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Welcome to TackleMMe

TackleMMe is a pipeline developed in MATLAB designed for tackling metabolic models exploration.

Concept

TackleMMe is based on the creation of one unique MATLAB object, called project. A project is made to store everything, from model building, to model analysis and model comparison. Tutorial scripts can be found in the BRCA example folder of our github.

A complete project object after running the entire pipeline (20262608_BRCAProject.mat), as well as intermediate objects after running the several steps of the pipeline are available here. Workspaces associated with the tutorials are also included.

The pipeline and tutorials has been organized in three main steps:

  1. Model building and project initialization

    The tutorial includes how to:

    • build context-specific models from RNA-seq data using rFASTCORMICS,
    • initialize a project,
    • add a model to an already existing project.

    Although TackleMMe offers the possibility to build context-specific models using rFASTCORMICS, any COBRA-format model can be stored inside a project. Mandatory fields required for project initialization are detailed in Project Initialization.

  2. Single Model Analysis

    Once a project is initialized and contains models, our pipeline allows to:

    • perform integrated tests on one or several models,
    • store the results as well as the parameters that have been used for each test,
    • update tests with new parameters,
    • generate a PDF report summarizing the results of the main tests.

    More details about the implemented analysis and the report can be found in Single Model Analysis

  3. Model Comparison

    Once single model analyses are completed and an active analysis is chosen for each model, the pipeline allows to:

    • compare models on a structural level (presence/absence of reactions, metabolites, and genes),
    • compare models on a functional level (FBA fluxes, FVA similarity, pathway enrichment),
    • compare models on a sampling level (flux distributions, inter-model KL divergence),
    • store all comparison results and generated plots in a dedicated comparisons field.

    More details about the available comparison types and their outputs can be found in Models Comparison.