Modelling gene expression via positive Boolean functions

In this work we propose an arti¯cial model for the generation of biologically plausible gene expression data to be used in the evaluation of the performance of gene selection and clustering methods. The model allows to ¯x in advance the set of relevant genes and the functional classes involved in the problem; the input-output relationship is constructed by synthesizing a positive Boolean function. Despite its simplicity, it is su±ciently rich to take account of the speci¯c peculiarities of gene expression data, including biological variability. A Java code had been developed to allow the user choose the model parameters according to the characteristics of the experiment he want to simulate. This permits to insert the arti¯cial model into a distributed system for microarray analysis, in particular one based on a Grid infrastructure.