GATOM

GATOM identifies regulated metabolic modules by integrating transcriptional and/or metabolomic data with atom transition networks to detect subnetworks that are significantly regulated between two conditions.

Key Features:

• Integration of omics data: Accepts transcriptional and/or metabolomic datasets to identify subnetworks most significantly regulated between two conditions.
• Atom transition network topology: Uses atom transition-based network topology to focus on the flow and transformation of atoms within metabolic pathways.
• Graph optimization: Formulates a variant of the maximum weight connected subgraph problem and provides an exact solver to identify relevant metabolic modules.
• Network construction pipelines: Constructs metabolic networks based on KEGG and Rhea databases, with the Rhea-based pipeline supporting lipidomics data.
• Software components: Implements computational components via the R packages mwcsr for solving the graph optimization problem and gatom for the pipeline implementation.

Scientific Applications:

• Systems biology and bioinformatics: Enables integrative analysis of metabolic regulation across omics datasets.
• Metabolic regulation studies: Identifies regulated metabolic modules and subnetworks between experimental conditions.
• Lipidomics analysis: Supports analysis of lipidomics data through the Rhea-based network pipeline.
• Disease and therapeutic research: Facilitates discovery related to disease mechanisms, drug development, and personalized medicine by revealing metabolic pathway regulation.

Methodology:

Integration of transcriptional and/or metabolomic datasets onto atom transition-based network topology; construction of networks from KEGG and Rhea; formulation of a variant of the maximum weight connected subgraph problem solved by an exact solver implemented in the mwcsr R package with the gatom package implementing the pipeline.