Totoro

Totoro integrates quantitative non-targeted metabolomic data from distinct metabolic states into genome-scale constraint-based metabolic models to predict reactions active during perturbation-induced transient metabolic states.

Key Features:

• Integration with Metabolomic Data: Integrates quantitative non-targeted metabolomic data representing distinct metabolic states into genome-scale metabolic models.
• Prediction of Active Reactions: Predicts which metabolic reactions are active during transient responses to perturbations.
• Applicability Across Models: Applicable to both small and large genome-scale models, demonstrated on Escherichia coli core model and the iJO1366 model.
• Constraint-Based Modeling: Employs constraint-based modeling approaches to combine metabolomic data with metabolic network structure.
• Implementation: Implemented in C++ and uses IBM ILOG CPLEX Optimization Studio for optimization.

Scientific Applications:

• Metabolic Pathway Analysis: Identifies active pathways during transient states such as nutrient pulses.
• Insights from Experimental Data: Applied to Escherichia coli growth experiments on glucose, pyruvate, and succinate to provide substrate-specific metabolic insights.
• Cross-Organism Modeling: Applicable to any organism with an available genome-scale metabolic model for comparative metabolic studies.

Methodology:

Constraint-based integration of quantitative non-targeted metabolomic data from two distinct metabolic states into genome-scale metabolic models to identify reactions active during transient perturbation-induced states.