Quantitative -omic data empowers bottom-up systems biology

Abstract

The large-scale generation of '-omic' data holds the potential to increase and deepen our understanding of biological phenomena, but the ability to synthesize information and extract knowledge from these data sets still represents a significant challenge. Bottom-up systems biology overcomes this hurdle through the integration of disparate -omic data types, and absolutely quantified experimental measurements allow for direct integration into quantitative, mechanistic models. The human red blood cell has served as a starting point for the application of systems biology approaches and has been the focus of a recent burst of generated quantitative metabolomics and proteomics data. Thus, the red blood cell represents the perfect case study through which to examine our ability to glean knowledge from the integration of multiple disparate data types.