Abstract

All biological processes are achieved through a complicated spatial and temporal dynamic regulation of genes (and thereby proteins). Understanding the dynamics of complex biological systems is thus a fundamental but challenging issue. Currently, the investigation of the dynamics of gene regulation in complex biological systems can be mainly divided into two aspects. On the one hand, attention is focused on recovering biological molecular network structure from dynamic genome-scale transcript expression profiles since the current knowledge of the manner in which genes regulate each other is far from being complete, even in model organisms. This recovering process is called reverse engineering of biological networks. We developed a new reverse engineering method, termed as trend correlation (TC) method to infer functional linkages among genes and the underlying biological networks directly from genome-scale time-series expression data. Following that, we have demonstrated how to systematically reveal genes involved in the suppressor function of human regulatory T cells and to provide the corresponding functional network by employing a systems biological strategy. On the other hand, current efforts are directed towards delineating the dynamic phenomena of biological systems and further deciphering mechanisms and/or principles underlying the complicated dynamic phenomena. We proposed a shifted cumulative regulation principle in which multiple regulators quantitatively control the expression of their target genes in the transcription regulatory network. In addition, a genome-scale network-based analysis strategy is proposed to identify molecular responsive networks with respect to conditional or pathogenic changes and identify ‘hidden’ key regulators. These works may thus provide the basis for revealing and analyzing a dynamic genome-scale genetic network in order to help our understanding of the complicated dynamic mechanisms that determine major human diseases.