The relationship between physiological stress response and variation in omics data

Abstract

Omics analysis is the cornerstone of systems biology. It offers comprehensive assessments of stress, interaction networks and connections to phenotype. Defining a stressed phenotype can be challenging, however, as stress response mechanisms can arise from a range of environmental conditions and experimental perturbations. Previous work from our lab noted the possibility of a relationship between stress in omics data and the variation of that data. This connection has yet to be clearly defined, and the cellular mechanisms responsible for the canalization of omics data remain a mystery. In this work I have taken advantage of the sensitivity of metabolomics and proteomics to detect cellular stress and characterize its relationship to variation. By utilizing coefficient of variation (CV) as a statistic of merit, the depth of the relationship between stress and variation can be uncovered. Once the model was clearly defined, a proteomics dataset with a large proportion of protein coverage was utilized to investigate what pathways might be responsible for the metabolite and protein canalization.