Christian Spieth, Felix Streichert, Nora Speer, and Andreas Zell
Multi-Objective Model Optimization for Inferring Gene Regulatory Networks
Proceedings of the Conference on Evolutionary Multi-Criterion Optimization (EMO 2005)
Abstract
With the invention of microarray technology, researchers are
able to measure the expression levels of ten thousands of genes in par-
allel at various time points of a biological process. The investigation of
gene regulatory networks has become one of the major topics in Systems
Biology. In this paper we address the problem of ¯nding gene regulatory
networks from experimental DNA microarray data. We suggest to use
a multi-objective evolutionary algorithm to identify the parameters of
a non-linear system given by the observed data. Currently, only limited
information on gene regulatory pathways is available in Systems Biology.
Not only the actual parameters of the examined system are unknown,
also the connectivity of the components is a priori not known. However,
this number is crucial for the inference process. Therefore, we propose a
method, which uses the connectivity as an optimization objective in ad-
dition to the data dissimilarity (relative standard error - RSE) between
experimental and simulated data.
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BibTeX
@INPROCEEDINGS{spieth05multiobjective,
author = {Christian Spieth and Felix Streichert and Nora Speer and Andreas Zell},
title = {Multi-Objective Model Optimization for Inferring Gene Regulatory Networks},
booktitle = {Proceedings of the Conference on Evolutionary Multi-Criterion Optimization (EMO 2005)},
year = {2005},
volume = {3410},
series = {LNCS},
pages = {607–620},
}