Abstract
Boolean Networks (BN) are established tools for modelling biological systems. However, their analysis is hindered by the state space explosion: the exponentially many states on the variables of a BN. We present an extension of the tool for model reduction ERODE with support for BNs and their reduction with a recent method called Backward Boolean Equivalence (BBE). BBE identifies maximal sets of variables that retain the same value whenever initialized equally. ERODE has been also extended to support importing and exporting between different formats and model repositories, enhancing interoperability with other tools.
Partially supported by the DFF project REDUCTO 9040-00224B, the Poul Due Jensen Grant 883901, the Villum Investigator Grant S4OS, and the PRIN project SEDUCE 2017TWRCNB.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The artifact can be downloaded from www.erode.eu/examples.html with further guidelines to replicate the experiments in this document.
References
Argyris, G., Lluch Lafuente, A., Tribastone, M., Tschaikowski, M., Vandin, A.: Reducing Boolean networks with backward Boolean equivalence. In: Cinquemani, E., Paulevé, L. (eds.) CMSB 2021. LNCS, vol. 12881, pp. 1–18. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85633-5_1
Azpeitia, E., Benítez, M., Vega, I., Villarreal, C., Alvarez-Buylla, E.R.: Single-cell and coupled GRN models of cell patterning in the Arabidopsis thaliana root stem cell niche. BMC Syst. Biol. 4(1), 1–19 (2010)
Cardelli, L., Tribastone, M., Tschaikowski, M., Vandin, A.: ERODE: a tool for the evaluation and reduction of ordinary differential equations. In: Legay, A., Margaria, T. (eds.) TACAS 2017. LNCS, vol. 10206, pp. 310–328. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-662-54580-5_19
Chaouiya, C., et al.: SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools. BMC Syst. Biol. 7(1), 1–15 (2013)
Chaouiya, C., Naldi, A., Thieffry, D.: Logical modelling of gene regulatory networks with GINsim. In: van Helden, J., Toussaint, A., Thieffry, D. (eds.) Bacterial Molecular Networks. Methods in Molecular Biology, vol. 804, pp. 463–479. Springer, New York (2012). https://doi.org/10.1007/978-1-61779-361-5_23
Coolen, M., Thieffry, D., Drivenes, Ø., Becker, T.S., Bally-Cuif, L.: miR-9 controls the timing of neurogenesis through the direct inhibition of antagonistic factors. Dev. Cell 22(5), 1052–1064 (2012)
de Moura, L., Bjørner, N.: Z3: an efficient SMT solver. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 337–340. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78800-3_24
Kauffman, S.: Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22(3), 437–467 (1969)
Malik-Sheriff, R.S., et al.: BioModels–15 years of sharing computational models in life science. Nucleic Acids Res. 48(D1), D407–D415 (2020). https://doi.org/10.1093/nar/gkz1055
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)
Naldi, A., Berenguier, D., Fauré, A., Lopez, F., Thieffry, D., Chaouiya, C.: Logical modelling of regulatory networks with GINsim 2.3. Biosystems 97(2), 134–139 (2009). https://doi.org/10.1016/j.biosystems.2009.04.008, https://www.sciencedirect.com/science/article/pii/S0303264709000665
Naldi, A., et al.: The CoLoMoTo interactive notebook: accessible and reproducible computational analyses for qualitative biological networks. Front. Physiol. 9, 680 (2018)
Naldi, A., Monteiro, P.T., Chaouiya, C.: Efficient handling of large signalling-regulatory networks by focusing on their core control. In: Gilbert, D., Heiner, M. (eds.) CMSB 2012. LNCS, pp. 288–306. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33636-2_17
Naldi, A., et al.: Cooperative development of logical modelling standards and tools with CoLoMoTo. Bioinformatics 31(7), 1154–1159 (2015)
Naldi, A., Remy, E., Thieffry, D., Chaouiya, C.: Dynamically consistent reduction of logical regulatory graphs. Theoret. Comput. Sci. 412(21), 2207–2218 (2011)
Paige, R., Tarjan, R.E.: Three partition refinement algorithms. SIAM J. Comput. 16(6), 973–989 (1987)
Veliz-Cuba, A.: Reduction of Boolean network models. J. Theor. Biol. 289, 167–172 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Argyris, G., Lafuente, A.L., Tribastone, M., Tschaikowski, M., Vandin, A. (2022). An Extension of ERODE to Reduce Boolean Networks By Backward Boolean Equivalence. In: Petre, I., Păun, A. (eds) Computational Methods in Systems Biology. CMSB 2022. Lecture Notes in Computer Science(), vol 13447. Springer, Cham. https://doi.org/10.1007/978-3-031-15034-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-15034-0_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15033-3
Online ISBN: 978-3-031-15034-0
eBook Packages: Computer ScienceComputer Science (R0)