Analysis of time-series regulatory networks

Abstract The vast majority of biological processes are dynamic, changing over time. Several studies profile high-throughput time-series data and use it for analyzing and modeling various biological processes. In this review, we focus on data, methods, and analysis for reconstructing dynamic regulatory network models from high-throughput time-series data sets. We discuss methods focused on a single data type, methods that integrate several omics data types, methods that integrate static and time-series data, and methods that focus on single-cell data. For each of these categories, we present some of the top methods and discuss their underlying assumptions, advantages, and potential shortcomings. As the quantity and types of time-series omics data continue to increase, we expect that these methods, and additional methods extending and improving them, would play an increasingly important role in our ability to accurately model biological processes.