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Computing Diverse Boolean Networks from Phosphoproteomic Time Series Data

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Computational Methods in Systems Biology (CMSB 2018)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 11095))

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Abstract

Logical modeling has been widely used to understand and expand the knowledge about protein interactions among different pathways. Realizing this, the caspo-ts system has been proposed recently to learn logical models from time series data. It uses Answer Set Programming to enumerate Boolean Networks (BNs) given prior knowledge networks and phosphoproteomic time series data. In the resulting sequence of solutions, similar BNs are typically clustered together. This can be problematic for large scale problems where we cannot explore the whole solution space in reasonable time. Our approach extends the caspo-ts system to cope with the important use case of finding diverse solutions of a problem with a large number of solutions. We first present the algorithm for finding diverse solutions and then we demonstrate the results of the proposed approach on two different benchmark scenarios in systems biology: (1) an artificial dataset to model TCR signaling and (2) the HPN-DREAM challenge dataset to model breast cancer cell lines.

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Notes

  1. 1.

    In the following, a diverse optimal solution is a solution which is minimal w.r.t. an objective function, there is no solution which is a subset of it, and it is different from previously enumerated solutions.

  2. 2.

    A clause can be seen as a reaction, where the proteins represented positively are available, and the proteins represented negatively are absent. A Boolean formula in DNF encompasses all possible reactions to update the value of a protein.

  3. 3.

    For example, when x has value 3000, the y value in blue gives the true positive rate among the solutions 2001 to 3000 computed by\(\textit{caspo-ts}^D\).

  4. 4.

    Note that the model checker could only verify 32 out of 46 solutions within one month for cell line BT20 in case of \(\textit{caspo-ts}^D\). There may exist more TPs for this cell line.

References

  1. Albert, R., Othmer, H.G.: The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. J. Theor. Biol. 223(1), 1–18 (2003)

    Article  MathSciNet  Google Scholar 

  2. Biane, C., Delaplace, F.: Abduction based drug target discovery using Boolean control network. In: Feret, J., Koeppl, H. (eds.) CMSB 2017. LNCS, vol. 10545, pp. 57–73. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67471-1_4

    Chapter  Google Scholar 

  3. Brewka, G., Delgrande, J., Romero, J., Schaub, T.: Implementing preferences with asprin. In: Calimeri, F., Ianni, G., Truszczynski, M. (eds.) LPNMR 2015. LNCS (LNAI), vol. 9345, pp. 158–172. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23264-5_15

    Chapter  Google Scholar 

  4. Calzone, L., et al.: Mathematical modelling of cell-fate decision in response to death receptor engagement. PLoS Comput. Biol. 6(3), e1000702 (2010)

    Article  MathSciNet  Google Scholar 

  5. Carlin, D.E., et al.: Prophetic granger causality to infer gene regulatory networks. PloS one 12(12), e0170340 (2017)

    Article  Google Scholar 

  6. Eiter, T., Erdem, E., Erdoğan, H., Fink, M.: Finding similar or diverse solutions in answer set programming. In: Hill, P.M., Warren, D.S. (eds.) ICLP 2009. LNCS, vol. 5649, pp. 342–356. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02846-5_29

    Chapter  Google Scholar 

  7. Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Clingo = asp + control: preliminary report. arXiv preprint arXiv:1405.3694 (2014)

  8. Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Kowalski, R., Bowen, K., (eds.) Proceedings of the Fifth International Conference and Symposium of Logic Programming (ICLP 1988), pp. 1070–1080. MIT Press (1988)

    Google Scholar 

  9. Guziolowski, C., et al.: Exhaustively characterizing feasible logic models of a signaling network using answer set programming. Bioinformatics 29(18), 2320–2326 (2013)

    Article  Google Scholar 

  10. Hebrard, E., Hnich, B., O’Sullivan, B., Walsh, T: Finding diverse and similar solutions in constraint programming. In: Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI 2005), pp. 372–377. AAAI Press (2005)

    Google Scholar 

  11. Hill, S.M., et al.: Inferring causal molecular networks: empirical assessment through a community-based effort. Nat. Methods 13(4), 310–318 (2016)

    Article  Google Scholar 

  12. Hill, S.M., et al.: Context specificity in causal signaling networks revealed by phosphoprotein profiling. Cell Syst. 4(1), 73–83 (2017)

    Article  Google Scholar 

  13. Kaminski, R., Schaub, T., Wanko, P.: A tutorial on hybrid answer set solving with clingo. In: Ianni, G., et al. (eds.) Reasoning Web 2017. LNCS, vol. 10370, pp. 167–203. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61033-7_6

    Chapter  Google Scholar 

  14. Kauffman, S.A.: The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, Oxford (1993)

    Google Scholar 

  15. Klamt, S.A., Saez-Rodriguez, J., Lindquist, J.A., Simeoni, L., Gilles, E.D.: A methodology for the structural and functional analysis of signaling and regulatory networks. BMC Bioinf. 7(1), 56 (2006)

    Article  Google Scholar 

  16. MacNamara, A., Terfve, C., Henriques, D., Bernabé, B.P., Saez-Rodriguez, J.: State-time spectrum of signal transduction logic models. Phys. Biol. 9(4), 045003 (2012)

    Article  Google Scholar 

  17. Mitsos, A., Melas, I.N., Siminelakis, P., Chairakaki, A.D., Saez-Rodriguez, J., Alexopoulos, L.G.: Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data. PLoS Comput. Biol. 5(12), e1000591 (2009)

    Article  Google Scholar 

  18. Nadel, A.: Generating diverse solutions in SAT. In: Sakallah, K.A., Simon, L. (eds.) SAT 2011. LNCS, vol. 6695, pp. 287–301. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21581-0_23

    Chapter  MATH  Google Scholar 

  19. Ostrowski, M., Paulevé, L., Schaub, T., Siegel, A., Guziolowski, C.: Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming. Biosystems 149, 139–153 (2016)

    Article  Google Scholar 

  20. Rau, A., Jaffrézic, F., Foulley, J.-L., Doerge, R.W.: An empirical Bayesian method for estimating biological networks from temporal microarray data. Stat. Appl. Genet. Mol. Biol. 9(1) (2010)

    Google Scholar 

  21. Romero, J., Schaub, T., Wanko, P.: Computing diverse optimal stable models. In: OASIcs-OpenAccess Series in Informatics, vol. 52. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2016)

    Google Scholar 

  22. Rosenblueth, D.A., Muñoz, S., Carrillo, M., Azpeitia, E.: Inference of Boolean networks from gene interaction graphs using a SAT solver. In: Dediu, A.-H., Martín-Vide, C., Truthe, B. (eds.) AlCoB 2014. LNCS, vol. 8542, pp. 235–246. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07953-0_19

    Chapter  Google Scholar 

  23. Sharan, R., Karp, M.: Reconstructing boolean models of signaling. J. Comput. Biol. 20(3), 249–257 (2013)

    Article  MathSciNet  Google Scholar 

  24. Shmulevich, I., Dougherty, E.R., Zhang, W.: Gene perturbation and intervention in probabilistic Boolean networks. Bioinformatics 18(10), 1319–1331 (2002)

    Article  Google Scholar 

  25. Simons, P.: Extending the stable model semantics with more expressive rules. In: Gelfond, M., Leone, N., Pfeifer, G. (eds.) LPNMR 1999. LNCS (LNAI), vol. 1730, pp. 305–316. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-46767-X_22

    Chapter  Google Scholar 

  26. Thakar, J., Albert, R.: Boolean models of within-host immune interactions. Curr. Opin. Microbiol. 13(3), 377–381 (2010)

    Article  Google Scholar 

  27. Videla, S., et al.: Revisiting the training of logic models of protein signaling networks with a ASP. In: Gilbert, D., Heiner, M. (eds.) CMSB 2012. LNCS, pp. 342–361. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33636-2_20

    Chapter  Google Scholar 

  28. Watterson, S., Marshall, S., Ghazal, P.: Logic models of pathway biology. Drug Discov. Today 13(9), 447–456 (2008)

    Article  Google Scholar 

  29. Wu, G., Dawson, E., Duong, A., Haw, R., Stein, L.: ReactomeFiviz: a Cytoscape app for pathway and network-based data analysis. F1000Research 3, 146 (2014)

    Google Scholar 

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Authors and Affiliations

  1. Laboratoire des Sciences du Numerique de Nantes, Ecole Centrale de Nantes, Nantes, France

    Misbah Razzaq & Carito Guziolowski

  2. University of Potsdam, Potsdam, Germany

    Roland Kaminski, Javier Romero & Torsten Schaub

  3. Laboratoire des Sciences du Numerique de Nantes, Universite de Nantes, Nantes, France

    Jeremie Bourdon

Authors
  1. Misbah Razzaq
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  6. Carito Guziolowski
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Correspondence to Carito Guziolowski .

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  1. Brno University of Technology, Brno, Czech Republic

    Milan Češka

  2. Masaryk University, Brno, Czech Republic

    David Šafránek

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Razzaq, M., Kaminski, R., Romero, J., Schaub, T., Bourdon, J., Guziolowski, C. (2018). Computing Diverse Boolean Networks from Phosphoproteomic Time Series Data. In: Češka, M., Šafránek, D. (eds) Computational Methods in Systems Biology. CMSB 2018. Lecture Notes in Computer Science(), vol 11095. Springer, Cham. https://doi.org/10.1007/978-3-319-99429-1_4

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  • DOI: https://doi.org/10.1007/978-3-319-99429-1_4

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