Method
Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana
-
* Corresponding authors: Anja Wille awille@inf.ethz.ch - Philip Zimmermann philip.zimmermann@ipw.biol.ethz.ch
1 Reverse Engineering Group, Swiss Federal Institute of Technology (ETH), Zurich
2 Colab, ETH, Zurich 8092, Switzerland
3 Seminar for Statistics, ETH, Zurich 8092, Switzerland
4 Institute for Plant Sciences and Functional Genomics Center Zurich, ETH, Zurich 8092, Switzerland
5 Computer Engineering and Networks Laboratory, ETH, Zurich 8092
6 Institute of Computational Science, ETH, Zurich 8092, Switzerland
Genome Biology 2004, 5:R92 doi:10.1186/gb-2004-5-11-r92
Published: 25 October 2004Abstract
We present a novel graphical Gaussian modeling approach for reverse engineering of genetic regulatory networks with many genes and few observations. When applying our approach to infer a gene network for isoprenoid biosynthesis in Arabidopsis thaliana, we detect modules of closely connected genes and candidate genes for possible cross-talk between the isoprenoid pathways. Genes of downstream pathways also fit well into the network. We evaluate our approach in a simulation study and using the yeast galactose network.