Genetic network models and statistical properties of gene expression data in knock-out experiments

It is shown here how gene knock-out experiments can be simulatedin Random Boolean Networks (RBN), which are well-knownsimplifiedmod els of genetic networks. The results of the simulations are presentedandcomparedwith those of actual experiments inS. cerevisiae. RBN with two incoming links per node have been considered, and the Boolean functions have been chosen at randomamong the set of so-calledcanalizing functions.Genes are knocked-out (i.e. silenced) one at a time, and the variations in the expression levels of the other genes, with respect tothe unperturbed case, are considered. Two important variables are defined: (i) avalanches, which measure the size of theperturbation generatedby knocking out a single gene, and(ii) susceptibilities, which measure how often the expression of a givengene is modified in these experiments.A remarkable observation is that the distributions of avalanches and susceptibilities are very robust, i.e. they are very similar indifferent random networks; this should be contrasted with the distribution of other variables that show a high variance in RBN.Moreover, the distribution of avalanches and susceptibilities of the RBN models are close to those observed in actual experimentsperformedwith S. cerevisiae, where the changes in gene expression levels have been recorded with DNA microarrays.These findings suggest that these distributions might be ‘‘generic’’ properties, common to a wide range of genetic models and realgenetic networks. The importance of such generic properties is discussed.