Computing Maximal and Minimal Trap Spaces of Boolean Networks
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.
Cite
@article{arxiv.1509.07731,
title = {Computing Maximal and Minimal Trap Spaces of Boolean Networks},
author = {Hannes Klarner and Alexander Bockmayr and Heike Siebert},
journal= {arXiv preprint arXiv:1509.07731},
year = {2015}
}