Deciphering modular and dynamic behaviors of transcriptional networks

Abstract

The coordinated and dynamic modulation or interaction of genes or proteins acts as an important mechanism used by a cell in functional regulation. Recent studies have shown that many transcriptional networks exhibit a scale-free topology and hierarchical modular architecture. It has also been shown that transcriptional networks or pathways are dynamic and behave only in certain ways and controlled manners in response to disease development, changing cellular conditions, and different environmental factors. Moreover, evolutionarily conserved and divergent transcriptional modules underline fundamental and species-specific molecular mechanisms controlling disease development or cellular phenotypes. Various computational algorithms have been developed to explore transcriptional networks and modules from gene expression data. In silico studies have also been made to mimic the dynamic behavior of regulatory networks, analyzing how disease or cellular phenotypes arise from the connectivity or networks of genes and their products. Here, we review the recent development in computational biology research on deciphering modular and dynamic behaviors of transcriptional networks, highlighting important findings. We also demonstrate how these computational algorithms can be applied in systems biology studies as on disease, stem cells, and drug discovery.

Fig. 1

Hierarchical and modular organization of the yeast transcriptional network. Genes are clustered into different modules. Some of the modules (e.g., protein biosynthesis) are organized in more than two hierarchical levels; large modules are composed of several smaller modules, giving a star-like topology. (Reproduced from (Tanay et al. 2004))

Fig. 2

A two-stage matrix decomposition of a microarray data set X (N genes and M samples) is obtained by ModulePro. The NICA extracts nonlinear independent components (columns in ) from X. At the PSMF stage, is approximated by the product of sparse matrix Y and low-rank Z. The values of all matrices are color coded by using a color heatmap, from dark green (minimum) to dark red (maximum). (Reproduced from (Li et al. 2007b))

Fig. 3

Results of cross-species transcriptional module analysis on the Oct4/Sox2/Nanog-directed regulatory network in human and mouse ESCs and EBs. (A) Heatmap presentation of the combined pair-wise correlation matrices of gene expression profiles in mouse (upper diagonal part) and human (lower diagonal part). Each column or row represents a human–mouse orthologous gene. Each block on the matrix presents the correlation level between the gene of the corresponding column and the gene of the row. The more reddish the color is, the more correlated the genes are on the expression profiles. The white color indicates zero correlation. (B) Heatmap of normalized gene expression values (red, over-expression in comparison to the mean expression value; green, under-expression, black, non change on the expression level). Each row represents an orthologous gene, and the position of the genes is the same as that on the row in the correlation matrix heatmap in A. The identified transcriptional modules are labeled as C1 through D7