Reverse Engineering Gene Networks
One of the main challenges in the era of post-genomic research is to develop methods to extract information from the vast amount of data generated by Genome-wide experimental data yield information not only at the single gene level, but also at the ‘systems’ level, i.e. how genes, proteins and metabolites interact with each other to perform a specific function. In order to ‘read’ such information new methods coming from quantitative sciences such as physics and engineering, have to be used. This interdisciplinary approach is at the core of Systems Biology. The identification of gene regulatory networks is of major importance in order to understand the working mechanisms of the cell in patho-physiological conditions. Once the biological process is formalized as a network, the outcome is a mathematical model of the biological process. This model can be explored to generate novel hypotheses on the functioning of disease-genes under study, to be then tested experimentally. Systems Biology approaches can be used also to identify novel pharmacological treatments of rare genetic diseases by identifying the most suitable compounds with the highest therapeutic efficacy. Our research aims at developing and applying experimental protocols and computational algorithms to infer gene regulatory networks to elucidate mechanisms of genetic diseases, and the mode of action of small molecules in order to identify therapeutic treatments.