Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations.

Area of Science:

• Systems Biology
• Metabolomics
• Computational Biology

Background:

• Metabolomic data analysis enhances understanding of cellular mechanisms and biological system responses.
• There is a need for advanced computational methods to analyze and integrate large volumes of biological data.

Purpose of the Study:

• To present Totoro, a novel constraint-based computational approach for integrating quantitative metabolomic data.
• To predict active metabolic reactions during transient states using genome-wide metabolic models.

Main Methods:

• Totoro integrates quantitative non-targeted metabolomic data from two metabolic states into genome-wide metabolic models.
• The method employs a constraint-based approach to predict reaction activity.
• Applied to Escherichia coli growth experiments with glucose, pyruvate, and succinate pulses using core and iJO1366 models.

Main Results:

• Totoro successfully predicted known active pathways in Escherichia coli under different substrate conditions.
• The method provided new insights into substrate-specific metabolisms.
• Demonstrated applicability to both small and large metabolic networks.

Conclusions:

• Totoro is an effective open-source tool for analyzing metabolomic data and metabolic models.
• The approach facilitates the study of cellular responses to perturbations and substrate utilization.
• Applicable to any organism with an available metabolic model.