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
Farinaccio, A., Vanneschi, L., Provero, P., Mauri, G., Giacobini, M. (2011). A New Evolutionary Gene Regulatory Network Reverse Engineering Tool. In Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics 9th European Conference, EvoBIO 2011, Torino, Italy, April 27-29, 2011. Proceedings (pp.13-24). Berlin : Springer [10.1007/978-3-642-20389-3_2].
A New Evolutionary Gene Regulatory Network Reverse Engineering Tool
FARINACCIO, ANTONELLA;VANNESCHI, LEONARDO;MAURI, GIANCARLO;
2011
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
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