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Reverse Engineering of Gene Regulation Networks with an Application to the DREAM4 in silico Network Challenge

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Handbook of Statistical Bioinformatics

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

Abstract

Despite much research, reverse engineering of gene regulation remains a challenging task due to a large number of genes involved and complex relationships among them. In this chapter, we review statistical methods for inferring gene regulation networks, specifically focusing on the methods for analyzing gene expression data. We then present a new reverse engineering method in order to efficiently utilize datasets from various perturbation experiments as well as to integrate these multiple sources of information. We apply our approach to the DREAM in silico network challenge to demonstrate its performance.

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Acknowledgements

Supported in part by NIH grants GM59507 and T15LM07056, a pilot grant from the Yale Pepper Center, NSF grant DMS0714817, and a fellowship from the China Scholarship Council.

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Authors and Affiliations

  1. Department of Epidemiology and Public Health, Yale University, New Haven, USA

    Hyonho Chun

  2. Program in Translational Informatics, Yale University, New Haven, USA

    Jia Kang

  3. Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, People’s Republic of China

    Xianghua Zhang

  4. School of Mathematical Sciences and Center for Theoretical Biology, Peking University, Beijing, People’s Republic of China

    Minghua Deng

  5. Program in Computational Biology, Yale University, New Haven, USA

    Haisu Ma

  6. Department of Genetics, Yale University, New Haven, USA

    Hongyu Zhao

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  1. Hyonho Chun
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  2. Jia Kang
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  3. Xianghua Zhang
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  4. Minghua Deng
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  5. Haisu Ma
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  6. Hongyu Zhao
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Corresponding author

Correspondence to Hongyu Zhao .

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Editors and Affiliations

  1. , Institute of Statistics, National Chiao Tung University, Ta Hsueh Road 1001, Hsinchu, 30050, Taiwan, Taiwan R.O.C.

    Henry Horng-Shing Lu

  2. , Department of Empirical Inference, MPI for Intelligent Systems, Spemannstraße 38, Tübingen, 72076, Germany

    Bernhard Schölkopf

  3. School of Medicine, Dept. Epidemiology & Public Health, Yale University, College Street 60, New Haven, 06520, Connecticut, USA

    Hongyu Zhao

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Chun, H., Kang, J., Zhang, X., Deng, M., Ma, H., Zhao, H. (2011). Reverse Engineering of Gene Regulation Networks with an Application to the DREAM4 in silico Network Challenge. In: Lu, HS., Schölkopf, B., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16345-6_22

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