Abstract
The construction of models of biological networks from prior knowledge and experimental data often leads to a multitude of candidate models. Devising a single model from them can require arbitrary choices, which may lead to strong biases in subsequent predictions.
We introduce here a methodology for a) synthesizing Boolean model ensembles satisfying a set of biologically relevant constraints and b) reasoning on the dynamics of the ensembles of models. The synthesis is performed using Answer-Set Programming, extending prior work to account for solution diversity and universal constraints on reachable fixed points, enabling an accurate specification of desired dynamics. The sampled models are then simulated and the results are aggregated through averaging or can be analyzed as a multi-dimensional distribution.
We illustrate our approach on a previously published Boolean model of a molecular network regulating the cell fate decisions in cancer progression. It appears that the ensemble-based approach to Boolean modelling brings new insights on the variability of synergistic interacting mutations effect concerning propensity of a cancer cell to metastasize.
S. Chevalier and V. Noël—Co-first authors.
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Code, data, and notebooks at https://doi.org/10.5281/zenodo.3938904; Synthesis has been performed on 36-cores CPUs @ 2.6 Ghz with 192 Go of RAM; first ensemble was generated at a rate of 5 s/model/CPU; second ensemble was generated at a rate of 3 min/model/CPU.
References
Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge (2003)
Chevalier, S., Froidevaux, C., Paulevé, L., Zinovyev, A.: Synthesis of Boolean networks from biological dynamical constraints using answer-set programming. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp. 34–41 (2019). https://doi.org/10.1109/ICTAI.2019.00014
Clarke, M.A., Fisher, J.: Executable cancer models: successes and challenges. Nat. Rev. Cancer 20, 343–354 (2020). https://doi.org/10.1038/s41568-020-0258-x
Cohen, D.P.A., Martignetti, L., Robine, S., Barillot, E., Zinovyev, A., Calzone, L.: Mathematical modelling of molecular pathways enabling tumour cell invasion and migration. PLoS Comput. Biol. 11(11), e1004571 (2015). https://doi.org/10.1371/journal.pcbi.1004571
Collombet, S., et al.: Logical modeling of lymphoid and myeloid cell specification and transdifferentiation. Proc. Nat. Acad. Sci. 114(23), 5792–5799 (2017). https://doi.org/10.1073/pnas.1610622114
Corblin, F., Tripodi, S., Fanchon, E., Ropers, D., Trilling, L.: A declarative constraint-based method for analyzing discrete genetic regulatory networks. Biosystems 98(2), 91–104 (2009). https://doi.org/10.1016/j.biosystems.2009.07.007
Dorier, J., Crespo, I., Niknejad, A., Liechti, R., Ebeling, M., Xenarios, I.: Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method. BMC Bioinform. 17(1), 410 (2016). https://doi.org/10.1186/s12859-016-1287-z
Eiter, T., Gottlob, G.: On the computational cost of disjunctive logic programming: propositional case. Ann. Math. Artif. Intell. 15(3), 289–323 (1995). https://doi.org/10.1007/BF01536399
Eiter, T., Ianni, G., Krennwallner, T.: Answer set programming: a primer. In: Tessaris, S., et al. (eds.) Reasoning Web 2009. LNCS, vol. 5689, pp. 40–110. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03754-2_2
Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Answer set solving in practice. Synth. Lect. Artif. Intell. Mach. Learn. 6, 1–23 (2012)
Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Clingo = ASP + control: preliminary report. CoRR abs/1405.3694 (2014)
Gillespie, D.T.: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J. Comput. Phys. 22(4), 403–434 (1976). https://doi.org/10.1016/0021-9991(76)90041-3
Goldfeder, J., Kugler, H.: BRE: IN - a backend for reasoning about interaction networks with temporal logic. In: Bortolussi, L., Sanguinetti, G. (eds.) CMSB 2019. LNCS, vol. 11773, pp. 289–295. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-31304-3_15
Kauffman, S.: A proposal for using the ensemble approach to understand genetic regulatory networks. J. Theor. Biol. 230(4), 581–590 (2004). https://doi.org/10.1016/j.jtbi.2003.12.017
Klarner, H., Bockmayr, A., Siebert, H.: Computing maximal and minimal trap spaces of Boolean networks. Nat. Comput. 14(4), 535–544 (2015). https://doi.org/10.1007/s11047-015-9520-7
Krawitz, P., Shmulevich, I.: Basin entropy in Boolean network ensembles. Phys. Rev. Lett. 98(15), 158701 (2007). https://doi.org/10.1103/physrevlett.98.158701
Lin, F., Zhao, Y.: ASSAT: computing answer sets of a logic program by SAT solvers. Artif. Intell. 157(1), 115–137 (2004). https://doi.org/10.1016/j.artint.2004.04.004
Lobo, J., Minker, J., Rajasekar, A.: Foundations of Disjunctive Logic Programming. MIT Press, Cambridge (1992)
Paulevé, L., Kolčák, J., Chatain, T., Haar, S.: Reconciling qualitative, abstract, and scalable modeling of biological networks. bioRxiv (2020). https://doi.org/10.1101/2020.03.22.998377
Razzaq, M., Kaminski, R., Romero, J., Schaub, T., Bourdon, J., Guziolowski, C.: Computing diverse Boolean networks from phosphoproteomic time series data. In: Češka, M., Šafránek, D. (eds.) CMSB 2018. LNCS, vol. 11095, pp. 59–74. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99429-1_4
Schwieger, R., Siebert, H.: Graph representations of monotonic Boolean model pools. In: Feret, J., Koeppl, H. (eds.) CMSB 2017. LNCS, vol. 10545, pp. 233–248. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67471-1_14
Stoll, G., et al.: MaBoSS 2.0: an environment for stochastic Boolean modeling. Bioinformatics 33(14), 2226–2228 (2017). https://doi.org/10.1093/bioinformatics/btx123
Stoll, G., Viara, E., Barillot, E., Calzone, L.: Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm. BMC Syst. Biol. 6(1), 116 (2012). https://doi.org/10.1186/1752-0509-6-116
Terfve, C., et al.: CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC Syst. Biol. 6(1), 133 (2012). https://doi.org/10.1186/1752-0509-6-133
Van Kampen, N.G.: Stochastic Processes in Physics and Chemistry, vol. 1. Elsevier, Amsterdam (1992)
Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemom. Intell. Lab. Syst. 2(1–3), 37–52 (1987). https://doi.org/10.1016/0169-7439(87)80084-9
Yordanov, B., Dunn, S.J., Kugler, H., Smith, A., Martello, G., Emmott, S.: A method to identify and analyze biological programs through automated reasoning. Syst. Biol. Appl. 2(1), 1–16 (2016). https://doi.org/10.1038/npjsba.2016.10
Zañudo, J.G., Steinway, S.N., Albert, R.: Discrete dynamic network modeling of oncogenic signaling: mechanistic insights for personalized treatment of cancer. Curr. Opin. Syst. Biol. 9, 1–10 (2018). https://doi.org/10.1016/j.coisb.2018.02.002
Acknowledgements
This work has been partially supported by Agence Nationale de la Recherche in the program Investissements d’Avenir (project No. ANR-19-P3IA-0001; PRAIRIE 3IA Institute), by ANR-FNR project “AlgoReCell” (ANR-16-CE12-0034), by ITMO Cancer, and by the Ministry of Science and Higher Education of the Russian Federation (project No. 14.Y26.31.0022). Experiments were carried out using the PlaFRIM experimental testbed,supported by Inria, CNRS (LABRI and IMB), Université de Bordeaux, Bordeaux INP and Conseil Régionald’Aquitaine (see https://www.plafrim.fr).
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Chevalier, S., Noël, V., Calzone, L., Zinovyev, A., Paulevé, L. (2020). Synthesis and Simulation of Ensembles of Boolean Networks for Cell Fate Decision. In: Abate, A., Petrov, T., Wolf, V. (eds) Computational Methods in Systems Biology. CMSB 2020. Lecture Notes in Computer Science(), vol 12314. Springer, Cham. https://doi.org/10.1007/978-3-030-60327-4_11
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