Informatics approaches for identifying biologic relationships in time-series data.

A vital goal of the genomic era is to identify biologic relationships between genes and gene products and to understand how these relationships influence phenotypes. Time course data contain a vast amount of causal and mechanistic information about complex systems, but experimental and informatics challenges must be overcome to produce and extract this information from biologic systems. Mathematical modeling and bioinformatics methods are being developed in anticipation of experiments involving the coordinated measurement of cellular and molecular quantities at various spatial and temporal scales. Experimental methods that probe at the nanoscale will facilitate the exploration of biologic systems at the single-cell and single-molecule level, but will also introduce special challenges for mathematical modeling because events at nanoscale concentrations are subject to the influence of intrinsic noise. This review addresses the progress, challenges, and frontiers in the field of time-series informatics. The ultimate goal of time-series informatics is to move beyond descriptive relationships and toward predictive models of emergent, or systemic, behaviors of biologic systems as a whole.