Developing and Testing Methods for Microarray Data Analysis Using an Artificial Life Framework

Dirk Repsilber, Jan T. Kim

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)


Microarray technology has resulted in large sets of gene expression data. Using these data to derive knowledge about the underlying mechanisms that control gene expression dynamics has become an important challenge. Adequate models of the fundamental principles of gene regulation, such as Artificial Life models of regulatory networks, are pivotal for progress in this area. In this contribution, we present a framework for simulating microarray gene expression experiments. Within this framework, artificial regulatory networks with a simple regulon structure are generated. Simulated expression profiles are obtained from these networks under a series of different environmental conditions. The expression profiles show a complex diversity. Consequently, success in using hierarchical clustering to detect groups of genes which form a regulon proves to depend strongly on the method which is used to quantify similarity between expression profiles. When measurements are noisy, even clusters of identically regulated genes are surprisingly difficult to detect. Finally, we suggest cluster support, a method based on overlaying multiple clustering trees, to find out which clusters in a tree are biologically significant.
Original languageEnglish
Title of host publicationAdvances in Artificial Life
EditorsWolfgang Banzhaf, Jens Ziegler, Thomas Christaller, Peter Dittrich, Jan T. Kim
Number of pages10
ISBN (Print)978-3-540-20057-4
Publication statusPublished - 2003
Event7th European Conference, ECAL 2003 - Dortmund, Germany
Duration: 14 Sep 200317 Sep 2003

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag, Berlin Heidelberg


Conference7th European Conference, ECAL 2003

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