TEAK: topology enrichment analysis framework for detecting activated biological subpathways

Area of Science:

• Systems Biology
• Computational Biology
• Genomics

Background:

• Effective analysis of gene expression data requires methods to identify relationships within biological pathways.
• Existing frameworks may not fully capture complex intergenic interactions.

Purpose of the Study:

• To develop and apply a novel analysis framework, TEAK, for mining gene expression data.
• To identify and characterize biologically relevant subpathways, including linear and nonlinear ones.

Main Methods:

• Developed the Topology Enrichment Analysis framework (TEAK) using a novel algorithm and Clique Percolation Method.
• Applied TEAK to analyze microarray data from Saccharomyces cerevisiae under stress conditions.
• Utilized Bayesian Information Criterion and Kullback-Leibler divergence for subpathway scoring.

Main Results:

• Identified linear sphingolipid metabolic subpathways activated during yeast nitrogen stress response.
• Discovered previously unreported fitness defects in dpl1Δ and lag1Δ mutants under nitrogen limitation.
• Identified a nonlinear glycerophospholipid metabolism subpathway involving SLC1, crucial for yeast filamentous growth.

Conclusions:

• TEAK is effective for analyzing gene expression data and uncovering complex pathway interactions.
• The study identified novel roles for sphingolipid and glycerophospholipid metabolism in yeast stress and filamentous growth.
• TEAK provides a powerful tool for discovering genotype-phenotype relationships in biological systems.