PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli
Karen Lemmens, Tijl De Bie, Thomas Dhollander, Sigrid De Keersmaecker, Inge Thijs, Geert Schoofs, Ami De Weerdt, Bart De Moor, Jozef Vanderleyden, Julio Collado-Vides, Kristof Engelen and Kathleen Marchal
Genome Biology Volume 10, Number R27, 2009.


We present DISTILLER, a data integration framework for the inference of transcriptional module networks. Experimental validation of predicted targets for the well-studied fumarate nitrate reductase regulator showed the effectiveness of our approach in Escherichia coli. In addition, the condition dependency and modularity of the inferred transcriptional network was studied. Surprisingly, the level of regulatory complexity seemed lower than that which would be expected from RegulonDB, indicating that complex regulatory programs tend to decrease the degree of modularity.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:4099
Deposited By:Tijl De Bie
Deposited On:24 March 2009