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Gene regulatory networks reconstruction from time series datasets using genetic programming: a comparison between tree-based and graph-based approaches

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Abstract

Genetic programming researchers have shown a growing interest in the study of gene regulatory networks in the last few years. Our team has also contributed to the field, by defining two systems for the automatic reverse engineering of gene regulatory networks called GRNGen and GeNet. In this paper, we revise this work by describing in detail the two approaches and empirically comparing them. The results we report, and in particular the fact that GeNet can be used on large networks while GRNGen cannot, encourage us to pursue the study of GeNet in the future. We conclude the paper by discussing the main research directions that we are planning to investigate to improve GeNet.

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Notes

  1. It is worth reminding that the fitness of an individual in GeNet is always calculated as the RMSE between the target time series dataset and the one reconstructed by the individual itself. Thus it has no relationship with the PPV and Se of the network. Furthermore, we also point out that the PPV and Se themselves could not have been used as fitness values, because, in order to calculate them, the target network must be known, while reverse engineering methods must work using only the information contained in the time series datasets.

  2. Exactly the same qualitative conclusions can be drawn for the switch-on dataset; we do not report the results here.

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Authors and Affiliations

  1. ISEGI, Universidade Nova de Lisboa, 1070-312, Lisboa, Portugal

    Leonardo Vanneschi

  2. INESC-ID, IST, Universidade Técnica de Lisboa, 1000-029, Lisboa, Portugal

    Leonardo Vanneschi

  3. Dipartimento di Informatica, Sistemistica e Comunicazione (D.I.S.Co.), Università di Milano-Bicocca, 20126, Milan, Italy

    Leonardo Vanneschi

  4. Mario Negri Institute for Pharmacological Research, Ranica, Italy

    Matteo Mondini

  5. Biozentrum, Universität Basel, Basel, Switzerland

    Martino Bertoni

  6. Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Via Adamello 16, 20139, Milan, Italy

    Alberto Ronchi

  7. Università di Milano-Bicocca, 20126, Milan, Italy

    Mattia Stefano

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  1. Leonardo Vanneschi
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  2. Matteo Mondini
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  3. Martino Bertoni
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  4. Alberto Ronchi
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  5. Mattia Stefano
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Correspondence to Leonardo Vanneschi.

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Vanneschi, L., Mondini, M., Bertoni, M. et al. Gene regulatory networks reconstruction from time series datasets using genetic programming: a comparison between tree-based and graph-based approaches. Genet Program Evolvable Mach 14, 431–455 (2013). https://doi.org/10.1007/s10710-013-9183-z

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