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Optimizing Topology and Parameters of Gene Regulatory Network Models from Time-Series Experiments
Christian Spieth, Felix Streichert, Nora Speer, and Andreas Zell
Centre for Bioinformatics Tübingen (ZBIT), University of Tübingen, Sand 1, D-72076 Tübingen, Germanyspieth@informatik.uni-tuebingen.de
http://www-ra.informatik.uni-tuebingen.de
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.
LNCS 3102, p. 461 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2004