TIGER

TIGER integrates genome-scale metabolic models with transcriptional regulation using mixed-integer linear programming (MILP) and gene-protein-reaction (GPR) logic to construct and analyze coupled metabolism–regulation models.

Key Features:

• Integration of Metabolic and Regulatory Networks: Combines genome-scale metabolic models with gene-protein-reaction (GPR) associations and transcriptional regulatory logic to represent metabolism–regulation interactions.
• Automated Conversion of Rules: Transforms generalized Boolean or multilevel regulatory rules into mixed-integer inequalities for incorporation into MILP formulations.
• Algorithm Implementation: Implements existing algorithms for integrating high-throughput expression data with genome-scale metabolic and transcriptional regulation models.
• Model Consistency and Improvement: Identifies inconsistencies and suggests improvements in coupled models, demonstrated on the Saccharomyces cerevisiae transcriptional regulatory network.
• Platform for Algorithm Development: Provides a consistent MILP-based framework for developing and extending algorithms and adding regulatory networks to genome-scale metabolic models.

Scientific Applications:

• Metabolic Flux Analysis: Enables constrained optimization analyses to predict relationships between metabolic fluxes and gene-encoded enzyme activities.
• Regulatory Network Modeling: Supports incorporation of transcriptional regulation into genome-scale metabolic models to study regulatory control of metabolism.
• High-throughput Data Integration: Integrates high-throughput expression data to refine and validate coupled metabolic–regulatory models against experimental datasets.

Methodology:

Encodes GPR relationships and regulatory logic using mixed-integer linear programming (MILP) by converting Boolean or multilevel rules into mixed-integer inequalities and includes implementations of algorithms for integrating high-throughput expression data with metabolic and transcriptional regulation models.