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Inferring Signaling and Gene Regulatory Network from Genetic and Genomic Information

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Handbook of Statistical Bioinformatics

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

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Abstract

Biological systems respond to environmental changes and genetic variations. One of the essential tasks of systems biology is to untangle the signaling and gene regulatory networks that respond to environmental changes or genetic variations. However, unwiring the complex gene regulatory program is extremely challenging due to the large number of variables involved in these regulatory programs. The traditional single gene centered strategy turns out to be both insufficient and inefficient for studying signaling and gene regulatory networks. With the emergence of various high throughput technologies, such as DNA microarray, ChIP-chip, etc., it becomes possible to interrogate the biological systems at genome scale efficiently and cost effectively. As these high throughput data are accumulating rapidly, there exists a clear demand for methods that effectively integrate these data to elucidate the complex behaviors of biological systems. In this chapter, we discuss several recently developed computational models that integrate diverse types of high throughput data, particularly, the genetic and genomic data, as examples for the systems approaches that untangle signaling and gene regulatory networks.

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Author information

Authors and Affiliations

  1. Merck & Co., Inc., Boston, MA, USA

    Zhidong Tu

  2. Sage Bionetwork, Seattle, WA, USA

    Jun Zhu

  3. University of Southern California, Los Angeles, CA, USA

    Fengzhu Sun

Authors
  1. Zhidong Tu
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  2. Jun Zhu
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  3. Fengzhu Sun
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Corresponding author

Correspondence to Fengzhu Sun .

Editor information

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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© 2011 Springer-Verlag Berlin Heidelberg

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Tu, Z., Zhu, J., Sun, F. (2011). Inferring Signaling and Gene Regulatory Network from Genetic and Genomic Information. 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_23

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