A new evolutionary gene regulatory network reverse engineering tool

Authors:
Antonella Farinaccio;Leonardo Vanneschi;Paolo Provero;Giancarlo Mauri;Mario Giacobini
Affiliations:
Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy;Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy;Computational Biology Unit, Molecular Biotechnology Center University of Torino, Italy and Department of Genetics, Biology and Biochemistry University of Torino, Italy;Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy;Computational Biology Unit, Molecular Biotechnology Center University of Torino, Italy and Department of Animal Production, Epidemiology and Ecology Faculty of Veterinary Medicine, University of T ...
Venue:
EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Year:
2011

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Abstract

We present a new reverse-engineering framework for gene regulatory network reconstruction. It works on temporal series of gene activation data and, using genetic programming, it extracts the activation functions of the different genes from those data. Successively, the gene regulatory network is reconstructed exploiting the automatic feature selection performed by genetic programming and its dynamics can be simulated using the previously extracted activation functions. The framework was tested on the well-known IRMA gene regulatory network, a simple network composed by five genes in the yeast Saccharomyces cerevisiae, defined in 2009 as a simplified biological model to benchmark reverse-engineering approaches. We show that the performances of the proposed framework on this benchmark network are encouraging.