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This article is part of the supplement: Selected articles from the Third International Symposium on Optimization and Systems Biology

Open Access Proceedings

Computational inference and analysis of genetic regulatory networks via a supervised combinatorial-optimization pattern

Binhua Tang1, Xuechen Wu2, Ge Tan3, Su-Shing Chen4, Qing Jing5 and Bairong Shen6*

Author Affiliations

1 Department of Bioinformatics, Tongji University, Shanghai, 212003, China

2 Institute of Protein Research, Tongji University, Shanghai, 212003, China

3 Department of Computer Science, ETH, Zurich, 8092, Switzerland

4 CISE and Systems Biology Lab, University of Florida, Gainesville, FLA, 32611, USA

5 Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China

6 Center for Systems Biology, Soochow University, Suzhou, 215006, China

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BMC Systems Biology 2010, 4(Suppl 2):S3  doi:10.1186/1752-0509-4-S2-S3

Published: 13 September 2010



Post-genome era brings about diverse categories of omics data. Inference and analysis of genetic regulatory networks act prominently in extracting inherent mechanisms, discovering and interpreting the related biological nature and living principles beneath mazy phenomena, and eventually promoting the well-beings of humankind.


A supervised combinatorial-optimization pattern based on information and signal-processing theories is introduced into the inference and analysis of genetic regulatory networks. An associativity measure is proposed to define the regulatory strength/connectivity, and a phase-shift metric determines regulatory directions among components of the reconstructed networks. Thus, it solves the undirected regulatory problems arising from most of current linear/nonlinear relevance methods. In case of computational and topological redundancy, we constrain the classified group size of pair candidates within a multiobjective combinatorial optimization (MOCO) pattern.


We testify the proposed approach on two real-world microarray datasets of different statistical characteristics. Thus, we reveal the inherent design mechanisms for genetic networks by quantitative means, facilitating further theoretic analysis and experimental design with diverse research purposes. Qualitative comparisons with other methods and certain related focuses needing further work are illustrated within the discussion section.