A survey of models for inference of gene regulatory networks

In this article, I present the biological backgrounds of microarray, ChIP-chip and ChIPSeq technologies and the application of computational methods in reverse engineering of gene regulatory networks (GRNs). The most commonly used GRNs models based on Boolean networks, Bayesian networks, relevance networks, differential and difference equations are described. A novel model for integration of prior biological knowledge in the GRNs inference is presented, too. The advantages and disadvantages of the described models are compared. The GRNs validation criteria are depicted. Current trends and further directions for GRNs inference using prior knowledge are given at the end of the paper

Integrate qualitative biological knowledge for gene regulatory network reconstruction with dynamic Bayesian networks

Reconstructing gene regulatory networks, especially the dynamic gene networks that reveal the temporal program of gene expression from microarray expression data, is essential in systems biology. To overcome the challenges posed by the noisy and under-sampled microarray data, developing data fusion methods to integrate legacy biological knowledge for gene network reconstruction is a promising direction. However, large amount of qualitative biological knowledge accumulated by previous research, albeit very valuable, has received less attention for reconstructing dynamic gene networks due to its incompatibility with the quantitative computational models.;In this dissertation, I introduce a novel method to fuse qualitative gene interaction information with quantitative microarray data under the Dynamic Bayesian Networks framework. This method extends the previous data integration methods by its capabilities of both utilizing qualitative biological knowledge by using Bayesian Networks without the involvement of human experts, and taking time-series data to produce dynamic gene networks. The experimental study shows that when compared with standard Dynamic Bayesian Networks method which only uses microarray data, our method excels by both accuracy and consistency