Abstract
Aeon is a recent tool which enables efficient analysis of long-term behaviour of asynchronous Boolean networks with unknown parameters. In this tool paper, we present a novel major release of Aeon (Aeon 2021) which introduces substantial new features compared to the original version. These include (i) enhanced static analysis functionality that verifies integrity of the Boolean network with its regulatory graph; (ii) state-space visualisation of individual attractors; (iii) stability analysis of network variables with respect to parameters; and finally, (iv) a novel decision-tree based interactive visualisation module allowing the exploration of complex relationships between parameters and network behaviour. Aeon 2021 is open-source, fully compatible with SBML-qual models, and available as an online application with an independent native compute engine responsible for resource-intensive tasks. The paper artefact is available via https://doi.org/10.5281/zenodo.5008293.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abou-Jaoudé, W., Monteiro, P.T.: On logical bifurcation diagrams. J. Theor. Biol. 466, 39–63 (2019)
Baudin, A., Paul, S., Su, C., Pang, J.: Controlling large Boolean networks with single-step perturbations. Bioinformatics 35(14), i558–i567 (07 2019)
Beneš, N., Brim, L., Kadlecaj, J., Pastva, S., Šafránek, D.: AEON: attractor bifurcation analysis of parametrised boolean networks. In: Lahiri, S.K., Wang, C. (eds.) CAV 2020. LNCS, vol. 12224, pp. 569–581. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-53288-8_28
Beneš, N., Brim, L., Pastva, S., Poláček, J., Šafránek, D.: Formal analysis of qualitative long-term behaviour in parametrised boolean networks. In: Ait-Ameur, Y., Qin, S. (eds.) ICFEM 2019. LNCS, vol. 11852, pp. 353–369. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32409-4_22
Beneš, N., Brim, L., Pastva, S., Šafránek, D.: Computing bottom SCCs symbolically using transition guided reduction. In: Computer Aided Verification (2021), accepted. Preprint available from authors
rg Benque, D., et al.: Bio Model Analyzer: Visual tool for modeling and analysis of biological networks. In: Computer Aided Verification. Lecture Notes in Computer Science, vol. 7358, pp. 686–692. Springer, Heidelberg (2012)
Berntenis, N., Ebeling, M.: Detection of attractors of large Boolean networks via exhaustive enumeration of appropriate subspaces of the state space. BMC Bioinf. 14, 361 (2013)
Chaouiya, C., Naldi, A., Thieffry, D.: Logical modelling of gene regulatory networks with GINsim. In: Bacterial Molecular Networks, pp. 463–479. Springer, Heidelberg (2012). https://doi.org/10.1007/978-1-61779-361-5_23
Cimatti, A., et al.: NuSMV 2: an opensource tool for symbolic model checking. In: Brinksma, E., Larsen, K.G. (eds.) CAV 2002. LNCS, vol. 2404, pp. 359–364. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45657-0_29
Feillet, C., et al.: Phase locking and multiple oscillating attractors for the coupled mammalian clock and cell cycle. Proc. Natl. Acad. Sci. 111(27), 9828–9833 (2014)
Franz, M., Lopes, C.T., Huck, G., Dong, Y., Sumer, O., Bader, G.D.: Cytoscape.js: a graph theory library for visualisation and analysis. Bioinformatics 32(2), 309–311 (2015)
Giacobbe, M., Guet, C.C., Gupta, A., Henzinger, T.A., Paixão, T., Petrov, T.: Model checking the evolution of gene regulatory networks. Acta Inf. 54(8), 765–787 (2017)
Helikar, T., et al.: The cell collective: toward an open and collaborative approach to systems biology. BMC Syst. Biol. 6(96), 1 (2012)
Klamt, S., Saez-Rodriguez, J., Gilles, E.D.: Structural and functional analysis of cellular networks with Cell NetAnalyzer. BMC Syst. Biol. 1(1), 2 (2007)
Klarner, H., Streck, A., Siebert, H.: PyBoolNet: a Python package for the generation, analysis and visualization of Boolean networks. Bioinformatics 33(5), 770–772 (2016)
Mizera, A., Pang, J., Su, C., Yuan, Q.: ASSA-PBN: a toolbox for probabilistic Boolean networks. IEEE/ACM Trans. Comput. Biol. Bioinf. (2018)
Müssel, C., Hopfensitz, M., Kestler, H.A.: BoolNet-an R package for generation, reconstruction and analysis of Boolean networks. Bioinformatics 26(10), 1378–1380 (2010)
de S. Cavalcante, H.L.D., Gauthier, D.J., Socolar, J.E.S., Zhang, R. : On the origin of chaos in autonomous Boolean networks (2010)
Saadatpour, A., et al.: Dynamical and structural analysis of a T-cell survival network identifies novel candidate therapeutic targets for large granular lymphocyte leukemia. PLoS Comput. Biol. 7(11), e1002267 (2011)
Shah, O.S., et al.: ATLANTIS - attractor landscape analysis toolbox for cell fate discovery and reprogramming. Sci. Rep. 8(1), 3554 (2018)
Streck, A., Thobe, K., Siebert, H.: Comparative statistical analysis of qualitative parametrization sets. In: Abate, A., Šafránek, D. (eds.) HSB 2015. LNCS, vol. 9271, pp. 20–34. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26916-0_2
Zou, Y.M.: Boolean networks with multiexpressions and parameters. IEEE/ACM Trans. Comput. Biol. Bioinf. 10, 584–592 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Beneš, N., Brim, L., Pastva, S., Šafránek, D. (2021). Aeon 2021: Bifurcation Decision Trees in Boolean Networks. In: Cinquemani, E., Paulevé, L. (eds) Computational Methods in Systems Biology. CMSB 2021. Lecture Notes in Computer Science(), vol 12881. Springer, Cham. https://doi.org/10.1007/978-3-030-85633-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-85633-5_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-85632-8
Online ISBN: 978-3-030-85633-5
eBook Packages: Computer ScienceComputer Science (R0)