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Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: Environmental factors

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
Article numbere10
Issue number1
Publication statusPublished - 2013
Publication typeA1 Journal article-refereed


The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I) observational gene expression data: normal environmental condition, (II) interventional gene expression data: growth in rich media, (III) interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.


  • Experimental design, Gene expression data, Gene regulatory networks, Interventional data, Statistical network inference