Abstract
Asymptotic behavior is often of particular interest when analyzing asynchronous Boolean networks representing biological systems such as signal transduction or gene regulatory networks. Methods based on a generalization of the steady state notion, the so-called symbolic steady states, can be exploited to investigate attractor properties as well as for model reduction techniques conserving attractors. In this paper, we propose a novel optimization-based method for computing all maximal symbolic steady states and motivate their use. n particular, we add a new result yielding a lower bound for the number of cyclic attractors and illustrate the methods with a short study of a MAPK pathway model.
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Klarner, H., Bockmayr, A., Siebert, H. (2014). Computing Symbolic Steady States of Boolean Networks. In: Wąs, J., Sirakoulis, G.C., Bandini, S. (eds) Cellular Automata. ACRI 2014. Lecture Notes in Computer Science, vol 8751. Springer, Cham. https://doi.org/10.1007/978-3-319-11520-7_59
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DOI: https://doi.org/10.1007/978-3-319-11520-7_59
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11519-1
Online ISBN: 978-3-319-11520-7
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