Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks

BMC Systems Biology

Yue Wang¹  David M Herrington³  Jianhua Xuan¹  Ie-Ming Shih⁴  Zhen Zhang⁴  Robert Clarke⁵  Eric P Hoffman²  Bai Zhang⁴  Ye Tian¹ 

[1] Department of Electrical & Computer Engineering, Virginia Tech, Arlington 22203, VA, USA;Research Center for Genetic Medicine, Children’s National Medical Center, Washington 20010, DC, USA;Department of Internal Medicine, Section on Cardiovascular Medicine, Wake Forest School of Medicine, Winston-Salem 27157, NC, USA;Department of Pathology, Johns Hopkins University, Baltimore 21231, MD, USA;Lombardi Comprehensive Cancer Center, Georgetown University, Washington 20057, DC, USA

关键词: Convex optimization;    Knowledge incorporation;    Systems biology;    Network analysis;    Network rewiring;    Differential dependency network;    Probabilistic graphical models;    Biological networks;   

Others  :  1159583
DOI  :  10.1186/s12918-014-0087-1

 received in 2014-02-24, accepted in 2014-07-15,  发布年份 2014

【 摘 要 】

Background

Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context-specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. While differential networks cannot be constructed by existing knowledge alone, novel incorporation of prior knowledge into data-driven approaches can improve the robustness and biological relevance of network inference. However, the major unresolved roadblocks include: big solution space but a small sample size; highly complex networks; imperfect prior knowledge; missing significance assessment; and heuristic structural parameter learning.

Results

To address these challenges, we formulated the inference of differential dependency networks that incorporate both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to “random” knowledge. Based on that scheme, we developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets. We then applied our method to yeast cell line and breast cancer microarray data and obtained biologically plausible results. The open-source R software package and the experimental data are freely available at http://www.cbil.ece.vt.edu/software.htm webcite.

Conclusions

Experiments on both synthetic and real data demonstrate the effectiveness of the knowledge-fused differential dependency network in revealing the statistically significant rewiring in biological networks. The method efficiently leverages data-driven evidence and existing biological knowledge while remaining robust to the false positive edges in the prior knowledge. The identified network rewiring events are supported by previous studies in the literature and also provide new mechanistic insight into the biological systems. We expect the knowledge-fused differential dependency network analysis, together with the open-source R package, to be an important and useful bioinformatics tool in biological network analyses.

【 授权许可】

   
2014 Tian et al.; licensee BioMed Central

【 图 表 】

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