Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana.
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.
Wille
Anja
A
Reverse Engineering Group, Swiss Federal Institute of Technology (ETH), Zurich. awille@inf.ethz.ch
Zimmermann
Philip
P
Vranová
Eva
E
Fürholz
Andreas
A
Laule
Oliver
O
Bleuler
Stefan
S
Hennig
Lars
L
Prelic
Amela
A
von Rohr
Peter
P
Thiele
Lothar
L
Zitzler
Eckart
E
Gruissem
Wilhelm
W
Bühlmann
Peter
P
D017360:Arabidopsis / Q000235:genetics*; D003196:Computer Graphics / Q000706:statistics & numerical data*; D003198:Computer Simulation / Q000706:statistics & numerical data; D005690:Galactose / Q000378:metabolism; D005800:Genes, Fungal / Q000235:genetics; D017343:Genes, Plant / Q000235:genetics* / Q000502:physiology; D008957:Models, Genetic; D016011:Normal Distribution; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D013729:Terpenes / Q000378:metabolism*
D013729:Terpenes; D005690:Galactose