Advertisement

Sequential Reprogramming of Boolean Networks Made Practical

  • Hugues Mandon
  • Cui Su
  • Stefan Haar
  • Jun Pang
  • Loïc PaulevéEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11773)

Abstract

We address the sequential reprogramming of gene regulatory networks modelled as Boolean networks. We develop an attractor-based sequential reprogramming method to compute all sequential reprogramming paths from a source attractor to a target attractor, where only attractors of the network are used as intermediates. Our method is more practical than existing reprogramming methods as it incorporates several practical constraints: (1) only biologically observable states, viz. attractors, can act as intermediates; (2) certain attractors, such as apoptosis, can be avoided as intermediates; (3) certain nodes can be avoided to perturb as they may be essential for cell survival or difficult to perturb with biomolecular techniques; and (4) given a threshold k, all sequential reprogramming paths with no more than k perturbations are computed. We compare our method with the minimal one-step reprogramming and the minimal sequential reprogramming on a variety of biological networks. The results show that our method can greatly reduce the number of perturbations compared to the one-step reprogramming, while having comparable results with the minimal sequential reprogramming. Moreover, our implementation is scalable for networks of more than 60 nodes.

Keywords

Cell reprogramming Boolean networks Attractors 

Notes

Acknowledgement

This research was supported by the ANR-FNR project AlgoReCell (ANR-16-CE12-0034; FNR INTER/ANR/15/11191283); Labex DigiCosme (project ANR-11-LABEX-0045-DIGICOSME) operated by ANR as part of the program “Investissement d’Avenir” Idex Paris-Saclay (ANR-11-IDEX-0003-02); and by the project SEC-PBN funded by University of Luxembourg. Cui Su was also partially supported by the COST Action IC1405.

References

  1. 1.
    Abou-Jaoudé, W., et al.: Model checking to assess T-helper cell plasticity. Front. Bioeng. Biotechnol. 2, 86 (2015)Google Scholar
  2. 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_4CrossRefGoogle Scholar
  3. 3.
    Chang, R., Shoemaker, R., Wang, W.: Systematic search for recipes to generate induced pluripotent stem cells. PLoS Comput. Biol. 7(12), e1002300 (2011)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Chudasama, V., Ovacik, M., Abernethy, D., Mager, D.: Logic-based and cellular pharmacodynamic modeling of Bortezomib responses in U266 human myeloma cells. J. Pharmacol. Exp. Ther. 354(3), 448–458 (2015)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Collombet, S., et al.: Logical modeling of lymphoid and myeloid cell specification and transdifferentiation. Proc. Nat. Acad. Sci. 114(23), 5792–5799 (2017)CrossRefGoogle Scholar
  7. 7.
    Crespo, I., Perumal, T.M., Jurkowski, W., del Sol, A.: Detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks. BMC Syst. Biol. 7(1), 140 (2013)CrossRefGoogle Scholar
  8. 8.
    Graf, T., Enver, T.: Forcing cells to change lineages. Nature 462(7273), 587–594 (2009)CrossRefGoogle Scholar
  9. 9.
    Herrmann, F., Groß, A., Zhou, D., Kestler, H.A., Kühl, M.: A Boolean model of the cardiac gene regulatory network determining first and second heart field identity. PLoS ONE 7, 1–10 (2012)Google Scholar
  10. 10.
    Jo, J., et al.: An integrated systems biology approach identifies positive cofactor 4 as a factor that increases reprogramming efficiency. Nucleic Acids Res. 44(3), 1203–1215 (2016)CrossRefGoogle Scholar
  11. 11.
    Krumsiek, J., Marr, C., Schroeder, T., Theis, F.J.: Hierarchical differentiation of myeloid progenitors is encoded in the transcription factor network. PLoS ONE 6(8), e22649 (2011)CrossRefGoogle Scholar
  12. 12.
    Mandon, H., Haar, S., Paulevé, L.: Temporal reprogramming of Boolean networks. In: Feret, J., Koeppl, H. (eds.) CMSB 2017. LNCS, vol. 10545, pp. 179–195. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67471-1_11CrossRefGoogle Scholar
  13. 13.
    Mizera, A., Pang, J., Qu, H., Yuan, Q.: Taming asynchrony for attractor detection in large Boolean networks. IEEE/ACM Trans. Comput. Biol. Bioinf. 16(1), 31–42 (2018)CrossRefGoogle Scholar
  14. 14.
    Mizera, A., Pang, J., Su, C., Yuan, Q.: ASSA-PBN: a toolbox for probabilistic Boolean networks. IEEE/ACM Trans. Comput. Biol. Bioinf. 15(4), 1203–1216 (2018)CrossRefGoogle Scholar
  15. 15.
    Offermann, B., et al.: Boolean modeling reveals the necessity of transcriptional regulation for bistability in PC12 cell differentiation. Front. Genet. 7, 44 (2016)CrossRefGoogle Scholar
  16. 16.
    Paul, S., Su, C., Pang, J., Mizera, A.: A decomposition-based approach towards the control of Boolean networks. In: Proceedings 9th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 11–20. ACM Press (2018)Google Scholar
  17. 17.
    Paul, S., Su, C., Pang, J., Mizera, A.: An efficient approach towards the source-target control of Boolean networks. IEEE/ACM Trans. Comput. Biol. Bioinf. (2019, accepted)Google Scholar
  18. 18.
    Remy, E., Rebouissou, S., Chaouiya, C., Zinovyev, A., Radvanyi, F., Calzone, L.: A modelling approach to explain mutually exclusive and co-occurring genetic alterations in bladder tumorigenesis. Cancer Res. 75, 4042–4052 (2015).  https://doi.org/10.1158/0008-5472.CAN-15-0602CrossRefGoogle Scholar
  19. 19.
    Ronquist, S., et al.: Algorithm for cellular reprogramming. Proc. Nat. Acad. Sci. 114(45), 11832–11837 (2017)CrossRefGoogle Scholar
  20. 20.
    Sahin, Ö., et al.: Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance. BMC Syst. Biol. 3(1), 1 (2009)CrossRefGoogle Scholar
  21. 21.
    Samaga, R., Von Kamp, A., Klamt, S.: Computing combinatorial intervention strategies and failure modes in signaling networks. J. Comput. Biol. 17(1), 39–53 (2010)MathSciNetCrossRefGoogle Scholar
  22. 22.
    del Sol, A., Buckley, N.J.: Concise review: a population shift view of cellular reprogramming. Stem Cells 32(6), 1367–1372 (2014)CrossRefGoogle Scholar
  23. 23.
    Takahashi, K., Yamanaka, S.: A decade of transcription factor-mediated reprogramming to pluripotency. Nat. Rev. Mol. Cell Biol. 17(3), 183–193 (2016)CrossRefGoogle Scholar
  24. 24.
    Zañudo, J.G.T., Albert, R.: Cell fate reprogramming by control of intracellular network dynamics. PLoS Comput. Biol. 11, 1–24 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hugues Mandon
    • 1
  • Cui Su
    • 2
  • Stefan Haar
    • 1
  • Jun Pang
    • 2
    • 3
  • Loïc Paulevé
    • 4
    Email author
  1. 1.LSV, ENS Paris-Saclay, Inria, CNRS, Université Paris-SaclayCachanFrance
  2. 2.SnTUniversity of LuxembourgLuxembourgLuxembourg
  3. 3.FSTCUniversity of LuxembourgEsch-sur-AlzetteLuxembourg
  4. 4.Univ. Bordeaux, Bordeaux INP, CNRS, LaBRI, UMR5800TalenceFrance

Personalised recommendations