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

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

Reverse Engineering Group, Swiss Federal Institute of Technology (ETH), Zurich

Colab, ETH, Zurich 8092, Switzerland

Seminar for Statistics, ETH, Zurich 8092, Switzerland

Institute for Plant Sciences and Functional Genomics Center Zurich, ETH, Zurich 8092, Switzerland

Computer Engineering and Networks Laboratory, ETH, Zurich 8092

Institute of Computational Science, ETH, Zurich 8092, Switzerland

author email corresponding author email

Genome Biology 2004, 5:R92doi:10.1186/gb-2004-5-11-r92

Published: 25 October 2004

Subject areas: Plant biology, Genome studies, Genetics

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.


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