StanDep

StanDep identifies core reactions for constructing context-specific genome-scale metabolic models (GEMs) by integrating transcriptomics with data-driven thresholding to retain lowly expressed enzymes and housekeeping genes.

Key Features:

• Expression Pattern Clustering: Clusters gene expression data across contexts to define groups with similar transcriptomic patterns.
• Data-Driven Thresholding: Applies data-dependent statistics, specifically cluster mean and standard deviation, to determine context-specific thresholds for each cluster.
• Inclusion of Housekeeping Reactions: Increases inclusion of housekeeping reactions often excluded by arbitrary-threshold methods.
• Transcriptomic Explanation for Lowly Expressed Reactions: Provides a transcriptomic basis for retaining lowly expressed reactions during model extraction.

Scientific Applications:

• Context-specific GEM reconstruction: Aids construction of context-specific metabolic models from transcriptomics within systems biology studies.
• Cancer cell-line modeling: Has been applied to generate hundreds of models for the NCI-60 cancer cell lines, enabling analysis of both context-specific and ubiquitous cellular functions.

Methodology:

Implemented as a MATLAB toolbox, StanDep uses heuristic methods to cluster genes by expression patterns and applies cluster-specific mean and standard deviation to define thresholds for core reactions integrated into GEMs.