Technological advances and research in areas such as genomics, transcriptomics and proteomics have produced huge masses of data that, in most cases, can only be analyzed using statistical, mathematical and computational approaches. These analyses have as main objective to generate knowledge that can help the development of methods for diagnosis, prognosis and treatment of diseases. In the context of gene regulatory networks, several mathematical models have been proposed in the literature, many of them based on mathematical concepts of dependence between random variables (gene expression levels), such as Pearson correlation, Hoeffding's D measure dependence, Contagion, and Granger causality. Although these methods are well accepted in mathematics, there are only few studies that characterize biological processes in the cell that result in such mathematical relationships, that is, the biological mechanisms that produce linear and non-linear correlations, correlation breakdowns, and information flow. This project aims to identify these types of mathematical associations in gene expression data from yeast and interpret them in biological point of view, by comparing the types of correlation with the information already known about the molecular mechanisms in the literature. This analysis will involve classes of mathematical correlations with molecular events in order to determine the biological processes that are not identifiable by means of mathematical techniques commonly used.
News published in Agência FAPESP Newsletter about the scholarship: