An Integrative Analysis of Time-varying Regulatory Networks From High-dimensional Data.

Directed networks have been widely used to describe many biological processes and functions. Understanding the structure of biological networks, especially regulatory networks, could help discover the mechanisms underlying important biological processes and pathogenesis of diseases. Most network inference methods assume the network structure is time-invariant or stationary. However, in some processes, the network structure is non-stationary or time-varying. The stationary network inference methods might not be able to directly used to reconstruct time-varying networks. Some non-stationary network learning methods have been proposed to infer the networks, but, the inferred networks are not regulatory networks which require activation and inhibition information. This work proposes an integrative approach, which combines the changepoint estimation, weighted network learning and searching, and model checking technique, to reconstruct time varying regulatory networks from high-dimensional time series data. We illustrate this approach to study the structure changes of Drosophila's regulatory networks in its life cycle.