Abstract
Boolean networks are typically used as simple models of gene regulatory networks. We use a particular class of Boolean networks called threshold Boolean networks defined by a weight matrix, a threshold vector, and an updating mode in this work. We consider the reconstruction of synthetic threshold Boolean networks that contain the same fixed points as the Mendoza and Alvarez-Buylla network of flower development by using an evolution strategy. We propose a characterization by computing topological and dynamical features of the inferred synthetic networks and then applying machine learning, particularly unsupervised learning techniques, to analyze these networks. We discover how these networks are clustered and what features are relevant to discriminate the cluster containing the Mendoza and Alvarez-Buylla network from all the other clusters.
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References
Boldhaus G, Klemm K (2010) Regulatory networks and connected components of the neutral space. Eur Phys J B 77:233–237
Ciliberti S, Martin OC, Wagner A (2007) Innovation and robustness in complex regulatory gene networks. PNAS 104:13591–13596
Ciliberti S, Martin OC, Wagner A (2007) Robustness can evolve gradually in complex regulatory gene networks with varying topology. PLoS Comput Biol 3:e15
Fauré A, Naldi A, Chaouiya C, Thieffry D (2006) Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle. Bioinformatics 22:e124–e131
Goles E, Montalva M, Ruz GA (2013) Deconstruction and dynamical robustness of regulatory networks: application to the yeast cell cycle networks. Bull Math Biol 75:939–966
Kauffman SA (1969) Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol 22:437–467
Lahdesmaki H, Shmulevich I, Yli-Harja O (2003) On learning gene regulatory networks under the boolean network model. Mach Learn 25:147–167
Liang S, Fuhrman S, Somogyi R (1998) Reveal, a general reverse engineering algorithm for inference of genetic network architectures. In: Pac Symp Biocomput, pp 18–29
Mendoza L, Alvarez-Buylla ER (1998) Dynamics of the genetic regulatory network for arabidopsis thaliana flower morphogenesis. J Theor Biol 193:307–319
Pham DT, Castellani M (2009) The bees algorithm: modelling foraging behaviour to solve continuous optimization problems. Proc IMechE Part C: J Mech Eng Sci 223:2919–2938
Ruz GA, Goles E (2010) Learning gene regulatory networks with predefined attractors for sequential updating schemes using simulated annealing. In: Proceedings of IEEE the ninth international conference on machine learning and applications (ICMLA 2010), pp 889–894
Ruz GA, Goles E (2012) Reconstruction and update robustness of the mammalian cell cycle network. In: 2012 ieee symposium on computational intelligence and computational biology, CIBCB 2012, pp 397–403
Ruz GA, Goles E (2013) Learning gene regulatory networks using the bees algorithm. Neural Comput Appl 22:63–70
Ruz GA, Goles E (2014) Neutral graph of regulatory Boolean networks using evolutionary computation. In: The 2014 ieee conference on computational intelligence in bioinformatics and computational biology (CIBCB 2014), pp 1–8
Ruz GA, Goles E, Montalva M, Fogel GB (2014) Dynamical and topological robustness of the mammalian cell cycle network: a reverse engineering approach. Biosystems 115:23–32
Ruz GA, Timmermann T, Barrera J, Goles E (2014) Neutral space analysis for a boolean network model of the fission yeast cell cycle network. Biol Res 47:64
Ruz GA, Timmermann T, Goles E (2015) Reconstruction of a GRN model of salt stress response in Arabidopsis using genetic algorithms. In: The 2015 ieee conference on computational intelligence in bioinformatics and computational biology (CIBCB 2015), pp 1–8
Ruz GA, Timmermann T, Goles E (2016) Neutral space analysis of gene regulatory network models of salt stress response in arabidopsis using evolutionary computation. In: The 2016 ieee congress on evolutionary computation (IEEE CEC 2016), pp 4281–4288
Ruz GA, Ashlock D, Ledger T, Goles E (2017) Inferring bistable lac operon Boolean regulatory networks using evolutionary computation. In: The 2017 ieee conference on computational intelligence in bioinformatics and computational biology (CIBCB 2017), pp 1–8
Ruz GA, Zúñiga A, Goles E (2018) A Boolean network model of bacterial quorum-sensing systems. Int J Data Min Bioinform 21:123–144
Timmermann T, González B, Ruz GA (2020) Reconstruction of a gene regulatory network of the induced systemic resistance defense response in Arabidopsis using boolean networks. BMC Bioinform 21:142
van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(86):2579–2605
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This work was funded by ANID FONDECYT 1180706 and ANID PIA/BASAL FB0002.
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Ruz, G.A. (2022). Analyzing Boolean Networks Through Unsupervised Learning. In: Adamatzky, A. (eds) Automata and Complexity. Emergence, Complexity and Computation, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-030-92551-2_14
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DOI: https://doi.org/10.1007/978-3-030-92551-2_14
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