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arXiv · 1509.07731

Computing Maximal and Minimal Trap Spaces of Boolean Networks

Hannes Klarner·Alexander Bockmayr·Heike Siebert
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Abstract

Asymptotic behaviors are often of particular interest when analyzing Boolean networks that represent biological systems such as signal trans- duction 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 com- puting 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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Hannes Klarner, Alexander Bockmayr, Heike Siebert. 2015-09-25. Computing Maximal and Minimal Trap Spaces of Boolean Networks. https://doi.org/10.1007/s11047-015-9520-7

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