Boolean Network Identification from Multiplex Time Series Data

  • Max Ostrowski
  • Loïc PaulevéEmail author
  • Torsten Schaub
  • Anne Siegel
  • Carito Guziolowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9308)


Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logical models can be learned from a prior knowledge network structure and multiplex phosphoproteomics data. However, most efficient and scalable training methods focus on the comparison of two time-points and assume that the system has reached an early steady state. In this paper, we generalize such a learning procedure to take into account the time series traces of phosphoproteomics data in order to discriminate Boolean networks according to their transient dynamics. To that goal, we exhibit a necessary condition that must be satisfied by a Boolean network dynamics to be consistent with a discretized time series trace. Based on this condition, we use a declarative programming approach (Answer Set Programming) to compute an over-approximation of the set of Boolean networks which fit best with experimental data. Combined with model-checking approaches, we end up with a global learning algorithm and compare it to learning approaches based on static data.


Time Series Time Series Data Boolean Network Interaction Graph Time Series Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Alexopoulos, L.G., Saez-Rodriguez, J., Cosgrove, B., Lauffenburger, D.A., Sorger, P.: Networks inferred from biochemical data reveal profound differences in toll-like receptor and inflammatory signaling between normal and transformed hepatocytes. Mol. Cell. Proteomics 9(9), 1849–1865 (2010)CrossRefGoogle Scholar
  2. 2.
    Aracena, J., Goles, E., Moreira, A., Salinas, L.: On the robustness of update schedules in boolean networks. Biosystems 97(1), 1–8 (2009)CrossRefGoogle Scholar
  3. 3.
    Baral, C.: Knowledge Representation. Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge (2003)CrossRefzbMATHGoogle Scholar
  4. 4.
    Berestovsky, N., Nakhleh, L.: An evaluation of methods for inferring boolean networks from time-series data. PLoS ONE 8(6), e66031 (2013)CrossRefGoogle Scholar
  5. 5.
    Cimatti, A., Clarke, E., Giunchiglia, E., Giunchiglia, F., Pistore, M., Roveri, M., Sebastiani, R., Tacchella, A.: 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) CrossRefGoogle Scholar
  6. 6.
    Gallet, E., Manceny, M., Le Gall, P., Ballarini, P.: An LTL model checking approach for biological parameter inference. In: Merz, S., Pang, J. (eds.) ICFEM 2014. LNCS, vol. 8829, pp. 155–170. Springer, Heidelberg (2014) Google Scholar
  7. 7.
    Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Answer set solving in practice. In: Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan and Claypool Publishers (2012)Google Scholar
  8. 8.
    Gebser, M., Kaufmann, B., Otero, R., Romero, J., Schaub, T., Wanko, P.: Domain-specific heuristics in answer set programming. In: Proceedings of the 27th National Conference on Artificial Intelligence (AAAI 2013), pp. 350–356. AAAI Press (2013)Google Scholar
  9. 9.
    Gebser, M., Kaufmann, B., Schaub, T.: Multi-threaded ASP solving with clasp. Theory and Pract. Log. Program. 12(4–5), 525–545 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Guziolowski, C., Videla, S., Eduati, F., Thiele, S., Cokelaer, T., Siegel, A., Saez-Rodriguez, J.: Exhaustively characterizing feasible logic models of a signaling network using answer set programming. Bioinformatics 29(18), 2320–2326 (2013)CrossRefGoogle Scholar
  11. 11.
    Harel, D., Kupferman, O., Vardi, M.Y.: On the complexity of verifying concurrent transition systems. Inf. Comput. 173(2), 143–161 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Kauffman, S.: Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22(3), 437–467 (1969)CrossRefGoogle Scholar
  13. 13.
    Klarner, H., Streck, A., Šafránek, D., Kolčák, J., Siebert, H.: Parameter identification and model ranking of thomas networks. In: Gilbert, D., Heiner, M. (eds.) CMSB 2012. LNCS, vol. 7605, pp. 207–226. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  14. 14.
    MacNamara, A., Terfve, C., Henriques, D., Bernabe, B.P., Saez-Rodriguez, J.: State-time spectrum of signal transduction logic models. Phys. Biol. 9(4), 045003 (2012)CrossRefGoogle Scholar
  15. 15.
    Saez-Rodriguez, J., Alexopoulos, L.G., Epperlein, J., Samaga, R., Lauffenburger, D.A., Klamt, S., Sorger, P.K.: Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Molecular Systems Biology 5, 331 (2009)CrossRefGoogle Scholar
  16. 16.
    Wang, R., Saadatpour, A., Albert, R.: Boolean modeling in systems biology: an overview of methodology and applications. Phys. Biol. 9(5), 055001 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Max Ostrowski
    • 1
  • Loïc Paulevé
    • 2
    Email author
  • Torsten Schaub
    • 1
  • Anne Siegel
    • 3
  • Carito Guziolowski
    • 4
  1. 1.Computer Science DepartmentPotsdam UniversityPostdamGermany
  2. 2.CNRSUniversité Paris-Sud LRI-UMR 8623OrsayFrance
  3. 3.CNRSUniversité de Rennes 1, IRISA-UMR 6074RennesFrance
  4. 4.École Centrale de NantesIRCCyN-UMR CNRS 6597NantesFrance

Personalised recommendations