Email updates

Keep up to date with the latest news and content from Genome Biology and BioMed Central.

Open Access Method

Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana

Anja Wille123*, Philip Zimmermann14*, Eva Vranová14, Andreas Fürholz14, Oliver Laule14, Stefan Bleuler15, Lars Hennig14, Amela Prelić15, Peter von Rohr16, Lothar Thiele15, Eckart Zitzler15, Wilhelm Gruissem14 and Peter Bühlmann13

Author affiliations

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

For all author emails, please log on.

Citation and License

Genome Biology 2004, 5:R92  doi:10.1186/gb-2004-5-11-r92

Published: 25 October 2004


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.