Towards the integration of computational systems biology and high-throughput data: a way to support differential analysis of microarray gene expression data

The paradigmatic shift occurred in biology that led first to high-throughput experimental techniques and later to computational systems biology must be applied also to the analysis paradigm of the relation between local models and data to obtain an effective prediction framework. In this work we show that the new relation between systems biology models and high-throughput data permits new integrations on the systemic scale like the use of in silico predictions to support the mining of gene expression datasets. We introduce a unifying notational framework in which we propose two applications concerning the use of system level models to support the differential analysis of microarray expression data. The approach is tested with a specific microarray experiment on the phosphate system in Saccharomyces cerevisiae and a computational model of the PHO pathway that supports the systems biology concepts.