XomicsToModel: omics data integration and generation of thermodynamically consistent metabolic models

Area of Science:

• Systems Biology
• Metabolic Modeling
• Bioinformatics

Background:

• Constraint-based modeling simulates biochemical systems, particularly metabolism.
• Existing methods for context-specific metabolic models lack thermodynamic consistency.
• Omics data integration is crucial for accurate metabolic reconstructions.

Purpose of the Study:

• To introduce XomicsToModel, a pipeline for extracting context-specific metabolic models.
• To ensure stoichiometric, thermodynamic, and flux consistency in extracted models.
• To enable accurate metabolic simulations across diverse biological contexts.

Main Methods:

• XomicsToModel integrates bibliomic, transcriptomic, proteomic, and metabolomic data.
• The pipeline uses a generic genome-scale metabolic reconstruction as a base.
• It semi-automates the extraction of context-specific, thermodynamically consistent models.

Main Results:

• The pipeline successfully extracts context-specific metabolic models that are stoichiometrically, thermodynamically, and flux consistent.
• XomicsToModel seamlessly integrates omics data, ensuring mechanistic accuracy and physicochemical consistency.
• Demonstrated utility in creating accurate metabolic models for various biological contexts.

Conclusions:

• XomicsToModel provides a robust method for generating high-fidelity metabolic models.
• The pipeline enhances the accuracy of metabolic simulations and predictions.
• Applicable to systems biology, drug development, and personalized medicine research.