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Inferring Gene Regulatory Networks from expression data: a computational challenge
Francesco Sambo
Department of Information Engineering
University of Padova


Reverse Engineering of Gene Regulatory Networks, i.e. the process of inferring cause-effect relations between genes from genome-wide experiments, is one of the most challenging tasks in present biology. Experimental data consist of DNA microarray measurements, which are snapshots of the level of activity of large sets of genes, under various conditions or in different temporal instants. The output of a Reverse Engineering algorithm is a Gene Regulatory Network, a graph in which nodes represent genes and edges correspond to causal relations between genes. In this talk I will first introduce the problem from a computational perspective, then I will briefly review the main algorithmic approaches to Reverse Engineering and describe CNET, our contribution to the solution of the problem. Performance of all the algorithms in literature still tends to be quite limited, thus I will present some results on the reliability of inferred gene networks and on the relations between performance of the algorithms and network structure, and I will show how this information can be used for designing better algorithms and optimal microarray experiments.


Gene Regulatory Networks