Christian Spieth, Felix Streichert, Nora Speer, and Andreas Zell

**
Optimizing Topology and Parameters of Gene Regulatory Network Models from Time-Series Experiments**

*
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2004)*

### Abstract

In this paper we address the problem of finding gene regulatory
networks from experimental DNA microarray data. Different
approaches to infer the dependencies of gene regulatory networks
by identifying parameters of mathematical models like complex
S-systems or simple Random Boolean Networks can be found in
literature. Due to the complexity of the inference problem some
researchers suggested Evolutionary Algorithms for this purpose. We
introduce enhancements to the Evolutionary Algorithm optimization
process to infer the parameters of the non-linear system given by
the observed data more reliably and precisely. Due to the limited
number of available data the inferring problem is under-determined
and ambiguous. Further on, the problem often is multi-modal and
therefore appropriate optimization strategies become necessary. We
propose a new method, which evolves the topology as well as the
parameters of the mathematical model to find the correct network.

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**BibTeX**

@INPROCEEDINGS{spieth04optimizing,

author = {Christian Spieth and Felix Streichert and Nora Speer and Andreas Zell},

title = {Optimizing Topology and Parameters of Gene Regulatory Network Models from Time-Series Experiments},

booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2004)},

year = {2004},

volume = {3102 (Part I)},

series = {LNCS},

pages = {461-470},

}