Tn-Core

Tn-Core integrates Tn-seq and RNA-seq data with genome-scale metabolic networks in Matlab to generate gene-centric, context-specific core metabolic network reconstructions for interpretation of essential genes.

Key Features:

• Integration of Experimental Data: Integrates Tn-seq and RNA-seq datasets to map experimental fitness and expression data onto metabolic networks.
• Gene-Centric Core Reconstructions: Constructs gene-centric, context-specific core metabolic network reconstructions that represent essential metabolism under defined conditions.
• Context-Specific Metabolic Models: Generates context-specific metabolic models that capture physiological states for downstream computational analyses.
• Genome-Scale Model Refinement: Contextualizes Tn-seq data with in silico genome-scale metabolic networks to refine and improve model accuracy.

Scientific Applications:

• Essential Gene Identification: Identifies essential genes and assigns their roles within metabolic networks using integrated experimental and computational data.
• Physiological Context Modeling: Produces comprehensive models that reflect organismal physiological contexts for condition-specific interpretation.
• Model Interpretation and Refinement: Refines genome-scale metabolic reconstructions to improve interpretation of genetic interactions and pathway essentiality.

Methodology:

Implemented in Matlab, Tn-Core systematically integrates Tn-seq and RNA-seq datasets with genome-scale metabolic networks to construct gene-centric, context-specific core metabolic reconstructions and to contextualize experimental fitness data within in silico models.