2004/03/11 by Mika Gustafsson, Gustafsson, Mika, Michael Hornquist +3
Biochemistry, Genetics and Molecular Biology · #FOS: Biological sciences #Molecular Networks (q-bio.MN) #q-bio.MN
paper · pdf · doi:10.48550/arxiv.q-bio/0403012
4 pages, submitted for publication
arxiv created 2004/03/11 · arxiv updated 2009/12/01
We perform a reverse engineering from the ``extended Spellman data'', consisting of 6178 mRNA levels measured by microarrays at 73 instances in four time series during the cell cycle of the yeast Saccharomyces cerevisae. By assuming a linear model of the genetic regulatory network, and imposing an extra constraint (the Lasso), we obtain a unique inference of coupling parameters. These parameters are transfered into an adjacent matrix, which is analyzed with respect to topological properties and biological relevance. We find a very broad distribution of outdegrees in the network, compatible with earlier findings for biological systems and totally incompatible with a random graph, and also indications of modules in the network. Finally, we show there is an excess of genes coding for transcription factors among the genes of highest outdegrees, a fact which indicates that our approach has biological relevance.