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Computing maximal and minimal trap spaces of Boolean networks

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An Erratum to this article was published on 10 January 2017

Abstract

Asymptotic behaviors are often of particular interest when analyzing Boolean networks that represent biological systems such as signal transduction or gene regulatory networks. Methods based on a generalization of the steady state notion, the so-called trap spaces, can be exploited to investigate attractor properties as well as for model reduction techniques. In this paper, we propose a novel optimization-based method for computing all minimal and maximal trap spaces and motivate their use. In particular, we add a new result yielding a lower bound for the number of cyclic attractors and illustrate the methods with a study of a MAPK pathway model. To test the efficiency and scalability of the method, we compare the performance of the ILP solver gurobi with the ASP solver potassco in a benchmark of random networks.

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Acknowledgments

We thank S. Videla, M. Ostrowski and T. Schaub of University of Potsdam for their help with the ASP formulation.

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Authors and Affiliations

  1. Freie Universität Berlin, FB Mathematik und Informatik, Arnimallee 6, 14195, Berlin, Germany

    Hannes Klarner, Alexander Bockmayr & Heike Siebert

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  1. Hannes Klarner
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  2. Alexander Bockmayr
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  3. Heike Siebert
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Corresponding author

Correspondence to Hannes Klarner.

Additional information

An erratum to this article is available at http://dx.doi.org/10.1007/s11047-016-9611-0.

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Klarner, H., Bockmayr, A. & Siebert, H. Computing maximal and minimal trap spaces of Boolean networks. Nat Comput 14, 535–544 (2015). https://doi.org/10.1007/s11047-015-9520-7

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  • DOI: https://doi.org/10.1007/s11047-015-9520-7

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