web

  • recon_model: The Recon3D model loaded from a web source using COBRApy.

  • This step loads the Recon3D model, a comprehensive genome-scale metabolic model. Recon3D integrates metabolic data from various human tissues and is widely used for studying human metabolism.

Structure of a COBRApy SBML Model

Ebrahim, A., Lerman, J. A., Palsson, B. O., & Hyduke, D. R. (2013).

COBRApy: COnstraints-Based Reconstruction and analysis for Python.

BMC Systems Biology, 7(1), 74. doi:10.1186/1752-0509-7-74

What’s inside each of them in CobraPy?

In COBRApy, a SBML model is structured as follows:

  • Reaction: Represents a biochemical reaction in the model.
    • id: A unique identifier for the reaction.
    • name: A descriptive name for the reaction.
    • metabolites: A dictionary of Metabolite objects and their stoichiometric coefficients in the reaction.
    • lower_bound: The lower bound of the reaction flux.
    • upper_bound: The upper bound of the reaction flux.
  • Metabolite: Represents a metabolite in the model.
    • id: A unique identifier for the metabolite.
    • name: A descriptive name for the metabolite.
    • formula: The chemical formula of the metabolite.
    • compartment: The compartment where the metabolite is located.
  • Gene: Represents a gene in the model.
    • id: A unique identifier for the gene.
    • name: A descriptive name for the gene.
    • reactions: A list of Reaction objects associated with the gene.

Process of Model Reconstruction

Load Recon3D model as the parent model

recon_model = cobra.io.web.load_model(model_id="Recon3D")
recon_model

Save Recon3D as .xml file.

cobra.io.write_sbml_model(recon_model, "recon_model.xml")

Reconstruction algorithm in gsmm needs,

  1. parent_model_path: Recon3D in this case.
  2. base_model_path: All the unneccessary genes, reactions and metabolites are removed, then the model is saved as base_model.
  3. gene_id_column: Expression data in our case has gene ids in the column called Gene_ID. We need to tell the algorithm that this is our gene id column.
parent_model_path = "recon_model.xml"
base_model_path = "recon_model.xml"
gene_id_column = "Gene_ID"

Running Model Reconstruction to get Context-Specific Model

run_model_reconstruction(..) takes care of the interface to run the main pipeline for reconstructing an optimized metabolic model from the provided expression data. It handles exceptions and prints error messages if reconstruction fails, returning None in case of errors.

epithelial_csm = run_model_reconstruction(model_path=parent_model_path,
                                          base_model_path=base_model_path,
                                          data_path=emt_expression_data_path,
                                          gene_id_column=gene_id_column,
                                          scores_column="Epithelial",
                                          )
epithelial_csm

Format of the EMT Data

Higher gene score indicates more epithelial sample and lower score indicates more mesenchymal sample

Running Model Reconstruction to get Context-Specific Model

run_model_reconstruction(..) takes care of the interface to run the main pipeline for reconstructing an optimized metabolic model from the provided expression data. It handles exceptions and prints error messages if reconstruction fails, returning None in case of errors.

epithelial_csm = run_model_reconstruction(model_path=parent_model_path,
                                          base_model_path=base_model_path,
                                          data_path=emt_expression_data_path,
                                          gene_id_column=gene_id_column,
                                          scores_column="Epithelial",
                                          )
epithelial_csm

mesenchymal_csm = run_model_reconstruction(model_path=parent_model_path,
                                          base_model_path=base_model_path,
                                          data_path=emt_expression_data_path,
                                          gene_id_column=gene_id_column,
                                          scores_column="Mesenchymal",
                                          )
mesenchymal_csm

Similarly we do so to get mesenchymal context specific metabolic model.

Save the CSMs for further analysis and plots

cobra.io.write_sbml_model(epithelial_csm, "epithelial_csm.xml")
cobra.io.write_sbml_model(mesenchymal_csm, "mesenchymal_csm.xml")

Analysis of Context Specific Models

Link to the python notebook this link

Define model_names with their associated paths in a dict

model_paths = {
    "epithelial_csm": "epithelial_csm.xml",
    "mesenchymal_csm": "mesenchymal_csm.xml"
}

Import analysis module from gsmm

analyse_and_save_fluxes(..) is a function that carries all the analysis and saves the data for further analysis to be utilised by visualisation module

Import analysis module from gsmm

analyse_and_save_fluxes(..) is a function that carries all the analysis and saves the data for further analysis to be utilised by visualisation module

from gsmm.csm.analyse_csm import analyse_and_save_fluxes

Import analysis module from gsmm

analyse_and_save_fluxes(..) is a function that carries all the analysis and saves the data for further analysis to be utilised by visualisation module

from gsmm.csm.analyse_csm import analyse_and_save_fluxes

analyse_and_save_fluxes(model_paths)

Clustermap of Reaction rates (Fluxes) in different Context Specific Models

Pearson Correlation coefficients of All the Reaction Fluxes compared across the models

Pearson Correlation coefficients of Sink Reaction fluxes, in this case it shows insignificance

Similarly, two or more models can be compared to get the relevant plot for significant observations.

Thank you!