A Gaussian graphical model for identifying significantly responsive regulatory networks from time series gene expression data

Abstract

With rapid accumulation of functional relationships between biological molecules, knowledge-based networks have been constructed and stocked in many databases. These networks provide the curated and comprehensive information for the functional linkages among genes and proteins, while their activities are highly related with specific phenotypes and conditions. To evaluate a knowledge-based network in a specific condition, measuring the consistency between its structure and the conditionally specific gene expression profiling data is an important criterion. In this work, we propose a Gaussian graphical model to evaluate the documented regulatory networks by the consistency between network architectures and time-series gene expression profiles. By developing a dynamical Bayesian network model, we derive a new method to evaluate gene regulatory networks in both simulated and true time series microarray data. The regulatory networks are evaluated by matching a network structure and gene expressions, which are achieved by randomly rewiring the regulatory structures. To demonstrate the effectiveness of our method, we identify the significant regulatory networks in response to the time series gene expression of circadian rhythm. Moreover, the knowledge-based networks are screened and ranked by their consistencies of structures based on dynamical gene expressions.