Chapter 8. Probabilistic Boolean Networks as Models for Gene Regulation

  1. Dr. Frank Emmert-Streib3,4 and
  2. Dr. Matthias Dehmer5
  1. Yufei Huang1 and
  2. Edward R. Dougherty2

Published Online: 16 SEP 2008

DOI: 10.1002/9783527622818.ch8

Analysis of Microarray Data: A Network-Based Approach

Analysis of Microarray Data: A Network-Based Approach

How to Cite

Huang, Y. and Dougherty, E. R. (2008) Probabilistic Boolean Networks as Models for Gene Regulation, in Analysis of Microarray Data: A Network-Based Approach (eds F. Emmert-Streib and M. Dehmer), Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany. doi: 10.1002/9783527622818.ch8

Editor Information

  1. 3

    University of Washington, Department of Biostatistics, University of Washington, Department of Genome Sciences, Seattle, WA 98195-5065, USA

  2. 4

    Stowers Institute for Medical Research, 1000 East 50th Street, Kansas City, MO 64110, USA

  3. 5

    Vienna University of Technology, Discrete Mathematics and Geometry, Wiedner Hauptstrasse 8–10/104, 1040 Vienna, Austria

Author Information

  1. 1

    University of Texas at San Antonio (UTSA), Department of Electrical and Compute Engineering, One UTSA Circle, San Antonio, TX 78249-0669, USA

  2. 2

    Texas A&M University, Department of Electrical and Computer Engineering, College Station, TX 77843, USA

Publication History

  1. Published Online: 16 SEP 2008
  2. Published Print: 13 FEB 2008

ISBN Information

Print ISBN: 9783527318223

Online ISBN: 9783527622818

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Keywords:

  • DNA microarrays;
  • analysis;
  • probabilistic Boolean networks (PBN);
  • models for gene regulation;
  • reverse engineering regulatory networks;
  • PBN-based microarray;
  • context-sensitive PBN;
  • melanoma application

Summary

This chapter contains sections titled:

  • Introduction

  • Modeling Gene Regulation with Probabilistic Boolean Networks

  • Reverse Engineering Regulatory Networks with PBN-Based Microarray Expression Data

  • Optimal Control of Context-Sensitive PBN

  • References