Open Access Method

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

Anja Wille1,2,3*, Philip Zimmermann1,4*, Eva Vranová1,4, Andreas Fürholz1,4, Oliver Laule1,4, Stefan Bleuler1,5, Lars Hennig1,4, Amela Prelić1,5, Peter von Rohr1,6, Lothar Thiele1,5, Eckart Zitzler1,5, Wilhelm Gruissem1,4 and Peter Bühlmann1,3

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.