Linear Modeling of Genetic Networks from Experimental Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
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The ability to measure the transcriptional response after a stimulus has drawn much attention to the underlying gene regulatory networks. Here, we evaluate the application of methods to reconstruct gene regulatory networks by applying them to the SOS response of E. coli, the budding yeast cell cycle and in silico models. For each network we define an a priori validation network, where each interaction is justified by at least one publication. In addition to the existing methods, we propose a SVD based method (NSS). Overall, most reconstruction methods perform well on in silico data sets, both in terms of topological reconstruction and predictability. For biological data sets the application of reconstruction methods is suitable to predict the expression of genes, whereas the topological reconstruction is only satisfactory with steadystate measurements. Surprisingly, the performance measured on in silico data does not correspond with the performance measured on biological data.