Abstract: The aim of the present work is to identify a connection network describing the expression profiles of the genes involved in the cellular cycle of the yeast. In order to derive such connections from the microarray data, a mathematical model is needed, whose states represent the expression levels of the genes. However, since the cellular dynamics exhibit a limit cycle behavior [1] (i.e. roughly speaking, a periodic response, which is robust to small perturbations), it is not possible to describe the whole phenomenon by means of only linear models. Therefore, we investigate the suitability of quadratic systems [2], for the description of the genetic network dynamics. A quadratic system is made up of a set of first order differential equations, with linear terms plus quadratic terms (i.e. multiplications between two state variables or between a state and an input variable).

Abstract: The increasing availability and maturity of DNA microarray technology has led to an explosion of cancer related studies. In order to evaluate, integrate, and cross-validate the accumulating amount of publicly available microarray data, new methods are needed. Statistical methods have been applied to raw microarray data to identify intersections of multiple gene expression signatures from a diverse collection of data sets. However, this analysis is very stringent and time consuming. Here we propose a fast method to detect common patterns in gene expression data by mining published lists of microarray experiments

Abstract: Cryptococcus neoformans is an opportunistic human pathogen of global importance. The two strains of C. neoformans serotype D JEC21 and B-3501A are highly related but markedly different in their pathogenicity. We report their genome sequences as an important step in the elucidation of the molecular basis for Cryptococcus virulence and for its difference with other fungal pathogens.