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A Logical Constraint–based Approach to Infer and Explore Diversity and Composition in Thresholded Boolean Automaton Networks

  • Quoc-Trung Vuong
  • Roselyne Chauvin
  • Sergiu Ivanov
  • Nicolas Glade
  • Laurent Trilling
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 689)

Abstract

Gene regulatory networks (GRN) are often modeled by Boolean networks to describe their structures and properties. Constraint logic programming (CPL) can be used to infer networks that satisfy constraints applied on their structure and their dynamics. Such approach yield complete satisfiable network sets that can be large. Having such complete sets allows to compare networks between each other and to understand how they can be constructed from other networks. In the present paper, we describe this inference approach applied to a particular class of thresholded Boolean automaton networks, a variation of Boolean neural networks, focusing on a necessary step to reduce the size of satisfiable sets to sets of non-redundant networks. For that purpose, we use a recent non-monotonic logic programming technology, namely Answer Set Programming (ASP). Our approach managed to yield complete network sets satisfying a given behavior, namely having a specific dynamics – a binary motif fixed in advance – on at least one node of networks of a given size. This allows us to illustrate how general rules could explain some relations of composition between these networks.

Keywords

Bioinformatics Genetic regulatory network Thresholded boolean automaton networks Answer set programming 

References

  1. 1.
    Ben Amor, H., Corblin, F., Fanchon, E., Elena, A., Trilling, L., Demongeot, J., Glade, N.: Formal methods for Hopfield-like networks. Acta Biotheor. 61, 21–39 (2013)CrossRefGoogle Scholar
  2. 2.
    Corblin, F., Tripodi, S., Fanchon, E., Ropers, D., Trilling, L.: A declarative constraint-based method for analyzing discrete genetic regulatory networks. Biosystems 2, 91–104 (2009)CrossRefGoogle Scholar
  3. 3.
    Elena, A., Ben-Amor, H., Glade, N., Demongeot, J.: Motifs in Regulatory Networks and their Structural Robustness. In: 8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008, Athens, Greece (2008). IEEE Trans. Inf. Tech. Biomed.Google Scholar
  4. 4.
    Elena, A.: Robustesse des réseaux d’automates booléens à seuil aux modes d’itération. Application à la modélisation des réseaux de régulation génétique. Ph.D. Thesis (French), Université Joseph Fourier (2009). https://tel.archives-ouvertes.fr/tel-00447564/
  5. 5.
    Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Schneider, M.: Potassco: the potsdam answer set solving collection. AI Comm. 24, 107–124 (2011)MathSciNetMATHGoogle Scholar
  6. 6.
    Gebser, M., Kaminski, R., Obermeier, P., Schaub, T.: Ricochet Robots Reloaded – A Case-study in Multi-shot ASP Solving. In: Advances in Knowledge Representation, Logic Programming and Abstract Argumentation. vol. 9060, pp. 17–32. LNCS (2015)Google Scholar
  7. 7.
    Glade, N., Elena, A., Corblin, F., Fanchon, E., Demongeot, J., Ben Amor H.: Determination, optimization and taxonomy of regulator networks. The example of Arabidopsis thaliana flower morphogenesis. IEEE AINA’ 11, IEEE Press, Piscataway, 488–494 (2011)Google Scholar
  8. 8.
    Glass, L., Kauffman, S.: The logical analysis of continuous, nonlinear biochemical control networks. J. Theor. Biol. 39, 103–129 (1973)CrossRefGoogle Scholar
  9. 9.
    Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. U.S.A. 79, 2554–2558 (1982)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Kauffman, S.: Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22, 437–467 (1969)MathSciNetCrossRefGoogle Scholar
  11. 11.
    McCulloch, W.S., Pitts, W.: A Logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Thomas, R.: On the relation between the logical structure of systems and their ability to generate multiple steady states or sustained oscillations. Springer Ser. Synerg. 9, 180–193 (1980)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Quoc-Trung Vuong
    • 1
  • Roselyne Chauvin
    • 2
  • Sergiu Ivanov
    • 3
  • Nicolas Glade
    • 1
  • Laurent Trilling
    • 1
  1. 1.Univ. Grenoble Alpes, CNRS, CHU Grenoble Alpes, Grenoble INPTIMC-IMAGGrenobleFrance
  2. 2.Radboud UniversiteitNijmegenNetherlands
  3. 3.IBISC LaboratoryEvry Val d’Essonne UniversityEvryFrance

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