Abstract
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.
M. Ostrowski and L. Paulevé—Co-first authors
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Notes
- 1.
Details in http://loicpauleve.name/cmsb15-suppl-A.pdf.
- 2.
Scripts and data available at http://loicpauleve.name/cmsb15-suppl.tbz2.
- 3.
Detailed results are given in http://loicpauleve.name/cmsb15-suppl-B.pdf.
References
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)
Aracena, J., Goles, E., Moreira, A., Salinas, L.: On the robustness of update schedules in boolean networks. Biosystems 97(1), 1–8 (2009)
Baral, C.: Knowledge Representation. Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge (2003)
Berestovsky, N., Nakhleh, L.: An evaluation of methods for inferring boolean networks from time-series data. PLoS ONE 8(6), e66031 (2013)
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)
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)
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)
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)
Gebser, M., Kaufmann, B., Schaub, T.: Multi-threaded ASP solving with clasp. Theory and Pract. Log. Program. 12(4–5), 525–545 (2012)
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)
Harel, D., Kupferman, O., Vardi, M.Y.: On the complexity of verifying concurrent transition systems. Inf. Comput. 173(2), 143–161 (2002)
Kauffman, S.: Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22(3), 437–467 (1969)
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)
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)
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)
Wang, R., Saadatpour, A., Albert, R.: Boolean modeling in systems biology: an overview of methodology and applications. Phys. Biol. 9(5), 055001 (2012)
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Ostrowski, M., Paulevé, L., Schaub, T., Siegel, A., Guziolowski, C. (2015). Boolean Network Identification from Multiplex Time Series Data. In: Roux, O., Bourdon, J. (eds) Computational Methods in Systems Biology. CMSB 2015. Lecture Notes in Computer Science(), vol 9308. Springer, Cham. https://doi.org/10.1007/978-3-319-23401-4_15
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