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

Anja Wille3,1,2*, Philip Zimmermann4,1*, Eva Vranová4,1, Andreas Fürholz4,1, Oliver Laule4,1, Stefan Bleuler1,5, Lars Hennig4,1, Amela Prelić1,5, Peter von Rohr1,6, Lothar Thiele1,5, Eckart Zitzler1,5, Wilhelm Gruissem4,1 and Peter Bühlmann3,1

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

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

Published: 25 October 2004

Abstract

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.