A Context-Specific, Literature-Supported Framework for Validating Stress Response Differentially Expressed Gene Sets

Computational models of stress responses identify genes underlying physiological adaptation, but their utility depends on rigorous validation. Often, gene activity reflects both adaptive mechanisms and noise. Here, we develop a framework that leverages public databases to support the subselection of biologically supported model genes for temperature-stress responses. We test our framework on a model that identified and categorized differentially expressed genes (DEGs) into Key-Response, Treatment-Specific, Noisy, and Support groups based on inter-individual gene expression variability before and after treatment. The first three groups were hypothesized to constitute a Principal Response. To