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Network analysis of systems elements

  • Daniel Schöner
  • Simon Barkow 
  • Stefan Bleuler
  • Anja Wille
  • Philip Zimmermann
  • Peter Bühlmann
  • Wilhelm Gruissem
  • Eckart Zitzler
Part of the Experientia Supplementum book series (EXS, volume 97)

Abstract

A central goal of postgenomic research is to assign a function to every predicted gene. Because genes often cooperate in order to establish and regulate cellular events the examination of a gene has also included the search for at least a few interacting genes. This requires a strong hypothesis about possible interaction partners, which has often been derived from what was known about the gene or protein beforehand. Many times, though, this prior knowledge has either been completely lacking, biased towards favored concepts, or only partial due to the theoretically vast interaction space. With the advent of high-throughput technology and robotics in biological research, it has become possible to study gene function on a global scale, monitoring entire genomes and proteomes at once. These systematic approaches aim at considering all possible dependencies between genes or their products, thereby exploring the interaction space at a systems scale. This chapter provides an introduction to network analysis and illustrates the corresponding concepts on the basis of gene expression data. First, an overview of existing methods for the identification of co-regulated genes is given. Second, the issue of topology inference is discussed and as an example a specific inference method is presented. And lastly, the application of these techniques is demonstrated for the Arabidopsis thaliana isoprenoid pathway.

Keywords

Network Analysis Gene Expression Data System Element Genetic Regulatory Network Isoprenoid Biosynthesis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Birkhäuser Verlag/Switzerland 2007

Authors and Affiliations

  • Daniel Schöner
    • 1
    • 4
  • Simon Barkow 
    • 2
    • 4
  • Stefan Bleuler
    • 2
    • 4
  • Anja Wille
    • 3
    • 4
  • Philip Zimmermann
    • 1
    • 4
  • Peter Bühlmann
    • 3
    • 4
  • Wilhelm Gruissem
    • 1
    • 4
  • Eckart Zitzler
    • 2
    • 4
  1. 1.Plant BiotechnologyInstitute of Plant SciencesSwitzerland
  2. 2.Computer Engineering and Networks LaboratorySwitzerland
  3. 3.Seminar for StatisticsSwiss Federal Institute of Technology (ETH)ZürichSwitzerland
  4. 4.Reverse Engineering GroupSwiss Federal Institute of Technology (ETH)Zürich

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