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An ASP-based approach for attractor enumeration in synchronous and asynchronous Boolean networks. (English) Zbl 07453124

Bogaerts, Bart (ed.) et al., Proceedings of the 35th international conference on logic programming (technical communications), ICLP 2019, Las Cruces, USA, September 20–25, 2019. Waterloo: Open Publishing Association (OPA). Electron. Proc. Theor. Comput. Sci. (EPTCS) 306, 295-301 (2019).
Summary: Boolean networks are conventionally used to represent and simulate gene regulatory networks. In the analysis of the dynamic of a Boolean network, the attractors are the objects of a special attention. In this work, we propose a novel approach based on Answer Set Programming (ASP) to express Boolean networks and simulate the dynamics of such networks. Our work focuses on the identification of the attractors, it relies on the exhaustive enumeration of all the attractors of synchronous and asynchronous Boolean networks. We applied and evaluated the proposed approach on real biological networks, and the obtained results indicate that this novel approach is promising.
For the entire collection see [Zbl 1464.68003].

MSC:

68N17 Logic programming

Software:

ASP-G
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Full Text: arXiv Link

References:

[1] Ferhat Ay, Fei Xu & Tamer Kahveci (2009): Scalable steady state analysis of Boolean biological regulatory networks. PloS one 4(12), p. e7992, doi:10.1371/journal.pone.0007992. · doi:10.1371/journal.pone.0007992
[2] Belad Benhamou & Pierre Siegel (2012): A New Semantics for Logic Programs Capturing and Extending the Stable Model Semantics. Tools with Artificial Intelligence (ICTAI), pp. 25-32, doi:10.1109/ICTAI.2012.167. · doi:10.1109/ICTAI.2012.167
[3] Maria I Davidich & Stefan Bornholdt (2008): Boolean network model predicts cell cycle sequence of fission yeast. PloS one 3(2), p. e1672, doi:10.1371/journal.pone.0001672. · doi:10.1371/journal.pone.0001672
[4] Hidde De Jong (2002): Modeling and simulation of genetic regulatory systems: a literature review. Journal of computational biology 9(1), pp. 67-103, doi:10.1089/10665270252833208. · doi:10.1089/10665270252833208
[5] Abhishek Garg, Alessandro Di Cara, Ioannis Xenarios, Luis Mendoza & Giovanni De Micheli (2008): Syn-chronous versus asynchronous modeling of gene regulatory networks. Bioinformatics 24(17), pp. 1917-1925, doi:10.1093/bioinformatics/btn336. · doi:10.1093/bioinformatics/btn336
[6] Abhishek Garg, Ioannis Xenarios, Luis Mendoza & Giovanni DeMicheli (2007): An efficient method for dynamic analysis of gene regulatory networks and in silico gene perturbation experiments, pp. 62-76. doi:10.1007/978-3-540-71681-5 5. · doi:10.1007/978-3-540-71681-5_5
[7] François Jacob & Jacques Monod (1961): Genetic regulatory mechanisms in the synthesis of proteins. Journal of molecular biology 3(3), pp. 318-356, doi:10.1016/S0022-2836(61)80072-7. · doi:10.1016/S0022-2836(61)80072-7
[8] Tarek Khaled, Belad Benhamou & Pierre Siegel (2018): A new method for computing stable models in logic programming. Tools with Artificial Intelligence (ICTAI), pp. 800-807, doi:10.1109/ICTAI.2018.00125. · doi:10.1109/ICTAI.2018.00125
[9] Mushthofa Mushthofa, Gustavo Torres, Yves Van de Peer, Kathleen Marchal & Martine De Cock (2014): ASP-G: an ASP-based method for finding attractors in genetic regulatory networks. Bioinformatics 30(21), pp. 3086-3092, doi:10.1093/bioinformatics/btu481. · doi:10.1093/bioinformatics/btu481
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