Methodology article

Discovery of time-delayed gene regulatory networks based on temporal gene expression profiling

Xia Li^1,3,4* , Shaoqi Rao^1,2,5* , Wei Jiang^1* , Chuanxing Li¹ , Yun Xiao¹ , Zheng Guo^1,3 , Qingpu Zhang³ , Lihong Wang¹ , Lei Du¹ , Jing Li¹ , Li Li¹ , Tianwen Zhang³ and Qing K Wang^2,5

¹  Department of Bioinformatics, Harbin Medical University, Harbin 150086, PR China

²  Departments of Cardiovascular Medicine and Molecular Cardiology, The Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, Ohio 44195, USA

³  Department of Computer Science, Harbin Institute of Technology, Harbin 150080, PR China

⁴  Biomedical Engineering Institute, Capital University of Medical Sciences, Beijing 100054, PR China

⁵  Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, 9500 Euclid Avenue, Cleveland, Ohio 44195, USA

author email corresponding author email* Contributed equally

BMC Bioinformatics 2006, 7:26doi:10.1186/1471-2105-7-26

Published:	18 January 2006

Abstract

Background

It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks.

Results

In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network) to address the underlying regulations of genes that can span any unit(s) of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings.

Conclusion

We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex gene regulations related to the development, aging and progressive pathogenesis of a complex disease where potential dependences between different experiment units might occurs.