Hierarchical decomposition of dynamically evolving regulatory networks

BMC Bioinformatics

Ahmet Ay²  Dihong Gong¹  Tamer Kahveci¹ 

[1] Department of Computer and Information Science and Engineering, University of Florida, Gainesville 32611, FL, USA

[2] Departments of Biology and Mathematics, Colgate University, Hamilton 13346, NY, USA

关键词: Network dynamics;    Gene regulatory networks;    Hierarchy;   

Others  :  1232566
DOI  :  10.1186/s12859-015-0529-9

 received in 2014-10-27, accepted in 2015-03-09,  发布年份 2015

【 摘 要 】

Background

Gene regulatory networks describe the interplay between genes and their products. These networks control almost every biological activity in the cell through interactions. The hierarchy of genes in these networks as defined by their interactions gives important insights into how these functions are governed. Accurately determining the hierarchy of genes is however a computationally difficult problem. This problem is further complicated by the fact that an intrinsic characteristic of regulatory networks is that the wiring of interactions can change over time. Determining how the hierarchy in the gene regulatory networks changes with dynamically evolving network topology remains to be an unsolved challenge.

Results

In this study, we develop a new method, named D-HIDEN (Dynamic-HIerarchical DEcomposition of Networks) to find the hierarchy of the genes in dynamically evolving gene regulatory network topologies. Unlike earlier methods, which recompute the hierarchy from scratch when the network topology changes, our method adapts the hierarchy based on the wiring of the interactions only for the nodes which have the potential to move in the hierarchy.

Conclusions

We compare D-HIDEN to five currently available hierarchical decomposition methods on synthetic and real gene regulatory networks. Our experiments demonstrate that D-HIDEN significantly outperforms existing methods in running time, accuracy, or both. Furthermore, our method is robust against dynamic changes in hierarchy. Our experiments on human gene regulatory networks suggest that our method may be used to reconstruct hierarchy in gene regulatory networks.

【 授权许可】

   
2015 Ay et al.; licensee BioMed Central.

【 图 表 】

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