Iteratively Inferring Gene Regulatory Networks with Virtual Knockout Experiments

Abstract. In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. We introduce enhancements to an 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. Therefore, we propose a new method, which will suggest necessary additional biological experiments to remove the ambiguities.

• Type: Conference
• Year: 2004
• Where: EVOW
• Authors: Christian Spieth, Felix Streichert, Nora Speer, Andreas Zell
• Keywords: Artificial Intelligence, DNA Microarray Data, Evolutionary Algorithm Optimization, EVOW 2004, Gene Regulatory Networks